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\uc785\uc7a5\uc5d0\uc11c\ub294 \uac1c\ubc1c\ud300\uc5d0\uc11c \uac1c\ubc1c\ud55c \uae30\ub2a5\uc774 \ubc30\ud3ec \uc2dc \uc7a5\uc560\ub97c \uc77c\uc73c\ud0a4\ub294 \ub4f1 \uc5ec\ub7ec \ubb38\uc81c\uac00 \uc0dd\uae30\uac8c \ub429\ub2c8\ub2e4."),(0,i.kt)("p",null,"\uc704\uc640 \uac19\uc740 \uc0c1\ud669\uc774 \ubc18\ubcf5\ub418\uba74 \uc870\uc9c1 \uc774\uae30\uc8fc\uc758\ub77c\uace0 \ubd88\ub9ac\ub294 \uc0ac\uc77c\ub85c \ud604\uc0c1\uc774 \uc0dd\uae38 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,i.kt)("p",null,(0,i.kt)("img",{alt:"silo",src:n(6651).Z,width:"892",height:"498"})),(0,i.kt)("blockquote",null,(0,i.kt)("p",{parentName:"blockquote"},"\uc0ac\uc77c\ub85c(silo)\ub294 \uace1\uc2dd\uc774\ub098 \uc0ac\ub8cc\ub97c \uc800\uc7a5\ud558\ub294 \uad74\ub69d \ubaa8\uc591\uc758 \ucc3d\uace0\ub97c \uc758\ubbf8\ud55c\ub2e4. \uc0ac\uc77c\ub85c\ub294 \ub3c5\ub9bd\uc801\uc73c\ub85c \uc874\uc7ac\ud558\uba70 \uc800\uc7a5\ub418\ub294 \ubb3c\ud488\uc774 \uc11c\ub85c \uc11e\uc774\uc9c0 \uc54a\ub3c4\ub85d \ucca0\uc800\ud788 \uad00\ub9ac\ud560 \uc218 \uc788\ub3c4\ub85d \ub3c4\uc640\uc900\ub2e4.",(0,i.kt)("br",{parentName:"p"}),"\n","\uc0ac\uc77c\ub85c \ud6a8\uacfc(Organizational Silos Effect)\ub294 \uc870\uc9c1 \ubd80\uc11c \uac04\uc5d0 \uc11c\ub85c \ud611\ub825\ud558\uc9c0 \uc54a\uace0 \ub0b4\ubd80 \uc774\uc775\ub9cc\uc744 \ucd94\uad6c\ud558\ub294 \ud604\uc0c1\uc744 \uc758\ubbf8\ud55c\ub2e4. \uc870\uc9c1 \ub0b4\uc5d0\uc11c \uac1c\ubcc4 \ubd80\uc11c\ub07c\ub9ac \uc11c\ub85c \ub2f4\uc744 \uc313\uace0 \uac01\uc790\uc758 \uc774\uc775\uc5d0\ub9cc \ubab0\ub450\ud558\ub294 \ubd80\uc11c \uc774\uae30\uc8fc\uc758\ub97c \uc77c\uceeb\ub294\ub2e4.")),(0,i.kt)("p",null,"\uc0ac\uc77c\ub85c \ud604\uc0c1\uc740 \uc11c\ube44\uc2a4 \ud488\uc9c8\uc758 \uc800\ud558\ub85c \uc774\uc5b4\uc9c0\uac8c \ub429\ub2c8\ub2e4. \uc774\ub7ec\ud55c \uc0ac\uc77c\ub85c \ud604\uc0c1\uc744 \ud574\uacb0\ud558\uae30 \uc704\ud574 \ub098\uc628 \uac83\uc774 \ubc14\ub85c 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\uc0c1\uc2b9\uc2dc\ud0b5\ub2c8\ub2e4.",(0,i.kt)("br",{parentName:"p"}),"\n","\uc55e\uc5d0\uc11c \uc124\uba85\ud55c \ub0b4\uc6a9\uc744 \uc885\ud569\ud558\uc790\uba74 DevOps\ub294 \uac1c\ubc1c\ud300\uacfc \uc6b4\uc601\ud300 \uac04\uc758 \ubb38\uc81c\uac00 \uc788\uc5c8\uace0 \uc774\ub97c \ud574\uacb0\ud558\uae30 \uc704\ud55c \ubc29\ubc95\ub860\uc785\ub2c8\ub2e4."),(0,i.kt)("h2",{id:"mlops"},"MLOps"),(0,i.kt)("h3",{id:"1-mlops"},"1) ML+Ops"),(0,i.kt)("p",null,"MLOps\ub294 Machine Learning \uacfc Operations\uc758 \ud569\uc131\uc5b4\ub85c DevOps\uc5d0\uc11c Dev\uac00 ML\ub85c \ubc14\ub00c\uc5c8\uc2b5\ub2c8\ub2e4. \uc774\uc81c \uc55e\uc5d0\uc11c \uc0b4\ud3b4\ubcf8 DevOps\ub97c \ud1b5\ud574 MLOps\uac00 \ubb34\uc5c7\uc778\uc9c0 \uc9d0\uc791\ud574 \ubcfc \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\u201cMLOps\ub294 \uba38\uc2e0\ub7ec\ub2dd\ud300\uacfc \uc6b4\uc601\ud300\uc758 \ubb38\uc81c\ub97c \ud574\uacb0\ud558\uae30 \uc704\ud55c \ubc29\ubc95\uc785\ub2c8\ub2e4.\u201d\n\uc774 \ub9d0\uc740 \uba38\uc2e0\ub7ec\ub2dd\ud300\uacfc \uc6b4\uc601\ud300 \uc0ac\uc774\uc5d0 \ubb38\uc81c\uac00 \ubc1c\uc0dd\ud588\ub2e4\ub294 \uc758\ubbf8\uc785\ub2c8\ub2e4. \uadf8\ub7fc \uc65c \uba38\uc2e0\ub7ec\ub2dd\ud300\uacfc \uc6b4\uc601\ud300\uc5d0\ub294 \ubb38\uc81c\uac00 \ubc1c\uc0dd\ud588\uc744\uae4c\uc694? \ub450 \ud300 \uac04\uc758 \ubb38\uc81c\ub97c \uc54c\uc544\ubcf4\uae30 \uc704\ud574\uc11c \ucd94\ucc9c\uc2dc\uc2a4\ud15c\uc744 \uc608\uc2dc\ub85c \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4."),(0,i.kt)("h4",{id:"rule-based"},"Rule Based"),(0,i.kt)("p",null,"\ucc98\uc74c \ucd94\ucc9c\uc2dc\uc2a4\ud15c\uc744 \ub9cc\ub4dc\ub294 \uacbd\uc6b0 \uac04\ub2e8\ud55c \uaddc\uce59\uc744 \uae30\ubc18\uc73c\ub85c \uc544\uc774\ud15c\uc744 \ucd94\ucc9c\ud569\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4\uc11c 1\uc8fc\uc77c\uac04 \ud310\ub9e4\ub7c9\uc774 \uac00\uc7a5 \ub9ce\uc740 \uc21c\uc11c\ub300\ub85c \ubcf4\uc5ec\uc8fc\ub294 \uc2dd\uc758 \ubc29\uc2dd\uc744 \uc774\uc6a9\ud569\ub2c8\ub2e4. \uc774 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\ub0b4\ub294 \ubaa8\ub378\uc744 \ud544\uc694\ud574\uc9c8 \uacbd\uc6b0 \ub525\ub7ec\ub2dd\uc744 \uc774\uc6a9\ud55c \ubaa8\ub378\uc744 \uac1c\ubc1c\ud558\uae30 \uc2dc\uc791\ud569\ub2c8\ub2e4. \uc774\ub54c \ub9cc\ub4dc\ub294 \ubaa8\ub378\uc740 \uba38\uc2e0\ub7ec\ub2dd\uacfc \uac19\uc774 \uc815\ud574\uc9c4 \uc8fc\uae30\uc5d0 \ub530\ub77c \ubaa8\ub378\uc744 \uc7ac\ud559\uc2b5 \ud6c4 \uc7ac\ubc30\ud3ec\ud569\ub2c8\ub2e4."),(0,i.kt)("p",null,(0,i.kt)("img",{alt:"graph",src:n(2994).Z,width:"752",height:"582"})),(0,i.kt)("p",null,"\uc704\uc5d0\uc11c \uc124\uba85\ud55c \uac83\uc744 x\ucd95\uc744 \ubaa8\ub378\uc758 \ubcf5\uc7a1\ub3c4, y\ucd95\uc744 \ubaa8\ub378\uc758 \uc131\ub2a5\uc73c\ub85c \ub450\uace0 \uadf8\ub798\ud504\ub85c \ud45c\ud604\ud55c\ub2e4\uba74 \ub2e4\uc74c\uacfc \uac19\uc774 \ubcf5\uc7a1\ub3c4\uac00 \uc62c\ub77c\uac08 \ub54c \ubaa8\ub378\uc758 \uc131\ub2a5\uc774 \uc62c\ub77c\uac00\ub294 \uc0c1\uc2b9 \uad00\uacc4\ub97c \uac16\uc2b5\ub2c8\ub2e4. \uba38\uc2e0\ub7ec\ub2dd\uc5d0\uc11c \ub525\ub7ec\ub2dd\uc73c\ub85c \ub118\uc5b4\uac08 \uba38\uc2e0\ub7ec\ub2dd \ud300\uc774 \uc0c8\ub85c \uc0dd\uae30\uac8c \ub429\ub2c8\ub2e4."),(0,i.kt)("p",null,"\ub9cc\uc57d \uad00\ub9ac\ud574\uc57c\ud560 \ubaa8\ub378\uc774 \uc801\ub2e4\uba74 \uc11c\ub85c \ud611\uc5c5\uc744 \ud1b5\ud574\uc11c \ucda9\ubd84\ud788 \ud574\uacb0\ud560 \uc218 \uc788\uc9c0\ub9cc \uac1c\ubc1c\ud574\uc57c \ud560 \ubaa8\ub378\uc774 \ub9ce\uc544\uc9c4\ub2e4\uba74 DevOps\uc758 \uacbd\uc6b0\uc640 \uac19\uc774 \uc0ac\uc77c\ub85c \ud604\uc0c1\uc774 \ub098\ud0c0\ub098\uac8c \ub429\ub2c8\ub2e4."),(0,i.kt)("p",null,"DevOps\uc758 \ubaa9\ud45c\uc640 \ub9de\ucdb0\uc11c \uc0dd\uac01\ud574\ubcf4\uba74 MLOps\uc758 \ubaa9\ud45c\ub294 \uac1c\ubc1c\ud55c \ubaa8\ub378\uc774 \uc815\uc0c1\uc801\uc73c\ub85c \ubc30\ud3ec\ub420 \uc218 \uc788\ub294\uc9c0 \ud14c\uc2a4\ud2b8\ud558\ub294 \uac83\uc785\ub2c8\ub2e4. \uac1c\ubc1c\ud300\uc5d0\uc11c \uac1c\ubc1c\ud55c \uae30\ub2a5\uc774 \uc815\uc0c1\uc801\uc73c\ub85c \ubc30\ud3ec\ub420 \uc218 \uc788\ub294\uc9c0 \ud655\uc778\ud558\ub294 \uac83\uc774 DevOps\uc758 \ubaa9\ud45c\uc600\ub2e4\uba74, MLOps\uc758 \ubaa9\ud45c\ub294 \uba38\uc2e0\ub7ec\ub2dd \ud300\uc5d0\uc11c \uac1c\ubc1c\ud55c \ubaa8\ub378\uc774 \uc815\uc0c1\uc801\uc73c\ub85c \ubc30\ud3ec\ub420 \uc218 \uc788\ub294\uc9c0 \ud655\uc778\ud558\ub294 \uac83\uc785\ub2c8\ub2e4."),(0,i.kt)("h3",{id:"2-ml---ops"},"2) ML -> Ops"),(0,i.kt)("p",null,"\ud558\uc9c0\ub9cc \ucd5c\uadfc \ub098\uc624\uace0 \uc788\ub294 MLOps \uad00\ub828 \uc81c\ud488\uacfc \uc124\uba85\uc744 \ubcf4\uba74 \uaf2d \uc55e\uc5d0\uc11c \uc124\uba85\ud55c \ubaa9\ud45c\ub9cc\uc744 \ub300\uc0c1\uc73c\ub85c \ud558\uace0 \uc788\uc9c0 \uc54a\uc2b5\ub2c8\ub2e4.\n\uc5b4\ub5a4 \uacbd\uc6b0\uc5d0\ub294 \uba38\uc2e0\ub7ec\ub2dd \ud300\uc5d0\uc11c \ub9cc\ub4e0 \ubaa8\ub378\uc744 \uc774\uc6a9\ud574 \uc9c1\uc811 \uc6b4\uc601\uc744 \ud560 \uc218 \uc788\ub3c4\ub85d \ub3c4\uc640\uc8fc\ub824\uace0 \ud569\ub2c8\ub2e4. \uc774\ub7ec\ud55c \ub2c8\uc988\ub294 \ucd5c\uadfc 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\uc785\uc7a5\uc5d0\uc11c\ub294 \uac1c\ubc1c\ud300\uc5d0\uc11c \uac1c\ubc1c\ud55c \uae30\ub2a5\uc774 \ubc30\ud3ec \uc2dc \uc7a5\uc560\ub97c \uc77c\uc73c\ud0a4\ub294 \ub4f1 \uc5ec\ub7ec \ubb38\uc81c\uac00 \uc0dd\uae30\uac8c \ub429\ub2c8\ub2e4."),(0,i.kt)("p",null,"\uc704\uc640 \uac19\uc740 \uc0c1\ud669\uc774 \ubc18\ubcf5\ub418\uba74 \uc870\uc9c1 \uc774\uae30\uc8fc\uc758\ub77c\uace0 \ubd88\ub9ac\ub294 \uc0ac\uc77c\ub85c \ud604\uc0c1\uc774 \uc0dd\uae38 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,i.kt)("p",null,(0,i.kt)("img",{alt:"silo",src:n(6651).Z,width:"892",height:"498"})),(0,i.kt)("blockquote",null,(0,i.kt)("p",{parentName:"blockquote"},"\uc0ac\uc77c\ub85c(silo)\ub294 \uace1\uc2dd\uc774\ub098 \uc0ac\ub8cc\ub97c \uc800\uc7a5\ud558\ub294 \uad74\ub69d \ubaa8\uc591\uc758 \ucc3d\uace0\ub97c \uc758\ubbf8\ud55c\ub2e4. \uc0ac\uc77c\ub85c\ub294 \ub3c5\ub9bd\uc801\uc73c\ub85c \uc874\uc7ac\ud558\uba70 \uc800\uc7a5\ub418\ub294 \ubb3c\ud488\uc774 \uc11c\ub85c \uc11e\uc774\uc9c0 \uc54a\ub3c4\ub85d \ucca0\uc800\ud788 \uad00\ub9ac\ud560 \uc218 \uc788\ub3c4\ub85d \ub3c4\uc640\uc900\ub2e4.",(0,i.kt)("br",{parentName:"p"}),"\n","\uc0ac\uc77c\ub85c \ud6a8\uacfc(Organizational Silos Effect)\ub294 \uc870\uc9c1 \ubd80\uc11c \uac04\uc5d0 \uc11c\ub85c \ud611\ub825\ud558\uc9c0 \uc54a\uace0 \ub0b4\ubd80 \uc774\uc775\ub9cc\uc744 \ucd94\uad6c\ud558\ub294 \ud604\uc0c1\uc744 \uc758\ubbf8\ud55c\ub2e4. \uc870\uc9c1 \ub0b4\uc5d0\uc11c \uac1c\ubcc4 \ubd80\uc11c\ub07c\ub9ac \uc11c\ub85c \ub2f4\uc744 \uc313\uace0 \uac01\uc790\uc758 \uc774\uc775\uc5d0\ub9cc \ubab0\ub450\ud558\ub294 \ubd80\uc11c \uc774\uae30\uc8fc\uc758\ub97c \uc77c\uceeb\ub294\ub2e4.")),(0,i.kt)("p",null,"\uc0ac\uc77c\ub85c \ud604\uc0c1\uc740 \uc11c\ube44\uc2a4 \ud488\uc9c8\uc758 \uc800\ud558\ub85c \uc774\uc5b4\uc9c0\uac8c \ub429\ub2c8\ub2e4. \uc774\ub7ec\ud55c \uc0ac\uc77c\ub85c \ud604\uc0c1\uc744 \ud574\uacb0\ud558\uae30 \uc704\ud574 \ub098\uc628 \uac83\uc774 \ubc14\ub85c 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\uc0c1\uc2b9\uc2dc\ud0b5\ub2c8\ub2e4.",(0,i.kt)("br",{parentName:"p"}),"\n","\uc55e\uc5d0\uc11c \uc124\uba85\ud55c \ub0b4\uc6a9\uc744 \uc885\ud569\ud558\uc790\uba74 DevOps\ub294 \uac1c\ubc1c\ud300\uacfc \uc6b4\uc601\ud300 \uac04\uc758 \ubb38\uc81c\uac00 \uc788\uc5c8\uace0 \uc774\ub97c \ud574\uacb0\ud558\uae30 \uc704\ud55c \ubc29\ubc95\ub860\uc785\ub2c8\ub2e4."),(0,i.kt)("h2",{id:"mlops"},"MLOps"),(0,i.kt)("h3",{id:"1-mlops"},"1) ML+Ops"),(0,i.kt)("p",null,"MLOps\ub294 Machine Learning \uacfc Operations\uc758 \ud569\uc131\uc5b4\ub85c DevOps\uc5d0\uc11c Dev\uac00 ML\ub85c \ubc14\ub00c\uc5c8\uc2b5\ub2c8\ub2e4. \uc774\uc81c \uc55e\uc5d0\uc11c \uc0b4\ud3b4\ubcf8 DevOps\ub97c \ud1b5\ud574 MLOps\uac00 \ubb34\uc5c7\uc778\uc9c0 \uc9d0\uc791\ud574 \ubcfc \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\u201cMLOps\ub294 \uba38\uc2e0\ub7ec\ub2dd\ud300\uacfc \uc6b4\uc601\ud300\uc758 \ubb38\uc81c\ub97c \ud574\uacb0\ud558\uae30 \uc704\ud55c \ubc29\ubc95\uc785\ub2c8\ub2e4.\u201d\n\uc774 \ub9d0\uc740 \uba38\uc2e0\ub7ec\ub2dd\ud300\uacfc \uc6b4\uc601\ud300 \uc0ac\uc774\uc5d0 \ubb38\uc81c\uac00 \ubc1c\uc0dd\ud588\ub2e4\ub294 \uc758\ubbf8\uc785\ub2c8\ub2e4. \uadf8\ub7fc \uc65c \uba38\uc2e0\ub7ec\ub2dd\ud300\uacfc \uc6b4\uc601\ud300\uc5d0\ub294 \ubb38\uc81c\uac00 \ubc1c\uc0dd\ud588\uc744\uae4c\uc694? \ub450 \ud300 \uac04\uc758 \ubb38\uc81c\ub97c \uc54c\uc544\ubcf4\uae30 \uc704\ud574\uc11c \ucd94\ucc9c\uc2dc\uc2a4\ud15c\uc744 \uc608\uc2dc\ub85c \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4."),(0,i.kt)("h4",{id:"rule-based"},"Rule Based"),(0,i.kt)("p",null,"\ucc98\uc74c \ucd94\ucc9c\uc2dc\uc2a4\ud15c\uc744 \ub9cc\ub4dc\ub294 \uacbd\uc6b0 \uac04\ub2e8\ud55c \uaddc\uce59\uc744 \uae30\ubc18\uc73c\ub85c \uc544\uc774\ud15c\uc744 \ucd94\ucc9c\ud569\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4\uc11c 1\uc8fc\uc77c\uac04 \ud310\ub9e4\ub7c9\uc774 \uac00\uc7a5 \ub9ce\uc740 \uc21c\uc11c\ub300\ub85c \ubcf4\uc5ec\uc8fc\ub294 \uc2dd\uc758 \ubc29\uc2dd\uc744 \uc774\uc6a9\ud569\ub2c8\ub2e4. \uc774 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\ub0b4\ub294 \ubaa8\ub378\uc744 \ud544\uc694\ud574\uc9c8 \uacbd\uc6b0 \ub525\ub7ec\ub2dd\uc744 \uc774\uc6a9\ud55c \ubaa8\ub378\uc744 \uac1c\ubc1c\ud558\uae30 \uc2dc\uc791\ud569\ub2c8\ub2e4. \uc774\ub54c \ub9cc\ub4dc\ub294 \ubaa8\ub378\uc740 \uba38\uc2e0\ub7ec\ub2dd\uacfc \uac19\uc774 \uc815\ud574\uc9c4 \uc8fc\uae30\uc5d0 \ub530\ub77c \ubaa8\ub378\uc744 \uc7ac\ud559\uc2b5 \ud6c4 \uc7ac\ubc30\ud3ec\ud569\ub2c8\ub2e4."),(0,i.kt)("p",null,(0,i.kt)("img",{alt:"graph",src:n(2994).Z,width:"752",height:"582"})),(0,i.kt)("p",null,"\uc704\uc5d0\uc11c \uc124\uba85\ud55c \uac83\uc744 x\ucd95\uc744 \ubaa8\ub378\uc758 \ubcf5\uc7a1\ub3c4, y\ucd95\uc744 \ubaa8\ub378\uc758 \uc131\ub2a5\uc73c\ub85c \ub450\uace0 \uadf8\ub798\ud504\ub85c \ud45c\ud604\ud55c\ub2e4\uba74 \ub2e4\uc74c\uacfc \uac19\uc774 \ubcf5\uc7a1\ub3c4\uac00 \uc62c\ub77c\uac08 \ub54c \ubaa8\ub378\uc758 \uc131\ub2a5\uc774 \uc62c\ub77c\uac00\ub294 \uc0c1\uc2b9 \uad00\uacc4\ub97c \uac16\uc2b5\ub2c8\ub2e4. \uba38\uc2e0\ub7ec\ub2dd\uc5d0\uc11c \ub525\ub7ec\ub2dd\uc73c\ub85c \ub118\uc5b4\uac08 \uba38\uc2e0\ub7ec\ub2dd \ud300\uc774 \uc0c8\ub85c \uc0dd\uae30\uac8c \ub429\ub2c8\ub2e4."),(0,i.kt)("p",null,"\ub9cc\uc57d \uad00\ub9ac\ud574\uc57c\ud560 \ubaa8\ub378\uc774 \uc801\ub2e4\uba74 \uc11c\ub85c \ud611\uc5c5\uc744 \ud1b5\ud574\uc11c \ucda9\ubd84\ud788 \ud574\uacb0\ud560 \uc218 \uc788\uc9c0\ub9cc \uac1c\ubc1c\ud574\uc57c \ud560 \ubaa8\ub378\uc774 \ub9ce\uc544\uc9c4\ub2e4\uba74 DevOps\uc758 \uacbd\uc6b0\uc640 \uac19\uc774 \uc0ac\uc77c\ub85c \ud604\uc0c1\uc774 \ub098\ud0c0\ub098\uac8c \ub429\ub2c8\ub2e4."),(0,i.kt)("p",null,"DevOps\uc758 \ubaa9\ud45c\uc640 \ub9de\ucdb0\uc11c \uc0dd\uac01\ud574\ubcf4\uba74 MLOps\uc758 \ubaa9\ud45c\ub294 \uac1c\ubc1c\ud55c \ubaa8\ub378\uc774 \uc815\uc0c1\uc801\uc73c\ub85c \ubc30\ud3ec\ub420 \uc218 \uc788\ub294\uc9c0 \ud14c\uc2a4\ud2b8\ud558\ub294 \uac83\uc785\ub2c8\ub2e4. \uac1c\ubc1c\ud300\uc5d0\uc11c \uac1c\ubc1c\ud55c \uae30\ub2a5\uc774 \uc815\uc0c1\uc801\uc73c\ub85c \ubc30\ud3ec\ub420 \uc218 \uc788\ub294\uc9c0 \ud655\uc778\ud558\ub294 \uac83\uc774 DevOps\uc758 \ubaa9\ud45c\uc600\ub2e4\uba74, MLOps\uc758 \ubaa9\ud45c\ub294 \uba38\uc2e0\ub7ec\ub2dd \ud300\uc5d0\uc11c \uac1c\ubc1c\ud55c \ubaa8\ub378\uc774 \uc815\uc0c1\uc801\uc73c\ub85c \ubc30\ud3ec\ub420 \uc218 \uc788\ub294\uc9c0 \ud655\uc778\ud558\ub294 \uac83\uc785\ub2c8\ub2e4."),(0,i.kt)("h3",{id:"2-ml---ops"},"2) ML -> Ops"),(0,i.kt)("p",null,"\ud558\uc9c0\ub9cc \ucd5c\uadfc \ub098\uc624\uace0 \uc788\ub294 MLOps \uad00\ub828 \uc81c\ud488\uacfc \uc124\uba85\uc744 \ubcf4\uba74 \uaf2d \uc55e\uc5d0\uc11c \uc124\uba85\ud55c \ubaa9\ud45c\ub9cc\uc744 \ub300\uc0c1\uc73c\ub85c \ud558\uace0 \uc788\uc9c0 \uc54a\uc2b5\ub2c8\ub2e4.\n\uc5b4\ub5a4 \uacbd\uc6b0\uc5d0\ub294 \uba38\uc2e0\ub7ec\ub2dd \ud300\uc5d0\uc11c \ub9cc\ub4e0 \ubaa8\ub378\uc744 \uc774\uc6a9\ud574 \uc9c1\uc811 \uc6b4\uc601\uc744 \ud560 \uc218 \uc788\ub3c4\ub85d \ub3c4\uc640\uc8fc\ub824\uace0 \ud569\ub2c8\ub2e4. \uc774\ub7ec\ud55c \ub2c8\uc988\ub294 \ucd5c\uadfc 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Prometheus & Grafana",permalink:"/docs/setup-components/install-components-pg"}},i={},p=[{value:"Seldon-Core",id:"seldon-core",level:2},{value:"Selon-Core \uc124\uce58",id:"selon-core-\uc124\uce58",level:2},{value:"Ambassador - Helm Repository \ucd94\uac00",id:"ambassador---helm-repository-\ucd94\uac00",level:3},{value:"Ambassador - Helm Repository \uc5c5\ub370\uc774\ud2b8",id:"ambassador---helm-repository-\uc5c5\ub370\uc774\ud2b8",level:3},{value:"Ambassador - Helm Install",id:"ambassador---helm-install",level:3},{value:"Seldon-Core - Helm Install",id:"seldon-core---helm-install",level:3},{value:"References",id:"references",level:2}],d={toc:p},c="wrapper";function m(e){let{components:n,...t}=e;return(0,o.kt)(c,(0,a.Z)({},d,t,{components:n,mdxType:"MDXLayout"}),(0,o.kt)("h2",{id:"seldon-core"},"Seldon-Core"),(0,o.kt)("p",null,"Seldon-Core\ub294 \ucfe0\ubc84\ub124\ud2f0\uc2a4 \ud658\uacbd\uc5d0 \uc218\ub9ce\uc740 \uba38\uc2e0\ub7ec\ub2dd \ubaa8\ub378\uc744 \ubc30\ud3ec\ud558\uace0 \uad00\ub9ac\ud560 \uc218 \uc788\ub294 \uc624\ud508\uc18c\uc2a4 \ud504\ub808\uc784\uc6cc\ud06c \uc911 \ud558\ub098\uc785\ub2c8\ub2e4.",(0,o.kt)("br",{parentName:"p"}),"\n","\ub354 \uc790\uc138\ud55c \ub0b4\uc6a9\uc740 Seldon-Core \uc758 \uacf5\uc2dd ",(0,o.kt)("a",{parentName:"p",href:"https://www.seldon.io/tech/products/core/"},"\uc81c\ud488 \uc124\uba85 \ud398\uc774\uc9c0")," \uc640 ",(0,o.kt)("a",{parentName:"p",href:"https://github.com/SeldonIO/seldon-core"},"\uae43\ud5d9")," \uadf8\ub9ac\uace0 API Deployment \ud30c\ud2b8\ub97c \ucc38\uace0\ud574\uc8fc\uc2dc\uae30\ub97c \ubc14\ub78d\ub2c8\ub2e4."),(0,o.kt)("h2",{id:"selon-core-\uc124\uce58"},"Selon-Core \uc124\uce58"),(0,o.kt)("p",null,"Seldon-Core\ub97c \uc0ac\uc6a9\ud558\uae30 \uc704\ud574\uc11c\ub294 \ucfe0\ubc84\ub124\ud2f0\uc2a4\uc758 \uc778\uadf8\ub808\uc2a4(Ingress)\ub97c \ub2f4\ub2f9\ud558\ub294 Ambassador \uc640 Istio \uc640 \uac19\uc740 ",(0,o.kt)("a",{parentName:"p",href:"https://docs.seldon.io/projects/seldon-core/en/latest/workflow/install.html"},"\ubaa8\ub4c8\uc774 \ud544\uc694\ud569\ub2c8\ub2e4"),".",(0,o.kt)("br",{parentName:"p"}),"\n","Seldon-Core \uc5d0\uc11c\ub294 Ambassador \uc640 Istio \ub9cc\uc744 \uacf5\uc2dd\uc801\uc73c\ub85c \uc9c0\uc6d0\ud558\uba70, ",(0,o.kt)("em",{parentName:"p"},"\ubaa8\ub450\uc758 MLOps"),"\uc5d0\uc11c\ub294 Ambassador\ub97c \uc0ac\uc6a9\ud574 Seldon-core\ub97c \uc0ac\uc6a9\ud558\ubbc0\ub85c Ambassador\ub97c \uc124\uce58\ud558\uaca0\uc2b5\ub2c8\ub2e4."),(0,o.kt)("h3",{id:"ambassador---helm-repository-\ucd94\uac00"},"Ambassador - Helm Repository \ucd94\uac00"),(0,o.kt)("pre",null,(0,o.kt)("code",{parentName:"pre",className:"language-bash"},"helm repo add datawire https://www.getambassador.io\n")),(0,o.kt)("p",null,"\ub2e4\uc74c\uacfc \uac19\uc740 \uba54\uc2dc\uc9c0\uac00 \ucd9c\ub825\ub418\uba74 \uc815\uc0c1\uc801\uc73c\ub85c \ucd94\uac00\ub41c \uac83\uc744 \uc758\ubbf8\ud569\ub2c8\ub2e4."),(0,o.kt)("pre",null,(0,o.kt)("code",{parentName:"pre",className:"language-bash"},'"datawire" has been added to your repositories\n')),(0,o.kt)("h3",{id:"ambassador---helm-repository-\uc5c5\ub370\uc774\ud2b8"},"Ambassador - Helm Repository \uc5c5\ub370\uc774\ud2b8"),(0,o.kt)("pre",null,(0,o.kt)("code",{parentName:"pre",className:"language-bash"},"helm repo update\n")),(0,o.kt)("p",null,"\ub2e4\uc74c\uacfc \uac19\uc740 \uba54\uc2dc\uc9c0\uac00 \ucd9c\ub825\ub418\uba74 \uc815\uc0c1\uc801\uc73c\ub85c \uc5c5\ub370\uc774\ud2b8\ub41c \uac83\uc744 \uc758\ubbf8\ud569\ub2c8\ub2e4."),(0,o.kt)("pre",null,(0,o.kt)("code",{parentName:"pre",className:"language-bash"},'Hang tight while we grab the latest from your chart repositories...\n...Successfully got an update from the "datawire" chart repository\nUpdate Complete. \u2388Happy Helming!\u2388\n')),(0,o.kt)("h3",{id:"ambassador---helm-install"},"Ambassador - Helm Install"),(0,o.kt)("p",null,"ambassador Chart 6.9.3 \ubc84\uc804\uc744 \uc124\uce58\ud569\ub2c8\ub2e4."),(0,o.kt)("pre",null,(0,o.kt)("code",{parentName:"pre",className:"language-bash"},"helm install ambassador datawire/ambassador \\\n --namespace seldon-system \\\n --create-namespace \\\n --set image.repository=quay.io/datawire/ambassador \\\n --set enableAES=false \\\n --set crds.keep=false \\\n --version 6.9.3\n")),(0,o.kt)("p",null,"\ub2e4\uc74c\uacfc \uac19\uc740 \uba54\uc2dc\uc9c0\uac00 \ucd9c\ub825\ub418\uc5b4\uc57c \ud569\ub2c8\ub2e4."),(0,o.kt)("pre",null,(0,o.kt)("code",{parentName:"pre",className:"language-bash"},"\uc0dd\ub7b5...\n\nW1206 17:01:36.026326 26635 warnings.go:70] rbac.authorization.k8s.io/v1beta1 Role is deprecated in v1.17+, unavailable in v1.22+; use rbac.authorization.k8s.io/v1 Role\nW1206 17:01:36.029764 26635 warnings.go:70] rbac.authorization.k8s.io/v1beta1 RoleBinding is deprecated in v1.17+, unavailable in v1.22+; use rbac.authorization.k8s.io/v1 RoleBinding\nNAME: ambassador\nLAST DEPLOYED: Mon Dec 6 17:01:34 2021\nNAMESPACE: seldon-system\nSTATUS: deployed\nREVISION: 1\nNOTES:\n-------------------------------------------------------------------------------\n Congratulations! You've successfully installed Ambassador!\n\n-------------------------------------------------------------------------------\nTo get the IP address of Ambassador, run the following commands:\nNOTE: It may take a few minutes for the LoadBalancer IP to be available.\n You can watch the status of by running 'kubectl get svc -w --namespace seldon-system ambassador'\n\n On GKE/Azure:\n export SERVICE_IP=$(kubectl get svc --namespace seldon-system ambassador -o jsonpath='{.status.loadBalancer.ingress[0].ip}')\n\n On AWS:\n export SERVICE_IP=$(kubectl get svc --namespace seldon-system ambassador -o jsonpath='{.status.loadBalancer.ingress[0].hostname}')\n\n echo http://$SERVICE_IP:\n\nFor help, visit our Slack at http://a8r.io/Slack or view the documentation online at https://www.getambassador.io.\n")),(0,o.kt)("p",null,"seldon-system \uc5d0 4 \uac1c\uc758 pod \uac00 Running \uc774 \ub420 \ub54c\uae4c\uc9c0 \uae30\ub2e4\ub9bd\ub2c8\ub2e4."),(0,o.kt)("pre",null,(0,o.kt)("code",{parentName:"pre",className:"language-bash"},"kubectl get pod -n seldon-system\n")),(0,o.kt)("pre",null,(0,o.kt)("code",{parentName:"pre",className:"language-bash"},"ambassador-7f596c8b57-4s9xh 1/1 Running 0 7m15s\nambassador-7f596c8b57-dt6lr 1/1 Running 0 7m15s\nambassador-7f596c8b57-h5l6f 1/1 Running 0 7m15s\nambassador-agent-77bccdfcd5-d5jxj 1/1 Running 0 7m15s\n")),(0,o.kt)("h3",{id:"seldon-core---helm-install"},"Seldon-Core - Helm Install"),(0,o.kt)("p",null,"seldon-core-operator Chart 1.11.2 \ubc84\uc804\uc744 \uc124\uce58\ud569\ub2c8\ub2e4."),(0,o.kt)("pre",null,(0,o.kt)("code",{parentName:"pre",className:"language-bash"},"helm install seldon-core seldon-core-operator \\\n --repo https://storage.googleapis.com/seldon-charts \\\n --namespace seldon-system \\\n --set usageMetrics.enabled=true \\\n --set ambassador.enabled=true \\\n --version 1.11.2\n")),(0,o.kt)("p",null,"\ub2e4\uc74c\uacfc \uac19\uc740 \uba54\uc2dc\uc9c0\uac00 \ucd9c\ub825\ub418\uc5b4\uc57c \ud569\ub2c8\ub2e4."),(0,o.kt)("pre",null,(0,o.kt)("code",{parentName:"pre",className:"language-bash"},"\uc0dd\ub7b5...\n\nW1206 17:05:38.336391 28181 warnings.go:70] admissionregistration.k8s.io/v1beta1 ValidatingWebhookConfiguration is deprecated in v1.16+, unavailable in v1.22+; use admissionregistration.k8s.io/v1 ValidatingWebhookConfiguration\nNAME: seldon-core\nLAST DEPLOYED: Mon Dec 6 17:05:34 2021\nNAMESPACE: seldon-system\nSTATUS: deployed\nREVISION: 1\nTEST SUITE: None\n")),(0,o.kt)("p",null,"seldon-system namespace \uc5d0 1 \uac1c\uc758 seldon-controller-manager pod \uac00 Running \uc774 \ub420 \ub54c\uae4c\uc9c0 \uae30\ub2e4\ub9bd\ub2c8\ub2e4."),(0,o.kt)("pre",null,(0,o.kt)("code",{parentName:"pre",className:"language-bash"},"kubectl get pod -n seldon-system | grep seldon-controller\n")),(0,o.kt)("pre",null,(0,o.kt)("code",{parentName:"pre",className:"language-bash"},"seldon-controller-manager-8457b8b5c7-r2frm 1/1 Running 0 2m22s\n")),(0,o.kt)("h2",{id:"references"},"References"),(0,o.kt)("ul",null,(0,o.kt)("li",{parentName:"ul"},(0,o.kt)("a",{parentName:"li",href:"https://docs.seldon.io/projects/seldon-core/en/latest/examples/server_examples.html#examples-server-examples--page-root"},"Example Model Servers with Seldon"))))}m.isMDXComponent=!0}}]); \ No newline at end of file diff --git a/assets/js/165f2d18.1a36a9b4.js b/assets/js/165f2d18.1a36a9b4.js deleted file mode 100644 index 2ef61e57..00000000 --- a/assets/js/165f2d18.1a36a9b4.js +++ /dev/null @@ -1 +0,0 @@ -"use strict";(self.webpackChunkv_2=self.webpackChunkv_2||[]).push([[6397],{3905:(e,t,r)=>{r.d(t,{Zo:()=>p,kt:()=>f});var o=r(7294);function n(e,t,r){return t in e?Object.defineProperty(e,t,{value:r,enumerable:!0,configurable:!0,writable:!0}):e[t]=r,e}function a(e,t){var r=Object.keys(e);if(Object.getOwnPropertySymbols){var o=Object.getOwnPropertySymbols(e);t&&(o=o.filter((function(t){return Object.getOwnPropertyDescriptor(e,t).enumerable}))),r.push.apply(r,o)}return r}function s(e){for(var t=1;t=0||(n[r]=e[r]);return n}(e,t);if(Object.getOwnPropertySymbols){var a=Object.getOwnPropertySymbols(e);for(o=0;o=0||Object.prototype.propertyIsEnumerable.call(e,r)&&(n[r]=e[r])}return n}var l=o.createContext({}),d=function(e){var t=o.useContext(l),r=t;return e&&(r="function"==typeof e?e(t):s(s({},t),e)),r},p=function(e){var t=d(e.components);return o.createElement(l.Provider,{value:t},e.children)},u="mdxType",c={inlineCode:"code",wrapper:function(e){var t=e.children;return o.createElement(o.Fragment,{},t)}},b=o.forwardRef((function(e,t){var r=e.components,n=e.mdxType,a=e.originalType,l=e.parentName,p=i(e,["components","mdxType","originalType","parentName"]),u=d(r),b=n,f=u["".concat(l,".").concat(b)]||u[b]||c[b]||a;return r?o.createElement(f,s(s({ref:t},p),{},{components:r})):o.createElement(f,s({ref:t},p))}));function f(e,t){var r=arguments,n=t&&t.mdxType;if("string"==typeof e||n){var a=r.length,s=new Array(a);s[0]=b;var i={};for(var l in t)hasOwnProperty.call(t,l)&&(i[l]=t[l]);i.originalType=e,i[u]="string"==typeof e?e:n,s[1]=i;for(var d=2;d{r.r(t),r.d(t,{assets:()=>l,contentTitle:()=>s,default:()=>c,frontMatter:()=>a,metadata:()=>i,toc:()=>d});var o=r(7462),n=(r(7294),r(3905));const a={title:"3. 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Kubeflow\uc758 \uc6d0\ud65c\ud55c \uc0ac\uc6a9\uc744 \uc704\ud574, \ucfe0\ubc84\ub124\ud2f0\uc2a4 \ubc84\uc804\uc740 v1.21.7\ub85c \uc9c0\uc815\ud558\uc5ec \uad6c\ucd95\ud558\uba70 ",(0,r.kt)("inlineCode",{parentName:"p"},"--extra-config"),"\ub97c \ucd94\uac00\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"minikube start --driver=none \\\n --kubernetes-version=v1.21.7 \\\n --extra-config=apiserver.service-account-signing-key-file=/var/lib/minikube/certs/sa.key \\\n --extra-config=apiserver.service-account-issuer=kubernetes.default.svc\n")),(0,r.kt)("h3",{id:"disable-default-addons"},"Disable default addons"),(0,r.kt)("p",null,"Minikube\ub97c \uc124\uce58\ud558\uba74 Default\ub85c \uc124\uce58\ub418\ub294 addon\uc774 \uc874\uc7ac\ud569\ub2c8\ub2e4. \uc774 \uc911 \uc800\ud76c\uac00 \uc0ac\uc6a9\ud558\uc9c0 \uc54a\uc744 addon\uc744 \ube44\ud65c\uc131\ud654\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"minikube addons disable storage-provisioner\nminikube addons disable default-storageclass\n")),(0,r.kt)("p",null,"\ubaa8\ub4e0 addon\uc774 \ube44\ud65c\uc131\ud654\ub41c \uac83\uc744 \ud655\uc778\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"minikube addons list\n")),(0,r.kt)("p",null,"\ub2e4\uc74c\uacfc \uac19\uc740 \uba54\uc2dc\uc9c0\uac00 \ubcf4\uc774\uba74 \uc815\uc0c1\uc801\uc73c\ub85c \uc124\uce58\ub41c \uac83\uc744 \uc758\ubbf8\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"root@ubuntu:/home/mlops# minikube addons list\n|-----------------------------|----------|--------------|-----------------------|\n| ADDON NAME | PROFILE | STATUS | MAINTAINER |\n|-----------------------------|----------|--------------|-----------------------|\n| ambassador | minikube | disabled | unknown (third-party) |\n| auto-pause | minikube | disabled | google |\n| csi-hostpath-driver | minikube | disabled | kubernetes |\n| dashboard | minikube | disabled | kubernetes |\n| default-storageclass | minikube | disabled | kubernetes |\n| efk | minikube | disabled | unknown (third-party) |\n| freshpod | minikube | disabled | google |\n| gcp-auth | minikube | disabled | google |\n| gvisor | minikube | disabled | google |\n| helm-tiller | minikube | disabled | unknown (third-party) |\n| ingress | minikube | disabled | unknown (third-party) |\n| ingress-dns | minikube | disabled | unknown (third-party) |\n| istio | minikube | disabled | unknown (third-party) |\n| istio-provisioner | minikube | disabled | unknown (third-party) |\n| kubevirt | minikube | disabled | unknown (third-party) |\n| logviewer | minikube | disabled | google |\n| metallb | minikube | disabled | unknown (third-party) |\n| metrics-server | minikube | disabled | kubernetes |\n| nvidia-driver-installer | minikube | disabled | google |\n| nvidia-gpu-device-plugin | minikube | disabled | unknown (third-party) |\n| olm | minikube | disabled | unknown (third-party) |\n| pod-security-policy | minikube | disabled | unknown (third-party) |\n| portainer | minikube | disabled | portainer.io |\n| registry | minikube | disabled | google |\n| registry-aliases | minikube | disabled | unknown (third-party) |\n| registry-creds | minikube | disabled | unknown (third-party) |\n| storage-provisioner | minikube | disabled | kubernetes |\n| storage-provisioner-gluster | minikube | disabled | unknown (third-party) |\n| volumesnapshots | minikube | disabled | kubernetes |\n|-----------------------------|----------|--------------|-----------------------|\n")),(0,r.kt)("h2",{id:"3-\ucfe0\ubc84\ub124\ud2f0\uc2a4-\ud074\ub77c\uc774\uc5b8\ud2b8-\uc14b\uc5c5"},"3. \ucfe0\ubc84\ub124\ud2f0\uc2a4 \ud074\ub77c\uc774\uc5b8\ud2b8 \uc14b\uc5c5"),(0,r.kt)("p",null,"\uc774\ubc88\uc5d0\ub294 ",(0,r.kt)("strong",{parentName:"p"},"\ud074\ub77c\uc774\uc5b8\ud2b8"),"\uc5d0 \ucfe0\ubc84\ub124\ud2f0\uc2a4\uc758 \uc6d0\ud65c\ud55c \uc0ac\uc6a9\uc744 \uc704\ud55c \ub3c4\uad6c\ub97c \uc124\uce58\ud569\ub2c8\ub2e4.\n",(0,r.kt)("strong",{parentName:"p"},"\ud074\ub77c\uc774\uc5b8\ud2b8"),"\uc640 ",(0,r.kt)("strong",{parentName:"p"},"\ud074\ub7ec\uc2a4\ud130")," \ub178\ub4dc\uac00 \ubd84\ub9ac\ub418\uc9c0 \uc54a\uc740 \uacbd\uc6b0\uc5d0\ub294 root user\ub85c \ubaa8\ub4e0 \uc791\uc5c5\uc744 \uc9c4\ud589\ud574\uc57c \ud568\uc5d0 \uc8fc\uc758\ubc14\ub78d\ub2c8\ub2e4."),(0,r.kt)("p",null,(0,r.kt)("strong",{parentName:"p"},"\ud074\ub77c\uc774\uc5b8\ud2b8"),"\uc640 ",(0,r.kt)("strong",{parentName:"p"},"\ud074\ub7ec\uc2a4\ud130")," \ub178\ub4dc\uac00 \ubd84\ub9ac\ub41c \uacbd\uc6b0, \uc6b0\uc120 kubernetes\uc758 \uad00\ub9ac\uc790 \uc778\uc99d \uc815\ubcf4\ub97c ",(0,r.kt)("strong",{parentName:"p"},"\ud074\ub77c\uc774\uc5b8\ud2b8"),"\ub85c 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\uc0dd\uc131\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"# \ud074\ub77c\uc774\uc5b8\ud2b8 \ub178\ub4dc\nmkdir -p /home/$USER/.kube\n"))),(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("p",{parentName:"li"},"\ud574\ub2f9 \ud30c\uc77c\uc5d0 2. \uc5d0\uc11c \ucd9c\ub825\ub41c \uc815\ubcf4\ub97c \ubd99\uc5ec\ub123\uc740 \ub4a4 \uc800\uc7a5\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"vi /home/$USER/.kube/config\n")))),(0,r.kt)("h2",{id:"4-\ucfe0\ubc84\ub124\ud2f0\uc2a4-\uae30\ubcf8-\ubaa8\ub4c8-\uc124\uce58"},"4. \ucfe0\ubc84\ub124\ud2f0\uc2a4 \uae30\ubcf8 \ubaa8\ub4c8 \uc124\uce58"),(0,r.kt)("p",null,(0,r.kt)("a",{parentName:"p",href:"/docs/1.0/setup-kubernetes/install-kubernetes-module"},"Setup Kubernetes Modules"),"\uc744 \ucc38\uace0\ud558\uc5ec \ub2e4\uc74c \ucef4\ud3ec\ub10c\ud2b8\ub4e4\uc744 \uc124\uce58\ud574 \uc8fc\uc2dc\uae30 \ubc14\ub78d\ub2c8\ub2e4."),(0,r.kt)("ul",null,(0,r.kt)("li",{parentName:"ul"},"helm"),(0,r.kt)("li",{parentName:"ul"},"kustomize"),(0,r.kt)("li",{parentName:"ul"},"CSI plugin"),(0,r.kt)("li",{parentName:"ul"},"[Optional]"," nvidia-docker, nvidia-device-plugin")),(0,r.kt)("h2",{id:"5-\uc815\uc0c1-\uc124\uce58-\ud655\uc778"},"5. \uc815\uc0c1 \uc124\uce58 \ud655\uc778"),(0,r.kt)("p",null,"\ucd5c\uc885\uc801\uc73c\ub85c node\uac00 Ready \uc778\uc9c0, OS, Docker, Kubernetes \ubc84\uc804\uc744 \ud655\uc778\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"kubectl get nodes -o wide\n")),(0,r.kt)("p",null,"\ub2e4\uc74c\uacfc \uac19\uc740 \uba54\uc2dc\uc9c0\uac00 \ubcf4\uc774\uba74 \uc815\uc0c1\uc801\uc73c\ub85c \uc124\uce58\ub41c \uac83\uc744 \uc758\ubbf8\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"NAME STATUS ROLES AGE VERSION INTERNAL-IP EXTERNAL-IP OS-IMAGE KERNEL-VERSION 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nvidia-driver-installer | minikube | disabled | google |\n| nvidia-gpu-device-plugin | minikube | disabled | unknown (third-party) |\n| olm | minikube | disabled | unknown (third-party) |\n| pod-security-policy | minikube | disabled | unknown (third-party) |\n| portainer | minikube | disabled | portainer.io |\n| registry | minikube | disabled | google |\n| registry-aliases | minikube | disabled | unknown (third-party) |\n| registry-creds | minikube | disabled | unknown (third-party) |\n| storage-provisioner | minikube | disabled | kubernetes |\n| storage-provisioner-gluster | minikube | disabled | unknown (third-party) |\n| volumesnapshots | minikube | disabled | kubernetes |\n|-----------------------------|----------|--------------|-----------------------|\n")),(0,r.kt)("h2",{id:"3-\ucfe0\ubc84\ub124\ud2f0\uc2a4-\ud074\ub77c\uc774\uc5b8\ud2b8-\uc14b\uc5c5"},"3. \ucfe0\ubc84\ub124\ud2f0\uc2a4 \ud074\ub77c\uc774\uc5b8\ud2b8 \uc14b\uc5c5"),(0,r.kt)("p",null,"\uc774\ubc88\uc5d0\ub294 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Component - Environment",description:"",sidebar_position:9,contributors:["Jongseob Jeon"]},i=void 0,l={unversionedId:"kubeflow/advanced-environment",id:"version-1.0/kubeflow/advanced-environment",title:"9. Component - Environment",description:"",source:"@site/versioned_docs/version-1.0/kubeflow/advanced-environment.md",sourceDirName:"kubeflow",slug:"/kubeflow/advanced-environment",permalink:"/docs/1.0/kubeflow/advanced-environment",draft:!1,editUrl:"https://github.com/mlops-for-all/mlops-for-all.github.io/tree/main/versioned_docs/version-1.0/kubeflow/advanced-environment.md",tags:[],version:"1.0",lastUpdatedBy:"conqrean",lastUpdatedAt:1705274857,formattedLastUpdatedAt:"2024\ub144 1\uc6d4 14\uc77c",sidebarPosition:9,frontMatter:{title:"9. Component - Environment",description:"",sidebar_position:9,contributors:["Jongseob Jeon"]},sidebar:"tutorialSidebar",previous:{title:"8. Component - InputPath/OutputPath",permalink:"/docs/1.0/kubeflow/advanced-component"},next:{title:"10. 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Component - InputPath/OutputPath"),"\uc5d0\uc11c \uc791\uc131\ud55c \ud30c\uc774\ud504\ub77c\uc778\uc744 \uc2e4\ud589\ud558\uba74 \uc2e4\ud328\ud558\uac8c \ub429\ub2c8\ub2e4. \uc65c \uc2e4\ud328\ud558\ub294\uc9c0 \uc54c\uc544\ubcf4\uace0 \uc815\uc0c1\uc801\uc73c\ub85c \uc2e4\ud589\ub420 \uc218 \uc788\ub3c4\ub85d \uc218\uc815\ud569\ub2c8\ub2e4."),(0,r.kt)("h3",{id:"convert-to-kubeflow-format"},"Convert to Kubeflow Format"),(0,r.kt)("p",null,(0,r.kt)("a",{parentName:"p",href:"/docs/1.0/kubeflow/advanced-component#convert-to-kubeflow-format"},"\uc55e\uc5d0\uc11c \uc791\uc131\ud55c \ucef4\ud3ec\ub10c\ud2b8"),"\ub97c yaml\ud30c\uc77c\ub85c \ubcc0\ud658\ud558\ub3c4\ub85d \ud558\uaca0\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-python"},'from kfp.components import InputPath, OutputPath, create_component_from_func\n\n\n@create_component_from_func\ndef train_from_csv(\n train_data_path: InputPath("csv"),\n train_target_path: InputPath("csv"),\n model_path: OutputPath("dill"),\n kernel: str,\n):\n import dill\n import pandas as pd\n\n from sklearn.svm import SVC\n\n train_data = pd.read_csv(train_data_path)\n train_target = pd.read_csv(train_target_path)\n\n clf = SVC(kernel=kernel)\n clf.fit(train_data, train_target)\n\n with open(model_path, mode="wb") as file_writer:\n dill.dump(clf, file_writer)\n\n\nif __name__ == "__main__":\n train_from_csv.component_spec.save("train_from_csv.yaml")\n')),(0,r.kt)("p",null,"\uc704\uc758 \uc2a4\ud06c\ub9bd\ud2b8\ub97c \uc2e4\ud589\ud558\uba74 \ub2e4\uc74c\uacfc \uac19\uc740 ",(0,r.kt)("inlineCode",{parentName:"p"},"train_from_csv.yaml")," \ud30c\uc77c\uc744 \uc5bb\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},'name: Train from csv\ninputs:\n- {name: train_data, type: csv}\n- {name: train_target, type: csv}\n- {name: model, type: dill}\n- {name: kernel, type: String}\nimplementation:\n container:\n image: python:3.7\n command:\n - sh\n - -ec\n - |\n program_path=$(mktemp)\n printf "%s" "$0" > "$program_path"\n python3 -u "$program_path" "$@"\n - |\n def train_from_csv(\n train_data_path,\n train_target_path,\n model_path,\n kernel,\n ):\n import dill\n import pandas as pd\n\n from sklearn.svm import SVC\n\n train_data = pd.read_csv(train_data_path)\n train_target = pd.read_csv(train_target_path)\n\n clf = SVC(kernel=kernel)\n clf.fit(train_data, train_target)\n\n with open(model_path, mode="wb") as file_writer:\n dill.dump(clf, file_writer)\n\n import argparse\n _parser = argparse.ArgumentParser(prog=\'Train from csv\', description=\'\')\n _parser.add_argument("--train-data", dest="train_data_path", type=str, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--train-target", dest="train_target_path", type=str, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--model", dest="model_path", type=str, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--kernel", dest="kernel", type=str, required=True, default=argparse.SUPPRESS)\n _parsed_args = vars(_parser.parse_args())\n\n _outputs = train_from_csv(**_parsed_args)\n args:\n - --train-data\n - {inputPath: train_data}\n - --train-target\n - {inputPath: train_target}\n - --model\n - {inputPath: model}\n - --kernel\n - {inputValue: kernel}\n')),(0,r.kt)("p",null,"\uc55e\uc11c ",(0,r.kt)("a",{parentName:"p",href:"/docs/1.0/kubeflow/basic-component#convert-to-kubeflow-format"},"Basic Usage Component"),"\uc5d0\uc11c \uc124\uba85\ud55c \ub0b4\uc6a9\uc5d0 \ub530\ub974\uba74 \uc774 \ucef4\ud3ec\ub10c\ud2b8\ub294 \ub2e4\uc74c\uacfc \uac19\uc774 \uc2e4\ud589\ub429\ub2c8\ub2e4."),(0,r.kt)("ol",null,(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("inlineCode",{parentName:"li"},"docker pull python:3.7")),(0,r.kt)("li",{parentName:"ol"},"run ",(0,r.kt)("inlineCode",{parentName:"li"},"command"))),(0,r.kt)("p",null,"\ud558\uc9c0\ub9cc \uc704\uc5d0\uc11c \uc0dd\uc131\ub41c 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\ub54c\ubb38\uc5d0 \uc2e4\ud589\uc774 \uc548 \ub418\ub294\uc9c0 \ub208\uce58\ucc44\uc2e0 \ubd84\ub4e4\ub3c4 \uc788\uc744 \uac83\uc785\ub2c8\ub2e4."),(0,r.kt)("p",null,(0,r.kt)("inlineCode",{parentName:"p"},"python:3.7")," \uc774\ubbf8\uc9c0\uc5d0\ub294 \uc6b0\ub9ac\uac00 \uc0ac\uc6a9\ud558\uace0\uc790 \ud558\ub294 ",(0,r.kt)("inlineCode",{parentName:"p"},"dill"),", ",(0,r.kt)("inlineCode",{parentName:"p"},"pandas"),", ",(0,r.kt)("inlineCode",{parentName:"p"},"sklearn")," \uc774 \uc124\uce58\ub418\uc5b4 \uc788\uc9c0 \uc54a\uc2b5\ub2c8\ub2e4.",(0,r.kt)("br",{parentName:"p"}),"\n","\uadf8\ub7ec\ubbc0\ub85c \uc2e4\ud589\ud560 \ub54c \ud574\ub2f9 \ud328\ud0a4\uc9c0\uac00 \uc874\uc7ac\ud558\uc9c0 \uc54a\ub294\ub2e4\ub294 \uc5d0\ub7ec\uc640 \ud568\uaed8 \uc2e4\ud589\uc774 \uc548 \ub429\ub2c8\ub2e4."),(0,r.kt)("p",null,"\uadf8\ub7fc \uc5b4\ub5bb\uac8c \ud328\ud0a4\uc9c0\ub97c \ucd94\uac00\ud560 \uc218 \uc788\uc744\uae4c\uc694?"),(0,r.kt)("h2",{id:"\ud328\ud0a4\uc9c0-\ucd94\uac00-\ubc29\ubc95"},"\ud328\ud0a4\uc9c0 \ucd94\uac00 \ubc29\ubc95"),(0,r.kt)("p",null,"Kubeflow\ub97c \ubcc0\ud658\ud558\ub294 \uacfc\uc815\uc5d0\uc11c \ub450 \uac00\uc9c0 \ubc29\ubc95\uc744 \ud1b5\ud574 \ud328\ud0a4\uc9c0\ub97c \ucd94\uac00\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,r.kt)("ol",null,(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("inlineCode",{parentName:"li"},"base_image")," \uc0ac\uc6a9"),(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("inlineCode",{parentName:"li"},"package_to_install")," \uc0ac\uc6a9")),(0,r.kt)("p",null,"\ucef4\ud3ec\ub10c\ud2b8\ub97c \ucef4\ud30c\uc77c\ud560 \ub54c \uc0ac\uc6a9\ud588\ub358 \ud568\uc218 ",(0,r.kt)("inlineCode",{parentName:"p"},"create_component_from_func")," \uac00 \uc5b4\ub5a4 argument\ub4e4\uc744 \ubc1b\uc744 \uc218 \uc788\ub294\uc9c0 \ud655\uc778\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"def create_component_from_func(\n func: Callable,\n output_component_file: Optional[str] = None,\n base_image: Optional[str] = None,\n packages_to_install: List[str] = None,\n annotations: Optional[Mapping[str, str]] = None,\n):\n")),(0,r.kt)("ul",null,(0,r.kt)("li",{parentName:"ul"},(0,r.kt)("inlineCode",{parentName:"li"},"func"),": \ucef4\ud3ec\ub10c\ud2b8\ub85c \ub9cc\ub4e4 \ucef4\ud3ec\ub10c\ud2b8 \ub798\ud37c \ud568\uc218"),(0,r.kt)("li",{parentName:"ul"},(0,r.kt)("inlineCode",{parentName:"li"},"base_image"),": \ucef4\ud3ec\ub10c\ud2b8 \ub798\ud37c\uac00 \uc2e4\ud589\ud560 \uc774\ubbf8\uc9c0"),(0,r.kt)("li",{parentName:"ul"},(0,r.kt)("inlineCode",{parentName:"li"},"packages_to_install"),": \ucef4\ud3ec\ub10c\ud2b8\uc5d0\uc11c \uc0ac\uc6a9\ud574\uc11c \ucd94\uac00\ub85c \uc124\uce58\ud574\uc57c \ud558\ub294 \ud328\ud0a4\uc9c0")),(0,r.kt)("h3",{id:"1-base_image"},"1. base_image"),(0,r.kt)("p",null,"\ucef4\ud3ec\ub10c\ud2b8\uac00 \uc2e4\ud589\ub418\ub294 \uc21c\uc11c\ub97c \uc880 \ub354 \uc790\uc138\ud788 \ub4e4\uc5ec\ub2e4\ubcf4\uba74 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4."),(0,r.kt)("ol",null,(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("inlineCode",{parentName:"li"},"docker pull base_image")),(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("inlineCode",{parentName:"li"},"pip install packages_to_install")),(0,r.kt)("li",{parentName:"ol"},"run ",(0,r.kt)("inlineCode",{parentName:"li"},"command"))),(0,r.kt)("p",null,"\ub9cc\uc57d \ucef4\ud3ec\ub10c\ud2b8\uac00 \uc0ac\uc6a9\ud558\ub294 base_image\uc5d0 \ud328\ud0a4\uc9c0\ub4e4\uc774 \uc804\ubd80 \uc124\uce58\ub418\uc5b4 \uc788\ub2e4\uba74 \ucd94\uac00\uc801\uc778 \ud328\ud0a4\uc9c0 \uc124\uce58 \uc5c6\uc774 \ubc14\ub85c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,r.kt)("p",null,"\uc608\ub97c \ub4e4\uc5b4, \uc774\ubc88 \ud398\uc774\uc9c0\uc5d0\uc11c\ub294 \ub2e4\uc74c\uacfc \uac19\uc740 Dockerfile\uc744 \uc791\uc131\ud558\uaca0\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-dockerfile"},"FROM python:3.7\n\nRUN pip install dill pandas scikit-learn\n")),(0,r.kt)("p",null,"\uc704\uc758 Dockerfile\uc744 \uc774\uc6a9\ud574 \uc774\ubbf8\uc9c0\ub97c \ube4c\ub4dc\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc2e4\uc2b5\uc5d0\uc11c \uc0ac\uc6a9\ud574\ubcfc \ub3c4\ucee4 \ud5c8\ube0c\ub294 ghcr\uc785\ub2c8\ub2e4.",(0,r.kt)("br",{parentName:"p"}),"\n","\uac01\uc790 \ud658\uacbd\uc5d0 \ub9de\ucd94\uc5b4\uc11c \ub3c4\ucee4 \ud5c8\ube0c\ub97c \uc120\ud0dd \ud6c4 \uc5c5\ub85c\ub4dc\ud558\uba74 \ub429\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"docker build . -f Dockerfile -t ghcr.io/mlops-for-all/base-image\ndocker push ghcr.io/mlops-for-all/base-image\n")),(0,r.kt)("p",null,"\uc774\uc81c base_image\ub97c \uc785\ub825\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-python"},'from functools import partial\nfrom kfp.components import InputPath, OutputPath, create_component_from_func\n\n@partial(\n create_component_from_func,\n base_image="ghcr.io/mlops-for-all/base-image:latest",\n)\ndef train_from_csv(\n train_data_path: InputPath("csv"),\n train_target_path: InputPath("csv"),\n model_path: OutputPath("dill"),\n kernel: str,\n):\n import dill\n import pandas as pd\n\n from sklearn.svm import SVC\n\n train_data = pd.read_csv(train_data_path)\n train_target = pd.read_csv(train_target_path)\n\n clf = SVC(kernel=kernel)\n clf.fit(train_data, train_target)\n\n with open(model_path, mode="wb") as file_writer:\n dill.dump(clf, file_writer)\n\nif __name__ == "__main__":\n train_from_csv.component_spec.save("train_from_csv.yaml")\n')),(0,r.kt)("p",null,"\uc774\uc81c \uc0dd\uc131\ub41c \ucef4\ud3ec\ub10c\ud2b8\ub97c \ucef4\ud30c\uc77c\ud558\uba74 \ub2e4\uc74c\uacfc \uac19\uc774 \ub098\uc635\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},'name: Train from csv\ninputs:\n- {name: train_data, type: csv}\n- {name: train_target, type: csv}\n- {name: kernel, type: String}\noutputs:\n- {name: model, type: dill}\nimplementation:\n container:\n image: ghcr.io/mlops-for-all/base-image:latest\n command:\n - sh\n - -ec\n - |\n program_path=$(mktemp)\n printf "%s" "$0" > "$program_path"\n python3 -u "$program_path" "$@"\n - |\n def _make_parent_dirs_and_return_path(file_path: str):\n import os\n os.makedirs(os.path.dirname(file_path), exist_ok=True)\n return file_path\n\n def train_from_csv(\n train_data_path,\n train_target_path,\n model_path,\n kernel,\n ):\n import dill\n import pandas as pd\n\n from sklearn.svm import SVC\n\n train_data = pd.read_csv(train_data_path)\n train_target = pd.read_csv(train_target_path)\n\n clf = SVC(kernel=kernel)\n clf.fit(train_data, train_target)\n\n with open(model_path, mode="wb") as file_writer:\n dill.dump(clf, file_writer)\n\n import argparse\n _parser = argparse.ArgumentParser(prog=\'Train from csv\', description=\'\')\n _parser.add_argument("--train-data", dest="train_data_path", type=str, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--train-target", dest="train_target_path", type=str, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--kernel", dest="kernel", type=str, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--model", dest="model_path", type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)\n _parsed_args = vars(_parser.parse_args())\n\n _outputs = train_from_csv(**_parsed_args)\n args:\n - --train-data\n - {inputPath: train_data}\n - --train-target\n - {inputPath: train_target}\n - --kernel\n - {inputValue: kernel}\n - --model\n - {outputPath: model}\n')),(0,r.kt)("p",null,"base_image\uac00 \uc6b0\ub9ac\uac00 \uc124\uc815\ud55c \uac12\uc73c\ub85c \ubc14\ub010 \uac83\uc744 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,r.kt)("h3",{id:"2-packages_to_install"},"2. packages_to_install"),(0,r.kt)("p",null,"\ud558\uc9c0\ub9cc \ud328\ud0a4\uc9c0\uac00 \ucd94\uac00\ub420 \ub54c\ub9c8\ub2e4 docker \uc774\ubbf8\uc9c0\ub97c \uacc4\uc18d\ud574\uc11c \uc0c8\ub85c \uc0dd\uc131\ud558\ub294 \uc791\uc5c5\uc740 \ub9ce\uc740 \uc2dc\uac04\uc774 \uc18c\uc694\ub429\ub2c8\ub2e4.\n\uc774 \ub54c, ",(0,r.kt)("inlineCode",{parentName:"p"},"packages_to_install")," argument \ub97c \uc0ac\uc6a9\ud558\uba74 \ud328\ud0a4\uc9c0\ub97c \ucee8\ud14c\uc774\ub108\uc5d0 \uc27d\uac8c \ucd94\uac00\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-python"},'from functools import partial\nfrom kfp.components import InputPath, OutputPath, create_component_from_func\n\n@partial(\n create_component_from_func,\n packages_to_install=["dill==0.3.4", "pandas==1.3.4", "scikit-learn==1.0.1"],\n)\ndef train_from_csv(\n train_data_path: InputPath("csv"),\n train_target_path: InputPath("csv"),\n model_path: OutputPath("dill"),\n kernel: str,\n):\n import dill\n import pandas as pd\n\n from sklearn.svm import SVC\n\n train_data = pd.read_csv(train_data_path)\n train_target = pd.read_csv(train_target_path)\n\n clf = SVC(kernel=kernel)\n clf.fit(train_data, train_target)\n\n with open(model_path, mode="wb") as file_writer:\n dill.dump(clf, file_writer)\n\nif __name__ == "__main__":\n train_from_csv.component_spec.save("train_from_csv.yaml")\n')),(0,r.kt)("p",null,"\uc2a4\ud06c\ub9bd\ud2b8\ub97c \uc2e4\ud589\ud558\uba74 \ub2e4\uc74c\uacfc \uac19\uc740 ",(0,r.kt)("inlineCode",{parentName:"p"},"train_from_csv.yaml")," \ud30c\uc77c\uc774 \uc0dd\uc131\ub429\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},'name: Train from csv\ninputs:\n- {name: train_data, type: csv}\n- {name: train_target, type: csv}\n- {name: kernel, type: String}\noutputs:\n- {name: model, type: dill}\nimplementation:\n container:\n image: python:3.7\n command:\n - sh\n - -c\n - (PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location\n \'dill==0.3.4\' \'pandas==1.3.4\' \'scikit-learn==1.0.1\' || PIP_DISABLE_PIP_VERSION_CHECK=1\n python3 -m pip install --quiet --no-warn-script-location \'dill==0.3.4\' \'pandas==1.3.4\'\n \'scikit-learn==1.0.1\' --user) && "$0" "$@"\n - sh\n - -ec\n - |\n program_path=$(mktemp)\n printf "%s" "$0" > "$program_path"\n python3 -u "$program_path" "$@"\n - |\n def _make_parent_dirs_and_return_path(file_path: str):\n import os\n os.makedirs(os.path.dirname(file_path), exist_ok=True)\n return file_path\n\n def train_from_csv(\n train_data_path,\n train_target_path,\n model_path,\n kernel,\n ):\n import dill\n import pandas as pd\n\n from sklearn.svm import SVC\n\n train_data = pd.read_csv(train_data_path)\n train_target = pd.read_csv(train_target_path)\n\n clf = SVC(kernel=kernel)\n clf.fit(train_data, train_target)\n\n with open(model_path, mode="wb") as file_writer:\n dill.dump(clf, file_writer)\n\n import argparse\n _parser = argparse.ArgumentParser(prog=\'Train from csv\', description=\'\')\n _parser.add_argument("--train-data", dest="train_data_path", type=str, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--train-target", dest="train_target_path", type=str, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--kernel", dest="kernel", type=str, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--model", dest="model_path", type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)\n _parsed_args = vars(_parser.parse_args())\n\n _outputs = train_from_csv(**_parsed_args)\n args:\n - --train-data\n - {inputPath: train_data}\n - --train-target\n - {inputPath: train_target}\n - --kernel\n - {inputValue: kernel}\n - --model\n - {outputPath: model}\n')),(0,r.kt)("p",null,"\uc704\uc5d0 \uc791\uc131\ud55c \ucef4\ud3ec\ub10c\ud2b8\uac00 \uc2e4\ud589\ub418\ub294 \uc21c\uc11c\ub97c \uc880 \ub354 \uc790\uc138\ud788 \ub4e4\uc5ec\ub2e4\ubcf4\uba74 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4."),(0,r.kt)("ol",null,(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("inlineCode",{parentName:"li"},"docker pull python:3.7")),(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("inlineCode",{parentName:"li"},"pip install dill==0.3.4 pandas==1.3.4 scikit-learn==1.0.1")),(0,r.kt)("li",{parentName:"ol"},"run ",(0,r.kt)("inlineCode",{parentName:"li"},"command"))),(0,r.kt)("p",null,"\uc0dd\uc131\ub41c yaml \ud30c\uc77c\uc744 \uc790\uc138\ud788 \ubcf4\uba74, \ub2e4\uc74c\uacfc \uac19\uc740 \uc904\uc774 \uc790\ub3d9\uc73c\ub85c \ucd94\uac00\ub418\uc5b4 \ud544\uc694\ud55c \ud328\ud0a4\uc9c0\uac00 \uc124\uce58\ub418\uae30 \ub54c\ubb38\uc5d0 \uc624\ub958 \uc5c6\uc774 \uc815\uc0c1\uc801\uc73c\ub85c \uc2e4\ud589\ub429\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"}," command:\n - sh\n - -c\n - (PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location\n 'dill==0.3.4' 'pandas==1.3.4' 'scikit-learn==1.0.1' || PIP_DISABLE_PIP_VERSION_CHECK=1\n python3 -m pip install --quiet --no-warn-script-location 'dill==0.3.4' 'pandas==1.3.4'\n 'scikit-learn==1.0.1' --user) && \"$0\" \"$@\"\n")))}_.isMDXComponent=!0}}]); \ No newline at end of file diff --git a/assets/js/167d5ab1.ae443784.js b/assets/js/167d5ab1.ae443784.js new file mode 100644 index 00000000..8d7a79a7 --- /dev/null +++ b/assets/js/167d5ab1.ae443784.js @@ -0,0 +1 @@ +"use strict";(self.webpackChunkv_2=self.webpackChunkv_2||[]).push([[4177],{3905:(n,e,t)=>{t.d(e,{Zo:()=>m,kt:()=>c});var a=t(7294);function r(n,e,t){return e in n?Object.defineProperty(n,e,{value:t,enumerable:!0,configurable:!0,writable:!0}):n[e]=t,n}function p(n,e){var 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Component - Environment",description:"",sidebar_position:9,contributors:["Jongseob Jeon"]},i=void 0,l={unversionedId:"kubeflow/advanced-environment",id:"version-1.0/kubeflow/advanced-environment",title:"9. Component - Environment",description:"",source:"@site/versioned_docs/version-1.0/kubeflow/advanced-environment.md",sourceDirName:"kubeflow",slug:"/kubeflow/advanced-environment",permalink:"/docs/1.0/kubeflow/advanced-environment",draft:!1,editUrl:"https://github.com/mlops-for-all/mlops-for-all.github.io/tree/main/versioned_docs/version-1.0/kubeflow/advanced-environment.md",tags:[],version:"1.0",lastUpdatedBy:"Aiden-Jeon",lastUpdatedAt:1706783082,formattedLastUpdatedAt:"2024\ub144 2\uc6d4 1\uc77c",sidebarPosition:9,frontMatter:{title:"9. Component - Environment",description:"",sidebar_position:9,contributors:["Jongseob Jeon"]},sidebar:"tutorialSidebar",previous:{title:"8. Component - InputPath/OutputPath",permalink:"/docs/1.0/kubeflow/advanced-component"},next:{title:"10. 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model_path: OutputPath("dill"),\n kernel: str,\n):\n import dill\n import pandas as pd\n\n from sklearn.svm import SVC\n\n train_data = pd.read_csv(train_data_path)\n train_target = pd.read_csv(train_target_path)\n\n clf = SVC(kernel=kernel)\n clf.fit(train_data, train_target)\n\n with open(model_path, mode="wb") as file_writer:\n dill.dump(clf, file_writer)\n\n\nif __name__ == "__main__":\n train_from_csv.component_spec.save("train_from_csv.yaml")\n')),(0,r.kt)("p",null,"\uc704\uc758 \uc2a4\ud06c\ub9bd\ud2b8\ub97c \uc2e4\ud589\ud558\uba74 \ub2e4\uc74c\uacfc \uac19\uc740 ",(0,r.kt)("inlineCode",{parentName:"p"},"train_from_csv.yaml")," \ud30c\uc77c\uc744 \uc5bb\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},'name: Train from csv\ninputs:\n- {name: train_data, type: csv}\n- {name: train_target, type: csv}\n- {name: model, type: dill}\n- {name: kernel, type: String}\nimplementation:\n container:\n image: python:3.7\n command:\n - sh\n - -ec\n - |\n program_path=$(mktemp)\n printf "%s" "$0" > "$program_path"\n python3 -u "$program_path" "$@"\n - |\n def train_from_csv(\n train_data_path,\n train_target_path,\n model_path,\n kernel,\n ):\n import dill\n import pandas as pd\n\n from sklearn.svm import SVC\n\n train_data = pd.read_csv(train_data_path)\n train_target = pd.read_csv(train_target_path)\n\n clf = SVC(kernel=kernel)\n clf.fit(train_data, train_target)\n\n with open(model_path, mode="wb") as file_writer:\n dill.dump(clf, file_writer)\n\n import argparse\n _parser = argparse.ArgumentParser(prog=\'Train from csv\', description=\'\')\n _parser.add_argument("--train-data", dest="train_data_path", type=str, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--train-target", dest="train_target_path", type=str, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--model", dest="model_path", type=str, required=True, default=argparse.SUPPRESS)\n 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Dockerfile\uc744 \uc791\uc131\ud558\uaca0\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-dockerfile"},"FROM python:3.7\n\nRUN pip install dill pandas scikit-learn\n")),(0,r.kt)("p",null,"\uc704\uc758 Dockerfile\uc744 \uc774\uc6a9\ud574 \uc774\ubbf8\uc9c0\ub97c \ube4c\ub4dc\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc2e4\uc2b5\uc5d0\uc11c \uc0ac\uc6a9\ud574\ubcfc \ub3c4\ucee4 \ud5c8\ube0c\ub294 ghcr\uc785\ub2c8\ub2e4.",(0,r.kt)("br",{parentName:"p"}),"\n","\uac01\uc790 \ud658\uacbd\uc5d0 \ub9de\ucd94\uc5b4\uc11c \ub3c4\ucee4 \ud5c8\ube0c\ub97c \uc120\ud0dd \ud6c4 \uc5c5\ub85c\ub4dc\ud558\uba74 \ub429\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"docker build . -f Dockerfile -t ghcr.io/mlops-for-all/base-image\ndocker push ghcr.io/mlops-for-all/base-image\n")),(0,r.kt)("p",null,"\uc774\uc81c base_image\ub97c \uc785\ub825\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-python"},'from functools import partial\nfrom kfp.components import InputPath, OutputPath, create_component_from_func\n\n@partial(\n create_component_from_func,\n base_image="ghcr.io/mlops-for-all/base-image:latest",\n)\ndef train_from_csv(\n train_data_path: InputPath("csv"),\n train_target_path: InputPath("csv"),\n model_path: OutputPath("dill"),\n kernel: str,\n):\n import dill\n import pandas as pd\n\n from sklearn.svm import SVC\n\n train_data = pd.read_csv(train_data_path)\n train_target = pd.read_csv(train_target_path)\n\n clf = SVC(kernel=kernel)\n clf.fit(train_data, train_target)\n\n with open(model_path, mode="wb") as file_writer:\n dill.dump(clf, file_writer)\n\nif __name__ == "__main__":\n train_from_csv.component_spec.save("train_from_csv.yaml")\n')),(0,r.kt)("p",null,"\uc774\uc81c \uc0dd\uc131\ub41c \ucef4\ud3ec\ub10c\ud2b8\ub97c \ucef4\ud30c\uc77c\ud558\uba74 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open(model_path, mode="wb") as file_writer:\n dill.dump(clf, file_writer)\n\n import argparse\n _parser = argparse.ArgumentParser(prog=\'Train from csv\', description=\'\')\n _parser.add_argument("--train-data", dest="train_data_path", type=str, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--train-target", dest="train_target_path", type=str, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--kernel", dest="kernel", type=str, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--model", dest="model_path", type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)\n _parsed_args = vars(_parser.parse_args())\n\n _outputs = train_from_csv(**_parsed_args)\n args:\n - --train-data\n - {inputPath: train_data}\n - --train-target\n - {inputPath: train_target}\n - --kernel\n - {inputValue: kernel}\n - --model\n - {outputPath: model}\n')),(0,r.kt)("p",null,"base_image\uac00 \uc6b0\ub9ac\uac00 \uc124\uc815\ud55c 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"scikit-learn==1.0.1"],\n)\ndef train_from_csv(\n train_data_path: InputPath("csv"),\n train_target_path: InputPath("csv"),\n model_path: OutputPath("dill"),\n kernel: str,\n):\n import dill\n import pandas as pd\n\n from sklearn.svm import SVC\n\n train_data = pd.read_csv(train_data_path)\n train_target = pd.read_csv(train_target_path)\n\n clf = SVC(kernel=kernel)\n clf.fit(train_data, train_target)\n\n with open(model_path, mode="wb") as file_writer:\n dill.dump(clf, file_writer)\n\nif __name__ == "__main__":\n train_from_csv.component_spec.save("train_from_csv.yaml")\n')),(0,r.kt)("p",null,"\uc2a4\ud06c\ub9bd\ud2b8\ub97c \uc2e4\ud589\ud558\uba74 \ub2e4\uc74c\uacfc \uac19\uc740 ",(0,r.kt)("inlineCode",{parentName:"p"},"train_from_csv.yaml")," \ud30c\uc77c\uc774 \uc0dd\uc131\ub429\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},'name: Train from csv\ninputs:\n- {name: train_data, type: csv}\n- {name: train_target, type: csv}\n- {name: kernel, type: String}\noutputs:\n- {name: model, type: dill}\nimplementation:\n container:\n image: python:3.7\n command:\n - sh\n - -c\n - (PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location\n \'dill==0.3.4\' \'pandas==1.3.4\' \'scikit-learn==1.0.1\' || PIP_DISABLE_PIP_VERSION_CHECK=1\n python3 -m pip install --quiet --no-warn-script-location \'dill==0.3.4\' \'pandas==1.3.4\'\n \'scikit-learn==1.0.1\' --user) && "$0" "$@"\n - sh\n - -ec\n - |\n program_path=$(mktemp)\n printf "%s" "$0" > "$program_path"\n python3 -u "$program_path" "$@"\n - |\n def _make_parent_dirs_and_return_path(file_path: str):\n import os\n os.makedirs(os.path.dirname(file_path), exist_ok=True)\n return file_path\n\n def train_from_csv(\n train_data_path,\n train_target_path,\n model_path,\n kernel,\n ):\n import dill\n import pandas as pd\n\n from sklearn.svm import SVC\n\n train_data = pd.read_csv(train_data_path)\n train_target = pd.read_csv(train_target_path)\n\n clf = SVC(kernel=kernel)\n clf.fit(train_data, train_target)\n\n with open(model_path, mode="wb") as file_writer:\n dill.dump(clf, file_writer)\n\n import argparse\n _parser = argparse.ArgumentParser(prog=\'Train from csv\', description=\'\')\n _parser.add_argument("--train-data", dest="train_data_path", type=str, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--train-target", dest="train_target_path", type=str, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--kernel", dest="kernel", type=str, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--model", dest="model_path", type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)\n _parsed_args = vars(_parser.parse_args())\n\n _outputs = train_from_csv(**_parsed_args)\n args:\n - --train-data\n - {inputPath: train_data}\n - --train-target\n - {inputPath: train_target}\n - --kernel\n - {inputValue: kernel}\n - --model\n - {outputPath: 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",(0,r.kt)("inlineCode",{parentName:"p"},"ubuntu:18.04")," \uc774\ubbf8\uc9c0\ub97c pull \ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"docker pull ubuntu:18.04\n")),(0,r.kt)("p",null,"\uc704 \uba85\ub839\uc5b4\ub97c \ud574\uc11d\ud558\uba74 ",(0,r.kt)("inlineCode",{parentName:"p"},"ubuntu")," \ub77c\ub294 \uc774\ub984\uc744 \uac00\uc9c4 \uc774\ubbf8\uc9c0 \uc911 ",(0,r.kt)("inlineCode",{parentName:"p"},"18.04")," \ud0dc\uadf8\uac00 \ub2ec\ub824\uc788\ub294 \uc774\ubbf8\uc9c0\ub97c \uac00\uc838\uc624\ub77c\ub294 \ub73b\uc785\ub2c8\ub2e4."),(0,r.kt)("p",null,"\ub9cc\uc57d, \uc815\uc0c1\uc801\uc73c\ub85c \uc218\ud589\ub41c\ub2e4\uba74 \ub2e4\uc74c\uacfc \ube44\uc2b7\ud558\uac8c \ucd9c\ub825\ub429\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"18.04: Pulling from library/ubuntu\n20d796c36622: Pull complete \nDigest: sha256:42cd9143b6060261187a72716906187294b8b66653b50d70bc7a90ccade5c984\nStatus: Downloaded newer image for ubuntu:18.04\ndocker.io/library/ubuntu:18.04\n")),(0,r.kt)("p",null,"\uc704\uc758 \uba85\ub839\uc5b4\ub97c \uc218\ud589\ud558\uba74 ",(0,r.kt)("a",{parentName:"p",href:"http://docker.io/library/"},"docker.io/library")," \ub77c\ub294 \uc774\ub984\uc758 registry \uc5d0\uc11c ubuntu:18.04 \ub77c\ub294 image \ub97c \uc5ec\ub7ec\ubd84\uc758 \ub178\ud2b8\ubd81\uc5d0 \ub2e4\uc6b4\ub85c\ub4dc \ubc1b\uac8c\ub429\ub2c8\ub2e4."),(0,r.kt)("ul",null,(0,r.kt)("li",{parentName:"ul"},"\ucc38\uace0\uc0ac\ud56d",(0,r.kt)("ul",{parentName:"li"},(0,r.kt)("li",{parentName:"ul"},"\ucd94\ud6c4 ",(0,r.kt)("a",{parentName:"li",href:"http://docker.io"},"docker.io")," \ub098 public \ud55c docker hub \uc640 \uac19\uc740 registry \ub300\uc2e0\uc5d0, \ud2b9\uc815 ",(0,r.kt)("strong",{parentName:"li"},"private")," \ud55c registry \uc5d0\uc11c docker image \ub97c \uac00\uc838\uc640\uc57c \ud558\ub294 \uacbd\uc6b0\uc5d0\ub294, ",(0,r.kt)("a",{parentName:"li",href:"https://docs.docker.com/engine/reference/commandline/login/"},(0,r.kt)("inlineCode",{parentName:"a"},"docker login"))," \uc744 \ud1b5\ud574\uc11c \ud2b9\uc815 registry \ub97c \ubc14\ub77c\ubcf4\ub3c4\ub85d \ud55c \ub4a4, docker pull \uc744 \uc218\ud589\ud558\ub294 \ud615\ud0dc\ub85c \uc0ac\uc6a9\ud569\ub2c8\ub2e4. \ud639\uc740 insecure registry \ub97c \uc124\uc815\ud558\ub294 ",(0,r.kt)("a",{parentName:"li",href:"https://stackoverflow.com/questions/42211380/add-insecure-registry-to-docker"},"\ubc29\uc548"),"\ub3c4 \ud65c\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,r.kt)("li",{parentName:"ul"},"\ud3d0\uc1c4\ub9dd\uc5d0\uc11c docker image \ub97c ",(0,r.kt)("inlineCode",{parentName:"li"},".tar")," \ud30c\uc77c\uacfc \uac19\uc740 \ud615\ud0dc\ub85c \uc800\uc7a5\ud558\uace0 \uacf5\uc720\ud560 \uc218 \uc788\ub3c4\ub85d ",(0,r.kt)("a",{parentName:"li",href:"https://docs.docker.com/engine/reference/commandline/save/"},(0,r.kt)("inlineCode",{parentName:"a"},"docker save")),", ",(0,r.kt)("a",{parentName:"li",href:"https://docs.docker.com/engine/reference/commandline/load/"},(0,r.kt)("inlineCode",{parentName:"a"},"docker load"))," \uc640 \uac19\uc740 \uba85\ub839\uc5b4\ub3c4 \uc874\uc7ac\ud569\ub2c8\ub2e4.")))),(0,r.kt)("h2",{id:"3-docker-images"},"3. Docker images"),(0,r.kt)("p",null,"\ub85c\uceec\uc5d0 \uc874\uc7ac\ud558\ub294 docker image \ub9ac\uc2a4\ud2b8\ub97c \ucd9c\ub825\ud558\ub294 \ucee4\ub9e8\ub4dc\uc785\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"docker images --help\n")),(0,r.kt)("p",null,"docker images\uc5d0\uc11c \uc0ac\uc6a9\ud560 \uc218 \uc788\ub294 argument\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"Usage: docker images [OPTIONS] [REPOSITORY[:TAG]]\n\nList images\n\nOptions:\n -a, --all Show all images (default hides intermediate images)\n --digests Show digests\n -f, --filter filter Filter output based on conditions provided\n --format string Pretty-print images using a Go template\n --no-trunc Don't truncate output\n -q, --quiet Only show image IDs\n")),(0,r.kt)("p",null,"\uc544\ub798 \uba85\ub839\uc5b4\ub97c \uc774\uc6a9\ud574 \uc9c1\uc811 \uc2e4\ud589\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"docker images\n")),(0,r.kt)("p",null,"\ub9cc\uc57d \ub3c4\ucee4\ub97c \ucd5c\ucd08 \uc124\uce58 \ud6c4 \uc774 \uc2e4\uc2b5\uc744 \uc9c4\ud589\ud55c\ub2e4\uba74 \ub2e4\uc74c\uacfc \ube44\uc2b7\ud558\uac8c \ucd9c\ub825\ub429\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"REPOSITORY TAG IMAGE ID CREATED SIZE\nubuntu 18.04 29e70752d7b2 2 days ago 56.7MB\n")),(0,r.kt)("p",null,"\uc904 \uc218 \uc788\ub294 argument\uc911 ",(0,r.kt)("inlineCode",{parentName:"p"},"-q"),"\ub97c \uc0ac\uc6a9\ud558\uba74 ",(0,r.kt)("inlineCode",{parentName:"p"},"IMAGE ID")," \ub9cc \ucd9c\ub825\ub429\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"docker images -q\n")),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"29e70752d7b2\n")),(0,r.kt)("h2",{id:"4-docker-ps"},"4. Docker ps"),(0,r.kt)("p",null,"\ud604\uc7ac \uc2e4\ud589 \uc911\uc778 \ub3c4\ucee4 \ucee8\ud14c\uc774\ub108 \ub9ac\uc2a4\ud2b8\ub97c \ucd9c\ub825\ud558\ub294 \ucee4\ub9e8\ub4dc\uc785\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"docker ps --help\n")),(0,r.kt)("p",null,"docker ps\uc5d0\uc11c \uc0ac\uc6a9\ud560 \uc218 \uc788\ub294 argument\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"Usage: docker ps [OPTIONS]\n\nList containers\n\nOptions:\n -a, --all Show all containers (default shows just running)\n -f, --filter filter Filter output based on conditions provided\n --format string Pretty-print containers using a Go template\n -n, --last int Show n last created containers (includes all states) (default -1)\n -l, --latest Show the latest created container (includes all states)\n --no-trunc Don't truncate output\n -q, --quiet Only display container IDs\n -s, --size Display total file sizes\n")),(0,r.kt)("p",null,"\uc544\ub798 \uba85\ub839\uc5b4\ub97c \uc774\uc6a9\ud574 \uc9c1\uc811 \uc2e4\ud589\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"docker ps\n")),(0,r.kt)("p",null,"\ud604\uc7ac \uc2e4\ud589 \uc911\uc778 \ucee8\ud14c\uc774\ub108\uac00 \uc5c6\ub2e4\uba74 \ub2e4\uc74c\uacfc \uac19\uc774 \ub098\uc635\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES\n")),(0,r.kt)("p",null,"\ub9cc\uc57d \uc2e4\ud589\ub418\ub294 \ucee8\ud14c\uc774\ub108\uac00 \uc788\ub2e4\uba74 \ub2e4\uc74c\uacfc \ube44\uc2b7\ud558\uac8c \ub098\uc635\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},'CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES\nc1e8f5e89d8d ubuntu "sleep 3600" 13 seconds ago Up 12 seconds trusting_newton\n')),(0,r.kt)("h2",{id:"5-docker-run"},"5. Docker run"),(0,r.kt)("p",null,"\ub3c4\ucee4 \ucee8\ud14c\uc774\ub108\ub97c \uc2e4\ud589\uc2dc\ud0a4\ub294 \ucee4\ub9e8\ub4dc\uc785\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"docker run --help\n")),(0,r.kt)("p",null,"docker run\uc744 \uc2e4\ud589\ud558\ub294 \uba85\ub839\uc5b4\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"Usage: docker run [OPTIONS] IMAGE [COMMAND] [ARG...]\n\nRun a command in a new container\n")),(0,r.kt)("p",null,"\uc5ec\uae30\uc11c \uc6b0\ub9ac\uac00 \ud655\uc778\ud574\uc57c \ud558\ub294 \uac83\uc740 \ubc14\ub85c docker run\uc740 \uc138 \uac1c \ud0c0\uc785\uc758 argument\ub97c \ubc1b\ub294\ub2e4\ub294 \uac83\uc744 \uc54c \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,r.kt)("ol",null,(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("inlineCode",{parentName:"li"},"[OPTIONS]")),(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("inlineCode",{parentName:"li"},"[COMMAND]")),(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("inlineCode",{parentName:"li"},"[ARG...]"))),(0,r.kt)("p",null,"\uc9c1\uc811 \ub3c4\ucee4 \ucee8\ud14c\uc774\ub108\ub97c \uc2e4\ud589\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"## Usage: docker run [OPTIONS] IMAGE [COMMAND] [ARG...]\ndocker run -it --name demo1 ubuntu:18.04 /bin/bash\n")),(0,r.kt)("ul",null,(0,r.kt)("li",{parentName:"ul"},(0,r.kt)("inlineCode",{parentName:"li"},"-it")," : ",(0,r.kt)("inlineCode",{parentName:"li"},"-i")," \uc635\uc158 + ",(0,r.kt)("inlineCode",{parentName:"li"},"-t")," \uc635\uc158",(0,r.kt)("ul",{parentName:"li"},(0,r.kt)("li",{parentName:"ul"},"container \ub97c \uc2e4\ud589\uc2dc\ud0b4\uacfc \ub3d9\uc2dc\uc5d0 interactive \ud55c terminal \ub85c \uc811\uc18d\uc2dc\ucf1c\uc8fc\ub294 \uc635\uc158"))),(0,r.kt)("li",{parentName:"ul"},(0,r.kt)("inlineCode",{parentName:"li"},"--name")," : name",(0,r.kt)("ul",{parentName:"li"},(0,r.kt)("li",{parentName:"ul"},"\ucee8\ud14c\uc774\ub108 id \ub300\uc2e0, \uad6c\ubd84\ud558\uae30 \uc27d\ub3c4\ub85d \uc9c0\uc815\ud574\uc8fc\ub294 \uc774\ub984"))),(0,r.kt)("li",{parentName:"ul"},(0,r.kt)("inlineCode",{parentName:"li"},"/bin/bash"),(0,r.kt)("ul",{parentName:"li"},(0,r.kt)("li",{parentName:"ul"},"\ucee8\ud14c\uc774\ub108\ub97c \uc2e4\ud589\uc2dc\ud0b4\uacfc \ub3d9\uc2dc\uc5d0 \uc2e4\ud589\ud560 \ucee4\ub9e8\ub4dc\ub85c, ",(0,r.kt)("inlineCode",{parentName:"li"},"/bin/bash")," \ub294 bash \uc258\uc744 \uc5ec\ub294 \uac83\uc744 \uc758\ubbf8\ud569\ub2c8\ub2e4.")))),(0,r.kt)("p",null,"\uc2e4\ud589 \ud6c4 ",(0,r.kt)("inlineCode",{parentName:"p"},"exit")," \uba85\ub839\uc5b4\ub97c \ud1b5\ud574 \ucee8\ud14c\uc774\ub108\ub97c \uc885\ub8cc\ud569\ub2c8\ub2e4."),(0,r.kt)("p",null,"\uc774 \uc81c \uc55e\uc11c \ubc30\uc6e0\ub358 ",(0,r.kt)("inlineCode",{parentName:"p"},"docker ps")," \uba85\ub839\uc5b4\ub97c \uce58\uba74 \ub2e4\uc74c\uacfc \uac19\uc774 \ub098\uc635\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES\n")),(0,r.kt)("p",null,"\uc2e4\ud589\ub418\uace0 \uc788\ub294 \ucee8\ud14c\uc774\ub108\uac00 \ub098\uc628\ub2e4\uace0 \ud588\uc9c0\ub9cc \uc5b4\uc9f8\uc11c\uc778\uc9c0 \ubc29\uae08 \uc2e4\ud589\ud55c \ucee8\ud14c\uc774\ub108\uac00 \ubcf4\uc774\uc9c0 \uc54a\uc2b5\ub2c8\ub2e4.\n\uadf8 \uc774\uc720\ub294 ",(0,r.kt)("inlineCode",{parentName:"p"},"docker ps"),"\ub294 \uae30\ubcf8\uac12\uc73c\ub85c \ud604\uc7ac \uc2e4\ud589 \uc911\uc778 \ucee8\ud14c\uc774\ub108\ub97c \ubcf4\uc5ec\uc8fc\uae30 \ub54c\ubb38\uc785\ub2c8\ub2e4."),(0,r.kt)("p",null,"\ub9cc\uc57d \uc885\ub8cc\ub41c \ucee8\ud14c\uc774\ub108\ub4e4\ub3c4 \ubcf4\uace0 \uc2f6\ub2e4\uba74 ",(0,r.kt)("inlineCode",{parentName:"p"},"-a")," \uc635\uc158\uc744 \uc8fc\uc5b4\uc57c \ud569\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"docker ps -a\n")),(0,r.kt)("p",null,"\uadf8\ub7ec\uba74 \ub2e4\uc74c\uacfc \uac19\uc774 \uc885\ub8cc\ub41c \ucee8\ud14c\uc774\ub108 \ubaa9\ub85d\ub3c4 \ub098\uc635\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},'CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES\n4c1aa74a382a ubuntu:18.04 "/bin/bash" 2 minutes ago Exited (0) 2 minutes ago demo1\n')),(0,r.kt)("h2",{id:"6-docker-exec"},"6. Docker exec"),(0,r.kt)("p",null,"Docker \ucee8\ud14c\uc774\ub108 \ub0b4\ubd80\uc5d0\uc11c \uba85\ub839\uc744 \ub0b4\ub9ac\uac70\ub098, \ub0b4\ubd80\ub85c \uc811\uc18d\ud558\ub294 \ucee4\ub9e8\ub4dc\uc785\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"docker exec --help\n")),(0,r.kt)("p",null,"\uc608\ub97c \ub4e4\uc5b4\uc11c \ub2e4\uc74c\uacfc \uac19\uc740 \uba85\ub839\uc5b4\ub97c \uc2e4\ud589\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"docker run -d --name demo2 ubuntu:18.04 sleep 3600\n")),(0,r.kt)("p",null,"\uc5ec\uae30\uc11c ",(0,r.kt)("inlineCode",{parentName:"p"},"-d")," \uc635\uc158\uc740 \ub3c4\ucee4 \ucee8\ud14c\uc774\ub108\ub97c \ubc31\uadf8\ub77c\uc6b4\ub4dc\uc5d0\uc11c \uc2e4\ud589\uc2dc\ucf1c\uc11c, \ucee8\ud14c\uc774\ub108\uc5d0\uc11c \uc811\uc18d \uc885\ub8cc\ub97c \ud558\ub354\ub77c\ub3c4, \uacc4\uc18d \uc2e4\ud589 \uc911\uc774 \ub418\ub3c4\ub85d \ud558\ub294 \ucee4\ub9e8\ub4dc\uc785\ub2c8\ub2e4."),(0,r.kt)("p",null,(0,r.kt)("inlineCode",{parentName:"p"},"docker ps"),"\ub97c \ud1b5\ud574 \ud604\uc7ac \uc2e4\ud589\uc911\uc778\uc9c0 \ud655\uc778\ud569\ub2c8\ub2e4."),(0,r.kt)("p",null,"\ub2e4\uc74c\uacfc \uac19\uc774 \uc2e4\ud589 \uc911\uc784\uc744 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},'CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES\nfc88a83e90f0 ubuntu:18.04 "sleep 3600" 4 seconds ago Up 3 seconds demo2\n')),(0,r.kt)("p",null,"\uc774\uc81c ",(0,r.kt)("inlineCode",{parentName:"p"},"docker exec")," \uba85\ub839\uc5b4\ub97c \ud1b5\ud574\uc11c \uc2e4\ud589\uc911\uc778 \ub3c4\ucee4 \ucee8\ud14c\uc774\ub108\uc5d0 \uc811\uc18d\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"docker exec -it demo2 /bin/bash\n")),(0,r.kt)("p",null,"\uc774 \uc804\uc758 ",(0,r.kt)("inlineCode",{parentName:"p"},"docker run"),"\uacfc \ub3d9\uc77c\ud558\uac8c container \ub0b4\ubd80\uc5d0 \uc811\uc18d\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,r.kt)("p",null,(0,r.kt)("inlineCode",{parentName:"p"},"exit"),"\uc744 \ud1b5\ud574 \uc885\ub8cc\ud569\ub2c8\ub2e4."),(0,r.kt)("h2",{id:"7-docker-logs"},"7. 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the repository\n --disable-content-trust Skip image verification (default true)\n --platform string Set platform if server is multi-platform capable\n -q, --quiet Suppress verbose output\n")),(0,r.kt)("p",null,"\uc5ec\uae30\uc11c \uc54c \uc218 \uc788\ub294 \uac83\uc740 \ubc14\ub85c docker pull\uc740 \ub450 \uac1c \ud0c0\uc785\uc758 argument\ub97c \ubc1b\ub294\ub2e4\ub294 \uac83\uc744 \uc54c \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,r.kt)("ol",null,(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("inlineCode",{parentName:"li"},"[OPTIONS]")),(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("inlineCode",{parentName:"li"},"NAME[:TAG|@DIGEST]"))),(0,r.kt)("p",null,"help\uc5d0\uc11c \ub098\uc628 ",(0,r.kt)("inlineCode",{parentName:"p"},"-a"),", -",(0,r.kt)("inlineCode",{parentName:"p"},"q")," \uc635\uc158\uc744 \uc0ac\uc6a9\ud558\uae30 \uc704\ud574\uc11c\ub294 NAME \uc55e\uc5d0\uc11c \uc0ac\uc6a9\ud574\uc57c \ud569\ub2c8\ub2e4."),(0,r.kt)("p",null,"\uc9c1\uc811 ",(0,r.kt)("inlineCode",{parentName:"p"},"ubuntu:18.04")," \uc774\ubbf8\uc9c0\ub97c pull \ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"docker pull ubuntu:18.04\n")),(0,r.kt)("p",null,"\uc704 \uba85\ub839\uc5b4\ub97c \ud574\uc11d\ud558\uba74 ",(0,r.kt)("inlineCode",{parentName:"p"},"ubuntu")," \ub77c\ub294 \uc774\ub984\uc744 \uac00\uc9c4 \uc774\ubbf8\uc9c0 \uc911 ",(0,r.kt)("inlineCode",{parentName:"p"},"18.04")," \ud0dc\uadf8\uac00 \ub2ec\ub824\uc788\ub294 \uc774\ubbf8\uc9c0\ub97c \uac00\uc838\uc624\ub77c\ub294 \ub73b\uc785\ub2c8\ub2e4."),(0,r.kt)("p",null,"\ub9cc\uc57d, \uc815\uc0c1\uc801\uc73c\ub85c \uc218\ud589\ub41c\ub2e4\uba74 \ub2e4\uc74c\uacfc \ube44\uc2b7\ud558\uac8c \ucd9c\ub825\ub429\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"18.04: Pulling from library/ubuntu\n20d796c36622: Pull complete \nDigest: sha256:42cd9143b6060261187a72716906187294b8b66653b50d70bc7a90ccade5c984\nStatus: Downloaded newer image for ubuntu:18.04\ndocker.io/library/ubuntu:18.04\n")),(0,r.kt)("p",null,"\uc704\uc758 \uba85\ub839\uc5b4\ub97c \uc218\ud589\ud558\uba74 ",(0,r.kt)("a",{parentName:"p",href:"http://docker.io/library/"},"docker.io/library")," \ub77c\ub294 \uc774\ub984\uc758 registry \uc5d0\uc11c ubuntu:18.04 \ub77c\ub294 image \ub97c \uc5ec\ub7ec\ubd84\uc758 \ub178\ud2b8\ubd81\uc5d0 \ub2e4\uc6b4\ub85c\ub4dc \ubc1b\uac8c\ub429\ub2c8\ub2e4."),(0,r.kt)("ul",null,(0,r.kt)("li",{parentName:"ul"},"\ucc38\uace0\uc0ac\ud56d",(0,r.kt)("ul",{parentName:"li"},(0,r.kt)("li",{parentName:"ul"},"\ucd94\ud6c4 ",(0,r.kt)("a",{parentName:"li",href:"http://docker.io"},"docker.io")," \ub098 public \ud55c docker hub \uc640 \uac19\uc740 registry \ub300\uc2e0\uc5d0, \ud2b9\uc815 ",(0,r.kt)("strong",{parentName:"li"},"private")," \ud55c registry \uc5d0\uc11c docker image \ub97c \uac00\uc838\uc640\uc57c \ud558\ub294 \uacbd\uc6b0\uc5d0\ub294, ",(0,r.kt)("a",{parentName:"li",href:"https://docs.docker.com/engine/reference/commandline/login/"},(0,r.kt)("inlineCode",{parentName:"a"},"docker login"))," \uc744 \ud1b5\ud574\uc11c \ud2b9\uc815 registry \ub97c \ubc14\ub77c\ubcf4\ub3c4\ub85d \ud55c \ub4a4, docker pull \uc744 \uc218\ud589\ud558\ub294 \ud615\ud0dc\ub85c \uc0ac\uc6a9\ud569\ub2c8\ub2e4. \ud639\uc740 insecure registry \ub97c \uc124\uc815\ud558\ub294 ",(0,r.kt)("a",{parentName:"li",href:"https://stackoverflow.com/questions/42211380/add-insecure-registry-to-docker"},"\ubc29\uc548"),"\ub3c4 \ud65c\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,r.kt)("li",{parentName:"ul"},"\ud3d0\uc1c4\ub9dd\uc5d0\uc11c docker image \ub97c ",(0,r.kt)("inlineCode",{parentName:"li"},".tar")," \ud30c\uc77c\uacfc \uac19\uc740 \ud615\ud0dc\ub85c \uc800\uc7a5\ud558\uace0 \uacf5\uc720\ud560 \uc218 \uc788\ub3c4\ub85d ",(0,r.kt)("a",{parentName:"li",href:"https://docs.docker.com/engine/reference/commandline/save/"},(0,r.kt)("inlineCode",{parentName:"a"},"docker save")),", ",(0,r.kt)("a",{parentName:"li",href:"https://docs.docker.com/engine/reference/commandline/load/"},(0,r.kt)("inlineCode",{parentName:"a"},"docker load"))," \uc640 \uac19\uc740 \uba85\ub839\uc5b4\ub3c4 \uc874\uc7ac\ud569\ub2c8\ub2e4.")))),(0,r.kt)("h2",{id:"3-docker-images"},"3. Docker images"),(0,r.kt)("p",null,"\ub85c\uceec\uc5d0 \uc874\uc7ac\ud558\ub294 docker image \ub9ac\uc2a4\ud2b8\ub97c \ucd9c\ub825\ud558\ub294 \ucee4\ub9e8\ub4dc\uc785\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"docker images --help\n")),(0,r.kt)("p",null,"docker images\uc5d0\uc11c \uc0ac\uc6a9\ud560 \uc218 \uc788\ub294 argument\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"Usage: docker images [OPTIONS] [REPOSITORY[:TAG]]\n\nList images\n\nOptions:\n -a, --all Show all images (default hides intermediate images)\n --digests Show digests\n -f, --filter filter Filter output based on conditions provided\n --format string Pretty-print images using a Go template\n --no-trunc Don't truncate output\n -q, --quiet Only show image IDs\n")),(0,r.kt)("p",null,"\uc544\ub798 \uba85\ub839\uc5b4\ub97c \uc774\uc6a9\ud574 \uc9c1\uc811 \uc2e4\ud589\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"docker images\n")),(0,r.kt)("p",null,"\ub9cc\uc57d \ub3c4\ucee4\ub97c \ucd5c\ucd08 \uc124\uce58 \ud6c4 \uc774 \uc2e4\uc2b5\uc744 \uc9c4\ud589\ud55c\ub2e4\uba74 \ub2e4\uc74c\uacfc \ube44\uc2b7\ud558\uac8c \ucd9c\ub825\ub429\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"REPOSITORY TAG IMAGE ID CREATED SIZE\nubuntu 18.04 29e70752d7b2 2 days ago 56.7MB\n")),(0,r.kt)("p",null,"\uc904 \uc218 \uc788\ub294 argument\uc911 ",(0,r.kt)("inlineCode",{parentName:"p"},"-q"),"\ub97c \uc0ac\uc6a9\ud558\uba74 ",(0,r.kt)("inlineCode",{parentName:"p"},"IMAGE ID")," \ub9cc \ucd9c\ub825\ub429\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"docker images -q\n")),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"29e70752d7b2\n")),(0,r.kt)("h2",{id:"4-docker-ps"},"4. Docker ps"),(0,r.kt)("p",null,"\ud604\uc7ac \uc2e4\ud589 \uc911\uc778 \ub3c4\ucee4 \ucee8\ud14c\uc774\ub108 \ub9ac\uc2a4\ud2b8\ub97c \ucd9c\ub825\ud558\ub294 \ucee4\ub9e8\ub4dc\uc785\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"docker ps --help\n")),(0,r.kt)("p",null,"docker ps\uc5d0\uc11c \uc0ac\uc6a9\ud560 \uc218 \uc788\ub294 argument\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"Usage: docker ps [OPTIONS]\n\nList containers\n\nOptions:\n -a, --all Show all containers (default shows just running)\n -f, --filter filter Filter output based on conditions provided\n --format string Pretty-print containers using a Go template\n -n, --last int Show n last created containers (includes all states) (default -1)\n -l, --latest Show the latest created container (includes all states)\n --no-trunc Don't truncate output\n -q, --quiet Only display container IDs\n -s, --size Display total file sizes\n")),(0,r.kt)("p",null,"\uc544\ub798 \uba85\ub839\uc5b4\ub97c \uc774\uc6a9\ud574 \uc9c1\uc811 \uc2e4\ud589\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"docker ps\n")),(0,r.kt)("p",null,"\ud604\uc7ac \uc2e4\ud589 \uc911\uc778 \ucee8\ud14c\uc774\ub108\uac00 \uc5c6\ub2e4\uba74 \ub2e4\uc74c\uacfc \uac19\uc774 \ub098\uc635\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES\n")),(0,r.kt)("p",null,"\ub9cc\uc57d \uc2e4\ud589\ub418\ub294 \ucee8\ud14c\uc774\ub108\uac00 \uc788\ub2e4\uba74 \ub2e4\uc74c\uacfc \ube44\uc2b7\ud558\uac8c \ub098\uc635\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},'CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES\nc1e8f5e89d8d ubuntu "sleep 3600" 13 seconds ago Up 12 seconds trusting_newton\n')),(0,r.kt)("h2",{id:"5-docker-run"},"5. Docker run"),(0,r.kt)("p",null,"\ub3c4\ucee4 \ucee8\ud14c\uc774\ub108\ub97c \uc2e4\ud589\uc2dc\ud0a4\ub294 \ucee4\ub9e8\ub4dc\uc785\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"docker run --help\n")),(0,r.kt)("p",null,"docker run\uc744 \uc2e4\ud589\ud558\ub294 \uba85\ub839\uc5b4\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"Usage: docker run [OPTIONS] IMAGE [COMMAND] [ARG...]\n\nRun a command in a new container\n")),(0,r.kt)("p",null,"\uc5ec\uae30\uc11c \uc6b0\ub9ac\uac00 \ud655\uc778\ud574\uc57c \ud558\ub294 \uac83\uc740 \ubc14\ub85c docker run\uc740 \uc138 \uac1c \ud0c0\uc785\uc758 argument\ub97c \ubc1b\ub294\ub2e4\ub294 \uac83\uc744 \uc54c \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,r.kt)("ol",null,(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("inlineCode",{parentName:"li"},"[OPTIONS]")),(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("inlineCode",{parentName:"li"},"[COMMAND]")),(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("inlineCode",{parentName:"li"},"[ARG...]"))),(0,r.kt)("p",null,"\uc9c1\uc811 \ub3c4\ucee4 \ucee8\ud14c\uc774\ub108\ub97c \uc2e4\ud589\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"## Usage: docker run [OPTIONS] IMAGE [COMMAND] [ARG...]\ndocker run -it --name demo1 ubuntu:18.04 /bin/bash\n")),(0,r.kt)("ul",null,(0,r.kt)("li",{parentName:"ul"},(0,r.kt)("inlineCode",{parentName:"li"},"-it")," : ",(0,r.kt)("inlineCode",{parentName:"li"},"-i")," \uc635\uc158 + ",(0,r.kt)("inlineCode",{parentName:"li"},"-t")," \uc635\uc158",(0,r.kt)("ul",{parentName:"li"},(0,r.kt)("li",{parentName:"ul"},"container \ub97c \uc2e4\ud589\uc2dc\ud0b4\uacfc \ub3d9\uc2dc\uc5d0 interactive \ud55c terminal \ub85c \uc811\uc18d\uc2dc\ucf1c\uc8fc\ub294 \uc635\uc158"))),(0,r.kt)("li",{parentName:"ul"},(0,r.kt)("inlineCode",{parentName:"li"},"--name")," : name",(0,r.kt)("ul",{parentName:"li"},(0,r.kt)("li",{parentName:"ul"},"\ucee8\ud14c\uc774\ub108 id \ub300\uc2e0, \uad6c\ubd84\ud558\uae30 \uc27d\ub3c4\ub85d \uc9c0\uc815\ud574\uc8fc\ub294 \uc774\ub984"))),(0,r.kt)("li",{parentName:"ul"},(0,r.kt)("inlineCode",{parentName:"li"},"/bin/bash"),(0,r.kt)("ul",{parentName:"li"},(0,r.kt)("li",{parentName:"ul"},"\ucee8\ud14c\uc774\ub108\ub97c \uc2e4\ud589\uc2dc\ud0b4\uacfc \ub3d9\uc2dc\uc5d0 \uc2e4\ud589\ud560 \ucee4\ub9e8\ub4dc\ub85c, ",(0,r.kt)("inlineCode",{parentName:"li"},"/bin/bash")," \ub294 bash \uc258\uc744 \uc5ec\ub294 \uac83\uc744 \uc758\ubbf8\ud569\ub2c8\ub2e4.")))),(0,r.kt)("p",null,"\uc2e4\ud589 \ud6c4 ",(0,r.kt)("inlineCode",{parentName:"p"},"exit")," \uba85\ub839\uc5b4\ub97c \ud1b5\ud574 \ucee8\ud14c\uc774\ub108\ub97c \uc885\ub8cc\ud569\ub2c8\ub2e4."),(0,r.kt)("p",null,"\uc774 \uc81c \uc55e\uc11c \ubc30\uc6e0\ub358 ",(0,r.kt)("inlineCode",{parentName:"p"},"docker ps")," \uba85\ub839\uc5b4\ub97c \uce58\uba74 \ub2e4\uc74c\uacfc \uac19\uc774 \ub098\uc635\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES\n")),(0,r.kt)("p",null,"\uc2e4\ud589\ub418\uace0 \uc788\ub294 \ucee8\ud14c\uc774\ub108\uac00 \ub098\uc628\ub2e4\uace0 \ud588\uc9c0\ub9cc \uc5b4\uc9f8\uc11c\uc778\uc9c0 \ubc29\uae08 \uc2e4\ud589\ud55c \ucee8\ud14c\uc774\ub108\uac00 \ubcf4\uc774\uc9c0 \uc54a\uc2b5\ub2c8\ub2e4.\n\uadf8 \uc774\uc720\ub294 ",(0,r.kt)("inlineCode",{parentName:"p"},"docker ps"),"\ub294 \uae30\ubcf8\uac12\uc73c\ub85c \ud604\uc7ac \uc2e4\ud589 \uc911\uc778 \ucee8\ud14c\uc774\ub108\ub97c \ubcf4\uc5ec\uc8fc\uae30 \ub54c\ubb38\uc785\ub2c8\ub2e4."),(0,r.kt)("p",null,"\ub9cc\uc57d \uc885\ub8cc\ub41c \ucee8\ud14c\uc774\ub108\ub4e4\ub3c4 \ubcf4\uace0 \uc2f6\ub2e4\uba74 ",(0,r.kt)("inlineCode",{parentName:"p"},"-a")," \uc635\uc158\uc744 \uc8fc\uc5b4\uc57c \ud569\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"docker ps -a\n")),(0,r.kt)("p",null,"\uadf8\ub7ec\uba74 \ub2e4\uc74c\uacfc \uac19\uc774 \uc885\ub8cc\ub41c \ucee8\ud14c\uc774\ub108 \ubaa9\ub85d\ub3c4 \ub098\uc635\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},'CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES\n4c1aa74a382a ubuntu:18.04 "/bin/bash" 2 minutes ago Exited (0) 2 minutes ago demo1\n')),(0,r.kt)("h2",{id:"6-docker-exec"},"6. Docker exec"),(0,r.kt)("p",null,"Docker \ucee8\ud14c\uc774\ub108 \ub0b4\ubd80\uc5d0\uc11c \uba85\ub839\uc744 \ub0b4\ub9ac\uac70\ub098, \ub0b4\ubd80\ub85c \uc811\uc18d\ud558\ub294 \ucee4\ub9e8\ub4dc\uc785\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"docker exec --help\n")),(0,r.kt)("p",null,"\uc608\ub97c \ub4e4\uc5b4\uc11c \ub2e4\uc74c\uacfc \uac19\uc740 \uba85\ub839\uc5b4\ub97c \uc2e4\ud589\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"docker run -d --name demo2 ubuntu:18.04 sleep 3600\n")),(0,r.kt)("p",null,"\uc5ec\uae30\uc11c ",(0,r.kt)("inlineCode",{parentName:"p"},"-d")," \uc635\uc158\uc740 \ub3c4\ucee4 \ucee8\ud14c\uc774\ub108\ub97c \ubc31\uadf8\ub77c\uc6b4\ub4dc\uc5d0\uc11c \uc2e4\ud589\uc2dc\ucf1c\uc11c, \ucee8\ud14c\uc774\ub108\uc5d0\uc11c \uc811\uc18d \uc885\ub8cc\ub97c \ud558\ub354\ub77c\ub3c4, \uacc4\uc18d \uc2e4\ud589 \uc911\uc774 \ub418\ub3c4\ub85d \ud558\ub294 \ucee4\ub9e8\ub4dc\uc785\ub2c8\ub2e4."),(0,r.kt)("p",null,(0,r.kt)("inlineCode",{parentName:"p"},"docker ps"),"\ub97c \ud1b5\ud574 \ud604\uc7ac \uc2e4\ud589\uc911\uc778\uc9c0 \ud655\uc778\ud569\ub2c8\ub2e4."),(0,r.kt)("p",null,"\ub2e4\uc74c\uacfc \uac19\uc774 \uc2e4\ud589 \uc911\uc784\uc744 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},'CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES\nfc88a83e90f0 ubuntu:18.04 "sleep 3600" 4 seconds ago Up 3 seconds demo2\n')),(0,r.kt)("p",null,"\uc774\uc81c ",(0,r.kt)("inlineCode",{parentName:"p"},"docker exec")," \uba85\ub839\uc5b4\ub97c \ud1b5\ud574\uc11c \uc2e4\ud589\uc911\uc778 \ub3c4\ucee4 \ucee8\ud14c\uc774\ub108\uc5d0 \uc811\uc18d\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"docker exec -it demo2 /bin/bash\n")),(0,r.kt)("p",null,"\uc774 \uc804\uc758 ",(0,r.kt)("inlineCode",{parentName:"p"},"docker run"),"\uacfc \ub3d9\uc77c\ud558\uac8c container \ub0b4\ubd80\uc5d0 \uc811\uc18d\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,r.kt)("p",null,(0,r.kt)("inlineCode",{parentName:"p"},"exit"),"\uc744 \ud1b5\ud574 \uc885\ub8cc\ud569\ub2c8\ub2e4."),(0,r.kt)("h2",{id:"7-docker-logs"},"7. Docker logs"),(0,r.kt)("p",null,"\ub3c4\ucee4 \ucee8\ud14c\uc774\ub108\uc758 log\ub97c \ud655\uc778\ud558\ub294 \ucee4\ub9e8\ub4dc \uc785\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"docker logs --help\n")),(0,r.kt)("p",null,"\ub2e4\uc74c\uacfc \uac19\uc740 \ucee8\ud14c\uc774\ub108\ub97c \uc2e4\ud589\uc2dc\ud0a4\ub3c4\ub85d \ud558\uaca0\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},'docker run --name demo3 -d busybox sh -c "while true; do $(echo date); sleep 1; done"\n')),(0,r.kt)("p",null,"\uc704 \uba85\ub839\uc5b4\ub97c \ud1b5\ud574\uc11c test \ub77c\ub294 \uc774\ub984\uc758 busybox \ucee8\ud14c\uc774\ub108\ub97c \ubc31\uadf8\ub77c\uc6b4\ub4dc\uc5d0\uc11c \ub3c4\ucee4 \ucee8\ud14c\uc774\ub108\ub85c \uc2e4\ud589\ud558\uc5ec, 1\ucd08\uc5d0 \ud55c \ubc88\uc529 \ud604\uc7ac \uc2dc\uac04\uc744 \ucd9c\ub825\ud558\ub3c4\ub85d \ud588\uc2b5\ub2c8\ub2e4."),(0,r.kt)("p",null,"\uc774\uc81c \uc544\ub798 \uba85\ub839\uc5b4\ub97c \ud1b5\ud574 log\ub97c \ud655\uc778\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"docker logs demo3\n")),(0,r.kt)("p",null,"\uc815\uc0c1\uc801\uc73c\ub85c \uc218\ud589\ub418\uba74 \uc544\ub798\uc640 \ube44\uc2b7\ud558\uac8c \ub098\uc635\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"Sun Mar 6 11:06:49 UTC 2022\nSun Mar 6 11:06:50 UTC 2022\nSun Mar 6 11:06:51 UTC 2022\nSun Mar 6 11:06:52 UTC 2022\nSun Mar 6 11:06:53 UTC 2022\nSun Mar 6 11:06:54 UTC 2022\n")),(0,r.kt)("p",null,"\uadf8\ub7f0\ub370 \uc774\ub807\uac8c \uc0ac\uc6a9\ud560 \uacbd\uc6b0 \uc5ec\ud0dc\uae4c\uc9c0 \ucc0d\ud78c log \ubc16\uc5d0 \ud655\uc778\ud560 \uc218 \uc5c6\uc2b5\ub2c8\ub2e4.",(0,r.kt)("br",{parentName:"p"}),"\n","\uc774 \ub54c ",(0,r.kt)("inlineCode",{parentName:"p"},"-f")," \uc635\uc158\uc744 \uc774\uc6a9\ud574 \uacc4\uc18d watch \ud558\uba70 \ucd9c\ub825\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"docker logs demo3 -f \n")),(0,r.kt)("h2",{id:"8-docker-stop"},"8. 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\uac1c\ubc1c(Development)\uacfc \uc6b4\uc601(Operations)\uc758 \ud569\uc131\uc5b4\ub85c\uc11c \uc18c\ud504\ud2b8\uc6e8\uc5b4 \uac1c\ubc1c\uc790\uc640 \uc815\ubcf4\uae30\uc220 \uc804\ubb38\uac00 \uac04\uc758 \uc18c\ud1b5, \ud611\uc5c5 \ubc0f \ud1b5\ud569\uc744 \uac15\uc870\ud558\ub294 \uac1c\ubc1c \ud658\uacbd\uc774\ub098 \ubb38\ud654\ub97c \ub9d0\ud569\ub2c8\ub2e4.\nDevOps\uc758 \ubaa9\uc801\uc740 \uc18c\ud504\ud2b8\uc6e8\uc5b4 \uac1c\ubc1c \uc870\uc9c1\uacfc \uc6b4\uc601 \uc870\uc9c1\uac04\uc758 \uc0c1\ud638 \uc758\uc874\uc801 \ub300\uc751\uc774\uba70 \uc870\uc9c1\uc774 \uc18c\ud504\ud2b8\uc6e8\uc5b4 \uc81c\ud488\uacfc \uc11c\ube44\uc2a4\ub97c \ube60\ub978 \uc2dc\uac04\uc5d0 \uac1c\ubc1c \ubc0f \ubc30\ud3ec\ud558\ub294 \uac83\uc744 \ubaa9\uc801\uc73c\ub85c \ud569\ub2c8\ub2e4."),(0,i.kt)("h3",{id:"silo-effect"},"Silo Effect"),(0,i.kt)("p",null,"\uadf8\ub7fc \uac04\ub2e8\ud55c \uc0c1\ud669 \uc124\uba85\uc744 \ud1b5\ud574 DevOps\uac00 \uc65c \ud544\uc694\ud55c\uc9c0 \uc54c\uc544\ubcf4\ub3c4\ub85d \ud558\uaca0\uc2b5\ub2c8\ub2e4."),(0,i.kt)("p",null,"\uc11c\ube44\uc2a4 \ucd08\uae30\uc5d0\ub294 \uc9c0\uc6d0\ud558\ub294 \uae30\ub2a5\uc774 \ub9ce\uc9c0 \uc54a\uc73c\uba70 \ud300 \ub610\ub294 \ud68c\uc0ac\uc758 \uaddc\ubaa8\uac00 \uc791\uc2b5\ub2c8\ub2e4. \uc774\ub54c\uc5d0\ub294 \uac1c\ubc1c\ud300\uacfc \uc6b4\uc601\ud300\uc758 \uad6c\ubd84\uc774 \uc5c6\uac70\ub098 \uc791\uc740 \uaddc\ubaa8\uc758 \ud300\uc73c\ub85c \uad6c\ubd84\ub418\uc5b4 \uc788\uc2b5\ub2c8\ub2e4. \ud575\uc2ec\uc740 \uaddc\ubaa8\uac00 \uc791\ub2e4\ub294 \uac83\uc5d0 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub54c\ub294 \uc11c\ub85c \uc18c\ud1b5\ud560 \uc218 \uc788\ub294 \uc811\uc810\uc774 \ub9ce\uace0, \uc9d1\uc911\ud574\uc57c \ud558\ub294 \uc11c\ube44\uc2a4\uac00 \uc801\uae30 \ub54c\ubb38\uc5d0 \ube60\ub974\uac8c \uc11c\ube44\uc2a4\ub97c \uac1c\uc120\ud574 \ub098\uac08 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,i.kt)("p",null,"\ud558\uc9c0\ub9cc \uc11c\ube44\uc2a4\uc758 \uaddc\ubaa8\uac00 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\uac1c\ubc1c(Development)\uacfc \uc6b4\uc601(Operations)\uc758 \ud569\uc131\uc5b4\ub85c\uc11c \uc18c\ud504\ud2b8\uc6e8\uc5b4 \uac1c\ubc1c\uc790\uc640 \uc815\ubcf4\uae30\uc220 \uc804\ubb38\uac00 \uac04\uc758 \uc18c\ud1b5, \ud611\uc5c5 \ubc0f \ud1b5\ud569\uc744 \uac15\uc870\ud558\ub294 \uac1c\ubc1c \ud658\uacbd\uc774\ub098 \ubb38\ud654\ub97c \ub9d0\ud569\ub2c8\ub2e4.\nDevOps\uc758 \ubaa9\uc801\uc740 \uc18c\ud504\ud2b8\uc6e8\uc5b4 \uac1c\ubc1c \uc870\uc9c1\uacfc \uc6b4\uc601 \uc870\uc9c1\uac04\uc758 \uc0c1\ud638 \uc758\uc874\uc801 \ub300\uc751\uc774\uba70 \uc870\uc9c1\uc774 \uc18c\ud504\ud2b8\uc6e8\uc5b4 \uc81c\ud488\uacfc \uc11c\ube44\uc2a4\ub97c \ube60\ub978 \uc2dc\uac04\uc5d0 \uac1c\ubc1c \ubc0f \ubc30\ud3ec\ud558\ub294 \uac83\uc744 \ubaa9\uc801\uc73c\ub85c \ud569\ub2c8\ub2e4."),(0,i.kt)("h3",{id:"silo-effect"},"Silo Effect"),(0,i.kt)("p",null,"\uadf8\ub7fc \uac04\ub2e8\ud55c \uc0c1\ud669 \uc124\uba85\uc744 \ud1b5\ud574 DevOps\uac00 \uc65c \ud544\uc694\ud55c\uc9c0 \uc54c\uc544\ubcf4\ub3c4\ub85d \ud558\uaca0\uc2b5\ub2c8\ub2e4."),(0,i.kt)("p",null,"\uc11c\ube44\uc2a4 \ucd08\uae30\uc5d0\ub294 \uc9c0\uc6d0\ud558\ub294 \uae30\ub2a5\uc774 \ub9ce\uc9c0 \uc54a\uc73c\uba70 \ud300 \ub610\ub294 \ud68c\uc0ac\uc758 \uaddc\ubaa8\uac00 \uc791\uc2b5\ub2c8\ub2e4. \uc774\ub54c\uc5d0\ub294 \uac1c\ubc1c\ud300\uacfc \uc6b4\uc601\ud300\uc758 \uad6c\ubd84\uc774 \uc5c6\uac70\ub098 \uc791\uc740 \uaddc\ubaa8\uc758 \ud300\uc73c\ub85c \uad6c\ubd84\ub418\uc5b4 \uc788\uc2b5\ub2c8\ub2e4. \ud575\uc2ec\uc740 \uaddc\ubaa8\uac00 \uc791\ub2e4\ub294 \uac83\uc5d0 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub54c\ub294 \uc11c\ub85c \uc18c\ud1b5\ud560 \uc218 \uc788\ub294 \uc811\uc810\uc774 \ub9ce\uace0, \uc9d1\uc911\ud574\uc57c \ud558\ub294 \uc11c\ube44\uc2a4\uac00 \uc801\uae30 \ub54c\ubb38\uc5d0 \ube60\ub974\uac8c \uc11c\ube44\uc2a4\ub97c \uac1c\uc120\ud574 \ub098\uac08 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,i.kt)("p",null,"\ud558\uc9c0\ub9cc \uc11c\ube44\uc2a4\uc758 \uaddc\ubaa8\uac00 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server --disable traefik --disable servicelb --disable local-storage --docker\n")),(0,a.kt)("p",null,"k3s\ub97c \uc124\uce58 \ud6c4 k3s config\ub97c \ud655\uc778\ud569\ub2c8\ub2e4"),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-bash"},"sudo cat /etc/rancher/k3s/k3s.yaml\n")),(0,a.kt)("p",null,"\uc815\uc0c1\uc801\uc73c\ub85c \uc124\uce58\ub418\uba74 \ub2e4\uc74c\uacfc \uac19\uc740 \ud56d\ubaa9\uc774 \ucd9c\ub825\ub429\ub2c8\ub2e4.",(0,a.kt)("br",{parentName:"p"}),"\n","(\ubcf4\uc548 \ubb38\uc81c\uc640 \uad00\ub828\ub41c \ud0a4\ub4e4\uc740 <...>\ub85c \uac00\ub838\uc2b5\ub2c8\ub2e4.)"),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-bash"},"apiVersion: v1\nclusters:\n- cluster:\n certificate-authority-data:\n <...>\n server: https://127.0.0.1:6443\n name: default\ncontexts:\n- context:\n cluster: default\n user: default\n name: default\ncurrent-context: default\nkind: Config\npreferences: {}\nusers:\n- name: default\n user:\n client-certificate-data:\n <...>\n client-key-data:\n <...>\n")),(0,a.kt)("h2",{id:"2-\ucfe0\ubc84\ub124\ud2f0\uc2a4-\ud074\ub7ec\uc2a4\ud130-\uc14b\uc5c5"},"2. \ucfe0\ubc84\ub124\ud2f0\uc2a4 \ud074\ub7ec\uc2a4\ud130 \uc14b\uc5c5"),(0,a.kt)("p",null,"k3s config\ub97c \ud074\ub7ec\uc2a4\ud130\uc758 kubeconfig\ub85c \uc0ac\uc6a9\ud558\uae30 \uc704\ud574\uc11c \ubcf5\uc0ac\ud569\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-bash"},"mkdir .kube\nsudo cp /etc/rancher/k3s/k3s.yaml .kube/config\n")),(0,a.kt)("p",null,"\ubcf5\uc0ac\ub41c config \ud30c\uc77c\uc5d0 user\uac00 \uc811\uadfc\ud560 \uc218 \uc788\ub294 \uad8c\ud55c\uc744 \uc90d\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-bash"},"sudo chown $USER:$USER .kube/config\n")),(0,a.kt)("h2",{id:"3-\ucfe0\ubc84\ub124\ud2f0\uc2a4-\ud074\ub77c\uc774\uc5b8\ud2b8-\uc14b\uc5c5"},"3. \ucfe0\ubc84\ub124\ud2f0\uc2a4 \ud074\ub77c\uc774\uc5b8\ud2b8 \uc14b\uc5c5"),(0,a.kt)("p",null,"\uc774\uc81c \ud074\ub7ec\uc2a4\ud130\uc5d0\uc11c \uc124\uc815\ud55c kubeconfig\ub97c \ub85c\uceec\ub85c \uc774\ub3d9\ud569\ub2c8\ub2e4.\n\ub85c\uceec\uc5d0\uc11c\ub294 \uacbd\ub85c\ub97c ",(0,a.kt)("inlineCode",{parentName:"p"},"~/.kube/config"),"\ub85c \uc124\uc815\ud569\ub2c8\ub2e4."),(0,a.kt)("p",null,"\ucc98\uc74c \ubcf5\uc0ac\ud55c config \ud30c\uc77c\uc5d0\ub294 server ip\uac00 ",(0,a.kt)("inlineCode",{parentName:"p"},"https://127.0.0.1:6443")," \uc73c\ub85c \ub418\uc5b4 \uc788\uc2b5\ub2c8\ub2e4.",(0,a.kt)("br",{parentName:"p"}),"\n","\uc774 \uac12\uc744 \ud074\ub7ec\uc2a4\ud130\uc758 ip\uc5d0 \ub9de\uac8c \uc218\uc815\ud569\ub2c8\ub2e4.",(0,a.kt)("br",{parentName:"p"}),"\n","(\uc774\ubc88 \ud398\uc774\uc9c0\uc5d0\uc11c \uc0ac\uc6a9\ud558\ub294 \ud074\ub7ec\uc2a4\ud130\uc758 ip\uc5d0 \ub9de\ucdb0\uc11c ",(0,a.kt)("inlineCode",{parentName:"p"},"https://192.168.0.19:6443")," \uc73c\ub85c \uc218\uc815\ud588\uc2b5\ub2c8\ub2e4.)"),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-bash"},"apiVersion: v1\nclusters:\n- cluster:\n certificate-authority-data:\n <...>\n server: https://192.168.0.19:6443\n name: default\ncontexts:\n- context:\n cluster: default\n user: default\n name: default\ncurrent-context: default\nkind: Config\npreferences: {}\nusers:\n- name: default\n user:\n client-certificate-data:\n <...>\n client-key-data:\n <...>\n")),(0,a.kt)("h2",{id:"4-\ucfe0\ubc84\ub124\ud2f0\uc2a4-\uae30\ubcf8-\ubaa8\ub4c8-\uc124\uce58"},"4. \ucfe0\ubc84\ub124\ud2f0\uc2a4 \uae30\ubcf8 \ubaa8\ub4c8 \uc124\uce58"),(0,a.kt)("p",null,(0,a.kt)("a",{parentName:"p",href:"/docs/1.0/setup-kubernetes/install-kubernetes-module"},"Setup Kubernetes Modules"),"\uc744 \ucc38\uace0\ud558\uc5ec \ub2e4\uc74c \ucef4\ud3ec\ub10c\ud2b8\ub4e4\uc744 \uc124\uce58\ud574 \uc8fc\uc2dc\uae30 \ubc14\ub78d\ub2c8\ub2e4."),(0,a.kt)("ul",null,(0,a.kt)("li",{parentName:"ul"},"helm"),(0,a.kt)("li",{parentName:"ul"},"kustomize"),(0,a.kt)("li",{parentName:"ul"},"CSI plugin"),(0,a.kt)("li",{parentName:"ul"},"[Optional]"," nvidia-docker, nvidia-device-plugin")),(0,a.kt)("h2",{id:"5-\uc815\uc0c1-\uc124\uce58-\ud655\uc778"},"5. \uc815\uc0c1 \uc124\uce58 \ud655\uc778"),(0,a.kt)("p",null,"\ucd5c\uc885\uc801\uc73c\ub85c node\uac00 Ready \uc778\uc9c0, OS, Docker, Kubernetes \ubc84\uc804\uc744 \ud655\uc778\ud569\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-bash"},"kubectl get nodes -o wide\n")),(0,a.kt)("p",null,"\ub2e4\uc74c\uacfc \uac19\uc740 \uba54\uc2dc\uc9c0\uac00 \ubcf4\uc774\uba74 \uc815\uc0c1\uc801\uc73c\ub85c \uc124\uce58\ub41c \uac83\uc744 \uc758\ubbf8\ud569\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-bash"},"NAME STATUS ROLES AGE VERSION INTERNAL-IP EXTERNAL-IP OS-IMAGE KERNEL-VERSION CONTAINER-RUNTIME\nubuntu Ready control-plane,master 11m v1.21.7+k3s1 192.168.0.19 Ubuntu 20.04.3 LTS 5.4.0-91-generic docker://20.10.11\n")),(0,a.kt)("h2",{id:"6-references"},"6. 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server --disable traefik --disable servicelb --disable local-storage --docker\n")),(0,a.kt)("p",null,"k3s\ub97c \uc124\uce58 \ud6c4 k3s config\ub97c \ud655\uc778\ud569\ub2c8\ub2e4"),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-bash"},"sudo cat /etc/rancher/k3s/k3s.yaml\n")),(0,a.kt)("p",null,"\uc815\uc0c1\uc801\uc73c\ub85c \uc124\uce58\ub418\uba74 \ub2e4\uc74c\uacfc \uac19\uc740 \ud56d\ubaa9\uc774 \ucd9c\ub825\ub429\ub2c8\ub2e4.",(0,a.kt)("br",{parentName:"p"}),"\n","(\ubcf4\uc548 \ubb38\uc81c\uc640 \uad00\ub828\ub41c \ud0a4\ub4e4\uc740 <...>\ub85c \uac00\ub838\uc2b5\ub2c8\ub2e4.)"),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-bash"},"apiVersion: v1\nclusters:\n- cluster:\n certificate-authority-data:\n <...>\n server: https://127.0.0.1:6443\n name: default\ncontexts:\n- context:\n cluster: default\n user: default\n name: default\ncurrent-context: default\nkind: Config\npreferences: {}\nusers:\n- name: default\n user:\n client-certificate-data:\n <...>\n client-key-data:\n <...>\n")),(0,a.kt)("h2",{id:"2-\ucfe0\ubc84\ub124\ud2f0\uc2a4-\ud074\ub7ec\uc2a4\ud130-\uc14b\uc5c5"},"2. \ucfe0\ubc84\ub124\ud2f0\uc2a4 \ud074\ub7ec\uc2a4\ud130 \uc14b\uc5c5"),(0,a.kt)("p",null,"k3s config\ub97c \ud074\ub7ec\uc2a4\ud130\uc758 kubeconfig\ub85c \uc0ac\uc6a9\ud558\uae30 \uc704\ud574\uc11c \ubcf5\uc0ac\ud569\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-bash"},"mkdir .kube\nsudo cp /etc/rancher/k3s/k3s.yaml .kube/config\n")),(0,a.kt)("p",null,"\ubcf5\uc0ac\ub41c config \ud30c\uc77c\uc5d0 user\uac00 \uc811\uadfc\ud560 \uc218 \uc788\ub294 \uad8c\ud55c\uc744 \uc90d\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-bash"},"sudo chown $USER:$USER .kube/config\n")),(0,a.kt)("h2",{id:"3-\ucfe0\ubc84\ub124\ud2f0\uc2a4-\ud074\ub77c\uc774\uc5b8\ud2b8-\uc14b\uc5c5"},"3. \ucfe0\ubc84\ub124\ud2f0\uc2a4 \ud074\ub77c\uc774\uc5b8\ud2b8 \uc14b\uc5c5"),(0,a.kt)("p",null,"\uc774\uc81c \ud074\ub7ec\uc2a4\ud130\uc5d0\uc11c \uc124\uc815\ud55c kubeconfig\ub97c \ub85c\uceec\ub85c \uc774\ub3d9\ud569\ub2c8\ub2e4.\n\ub85c\uceec\uc5d0\uc11c\ub294 \uacbd\ub85c\ub97c ",(0,a.kt)("inlineCode",{parentName:"p"},"~/.kube/config"),"\ub85c \uc124\uc815\ud569\ub2c8\ub2e4."),(0,a.kt)("p",null,"\ucc98\uc74c \ubcf5\uc0ac\ud55c config \ud30c\uc77c\uc5d0\ub294 server ip\uac00 ",(0,a.kt)("inlineCode",{parentName:"p"},"https://127.0.0.1:6443")," \uc73c\ub85c \ub418\uc5b4 \uc788\uc2b5\ub2c8\ub2e4.",(0,a.kt)("br",{parentName:"p"}),"\n","\uc774 \uac12\uc744 \ud074\ub7ec\uc2a4\ud130\uc758 ip\uc5d0 \ub9de\uac8c \uc218\uc815\ud569\ub2c8\ub2e4.",(0,a.kt)("br",{parentName:"p"}),"\n","(\uc774\ubc88 \ud398\uc774\uc9c0\uc5d0\uc11c \uc0ac\uc6a9\ud558\ub294 \ud074\ub7ec\uc2a4\ud130\uc758 ip\uc5d0 \ub9de\ucdb0\uc11c ",(0,a.kt)("inlineCode",{parentName:"p"},"https://192.168.0.19:6443")," \uc73c\ub85c \uc218\uc815\ud588\uc2b5\ub2c8\ub2e4.)"),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-bash"},"apiVersion: v1\nclusters:\n- cluster:\n certificate-authority-data:\n <...>\n server: https://192.168.0.19:6443\n name: default\ncontexts:\n- context:\n cluster: default\n user: default\n name: default\ncurrent-context: default\nkind: Config\npreferences: {}\nusers:\n- name: default\n user:\n client-certificate-data:\n <...>\n client-key-data:\n <...>\n")),(0,a.kt)("h2",{id:"4-\ucfe0\ubc84\ub124\ud2f0\uc2a4-\uae30\ubcf8-\ubaa8\ub4c8-\uc124\uce58"},"4. \ucfe0\ubc84\ub124\ud2f0\uc2a4 \uae30\ubcf8 \ubaa8\ub4c8 \uc124\uce58"),(0,a.kt)("p",null,(0,a.kt)("a",{parentName:"p",href:"/docs/1.0/setup-kubernetes/install-kubernetes-module"},"Setup Kubernetes Modules"),"\uc744 \ucc38\uace0\ud558\uc5ec \ub2e4\uc74c \ucef4\ud3ec\ub10c\ud2b8\ub4e4\uc744 \uc124\uce58\ud574 \uc8fc\uc2dc\uae30 \ubc14\ub78d\ub2c8\ub2e4."),(0,a.kt)("ul",null,(0,a.kt)("li",{parentName:"ul"},"helm"),(0,a.kt)("li",{parentName:"ul"},"kustomize"),(0,a.kt)("li",{parentName:"ul"},"CSI plugin"),(0,a.kt)("li",{parentName:"ul"},"[Optional]"," nvidia-docker, nvidia-device-plugin")),(0,a.kt)("h2",{id:"5-\uc815\uc0c1-\uc124\uce58-\ud655\uc778"},"5. \uc815\uc0c1 \uc124\uce58 \ud655\uc778"),(0,a.kt)("p",null,"\ucd5c\uc885\uc801\uc73c\ub85c node\uac00 Ready \uc778\uc9c0, OS, Docker, Kubernetes \ubc84\uc804\uc744 \ud655\uc778\ud569\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-bash"},"kubectl get nodes -o wide\n")),(0,a.kt)("p",null,"\ub2e4\uc74c\uacfc \uac19\uc740 \uba54\uc2dc\uc9c0\uac00 \ubcf4\uc774\uba74 \uc815\uc0c1\uc801\uc73c\ub85c \uc124\uce58\ub41c \uac83\uc744 \uc758\ubbf8\ud569\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-bash"},"NAME STATUS ROLES AGE VERSION INTERNAL-IP EXTERNAL-IP OS-IMAGE KERNEL-VERSION CONTAINER-RUNTIME\nubuntu Ready control-plane,master 11m v1.21.7+k3s1 192.168.0.19 Ubuntu 20.04.3 LTS 5.4.0-91-generic docker://20.10.11\n")),(0,a.kt)("h2",{id:"6-references"},"6. References"),(0,a.kt)("ul",null,(0,a.kt)("li",{parentName:"ul"},(0,a.kt)("a",{parentName:"li",href:"https://rancher.com/docs/k3s/latest/en/installation/install-options/"},"https://rancher.com/docs/k3s/latest/en/installation/install-options/"))))}k.isMDXComponent=!0}}]); \ No newline at end of file diff --git a/assets/js/38642333.225fa8fa.js b/assets/js/38642333.225fa8fa.js deleted file mode 100644 index 9f6d7298..00000000 --- a/assets/js/38642333.225fa8fa.js +++ /dev/null @@ -1 +0,0 @@ -"use strict";(self.webpackChunkv_2=self.webpackChunkv_2||[]).push([[6643],{3905:(e,n,t)=>{t.d(n,{Zo:()=>d,kt:()=>g});var a=t(7294);function l(e,n,t){return n in e?Object.defineProperty(e,n,{value:t,enumerable:!0,configurable:!0,writable:!0}):e[n]=t,e}function r(e,n){var t=Object.keys(e);if(Object.getOwnPropertySymbols){var a=Object.getOwnPropertySymbols(e);n&&(a=a.filter((function(n){return Object.getOwnPropertyDescriptor(e,n).enumerable}))),t.push.apply(t,a)}return t}function s(e){for(var n=1;n=0||(l[t]=e[t]);return l}(e,n);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);for(a=0;a=0||Object.prototype.propertyIsEnumerable.call(e,t)&&(l[t]=e[t])}return l}var i=a.createContext({}),p=function(e){var n=a.useContext(i),t=n;return e&&(t="function"==typeof e?e(n):s(s({},n),e)),t},d=function(e){var n=p(e.components);return a.createElement(i.Provider,{value:n},e.children)},m="mdxType",c={inlineCode:"code",wrapper:function(e){var n=e.children;return a.createElement(a.Fragment,{},n)}},u=a.forwardRef((function(e,n){var t=e.components,l=e.mdxType,r=e.originalType,i=e.parentName,d=o(e,["components","mdxType","originalType","parentName"]),m=p(t),u=l,g=m["".concat(i,".").concat(u)]||m[u]||c[u]||r;return t?a.createElement(g,s(s({ref:n},d),{},{components:t})):a.createElement(g,s({ref:n},d))}));function g(e,n){var t=arguments,l=n&&n.mdxType;if("string"==typeof e||l){var r=t.length,s=new Array(r);s[0]=u;var o={};for(var i in n)hasOwnProperty.call(n,i)&&(o[i]=n[i]);o.originalType=e,o[m]="string"==typeof e?e:l,s[1]=o;for(var p=2;p{t.r(n),t.d(n,{assets:()=>i,contentTitle:()=>s,default:()=>c,frontMatter:()=>r,metadata:()=>o,toc:()=>p});var a=t(7462),l=(t(7294),t(3905));const r={title:"2. 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\uc785\ub2c8\ub2e4.",(0,p.kt)("br",{parentName:"p"}),"\n","\uc124\uc815\ud558\ub294 \ubc29\ubc95\uc740 \ud30c\uc774\ud504\ub77c\uc778\uc5d0\uc11c \ucef4\ud3ec\ub10c\ud2b8\uc5d0 \ub2e4\uc74c\uacfc \uac19\uc774 ",(0,p.kt)("inlineCode",{parentName:"p"},"set_display_name")," ",(0,p.kt)("a",{parentName:"p",href:"https://kubeflow-pipelines.readthedocs.io/en/latest/source/kfp.dsl.html#kfp.dsl.ContainerOp.set_display_name"},"attribute"),"\ub97c \uc774\uc6a9\ud558\uba74 \ub429\ub2c8\ub2e4."),(0,p.kt)("pre",null,(0,p.kt)("code",{parentName:"pre",className:"language-python"},'import kfp\nfrom kfp.components import create_component_from_func\nfrom kfp.dsl import pipeline\n\n\n@create_component_from_func\ndef print_and_return_number(number: int) -> int:\n print(number)\n return number\n\n\n@create_component_from_func\ndef sum_and_print_numbers(number_1: int, number_2: int):\n print(number_1 + number_2)\n\n\n@pipeline(name="example_pipeline")\ndef example_pipeline(number_1: int, number_2: int):\n number_1_result = print_and_return_number(number_1).set_display_name("This is number 1")\n number_2_result = print_and_return_number(number_2).set_display_name("This is number 2")\n sum_result = sum_and_print_numbers(\n number_1=number_1_result.output, number_2=number_2_result.output\n ).set_display_name("This is sum of number 1 and number 2")\n\n\nif __name__ == "__main__":\n kfp.compiler.Compiler().compile(example_pipeline, "example_pipeline.yaml")\n')),(0,p.kt)("p",null,"\uc774 \uc2a4\ud06c\ub9bd\ud2b8\ub97c \uc2e4\ud589\ud574\uc11c \ub098\uc628 ",(0,p.kt)("inlineCode",{parentName:"p"},"example_pipeline.yaml"),"\uc744 \ud655\uc778\ud558\uba74 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4."),(0,p.kt)("p",null,(0,p.kt)("details",null,(0,p.kt)("summary",null,"example_pipeline.yaml"),(0,p.kt)("pre",null,(0,p.kt)("code",{parentName:"pre",className:"language-bash"},'apiVersion: argoproj.io/v1alpha1\nkind: Workflow\nmetadata:\n generateName: example-pipeline-\n annotations: {pipelines.kubeflow.org/kfp_sdk_version: 1.8.9, pipelines.kubeflow.org/pipeline_compilation_time: \'2021-12-09T18:11:43.193190\',\n pipelines.kubeflow.org/pipeline_spec: \'{"inputs": [{"name": "number_1", "type":\n "Integer"}, {"name": "number_2", "type": "Integer"}], "name": "example_pipeline"}\'}\n labels: {pipelines.kubeflow.org/kfp_sdk_version: 1.8.9}\nspec:\n entrypoint: example-pipeline\n templates:\n - name: example-pipeline\n inputs:\n parameters:\n - {name: number_1}\n - {name: number_2}\n dag:\n tasks:\n - name: print-and-return-number\n template: print-and-return-number\n arguments:\n parameters:\n - {name: number_1, value: \'{{inputs.parameters.number_1}}\'}\n - name: print-and-return-number-2\n template: print-and-return-number-2\n arguments:\n parameters:\n - {name: number_2, value: \'{{inputs.parameters.number_2}}\'}\n - name: sum-and-print-numbers\n template: sum-and-print-numbers\n dependencies: [print-and-return-number, print-and-return-number-2]\n arguments:\n parameters:\n - {name: print-and-return-number-2-Output, value: \'{{tasks.print-and-return-number-2.outputs.parameters.print-and-return-number-2-Output}}\'}\n - {name: print-and-return-number-Output, value: \'{{tasks.print-and-return-number.outputs.parameters.print-and-return-number-Output}}\'}\n - name: print-and-return-number\n container:\n args: [--number, \'{{inputs.parameters.number_1}}\', \'----output-paths\', /tmp/outputs/Output/data]\n command:\n - sh\n - -ec\n - |\n program_path=$(mktemp)\n printf "%s" "$0" > "$program_path"\n python3 -u "$program_path" "$@"\n - |\n def print_and_return_number(number):\n print(number)\n return number\n\n def _serialize_int(int_value: int) -> str:\n if isinstance(int_value, str):\n return int_value\n if not isinstance(int_value, int):\n raise TypeError(\'Value "{}" has type "{}" instead of int.\'.format(\n str(int_value), str(type(int_value))))\n return str(int_value)\n\n import argparse\n _parser = argparse.ArgumentParser(prog=\'Print and return number\', description=\'\')\n _parser.add_argument("--number", dest="number", type=int, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("----output-paths", dest="_output_paths", type=str, nargs=1)\n _parsed_args = vars(_parser.parse_args())\n _output_files = _parsed_args.pop("_output_paths", [])\n\n _outputs = print_and_return_number(**_parsed_args)\n\n _outputs = [_outputs]\n\n _output_serializers = [\n _serialize_int,\n\n ]\n\n import os\n for idx, output_file in enumerate(_output_files):\n try:\n os.makedirs(os.path.dirname(output_file))\n except OSError:\n pass\n with open(output_file, \'w\') as f:\n f.write(_output_serializers[idx](_outputs[idx]))\n image: python:3.7\n inputs:\n parameters:\n - {name: number_1}\n outputs:\n parameters:\n - name: print-and-return-number-Output\n valueFrom: {path: /tmp/outputs/Output/data}\n artifacts:\n - {name: print-and-return-number-Output, path: /tmp/outputs/Output/data}\n metadata:\n annotations: {pipelines.kubeflow.org/task_display_name: This is number 1, pipelines.kubeflow.org/component_spec: \'{"implementation":\n {"container": {"args": ["--number", {"inputValue": "number"}, "----output-paths",\n {"outputPath": "Output"}], "command": ["sh", "-ec", "program_path=$(mktemp)\\nprintf\n \\"%s\\" \\"$0\\" > \\"$program_path\\"\\npython3 -u \\"$program_path\\" \\"$@\\"\\n",\n "def print_and_return_number(number):\\n print(number)\\n return number\\n\\ndef\n _serialize_int(int_value: int) -> str:\\n if isinstance(int_value, str):\\n return\n int_value\\n if not isinstance(int_value, int):\\n raise TypeError(\'\'Value\n \\"{}\\" has type \\"{}\\" instead of int.\'\'.format(\\n str(int_value),\n str(type(int_value))))\\n return str(int_value)\\n\\nimport argparse\\n_parser\n = argparse.ArgumentParser(prog=\'\'Print and return number\'\', description=\'\'\'\')\\n_parser.add_argument(\\"--number\\",\n dest=\\"number\\", type=int, required=True, default=argparse.SUPPRESS)\\n_parser.add_argument(\\"----output-paths\\",\n dest=\\"_output_paths\\", type=str, nargs=1)\\n_parsed_args = vars(_parser.parse_args())\\n_output_files\n = _parsed_args.pop(\\"_output_paths\\", [])\\n\\n_outputs = print_and_return_number(**_parsed_args)\\n\\n_outputs\n = [_outputs]\\n\\n_output_serializers = [\\n _serialize_int,\\n\\n]\\n\\nimport\n os\\nfor idx, output_file in enumerate(_output_files):\\n try:\\n os.makedirs(os.path.dirname(output_file))\\n except\n OSError:\\n pass\\n with open(output_file, \'\'w\'\') as f:\\n f.write(_output_serializers[idx](_outputs[idx]))\\n"],\n "image": "python:3.7"}}, "inputs": [{"name": "number", "type": "Integer"}],\n "name": "Print and return number", "outputs": [{"name": "Output", "type":\n "Integer"}]}\', pipelines.kubeflow.org/component_ref: \'{}\', pipelines.kubeflow.org/arguments.parameters: \'{"number":\n "{{inputs.parameters.number_1}}"}\'}\n labels:\n pipelines.kubeflow.org/kfp_sdk_version: 1.8.9\n pipelines.kubeflow.org/pipeline-sdk-type: kfp\n pipelines.kubeflow.org/enable_caching: "true"\n - name: print-and-return-number-2\n container:\n args: [--number, \'{{inputs.parameters.number_2}}\', \'----output-paths\', /tmp/outputs/Output/data]\n command:\n - sh\n - -ec\n - |\n program_path=$(mktemp)\n printf "%s" "$0" > "$program_path"\n python3 -u "$program_path" "$@"\n - |\n def print_and_return_number(number):\n print(number)\n return number\n\n def _serialize_int(int_value: int) -> str:\n if isinstance(int_value, str):\n return int_value\n if not isinstance(int_value, int):\n raise TypeError(\'Value "{}" has type "{}" instead of int.\'.format(\n str(int_value), str(type(int_value))))\n return str(int_value)\n\n import argparse\n _parser = argparse.ArgumentParser(prog=\'Print and return number\', description=\'\')\n _parser.add_argument("--number", dest="number", type=int, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("----output-paths", dest="_output_paths", type=str, nargs=1)\n _parsed_args = vars(_parser.parse_args())\n _output_files = _parsed_args.pop("_output_paths", [])\n\n _outputs = print_and_return_number(**_parsed_args)\n\n _outputs = [_outputs]\n\n _output_serializers = [\n _serialize_int,\n\n ]\n\n import os\n for idx, output_file in enumerate(_output_files):\n try:\n os.makedirs(os.path.dirname(output_file))\n except OSError:\n pass\n with open(output_file, \'w\') as f:\n f.write(_output_serializers[idx](_outputs[idx]))\n image: python:3.7\n inputs:\n parameters:\n - {name: number_2}\n outputs:\n parameters:\n - name: print-and-return-number-2-Output\n valueFrom: {path: /tmp/outputs/Output/data}\n artifacts:\n - {name: print-and-return-number-2-Output, path: /tmp/outputs/Output/data}\n metadata:\n annotations: {pipelines.kubeflow.org/task_display_name: This is number 2, pipelines.kubeflow.org/component_spec: \'{"implementation":\n {"container": {"args": ["--number", {"inputValue": "number"}, "----output-paths",\n {"outputPath": "Output"}], "command": ["sh", "-ec", "program_path=$(mktemp)\\nprintf\n \\"%s\\" \\"$0\\" > \\"$program_path\\"\\npython3 -u \\"$program_path\\" \\"$@\\"\\n",\n "def print_and_return_number(number):\\n print(number)\\n return number\\n\\ndef\n _serialize_int(int_value: int) -> str:\\n if isinstance(int_value, str):\\n return\n int_value\\n if not isinstance(int_value, int):\\n raise TypeError(\'\'Value\n \\"{}\\" has type \\"{}\\" instead of int.\'\'.format(\\n str(int_value),\n str(type(int_value))))\\n return str(int_value)\\n\\nimport argparse\\n_parser\n = argparse.ArgumentParser(prog=\'\'Print and return number\'\', description=\'\'\'\')\\n_parser.add_argument(\\"--number\\",\n dest=\\"number\\", type=int, required=True, default=argparse.SUPPRESS)\\n_parser.add_argument(\\"----output-paths\\",\n dest=\\"_output_paths\\", type=str, nargs=1)\\n_parsed_args = vars(_parser.parse_args())\\n_output_files\n = _parsed_args.pop(\\"_output_paths\\", [])\\n\\n_outputs = print_and_return_number(**_parsed_args)\\n\\n_outputs\n = [_outputs]\\n\\n_output_serializers = [\\n _serialize_int,\\n\\n]\\n\\nimport\n os\\nfor idx, output_file in enumerate(_output_files):\\n try:\\n os.makedirs(os.path.dirname(output_file))\\n except\n OSError:\\n pass\\n with open(output_file, \'\'w\'\') as f:\\n f.write(_output_serializers[idx](_outputs[idx]))\\n"],\n "image": "python:3.7"}}, "inputs": [{"name": "number", "type": "Integer"}],\n "name": "Print and return number", "outputs": [{"name": "Output", "type":\n "Integer"}]}\', pipelines.kubeflow.org/component_ref: \'{}\', pipelines.kubeflow.org/arguments.parameters: \'{"number":\n "{{inputs.parameters.number_2}}"}\'}\n labels:\n pipelines.kubeflow.org/kfp_sdk_version: 1.8.9\n pipelines.kubeflow.org/pipeline-sdk-type: kfp\n pipelines.kubeflow.org/enable_caching: "true"\n - name: sum-and-print-numbers\n container:\n args: [--number-1, \'{{inputs.parameters.print-and-return-number-Output}}\', --number-2,\n \'{{inputs.parameters.print-and-return-number-2-Output}}\']\n command:\n - sh\n - -ec\n - |\n program_path=$(mktemp)\n printf "%s" "$0" > "$program_path"\n python3 -u "$program_path" "$@"\n - |\n def sum_and_print_numbers(number_1, number_2):\n print(number_1 + number_2)\n\n import argparse\n _parser = argparse.ArgumentParser(prog=\'Sum and print numbers\', description=\'\')\n _parser.add_argument("--number-1", dest="number_1", type=int, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--number-2", dest="number_2", type=int, required=True, default=argparse.SUPPRESS)\n _parsed_args = vars(_parser.parse_args())\n\n _outputs = sum_and_print_numbers(**_parsed_args)\n image: python:3.7\n inputs:\n parameters:\n - {name: print-and-return-number-2-Output}\n - {name: print-and-return-number-Output}\n metadata:\n annotations: {pipelines.kubeflow.org/task_display_name: This is sum of number\n 1 and number 2, pipelines.kubeflow.org/component_spec: \'{"implementation":\n {"container": {"args": ["--number-1", {"inputValue": "number_1"}, "--number-2",\n {"inputValue": "number_2"}], "command": ["sh", "-ec", "program_path=$(mktemp)\\nprintf\n \\"%s\\" \\"$0\\" > \\"$program_path\\"\\npython3 -u \\"$program_path\\" \\"$@\\"\\n",\n "def sum_and_print_numbers(number_1, number_2):\\n print(number_1 + number_2)\\n\\nimport\n argparse\\n_parser = argparse.ArgumentParser(prog=\'\'Sum and print numbers\'\',\n description=\'\'\'\')\\n_parser.add_argument(\\"--number-1\\", dest=\\"number_1\\",\n type=int, required=True, default=argparse.SUPPRESS)\\n_parser.add_argument(\\"--number-2\\",\n dest=\\"number_2\\", type=int, required=True, default=argparse.SUPPRESS)\\n_parsed_args\n = vars(_parser.parse_args())\\n\\n_outputs = sum_and_print_numbers(**_parsed_args)\\n"],\n "image": "python:3.7"}}, "inputs": [{"name": "number_1", "type": "Integer"},\n {"name": "number_2", "type": "Integer"}], "name": "Sum and print numbers"}\',\n pipelines.kubeflow.org/component_ref: \'{}\', pipelines.kubeflow.org/arguments.parameters: \'{"number_1":\n "{{inputs.parameters.print-and-return-number-Output}}", "number_2": "{{inputs.parameters.print-and-return-number-2-Output}}"}\'}\n labels:\n pipelines.kubeflow.org/kfp_sdk_version: 1.8.9\n pipelines.kubeflow.org/pipeline-sdk-type: kfp\n pipelines.kubeflow.org/enable_caching: "true"\n arguments:\n parameters:\n - {name: number_1}\n - {name: number_2}\n serviceAccountName: pipeline-runner\n')))),(0,p.kt)("p",null,"\uc774 \uc804\uc758 \ud30c\uc77c\uacfc \ube44\uad50\ud558\uba74 ",(0,p.kt)("inlineCode",{parentName:"p"},"pipelines.kubeflow.org/task_display_name")," key\uac00 \uc0c8\ub85c \uc0dd\uc131\ub418\uc5c8\uc2b5\ub2c8\ub2e4."),(0,p.kt)("h3",{id:"ui-in-kubeflow"},"UI in Kubeflow"),(0,p.kt)("p",null,"\uc704\uc5d0\uc11c \ub9cc\ub4e0 \ud30c\uc77c\uc744 \uc774\uc6a9\ud574 \uc774\uc804\uc5d0 \uc0dd\uc131\ud55c ",(0,p.kt)("a",{parentName:"p",href:"/docs/1.0/kubeflow/basic-pipeline-upload#upload-pipeline-version"},"\ud30c\uc774\ud504\ub77c\uc778"),"\uc758 \ubc84\uc804\uc744 \uc62c\ub9ac\uaca0\uc2b5\ub2c8\ub2e4."),(0,p.kt)("p",null,(0,p.kt)("img",{alt:"adv-pipeline-0.png",src:r(139).Z,width:"3360",height:"2100"})),(0,p.kt)("p",null,"\uadf8\ub7ec\uba74 \uc704\uc640 \uac19\uc774 \uc124\uc815\ud55c \uc774\ub984\uc774 \ub178\ucd9c\ub418\ub294 \uac83\uc744 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,p.kt)("h2",{id:"resources"},"Resources"),(0,p.kt)("h3",{id:"gpu"},"GPU"),(0,p.kt)("p",null,"\ud2b9\ubcc4\ud55c \uc124\uc815\uc774 \uc5c6\ub2e4\uba74 \ud30c\uc774\ud504\ub77c\uc778\uc740 \ucef4\ud3ec\ub10c\ud2b8\ub97c \ucfe0\ubc84\ub124\ud2f0\uc2a4 \ud30c\ub4dc(pod)\ub85c \uc2e4\ud589\ud560 \ub54c, \uae30\ubcf8 \ub9ac\uc18c\uc2a4 \uc2a4\ud399\uc73c\ub85c \uc2e4\ud589\ud558\uac8c \ub429\ub2c8\ub2e4.",(0,p.kt)("br",{parentName:"p"}),"\n","\ub9cc\uc57d GPU\ub97c \uc0ac\uc6a9\ud574 \ubaa8\ub378\uc744 \ud559\uc2b5\ud574\uc57c \ud560 \ub54c \ucfe0\ubc84\ub124\ud2f0\uc2a4\uc0c1\uc5d0\uc11c GPU\ub97c \ud560\ub2f9\ubc1b\uc9c0 \ubabb\ud574 \uc81c\ub300\ub85c \ud559\uc2b5\uc774 \uc774\ub8e8\uc5b4\uc9c0\uc9c0 \uc54a\uc2b5\ub2c8\ub2e4.",(0,p.kt)("br",{parentName:"p"}),"\n","\uc774\ub97c \uc704\ud574 ",(0,p.kt)("inlineCode",{parentName:"p"},"set_gpu_limit()")," ",(0,p.kt)("a",{parentName:"p",href:"https://kubeflow-pipelines.readthedocs.io/en/latest/source/kfp.dsl.html?highlight=set_gpu_limit#kfp.dsl.UserContainer.set_gpu_limit"},"attribute"),"\uc744 \uc774\uc6a9\ud574 \uc124\uc815\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,p.kt)("pre",null,(0,p.kt)("code",{parentName:"pre",className:"language-python"},'import kfp\nfrom kfp.components import create_component_from_func\nfrom kfp.dsl import pipeline\n\n\n@create_component_from_func\ndef print_and_return_number(number: int) -> int:\n print(number)\n return number\n\n\n@create_component_from_func\ndef sum_and_print_numbers(number_1: int, number_2: int):\n print(number_1 + number_2)\n\n\n@pipeline(name="example_pipeline")\ndef example_pipeline(number_1: int, number_2: int):\n number_1_result = print_and_return_number(number_1).set_display_name("This is number 1")\n number_2_result = print_and_return_number(number_2).set_display_name("This is number 2")\n sum_result = sum_and_print_numbers(\n number_1=number_1_result.output, number_2=number_2_result.output\n ).set_display_name("This is sum of number 1 and number 2").set_gpu_limit(1)\n\n\nif __name__ == "__main__":\n kfp.compiler.Compiler().compile(example_pipeline, "example_pipeline.yaml")\n')),(0,p.kt)("p",null,"\uc704\uc758 \uc2a4\ud06c\ub9bd\ud2b8\ub97c \uc2e4\ud589\ud558\uba74 \uc0dd\uc131\ub41c \ud30c\uc77c\uc5d0\uc11c ",(0,p.kt)("inlineCode",{parentName:"p"},"sum-and-print-numbers"),"\ub97c \uc790\uc138\ud788 \ubcf4\uba74 resources\uc5d0 ",(0,p.kt)("inlineCode",{parentName:"p"},"{nvidia.com/gpu: 1}")," \ub3c4 \ucd94\uac00\ub41c \uac83\uc744 \ubcfc \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\uc774\ub97c \ud1b5\ud574 GPU\ub97c \ud560\ub2f9\ubc1b\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,p.kt)("pre",null,(0,p.kt)("code",{parentName:"pre",className:"language-bash"},' - name: sum-and-print-numbers\n container:\n args: [--number-1, \'{{inputs.parameters.print-and-return-number-Output}}\', --number-2,\n \'{{inputs.parameters.print-and-return-number-2-Output}}\']\n command:\n - sh\n - -ec\n - |\n program_path=$(mktemp)\n printf "%s" "$0" > "$program_path"\n python3 -u "$program_path" "$@"\n - |\n def sum_and_print_numbers(number_1, number_2):\n print(number_1 + number_2)\n\n import argparse\n _parser = argparse.ArgumentParser(prog=\'Sum and print numbers\', description=\'\')\n _parser.add_argument("--number-1", dest="number_1", type=int, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--number-2", dest="number_2", type=int, required=True, default=argparse.SUPPRESS)\n _parsed_args = vars(_parser.parse_args())\n\n _outputs = sum_and_print_numbers(**_parsed_args)\n image: python:3.7\n resources:\n limits: {nvidia.com/gpu: 1}\n')),(0,p.kt)("h3",{id:"cpu"},"CPU"),(0,p.kt)("p",null,"cpu\uc758 \uac1c\uc218\ub97c \uc815\ud558\uae30 \uc704\ud574\uc11c \uc774\uc6a9\ud558\ub294 \ud568\uc218\ub294 ",(0,p.kt)("inlineCode",{parentName:"p"},".set_cpu_limit()")," ",(0,p.kt)("a",{parentName:"p",href:"https://kubeflow-pipelines.readthedocs.io/en/latest/source/kfp.dsl.html?highlight=set_gpu_limit#kfp.dsl.Sidecar.set_cpu_limit"},"attribute"),"\uc744 \uc774\uc6a9\ud574 \uc124\uc815\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.",(0,p.kt)("br",{parentName:"p"}),"\n","gpu\uc640\ub294 \ub2e4\ub978 \uc810\uc740 int\uac00 \uc544\ub2cc string\uc73c\ub85c \uc785\ub825\ud574\uc57c \ud55c\ub2e4\ub294 \uc810\uc785\ub2c8\ub2e4."),(0,p.kt)("pre",null,(0,p.kt)("code",{parentName:"pre",className:"language-python"},'import kfp\nfrom kfp.components import create_component_from_func\nfrom kfp.dsl import pipeline\n\n\n@create_component_from_func\ndef print_and_return_number(number: int) -> int:\n print(number)\n return number\n\n\n@create_component_from_func\ndef sum_and_print_numbers(number_1: int, number_2: int):\n print(number_1 + number_2)\n\n\n@pipeline(name="example_pipeline")\ndef example_pipeline(number_1: int, number_2: int):\n number_1_result = print_and_return_number(number_1).set_display_name("This is number 1")\n number_2_result = print_and_return_number(number_2).set_display_name("This is number 2")\n sum_result = sum_and_print_numbers(\n number_1=number_1_result.output, number_2=number_2_result.output\n ).set_display_name("This is sum of number 1 and number 2").set_gpu_limit(1).set_cpu_limit("16")\n\n\nif __name__ == "__main__":\n kfp.compiler.Compiler().compile(example_pipeline, "example_pipeline.yaml")\n')),(0,p.kt)("p",null,"\ubc14\ub010 \ubd80\ubd84\ub9cc \ud655\uc778\ud558\uba74 \ub2e4\uc74c\uacfc 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number_2)\n\n\n@pipeline(name="example_pipeline")\ndef example_pipeline(number_1: int, number_2: int):\n number_1_result = print_and_return_number(number_1).set_display_name("This is number 1")\n number_2_result = print_and_return_number(number_2).set_display_name("This is number 2")\n sum_result = sum_and_print_numbers(\n number_1=number_1_result.output, number_2=number_2_result.output\n ).set_display_name("This is sum of number 1 and number 2").set_gpu_limit(1).set_memory_limit("1G")\n\n\nif __name__ == "__main__":\n kfp.compiler.Compiler().compile(example_pipeline, "example_pipeline.yaml")\n\n')),(0,p.kt)("p",null,"\ubc14\ub010 \ubd80\ubd84\ub9cc \ud655\uc778\ud558\uba74 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4."),(0,p.kt)("pre",null,(0,p.kt)("code",{parentName:"pre",className:"language-bash"}," resources:\n limits: {nvidia.com/gpu: 1, memory: 1G}\n")))}l.isMDXComponent=!0},139:(n,e,r)=>{r.d(e,{Z:()=>t});const 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number_1_result = print_and_return_number(number_1).set_display_name("This is number 1")\n number_2_result = print_and_return_number(number_2).set_display_name("This is number 2")\n sum_result = sum_and_print_numbers(\n number_1=number_1_result.output, number_2=number_2_result.output\n ).set_display_name("This is sum of number 1 and number 2")\n\n\nif __name__ == "__main__":\n kfp.compiler.Compiler().compile(example_pipeline, "example_pipeline.yaml")\n')),(0,p.kt)("p",null,"\uc774 \uc2a4\ud06c\ub9bd\ud2b8\ub97c \uc2e4\ud589\ud574\uc11c \ub098\uc628 ",(0,p.kt)("inlineCode",{parentName:"p"},"example_pipeline.yaml"),"\uc744 \ud655\uc778\ud558\uba74 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4."),(0,p.kt)("p",null,(0,p.kt)("details",null,(0,p.kt)("summary",null,"example_pipeline.yaml"),(0,p.kt)("pre",null,(0,p.kt)("code",{parentName:"pre",className:"language-bash"},'apiVersion: argoproj.io/v1alpha1\nkind: Workflow\nmetadata:\n generateName: example-pipeline-\n annotations: {pipelines.kubeflow.org/kfp_sdk_version: 1.8.9, pipelines.kubeflow.org/pipeline_compilation_time: \'2021-12-09T18:11:43.193190\',\n pipelines.kubeflow.org/pipeline_spec: \'{"inputs": [{"name": "number_1", "type":\n "Integer"}, {"name": "number_2", "type": "Integer"}], "name": "example_pipeline"}\'}\n labels: {pipelines.kubeflow.org/kfp_sdk_version: 1.8.9}\nspec:\n entrypoint: example-pipeline\n templates:\n - name: example-pipeline\n inputs:\n parameters:\n - {name: number_1}\n - {name: number_2}\n dag:\n tasks:\n - name: print-and-return-number\n template: print-and-return-number\n arguments:\n parameters:\n - {name: number_1, value: \'{{inputs.parameters.number_1}}\'}\n - name: print-and-return-number-2\n template: print-and-return-number-2\n arguments:\n parameters:\n - {name: number_2, value: \'{{inputs.parameters.number_2}}\'}\n - name: sum-and-print-numbers\n template: sum-and-print-numbers\n dependencies: [print-and-return-number, print-and-return-number-2]\n arguments:\n parameters:\n - {name: print-and-return-number-2-Output, value: \'{{tasks.print-and-return-number-2.outputs.parameters.print-and-return-number-2-Output}}\'}\n - {name: print-and-return-number-Output, value: \'{{tasks.print-and-return-number.outputs.parameters.print-and-return-number-Output}}\'}\n - name: print-and-return-number\n container:\n args: [--number, \'{{inputs.parameters.number_1}}\', \'----output-paths\', /tmp/outputs/Output/data]\n command:\n - sh\n - -ec\n - |\n program_path=$(mktemp)\n printf "%s" "$0" > "$program_path"\n python3 -u "$program_path" "$@"\n - |\n def print_and_return_number(number):\n print(number)\n return number\n\n def _serialize_int(int_value: int) -> str:\n if isinstance(int_value, str):\n return int_value\n if not isinstance(int_value, int):\n raise TypeError(\'Value "{}" has type "{}" instead of int.\'.format(\n str(int_value), str(type(int_value))))\n return str(int_value)\n\n import argparse\n _parser = argparse.ArgumentParser(prog=\'Print and return number\', description=\'\')\n _parser.add_argument("--number", dest="number", type=int, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("----output-paths", dest="_output_paths", type=str, nargs=1)\n _parsed_args = vars(_parser.parse_args())\n _output_files = _parsed_args.pop("_output_paths", [])\n\n _outputs = print_and_return_number(**_parsed_args)\n\n _outputs = [_outputs]\n\n _output_serializers = [\n _serialize_int,\n\n ]\n\n import os\n for idx, output_file in enumerate(_output_files):\n try:\n os.makedirs(os.path.dirname(output_file))\n except OSError:\n pass\n with open(output_file, \'w\') as f:\n f.write(_output_serializers[idx](_outputs[idx]))\n image: python:3.7\n inputs:\n parameters:\n - {name: number_1}\n outputs:\n parameters:\n - name: print-and-return-number-Output\n valueFrom: {path: /tmp/outputs/Output/data}\n artifacts:\n - {name: print-and-return-number-Output, path: /tmp/outputs/Output/data}\n metadata:\n annotations: {pipelines.kubeflow.org/task_display_name: This is number 1, pipelines.kubeflow.org/component_spec: \'{"implementation":\n {"container": {"args": ["--number", {"inputValue": "number"}, "----output-paths",\n {"outputPath": "Output"}], "command": ["sh", "-ec", "program_path=$(mktemp)\\nprintf\n \\"%s\\" \\"$0\\" > \\"$program_path\\"\\npython3 -u \\"$program_path\\" \\"$@\\"\\n",\n "def print_and_return_number(number):\\n print(number)\\n return number\\n\\ndef\n _serialize_int(int_value: int) -> str:\\n if isinstance(int_value, str):\\n return\n int_value\\n if not isinstance(int_value, int):\\n raise TypeError(\'\'Value\n \\"{}\\" has type \\"{}\\" instead of int.\'\'.format(\\n str(int_value),\n str(type(int_value))))\\n return str(int_value)\\n\\nimport argparse\\n_parser\n = argparse.ArgumentParser(prog=\'\'Print and return number\'\', description=\'\'\'\')\\n_parser.add_argument(\\"--number\\",\n dest=\\"number\\", type=int, required=True, default=argparse.SUPPRESS)\\n_parser.add_argument(\\"----output-paths\\",\n dest=\\"_output_paths\\", type=str, nargs=1)\\n_parsed_args = vars(_parser.parse_args())\\n_output_files\n = _parsed_args.pop(\\"_output_paths\\", [])\\n\\n_outputs = print_and_return_number(**_parsed_args)\\n\\n_outputs\n = [_outputs]\\n\\n_output_serializers = [\\n _serialize_int,\\n\\n]\\n\\nimport\n os\\nfor idx, output_file in enumerate(_output_files):\\n try:\\n os.makedirs(os.path.dirname(output_file))\\n except\n OSError:\\n pass\\n with open(output_file, \'\'w\'\') as f:\\n f.write(_output_serializers[idx](_outputs[idx]))\\n"],\n "image": "python:3.7"}}, "inputs": [{"name": "number", "type": "Integer"}],\n "name": "Print and return number", "outputs": [{"name": "Output", "type":\n "Integer"}]}\', pipelines.kubeflow.org/component_ref: \'{}\', pipelines.kubeflow.org/arguments.parameters: \'{"number":\n "{{inputs.parameters.number_1}}"}\'}\n labels:\n pipelines.kubeflow.org/kfp_sdk_version: 1.8.9\n pipelines.kubeflow.org/pipeline-sdk-type: kfp\n pipelines.kubeflow.org/enable_caching: "true"\n - name: print-and-return-number-2\n container:\n args: [--number, \'{{inputs.parameters.number_2}}\', \'----output-paths\', /tmp/outputs/Output/data]\n command:\n - sh\n - -ec\n - |\n program_path=$(mktemp)\n printf "%s" "$0" > "$program_path"\n python3 -u "$program_path" "$@"\n - |\n def print_and_return_number(number):\n print(number)\n return number\n\n def _serialize_int(int_value: int) -> str:\n if isinstance(int_value, str):\n return int_value\n if not isinstance(int_value, int):\n raise TypeError(\'Value "{}" has type "{}" instead of int.\'.format(\n str(int_value), str(type(int_value))))\n return str(int_value)\n\n import argparse\n _parser = argparse.ArgumentParser(prog=\'Print and return number\', description=\'\')\n _parser.add_argument("--number", dest="number", type=int, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("----output-paths", dest="_output_paths", type=str, nargs=1)\n _parsed_args = vars(_parser.parse_args())\n _output_files = _parsed_args.pop("_output_paths", [])\n\n _outputs = print_and_return_number(**_parsed_args)\n\n _outputs = [_outputs]\n\n _output_serializers = [\n _serialize_int,\n\n ]\n\n import os\n for idx, output_file in enumerate(_output_files):\n try:\n os.makedirs(os.path.dirname(output_file))\n except OSError:\n pass\n with open(output_file, \'w\') as f:\n f.write(_output_serializers[idx](_outputs[idx]))\n image: python:3.7\n inputs:\n parameters:\n - {name: number_2}\n outputs:\n parameters:\n - name: print-and-return-number-2-Output\n valueFrom: {path: /tmp/outputs/Output/data}\n artifacts:\n - {name: print-and-return-number-2-Output, path: /tmp/outputs/Output/data}\n metadata:\n annotations: {pipelines.kubeflow.org/task_display_name: This is number 2, pipelines.kubeflow.org/component_spec: \'{"implementation":\n {"container": {"args": ["--number", {"inputValue": "number"}, "----output-paths",\n {"outputPath": "Output"}], "command": ["sh", "-ec", "program_path=$(mktemp)\\nprintf\n \\"%s\\" \\"$0\\" > \\"$program_path\\"\\npython3 -u \\"$program_path\\" \\"$@\\"\\n",\n "def print_and_return_number(number):\\n print(number)\\n return number\\n\\ndef\n _serialize_int(int_value: int) -> str:\\n if isinstance(int_value, str):\\n return\n int_value\\n if not isinstance(int_value, int):\\n raise TypeError(\'\'Value\n \\"{}\\" has type \\"{}\\" instead of int.\'\'.format(\\n str(int_value),\n str(type(int_value))))\\n return str(int_value)\\n\\nimport argparse\\n_parser\n = argparse.ArgumentParser(prog=\'\'Print and return number\'\', description=\'\'\'\')\\n_parser.add_argument(\\"--number\\",\n dest=\\"number\\", type=int, required=True, default=argparse.SUPPRESS)\\n_parser.add_argument(\\"----output-paths\\",\n dest=\\"_output_paths\\", type=str, nargs=1)\\n_parsed_args = vars(_parser.parse_args())\\n_output_files\n = _parsed_args.pop(\\"_output_paths\\", [])\\n\\n_outputs = print_and_return_number(**_parsed_args)\\n\\n_outputs\n = [_outputs]\\n\\n_output_serializers = [\\n _serialize_int,\\n\\n]\\n\\nimport\n os\\nfor idx, output_file in enumerate(_output_files):\\n try:\\n os.makedirs(os.path.dirname(output_file))\\n except\n OSError:\\n pass\\n with open(output_file, \'\'w\'\') as f:\\n f.write(_output_serializers[idx](_outputs[idx]))\\n"],\n "image": "python:3.7"}}, "inputs": [{"name": "number", "type": "Integer"}],\n "name": "Print and return number", "outputs": [{"name": "Output", "type":\n "Integer"}]}\', pipelines.kubeflow.org/component_ref: \'{}\', pipelines.kubeflow.org/arguments.parameters: \'{"number":\n "{{inputs.parameters.number_2}}"}\'}\n labels:\n pipelines.kubeflow.org/kfp_sdk_version: 1.8.9\n pipelines.kubeflow.org/pipeline-sdk-type: kfp\n pipelines.kubeflow.org/enable_caching: "true"\n - name: sum-and-print-numbers\n container:\n args: [--number-1, \'{{inputs.parameters.print-and-return-number-Output}}\', --number-2,\n \'{{inputs.parameters.print-and-return-number-2-Output}}\']\n command:\n - sh\n - -ec\n - |\n program_path=$(mktemp)\n printf "%s" "$0" > "$program_path"\n python3 -u "$program_path" "$@"\n - |\n def sum_and_print_numbers(number_1, number_2):\n print(number_1 + number_2)\n\n import argparse\n _parser = argparse.ArgumentParser(prog=\'Sum and print numbers\', description=\'\')\n _parser.add_argument("--number-1", dest="number_1", type=int, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--number-2", dest="number_2", type=int, required=True, default=argparse.SUPPRESS)\n _parsed_args = vars(_parser.parse_args())\n\n _outputs = sum_and_print_numbers(**_parsed_args)\n image: python:3.7\n inputs:\n parameters:\n - {name: print-and-return-number-2-Output}\n - {name: print-and-return-number-Output}\n metadata:\n annotations: {pipelines.kubeflow.org/task_display_name: This is sum of number\n 1 and number 2, pipelines.kubeflow.org/component_spec: \'{"implementation":\n {"container": {"args": ["--number-1", {"inputValue": "number_1"}, "--number-2",\n {"inputValue": "number_2"}], "command": ["sh", "-ec", "program_path=$(mktemp)\\nprintf\n \\"%s\\" \\"$0\\" > \\"$program_path\\"\\npython3 -u \\"$program_path\\" \\"$@\\"\\n",\n "def sum_and_print_numbers(number_1, number_2):\\n print(number_1 + number_2)\\n\\nimport\n argparse\\n_parser = argparse.ArgumentParser(prog=\'\'Sum and print numbers\'\',\n description=\'\'\'\')\\n_parser.add_argument(\\"--number-1\\", dest=\\"number_1\\",\n type=int, required=True, default=argparse.SUPPRESS)\\n_parser.add_argument(\\"--number-2\\",\n dest=\\"number_2\\", type=int, required=True, default=argparse.SUPPRESS)\\n_parsed_args\n = vars(_parser.parse_args())\\n\\n_outputs = sum_and_print_numbers(**_parsed_args)\\n"],\n "image": "python:3.7"}}, "inputs": [{"name": "number_1", "type": "Integer"},\n {"name": "number_2", "type": "Integer"}], "name": "Sum and print numbers"}\',\n pipelines.kubeflow.org/component_ref: \'{}\', pipelines.kubeflow.org/arguments.parameters: \'{"number_1":\n "{{inputs.parameters.print-and-return-number-Output}}", "number_2": "{{inputs.parameters.print-and-return-number-2-Output}}"}\'}\n labels:\n pipelines.kubeflow.org/kfp_sdk_version: 1.8.9\n pipelines.kubeflow.org/pipeline-sdk-type: kfp\n pipelines.kubeflow.org/enable_caching: "true"\n arguments:\n parameters:\n - {name: number_1}\n - {name: number_2}\n serviceAccountName: pipeline-runner\n')))),(0,p.kt)("p",null,"\uc774 \uc804\uc758 \ud30c\uc77c\uacfc \ube44\uad50\ud558\uba74 ",(0,p.kt)("inlineCode",{parentName:"p"},"pipelines.kubeflow.org/task_display_name")," key\uac00 \uc0c8\ub85c \uc0dd\uc131\ub418\uc5c8\uc2b5\ub2c8\ub2e4."),(0,p.kt)("h3",{id:"ui-in-kubeflow"},"UI in Kubeflow"),(0,p.kt)("p",null,"\uc704\uc5d0\uc11c \ub9cc\ub4e0 \ud30c\uc77c\uc744 \uc774\uc6a9\ud574 \uc774\uc804\uc5d0 \uc0dd\uc131\ud55c 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sum_and_print_numbers(number_1: int, number_2: int):\n print(number_1 + number_2)\n\n\n@pipeline(name="example_pipeline")\ndef example_pipeline(number_1: int, number_2: int):\n number_1_result = print_and_return_number(number_1).set_display_name("This is number 1")\n number_2_result = print_and_return_number(number_2).set_display_name("This is number 2")\n sum_result = sum_and_print_numbers(\n number_1=number_1_result.output, number_2=number_2_result.output\n ).set_display_name("This is sum of number 1 and number 2").set_gpu_limit(1)\n\n\nif __name__ == "__main__":\n kfp.compiler.Compiler().compile(example_pipeline, "example_pipeline.yaml")\n')),(0,p.kt)("p",null,"\uc704\uc758 \uc2a4\ud06c\ub9bd\ud2b8\ub97c \uc2e4\ud589\ud558\uba74 \uc0dd\uc131\ub41c \ud30c\uc77c\uc5d0\uc11c ",(0,p.kt)("inlineCode",{parentName:"p"},"sum-and-print-numbers"),"\ub97c \uc790\uc138\ud788 \ubcf4\uba74 resources\uc5d0 ",(0,p.kt)("inlineCode",{parentName:"p"},"{nvidia.com/gpu: 1}")," \ub3c4 \ucd94\uac00\ub41c \uac83\uc744 \ubcfc \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\uc774\ub97c \ud1b5\ud574 GPU\ub97c \ud560\ub2f9\ubc1b\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,p.kt)("pre",null,(0,p.kt)("code",{parentName:"pre",className:"language-bash"},' - name: sum-and-print-numbers\n container:\n args: [--number-1, \'{{inputs.parameters.print-and-return-number-Output}}\', --number-2,\n \'{{inputs.parameters.print-and-return-number-2-Output}}\']\n command:\n - sh\n - -ec\n - |\n program_path=$(mktemp)\n printf "%s" "$0" > "$program_path"\n python3 -u "$program_path" "$@"\n - |\n def sum_and_print_numbers(number_1, number_2):\n print(number_1 + number_2)\n\n import argparse\n _parser = argparse.ArgumentParser(prog=\'Sum and print numbers\', description=\'\')\n _parser.add_argument("--number-1", dest="number_1", type=int, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--number-2", dest="number_2", type=int, required=True, default=argparse.SUPPRESS)\n _parsed_args = vars(_parser.parse_args())\n\n _outputs = sum_and_print_numbers(**_parsed_args)\n image: python:3.7\n resources:\n limits: {nvidia.com/gpu: 1}\n')),(0,p.kt)("h3",{id:"cpu"},"CPU"),(0,p.kt)("p",null,"cpu\uc758 \uac1c\uc218\ub97c \uc815\ud558\uae30 \uc704\ud574\uc11c \uc774\uc6a9\ud558\ub294 \ud568\uc218\ub294 ",(0,p.kt)("inlineCode",{parentName:"p"},".set_cpu_limit()")," ",(0,p.kt)("a",{parentName:"p",href:"https://kubeflow-pipelines.readthedocs.io/en/latest/source/kfp.dsl.html?highlight=set_gpu_limit#kfp.dsl.Sidecar.set_cpu_limit"},"attribute"),"\uc744 \uc774\uc6a9\ud574 \uc124\uc815\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.",(0,p.kt)("br",{parentName:"p"}),"\n","gpu\uc640\ub294 \ub2e4\ub978 \uc810\uc740 int\uac00 \uc544\ub2cc string\uc73c\ub85c \uc785\ub825\ud574\uc57c \ud55c\ub2e4\ub294 \uc810\uc785\ub2c8\ub2e4."),(0,p.kt)("pre",null,(0,p.kt)("code",{parentName:"pre",className:"language-python"},'import kfp\nfrom kfp.components import create_component_from_func\nfrom kfp.dsl import pipeline\n\n\n@create_component_from_func\ndef print_and_return_number(number: int) -> int:\n print(number)\n return number\n\n\n@create_component_from_func\ndef sum_and_print_numbers(number_1: int, number_2: int):\n print(number_1 + number_2)\n\n\n@pipeline(name="example_pipeline")\ndef example_pipeline(number_1: int, number_2: int):\n number_1_result = print_and_return_number(number_1).set_display_name("This is number 1")\n number_2_result = print_and_return_number(number_2).set_display_name("This is number 2")\n sum_result = sum_and_print_numbers(\n number_1=number_1_result.output, number_2=number_2_result.output\n ).set_display_name("This is sum of number 1 and number 2").set_gpu_limit(1).set_cpu_limit("16")\n\n\nif __name__ == "__main__":\n kfp.compiler.Compiler().compile(example_pipeline, "example_pipeline.yaml")\n')),(0,p.kt)("p",null,"\ubc14\ub010 \ubd80\ubd84\ub9cc \ud655\uc778\ud558\uba74 \ub2e4\uc74c\uacfc 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number_2)\n\n\n@pipeline(name="example_pipeline")\ndef example_pipeline(number_1: int, number_2: int):\n number_1_result = print_and_return_number(number_1).set_display_name("This is number 1")\n number_2_result = print_and_return_number(number_2).set_display_name("This is number 2")\n sum_result = sum_and_print_numbers(\n number_1=number_1_result.output, number_2=number_2_result.output\n ).set_display_name("This is sum of number 1 and number 2").set_gpu_limit(1).set_memory_limit("1G")\n\n\nif __name__ == "__main__":\n kfp.compiler.Compiler().compile(example_pipeline, "example_pipeline.yaml")\n\n')),(0,p.kt)("p",null,"\ubc14\ub010 \ubd80\ubd84\ub9cc \ud655\uc778\ud558\uba74 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4."),(0,p.kt)("pre",null,(0,p.kt)("code",{parentName:"pre",className:"language-bash"}," resources:\n limits: {nvidia.com/gpu: 1, memory: 1G}\n")))}l.isMDXComponent=!0},139:(n,e,r)=>{r.d(e,{Z:()=>t});const 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image.",sidebar_position:5,contributors:["Jongseob Jeon","Jaeyeon Kim"]},sidebar:"preSidebar",previous:{title:"[Practice] Docker command",permalink:"/docs/prerequisites/docker/command"},next:{title:"[Practice] Docker Advanced",permalink:"/docs/prerequisites/docker/advanced"}},p={},c=[{value:"1. Dockerfile \ub9cc\ub4e4\uae30",id:"1-dockerfile-\ub9cc\ub4e4\uae30",level:2},{value:"2. Dockerfile \ub0b4\uc7a5 \uba85\ub839\uc5b4",id:"2-dockerfile-\ub0b4\uc7a5-\uba85\ub839\uc5b4",level:2},{value:"FROM",id:"from",level:3},{value:"COPY",id:"copy",level:3},{value:"RUN",id:"run",level:3},{value:"CMD",id:"cmd",level:3},{value:"WORKDIR",id:"workdir",level:3},{value:"ENV",id:"env",level:3},{value:"EXPOSE",id:"expose",level:3},{value:"3. \uac04\ub2e8\ud55c Dockerfile \uc791\uc131\ud574\ubcf4\uae30",id:"3-\uac04\ub2e8\ud55c-dockerfile-\uc791\uc131\ud574\ubcf4\uae30",level:2},{value:"4. Docker build from Dockerfile",id:"4-docker-build-from-dockerfile",level:2},{value:"5. Docker run from Dockerfile",id:"5-docker-run-from-dockerfile",level:2},{value:"6. Docker run with env",id:"6-docker-run-with-env",level:2}],k={toc:c},m="wrapper";function d(e){let{components:t,...r}=e;return(0,a.kt)(m,(0,n.Z)({},k,r,{components:t,mdxType:"MDXLayout"}),(0,a.kt)("h2",{id:"1-dockerfile-\ub9cc\ub4e4\uae30"},"1. Dockerfile \ub9cc\ub4e4\uae30"),(0,a.kt)("p",null,"\ub3c4\ucee4 \uc774\ubbf8\uc9c0\ub97c \ub9cc\ub4dc\ub294 \uac00\uc7a5 \uc26c\uc6b4 \ubc29\ubc95\uc740 \ub3c4\ucee4\uc5d0\uc11c \uc81c\uacf5\ud558\ub294 \ud15c\ud50c\ub9bf\uc778 Dockerfile\uc744 \uc0ac\uc6a9\ud558\ub294 \uac83\uc785\ub2c8\ub2e4.",(0,a.kt)("br",{parentName:"p"}),"\n","\uc774\uc678\uc5d0\ub294 running container \ub97c docker image \ub85c \ub9cc\ub4dc\ub294 ",(0,a.kt)("inlineCode",{parentName:"p"},"docker commit")," \ub4f1\uc744 \ud65c\uc6a9\ud558\ub294 \ubc29\ubc95\uc774 \uc788\uc2b5\ub2c8\ub2e4."),(0,a.kt)("ul",null,(0,a.kt)("li",{parentName:"ul"},(0,a.kt)("inlineCode",{parentName:"li"},"Dockerfile"),(0,a.kt)("ul",{parentName:"li"},(0,a.kt)("li",{parentName:"ul"},"\uc0ac\uc6a9\uc790\uac00 \ub3c4\ucee4 \uc774\ubbf8\uc9c0\ub97c \uc27d\uac8c \ub9cc\ub4e4 \uc218 \uc788\ub3c4\ub85d, \uc81c\uacf5\ud558\ub294 \ud15c\ud50c\ub9bf"),(0,a.kt)("li",{parentName:"ul"},"\ud30c\uc77c\uba85\uc740 \uaf2d ",(0,a.kt)("inlineCode",{parentName:"li"},"Dockerfile")," \uc774 \uc544\ub2c8\uc5b4\ub3c4 \uc0c1\uad00\uc5c6\uc9c0\ub9cc, ",(0,a.kt)("inlineCode",{parentName:"li"},"docker build")," \uc218\ud589 \uc2dc, default \ub85c \uc0ac\uc6a9\ud558\ub294 \ud30c\uc77c\uba85\uc774 ",(0,a.kt)("inlineCode",{parentName:"li"},"Dockerfile")," \uc785\ub2c8\ub2e4."),(0,a.kt)("li",{parentName:"ul"},"\ub3c4\ucee4 \uc774\ubbf8\uc9c0\ub97c \ub9cc\ub4dc\ub294 ",(0,a.kt)("inlineCode",{parentName:"li"},"docker build")," \ub97c \uc218\ud589\ud560 \ub54c, ",(0,a.kt)("inlineCode",{parentName:"li"},"-f")," \uc635\uc158\uc744 \uc8fc\uba74 \ub2e4\ub978 \ud30c\uc77c\uba85\uc73c\ub85c\ub3c4 \uc0ac\uc6a9 \uac00\ub2a5\ud569\ub2c8\ub2e4.",(0,a.kt)("ul",{parentName:"li"},(0,a.kt)("li",{parentName:"ul"},"ex) ",(0,a.kt)("inlineCode",{parentName:"li"},"docker build -f dockerfile-asdf .")," \ub3c4 \uac00\ub2a5")))))),(0,a.kt)("ol",null,(0,a.kt)("li",{parentName:"ol"},(0,a.kt)("p",{parentName:"li"},"\uc2e4\uc2b5\uc744 \uc704\ud574\uc11c \ud3b8\ud55c \ub514\ub809\ud1a0\ub9ac\ub85c \uc774\ub3d9\ud569\ub2c8\ub2e4."),(0,a.kt)("pre",{parentName:"li"},(0,a.kt)("code",{parentName:"pre",className:"language-bash"},"cd \n"))),(0,a.kt)("li",{parentName:"ol"},(0,a.kt)("p",{parentName:"li"},"docker-practice \ub77c\ub294 \uc774\ub984\uc758 \ud3f4\ub354\ub97c \uc0dd\uc131\ud569\ub2c8\ub2e4."),(0,a.kt)("pre",{parentName:"li"},(0,a.kt)("code",{parentName:"pre",className:"language-bash"},"mkdir docker-practice\n"))),(0,a.kt)("li",{parentName:"ol"},(0,a.kt)("p",{parentName:"li"},"docker-practice \ud3f4\ub354\ub85c \uc774\ub3d9\ud569\ub2c8\ub2e4."),(0,a.kt)("pre",{parentName:"li"},(0,a.kt)("code",{parentName:"pre",className:"language-bash"},"cd docker-practice\n"))),(0,a.kt)("li",{parentName:"ol"},(0,a.kt)("p",{parentName:"li"},"Dockerfile \uc774\ub77c\ub294 \ube48 \ud30c\uc77c\uc744 \uc0dd\uc131\ud569\ub2c8\ub2e4."),(0,a.kt)("pre",{parentName:"li"},(0,a.kt)("code",{parentName:"pre",className:"language-bash"},"touch Dockerfile\n"))),(0,a.kt)("li",{parentName:"ol"},(0,a.kt)("p",{parentName:"li"},"\uc815\uc0c1\uc801\uc73c\ub85c \uc0dd\uc131\ub418\uc5c8\ub294\uc9c0 \ud655\uc778\ud569\ub2c8\ub2e4."),(0,a.kt)("pre",{parentName:"li"},(0,a.kt)("code",{parentName:"pre",className:"language-bash"},"ls\n")))),(0,a.kt)("h2",{id:"2-dockerfile-\ub0b4\uc7a5-\uba85\ub839\uc5b4"},"2. Dockerfile \ub0b4\uc7a5 \uba85\ub839\uc5b4"),(0,a.kt)("p",null,"Dockerfile \uc5d0\uc11c \uc0ac\uc6a9\ud560 \uc218 \uc788\ub294 \uae30\ubcf8\uc801\uc778 \uba85\ub839\uc5b4\uc5d0 \ub300\ud574\uc11c \ud558\ub098\uc529 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4."),(0,a.kt)("h3",{id:"from"},"FROM"),(0,a.kt)("p",null,"Dockerfile \uc774 base image \ub85c \uc5b4\ub5a0\ud55c \uc774\ubbf8\uc9c0\ub97c \uc0ac\uc6a9\ud560 \uac83\uc778\uc9c0\ub97c \uba85\uc2dc\ud558\ub294 \uba85\ub839\uc5b4\uc785\ub2c8\ub2e4.",(0,a.kt)("br",{parentName:"p"}),"\n","\ub3c4\ucee4 \uc774\ubbf8\uc9c0\ub97c \ub9cc\ub4e4 \ub54c, \uc544\ubb34\uac83\ub3c4 \uc5c6\ub294 \ube48 \ud658\uacbd\uc5d0\uc11c\ubd80\ud130 \ud558\ub098\ud558\ub098\uc529 \uc81c\uac00 \uc758\ub3c4\ud55c \ud658\uacbd\uc744 \ub9cc\ub4e4\uc5b4\uac00\ub294\uac8c \uc544\ub2c8\ub77c, python 3.9 \ubc84\uc804\uc774 \uc124\uce58\ub41c \ud658\uacbd\uc744 \ubca0\uc774\uc2a4\ub85c\ud574\ub450\uace0, \uc800\ub294 pytorch \ub97c \uc124\uce58\ud558\uace0, \uc81c \uc18c\uc2a4\ucf54\ub4dc\ub9cc \ub123\uc5b4\ub450\ub294 \ud615\ud0dc\ub85c \ud65c\uc6a9\ud560 \uc218\uac00 \uc788\uc2b5\ub2c8\ub2e4.",(0,a.kt)("br",{parentName:"p"}),"\n","\uc774\ub7ec\ud55c \uacbd\uc6b0\uc5d0\ub294 ",(0,a.kt)("inlineCode",{parentName:"p"},"python:3.9"),", ",(0,a.kt)("inlineCode",{parentName:"p"},"python-3.9-alpine"),", ... \ub4f1\uc758 \uc798 \ub9cc\ub4e4\uc5b4\uc9c4 \uc774\ubbf8\uc9c0\ub97c \ubca0\uc774\uc2a4\ub85c \ud65c\uc6a9\ud569\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-docker"},"FROM [:] [AS ]\n\n# \uc608\uc2dc\nFROM ubuntu\nFROM ubuntu:18.04\nFROM nginx:latest AS ngx\n")),(0,a.kt)("h3",{id:"copy"},"COPY"),(0,a.kt)("p",null,(0,a.kt)("strong",{parentName:"p"},"host(\ub85c\uceec)\uc5d0\uc11c\uc758 ",(0,a.kt)("inlineCode",{parentName:"strong"},""))," \uacbd\ub85c\uc758 \ud30c\uc77c \ud639\uc740 \ub514\ub809\ud1a0\ub9ac\ub97c ",(0,a.kt)("strong",{parentName:"p"},"container \ub0b4\ubd80\uc5d0\uc11c\uc758 ",(0,a.kt)("inlineCode",{parentName:"strong"},""))," \uacbd\ub85c\uc5d0 \ubcf5\uc0ac\ud558\ub294 \uba85\ub839\uc5b4\uc785\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-docker"},"COPY ... \n\n# \uc608\uc2dc\nCOPY a.txt /some-directory/b.txt\nCOPY my-directory /some-directory-2\n")),(0,a.kt)("p",null,(0,a.kt)("inlineCode",{parentName:"p"},"ADD")," \ub294 ",(0,a.kt)("inlineCode",{parentName:"p"},"COPY")," \uc640 \ube44\uc2b7\ud558\uc9c0\ub9cc \ucd94\uac00\uc801\uc778 \uae30\ub2a5\uc744 \ud488\uace0 \uc788\uc2b5\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-docker"},"# 1 - \ud638\uc2a4\ud2b8\uc5d0 \uc555\ucd95\ub418\uc5b4\uc788\ub294 \ud30c\uc77c\uc744 \ud480\uba74\uc11c \ucee8\ud14c\uc774\ub108 \ub0b4\ubd80\ub85c copy \ud560 \uc218 \uc788\uc74c\nADD scripts.tar.gz /tmp\n# 2 - Remote URLs \uc5d0 \uc788\ub294 \ud30c\uc77c\uc744 \uc18c\uc2a4 \uacbd\ub85c\ub85c \uc9c0\uc815\ud560 \uc218 \uc788\uc74c\nADD http://www.example.com/script.sh /tmp\n\n# \uc704 \ub450 \uac00\uc9c0 \uae30\ub2a5\uc744 \uc0ac\uc6a9\ud558\uace0 \uc2f6\uc744 \uacbd\uc6b0\uc5d0\ub9cc COPY \ub300\uc2e0 ADD \ub97c \uc0ac\uc6a9\ud558\ub294 \uac83\uc744 \uad8c\uc7a5\n")),(0,a.kt)("h3",{id:"run"},"RUN"),(0,a.kt)("p",null,"\uba85\uc2dc\ud55c \ucee4\ub9e8\ub4dc\ub97c \ub3c4\ucee4 \ucee8\ud14c\uc774\ub108 \ub0b4\ubd80\uc5d0\uc11c \uc2e4\ud589\ud558\ub294 \uba85\ub839\uc5b4\uc785\ub2c8\ub2e4.",(0,a.kt)("br",{parentName:"p"}),"\n","\ub3c4\ucee4 \uc774\ubbf8\uc9c0\ub294 \ud574\ub2f9 \ucee4\ub9e8\ub4dc\ub4e4\uc774 \uc2e4\ud589\ub41c \uc0c1\ud0dc\ub97c \uc720\uc9c0\ud569\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-docker"},'RUN \nRUN ["executable-command", "parameter1", "parameter2"]\n\n# \uc608\uc2dc\nRUN pip install torch\nRUN pip install -r requirements.txt\n')),(0,a.kt)("h3",{id:"cmd"},"CMD"),(0,a.kt)("p",null,"\uba85\uc2dc\ud55c \ucee4\ub9e8\ub4dc\ub97c \ub3c4\ucee4 \ucee8\ud14c\uc774\ub108\uac00 ",(0,a.kt)("strong",{parentName:"p"},"\uc2dc\uc791\ub420 \ub54c"),", \uc2e4\ud589\ud558\ub294 \uac83\uc744 \uba85\uc2dc\ud558\ub294 \uba85\ub839\uc5b4\uc785\ub2c8\ub2e4.",(0,a.kt)("br",{parentName:"p"}),"\n","\ube44\uc2b7\ud55c \uc5ed\ud560\uc744 \ud558\ub294 \uba85\ub839\uc5b4\ub85c ",(0,a.kt)("strong",{parentName:"p"},"ENTRYPOINT")," \uac00 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \ub458\uc758 \ucc28\uc774\uc5d0 \ub300\ud574\uc11c\ub294 ",(0,a.kt)("strong",{parentName:"p"},"\ub4a4\uc5d0\uc11c")," \ub2e4\ub8f9\ub2c8\ub2e4.",(0,a.kt)("br",{parentName:"p"}),"\n","\ud558\ub098\uc758 \ub3c4\ucee4 \uc774\ubbf8\uc9c0\uc5d0\uc11c\ub294 \ud558\ub098\uc758 ",(0,a.kt)("strong",{parentName:"p"},"CMD")," \ub9cc \uc2e4\ud589\ud560 \uc218 \uc788\ub2e4\ub294 \uc810\uc5d0\uc11c ",(0,a.kt)("strong",{parentName:"p"},"RUN")," \uba85\ub839\uc5b4\uc640 \ub2e4\ub985\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-docker"},'CMD \nCMD ["executable-command", "parameter1", "parameter2"]\nCMD ["parameter1", "parameter2"] # ENTRYPOINT \uc640 \ud568\uaed8 \uc0ac\uc6a9\ub420 \ub54c\n\n# \uc608\uc2dc\nCMD python main.py\n')),(0,a.kt)("h3",{id:"workdir"},"WORKDIR"),(0,a.kt)("p",null,"\uc774\ud6c4 \ucd94\uac00\ub420 \uba85\ub839\uc5b4\ub97c \ucee8\ud14c\uc774\ub108 \ub0b4\uc758 \uc5b4\ub5a4 \ub514\ub809\ud1a0\ub9ac\uc5d0\uc11c \uc218\ud589\ud560 \uac83\uc778\uc9c0\ub97c \uba85\uc2dc\ud558\ub294 \uba85\ub839\uc5b4\uc785\ub2c8\ub2e4.",(0,a.kt)("br",{parentName:"p"}),"\n","\ub9cc\uc57d, \ud574\ub2f9 \ub514\ub809\ud1a0\ub9ac\uac00 \uc5c6\ub2e4\uba74 \uc0dd\uc131\ud569\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-docker"},"WORKDIR /path/to/workdir\n\n# \uc608\uc2dc\nWORKDIR /home/demo\nRUN pwd # /home/demo \uac00 \ucd9c\ub825\ub428\n")),(0,a.kt)("h3",{id:"env"},"ENV"),(0,a.kt)("p",null,"\ucee8\ud14c\uc774\ub108 \ub0b4\ubd80\uc5d0\uc11c \uc9c0\uc18d\uc801\uc73c\ub85c \uc0ac\uc6a9\ub420 environment variable \uc758 \uac12\uc744 \uc124\uc815\ud558\ub294 \uba85\ub839\uc5b4\uc785\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-docker"},"ENV \nENV =\n\n# \uc608\uc2dc\n# default \uc5b8\uc5b4 \uc124\uc815\nRUN locale-gen ko_KR.UTF-8\nENV LANG ko_KR.UTF-8\nENV LANGUAGE ko_KR.UTF-8\nENV LC_ALL ko_KR.UTF-8\n")),(0,a.kt)("h3",{id:"expose"},"EXPOSE"),(0,a.kt)("p",null,"\ucee8\ud14c\uc774\ub108\uc5d0\uc11c \ub6ab\uc5b4\uc904 \ud3ec\ud2b8/\ud504\ub85c\ud1a0\ucf5c\uc744 \uc9c0\uc815\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.",(0,a.kt)("br",{parentName:"p"}),"\n",(0,a.kt)("inlineCode",{parentName:"p"},"")," \uc744 \uc9c0\uc815\ud558\uc9c0 \uc54a\uc73c\uba74 TCP \uac00 \ub514\ud3f4\ud2b8\ub85c \uc124\uc815\ub429\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-docker"},"EXPOSE \nEXPOSE /\n\n# \uc608\uc2dc\nEXPOSE 8080\n")),(0,a.kt)("h2",{id:"3-\uac04\ub2e8\ud55c-dockerfile-\uc791\uc131\ud574\ubcf4\uae30"},"3. \uac04\ub2e8\ud55c Dockerfile \uc791\uc131\ud574\ubcf4\uae30"),(0,a.kt)("p",null,(0,a.kt)("inlineCode",{parentName:"p"},"vim Dockerfile")," \ud639\uc740 vscode \ub4f1 \ubcf8\uc778\uc774 \uc0ac\uc6a9\ud558\ub294 \ud3b8\uc9d1\uae30\ub85c ",(0,a.kt)("inlineCode",{parentName:"p"},"Dockerfile")," \uc744 \uc5f4\uc5b4 \ub2e4\uc74c\uacfc \uac19\uc774 \uc791\uc131\ud574\uc90d\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-docker"},"# base image \ub97c ubuntu 18.04 \ub85c \uc124\uc815\ud569\ub2c8\ub2e4.\nFROM ubuntu:18.04\n\n# apt-get update \uba85\ub839\uc744 \uc2e4\ud589\ud569\ub2c8\ub2e4.\nRUN apt-get update\n\n# TEST env var\uc758 \uac12\uc744 hello \ub85c \uc9c0\uc815\ud569\ub2c8\ub2e4.\nENV TEST hello\n\n# DOCKER CONTAINER \uac00 \uc2dc\uc791\ub420 \ub54c, \ud658\uacbd\ubcc0\uc218 TEST \uc758 \uac12\uc744 \ucd9c\ub825\ud569\ub2c8\ub2e4.\nCMD echo $TEST\n")),(0,a.kt)("h2",{id:"4-docker-build-from-dockerfile"},"4. Docker build from Dockerfile"),(0,a.kt)("p",null,(0,a.kt)("inlineCode",{parentName:"p"},"docker build")," \uba85\ub839\uc5b4\ub85c Dockerfile \ub85c\ubd80\ud130 Docker Image \ub97c \ub9cc\ub4e4\uc5b4\ubd05\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-bash"},"docker build --help\n")),(0,a.kt)("p",null,"Dockerfile \uc774 \uc788\ub294 \uacbd\ub85c\uc5d0\uc11c \ub2e4\uc74c \uba85\ub839\uc744 \uc2e4\ud589\ud569\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-bash"},"docker build -t my-image:v1.0.0 .\n")),(0,a.kt)("p",null,"\uc704 \ucee4\ub9e8\ub4dc\ub97c \uc124\uba85\ud558\uba74 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4."),(0,a.kt)("ul",null,(0,a.kt)("li",{parentName:"ul"},(0,a.kt)("inlineCode",{parentName:"li"},".")," : ",(0,a.kt)("strong",{parentName:"li"},"\ud604\uc7ac \uacbd\ub85c"),"\uc5d0 \uc788\ub294 Dockerfile \ub85c\ubd80\ud130"),(0,a.kt)("li",{parentName:"ul"},(0,a.kt)("inlineCode",{parentName:"li"},"-t")," : my-image \ub77c\ub294 ",(0,a.kt)("strong",{parentName:"li"},"\uc774\ub984"),"\uacfc v1.0.0 \uc774\ub77c\ub294 ",(0,a.kt)("strong",{parentName:"li"},"\ud0dc\uadf8"),"\ub85c ",(0,a.kt)("strong",{parentName:"li"},"\uc774\ubbf8\uc9c0"),"\ub97c"),(0,a.kt)("li",{parentName:"ul"},"\ube4c\ub4dc\ud558\uaca0\ub2e4\ub77c\ub294 \uba85\ub839\uc5b4")),(0,a.kt)("p",null,"\uc815\uc0c1\uc801\uc73c\ub85c \uc774\ubbf8\uc9c0 \ube4c\ub4dc\ub418\uc5c8\ub294\uc9c0 \ud655\uc778\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-bash"},"# grep : my-image \uac00 \uc788\ub294\uc9c0\ub97c \uc7a1\uc544\ub0b4\ub294 (grep) \ud558\ub294 \uba85\ub839\uc5b4\ndocker images | grep my-image\n")),(0,a.kt)("p",null,"\uc815\uc0c1\uc801\uc73c\ub85c \uc218\ud589\ub41c\ub2e4\uba74 \ub2e4\uc74c\uacfc \uac19\uc774 \ucd9c\ub825\ub429\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-bash"},"my-image v1.0.0 143114710b2d 3 seconds ago 87.9MB\n")),(0,a.kt)("h2",{id:"5-docker-run-from-dockerfile"},"5. Docker run from Dockerfile"),(0,a.kt)("p",null,"\uadf8\ub7fc \uc774\uc81c \ubc29\uae08 \ube4c\ub4dc\ud55c ",(0,a.kt)("inlineCode",{parentName:"p"},"my-image:v1.0.0")," \uc774\ubbf8\uc9c0\ub85c docker \ucee8\ud14c\uc774\ub108\ub97c ",(0,a.kt)("strong",{parentName:"p"},"run")," \ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-bash"},"docker run my-image:v1.0.0\n")),(0,a.kt)("p",null,"\uc815\uc0c1\uc801\uc73c\ub85c \uc218\ud589\ub41c\ub2e4\uba74 \ub2e4\uc74c\uacfc \uac19\uc774 \ub098\uc635\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-bash"},"hello\n")),(0,a.kt)("h2",{id:"6-docker-run-with-env"},"6. 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\uc18c\uc2a4\ucf54\ub4dc\ub9cc \ub123\uc5b4\ub450\ub294 \ud615\ud0dc\ub85c \ud65c\uc6a9\ud560 \uc218\uac00 \uc788\uc2b5\ub2c8\ub2e4.",(0,a.kt)("br",{parentName:"p"}),"\n","\uc774\ub7ec\ud55c \uacbd\uc6b0\uc5d0\ub294 ",(0,a.kt)("inlineCode",{parentName:"p"},"python:3.9"),", ",(0,a.kt)("inlineCode",{parentName:"p"},"python-3.9-alpine"),", ... \ub4f1\uc758 \uc798 \ub9cc\ub4e4\uc5b4\uc9c4 \uc774\ubbf8\uc9c0\ub97c \ubca0\uc774\uc2a4\ub85c \ud65c\uc6a9\ud569\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-docker"},"FROM [:] [AS ]\n\n# \uc608\uc2dc\nFROM ubuntu\nFROM ubuntu:18.04\nFROM nginx:latest AS ngx\n")),(0,a.kt)("h3",{id:"copy"},"COPY"),(0,a.kt)("p",null,(0,a.kt)("strong",{parentName:"p"},"host(\ub85c\uceec)\uc5d0\uc11c\uc758 ",(0,a.kt)("inlineCode",{parentName:"strong"},""))," \uacbd\ub85c\uc758 \ud30c\uc77c \ud639\uc740 \ub514\ub809\ud1a0\ub9ac\ub97c ",(0,a.kt)("strong",{parentName:"p"},"container \ub0b4\ubd80\uc5d0\uc11c\uc758 ",(0,a.kt)("inlineCode",{parentName:"strong"},""))," \uacbd\ub85c\uc5d0 \ubcf5\uc0ac\ud558\ub294 \uba85\ub839\uc5b4\uc785\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-docker"},"COPY ... \n\n# \uc608\uc2dc\nCOPY a.txt /some-directory/b.txt\nCOPY my-directory /some-directory-2\n")),(0,a.kt)("p",null,(0,a.kt)("inlineCode",{parentName:"p"},"ADD")," \ub294 ",(0,a.kt)("inlineCode",{parentName:"p"},"COPY")," \uc640 \ube44\uc2b7\ud558\uc9c0\ub9cc \ucd94\uac00\uc801\uc778 \uae30\ub2a5\uc744 \ud488\uace0 \uc788\uc2b5\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-docker"},"# 1 - \ud638\uc2a4\ud2b8\uc5d0 \uc555\ucd95\ub418\uc5b4\uc788\ub294 \ud30c\uc77c\uc744 \ud480\uba74\uc11c \ucee8\ud14c\uc774\ub108 \ub0b4\ubd80\ub85c copy \ud560 \uc218 \uc788\uc74c\nADD scripts.tar.gz /tmp\n# 2 - Remote URLs \uc5d0 \uc788\ub294 \ud30c\uc77c\uc744 \uc18c\uc2a4 \uacbd\ub85c\ub85c \uc9c0\uc815\ud560 \uc218 \uc788\uc74c\nADD http://www.example.com/script.sh /tmp\n\n# \uc704 \ub450 \uac00\uc9c0 \uae30\ub2a5\uc744 \uc0ac\uc6a9\ud558\uace0 \uc2f6\uc744 \uacbd\uc6b0\uc5d0\ub9cc COPY \ub300\uc2e0 ADD \ub97c \uc0ac\uc6a9\ud558\ub294 \uac83\uc744 \uad8c\uc7a5\n")),(0,a.kt)("h3",{id:"run"},"RUN"),(0,a.kt)("p",null,"\uba85\uc2dc\ud55c \ucee4\ub9e8\ub4dc\ub97c \ub3c4\ucee4 \ucee8\ud14c\uc774\ub108 \ub0b4\ubd80\uc5d0\uc11c \uc2e4\ud589\ud558\ub294 \uba85\ub839\uc5b4\uc785\ub2c8\ub2e4.",(0,a.kt)("br",{parentName:"p"}),"\n","\ub3c4\ucee4 \uc774\ubbf8\uc9c0\ub294 \ud574\ub2f9 \ucee4\ub9e8\ub4dc\ub4e4\uc774 \uc2e4\ud589\ub41c \uc0c1\ud0dc\ub97c \uc720\uc9c0\ud569\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-docker"},'RUN \nRUN ["executable-command", "parameter1", "parameter2"]\n\n# \uc608\uc2dc\nRUN pip install torch\nRUN pip install -r requirements.txt\n')),(0,a.kt)("h3",{id:"cmd"},"CMD"),(0,a.kt)("p",null,"\uba85\uc2dc\ud55c \ucee4\ub9e8\ub4dc\ub97c \ub3c4\ucee4 \ucee8\ud14c\uc774\ub108\uac00 ",(0,a.kt)("strong",{parentName:"p"},"\uc2dc\uc791\ub420 \ub54c"),", \uc2e4\ud589\ud558\ub294 \uac83\uc744 \uba85\uc2dc\ud558\ub294 \uba85\ub839\uc5b4\uc785\ub2c8\ub2e4.",(0,a.kt)("br",{parentName:"p"}),"\n","\ube44\uc2b7\ud55c \uc5ed\ud560\uc744 \ud558\ub294 \uba85\ub839\uc5b4\ub85c ",(0,a.kt)("strong",{parentName:"p"},"ENTRYPOINT")," \uac00 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \ub458\uc758 \ucc28\uc774\uc5d0 \ub300\ud574\uc11c\ub294 ",(0,a.kt)("strong",{parentName:"p"},"\ub4a4\uc5d0\uc11c")," \ub2e4\ub8f9\ub2c8\ub2e4.",(0,a.kt)("br",{parentName:"p"}),"\n","\ud558\ub098\uc758 \ub3c4\ucee4 \uc774\ubbf8\uc9c0\uc5d0\uc11c\ub294 \ud558\ub098\uc758 ",(0,a.kt)("strong",{parentName:"p"},"CMD")," \ub9cc \uc2e4\ud589\ud560 \uc218 \uc788\ub2e4\ub294 \uc810\uc5d0\uc11c ",(0,a.kt)("strong",{parentName:"p"},"RUN")," \uba85\ub839\uc5b4\uc640 \ub2e4\ub985\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-docker"},'CMD \nCMD ["executable-command", "parameter1", "parameter2"]\nCMD ["parameter1", "parameter2"] # ENTRYPOINT \uc640 \ud568\uaed8 \uc0ac\uc6a9\ub420 \ub54c\n\n# \uc608\uc2dc\nCMD python main.py\n')),(0,a.kt)("h3",{id:"workdir"},"WORKDIR"),(0,a.kt)("p",null,"\uc774\ud6c4 \ucd94\uac00\ub420 \uba85\ub839\uc5b4\ub97c \ucee8\ud14c\uc774\ub108 \ub0b4\uc758 \uc5b4\ub5a4 \ub514\ub809\ud1a0\ub9ac\uc5d0\uc11c \uc218\ud589\ud560 \uac83\uc778\uc9c0\ub97c \uba85\uc2dc\ud558\ub294 \uba85\ub839\uc5b4\uc785\ub2c8\ub2e4.",(0,a.kt)("br",{parentName:"p"}),"\n","\ub9cc\uc57d, \ud574\ub2f9 \ub514\ub809\ud1a0\ub9ac\uac00 \uc5c6\ub2e4\uba74 \uc0dd\uc131\ud569\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-docker"},"WORKDIR /path/to/workdir\n\n# \uc608\uc2dc\nWORKDIR /home/demo\nRUN pwd # /home/demo \uac00 \ucd9c\ub825\ub428\n")),(0,a.kt)("h3",{id:"env"},"ENV"),(0,a.kt)("p",null,"\ucee8\ud14c\uc774\ub108 \ub0b4\ubd80\uc5d0\uc11c \uc9c0\uc18d\uc801\uc73c\ub85c \uc0ac\uc6a9\ub420 environment variable \uc758 \uac12\uc744 \uc124\uc815\ud558\ub294 \uba85\ub839\uc5b4\uc785\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-docker"},"ENV \nENV =\n\n# \uc608\uc2dc\n# default \uc5b8\uc5b4 \uc124\uc815\nRUN locale-gen ko_KR.UTF-8\nENV LANG ko_KR.UTF-8\nENV LANGUAGE ko_KR.UTF-8\nENV LC_ALL ko_KR.UTF-8\n")),(0,a.kt)("h3",{id:"expose"},"EXPOSE"),(0,a.kt)("p",null,"\ucee8\ud14c\uc774\ub108\uc5d0\uc11c \ub6ab\uc5b4\uc904 \ud3ec\ud2b8/\ud504\ub85c\ud1a0\ucf5c\uc744 \uc9c0\uc815\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.",(0,a.kt)("br",{parentName:"p"}),"\n",(0,a.kt)("inlineCode",{parentName:"p"},"")," \uc744 \uc9c0\uc815\ud558\uc9c0 \uc54a\uc73c\uba74 TCP \uac00 \ub514\ud3f4\ud2b8\ub85c \uc124\uc815\ub429\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-docker"},"EXPOSE \nEXPOSE /\n\n# \uc608\uc2dc\nEXPOSE 8080\n")),(0,a.kt)("h2",{id:"3-\uac04\ub2e8\ud55c-dockerfile-\uc791\uc131\ud574\ubcf4\uae30"},"3. \uac04\ub2e8\ud55c Dockerfile \uc791\uc131\ud574\ubcf4\uae30"),(0,a.kt)("p",null,(0,a.kt)("inlineCode",{parentName:"p"},"vim Dockerfile")," \ud639\uc740 vscode \ub4f1 \ubcf8\uc778\uc774 \uc0ac\uc6a9\ud558\ub294 \ud3b8\uc9d1\uae30\ub85c ",(0,a.kt)("inlineCode",{parentName:"p"},"Dockerfile")," \uc744 \uc5f4\uc5b4 \ub2e4\uc74c\uacfc \uac19\uc774 \uc791\uc131\ud574\uc90d\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-docker"},"# base image \ub97c ubuntu 18.04 \ub85c \uc124\uc815\ud569\ub2c8\ub2e4.\nFROM ubuntu:18.04\n\n# apt-get update \uba85\ub839\uc744 \uc2e4\ud589\ud569\ub2c8\ub2e4.\nRUN apt-get update\n\n# TEST env var\uc758 \uac12\uc744 hello \ub85c \uc9c0\uc815\ud569\ub2c8\ub2e4.\nENV TEST hello\n\n# DOCKER CONTAINER \uac00 \uc2dc\uc791\ub420 \ub54c, \ud658\uacbd\ubcc0\uc218 TEST \uc758 \uac12\uc744 \ucd9c\ub825\ud569\ub2c8\ub2e4.\nCMD echo $TEST\n")),(0,a.kt)("h2",{id:"4-docker-build-from-dockerfile"},"4. Docker build from Dockerfile"),(0,a.kt)("p",null,(0,a.kt)("inlineCode",{parentName:"p"},"docker build")," \uba85\ub839\uc5b4\ub85c Dockerfile \ub85c\ubd80\ud130 Docker Image \ub97c \ub9cc\ub4e4\uc5b4\ubd05\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-bash"},"docker build --help\n")),(0,a.kt)("p",null,"Dockerfile \uc774 \uc788\ub294 \uacbd\ub85c\uc5d0\uc11c \ub2e4\uc74c \uba85\ub839\uc744 \uc2e4\ud589\ud569\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-bash"},"docker build -t my-image:v1.0.0 .\n")),(0,a.kt)("p",null,"\uc704 \ucee4\ub9e8\ub4dc\ub97c \uc124\uba85\ud558\uba74 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4."),(0,a.kt)("ul",null,(0,a.kt)("li",{parentName:"ul"},(0,a.kt)("inlineCode",{parentName:"li"},".")," : ",(0,a.kt)("strong",{parentName:"li"},"\ud604\uc7ac \uacbd\ub85c"),"\uc5d0 \uc788\ub294 Dockerfile \ub85c\ubd80\ud130"),(0,a.kt)("li",{parentName:"ul"},(0,a.kt)("inlineCode",{parentName:"li"},"-t")," : my-image \ub77c\ub294 ",(0,a.kt)("strong",{parentName:"li"},"\uc774\ub984"),"\uacfc v1.0.0 \uc774\ub77c\ub294 ",(0,a.kt)("strong",{parentName:"li"},"\ud0dc\uadf8"),"\ub85c ",(0,a.kt)("strong",{parentName:"li"},"\uc774\ubbf8\uc9c0"),"\ub97c"),(0,a.kt)("li",{parentName:"ul"},"\ube4c\ub4dc\ud558\uaca0\ub2e4\ub77c\ub294 \uba85\ub839\uc5b4")),(0,a.kt)("p",null,"\uc815\uc0c1\uc801\uc73c\ub85c \uc774\ubbf8\uc9c0 \ube4c\ub4dc\ub418\uc5c8\ub294\uc9c0 \ud655\uc778\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-bash"},"# grep : my-image \uac00 \uc788\ub294\uc9c0\ub97c \uc7a1\uc544\ub0b4\ub294 (grep) \ud558\ub294 \uba85\ub839\uc5b4\ndocker images | grep my-image\n")),(0,a.kt)("p",null,"\uc815\uc0c1\uc801\uc73c\ub85c \uc218\ud589\ub41c\ub2e4\uba74 \ub2e4\uc74c\uacfc \uac19\uc774 \ucd9c\ub825\ub429\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-bash"},"my-image v1.0.0 143114710b2d 3 seconds ago 87.9MB\n")),(0,a.kt)("h2",{id:"5-docker-run-from-dockerfile"},"5. Docker run from Dockerfile"),(0,a.kt)("p",null,"\uadf8\ub7fc \uc774\uc81c \ubc29\uae08 \ube4c\ub4dc\ud55c ",(0,a.kt)("inlineCode",{parentName:"p"},"my-image:v1.0.0")," \uc774\ubbf8\uc9c0\ub85c docker \ucee8\ud14c\uc774\ub108\ub97c ",(0,a.kt)("strong",{parentName:"p"},"run")," \ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-bash"},"docker run my-image:v1.0.0\n")),(0,a.kt)("p",null,"\uc815\uc0c1\uc801\uc73c\ub85c \uc218\ud589\ub41c\ub2e4\uba74 \ub2e4\uc74c\uacfc \uac19\uc774 \ub098\uc635\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-bash"},"hello\n")),(0,a.kt)("h2",{id:"6-docker-run-with-env"},"6. Docker run with env"),(0,a.kt)("p",null,"\uc774\ubc88\uc5d0\ub294 \ubc29\uae08 \ube4c\ub4dc\ud55c ",(0,a.kt)("inlineCode",{parentName:"p"},"my-image:v1.0.0")," \uc774\ubbf8\uc9c0\ub97c \uc2e4\ud589\ud558\ub294 \uc2dc\uc810\uc5d0, ",(0,a.kt)("inlineCode",{parentName:"p"},"TEST")," env var \uc758 \uac12\uc744 \ubcc0\uacbd\ud558\uc5ec docker \ucee8\ud14c\uc774\ub108\ub97c run \ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-bash"},"docker run -e TEST=bye my-image:v1.0.0\n")),(0,a.kt)("p",null,"\uc815\uc0c1\uc801\uc73c\ub85c \uc218\ud589\ub41c\ub2e4\uba74 \ub2e4\uc74c\uacfc \uac19\uc774 \ub098\uc635\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-bash"},"bye\n")))}d.isMDXComponent=!0}}]); \ No newline at end of file diff --git a/assets/js/44d64813.7c919c8e.js b/assets/js/44d64813.7c919c8e.js new file mode 100644 index 00000000..83138c0e --- /dev/null +++ b/assets/js/44d64813.7c919c8e.js @@ -0,0 +1 @@ +"use strict";(self.webpackChunkv_2=self.webpackChunkv_2||[]).push([[4023],{3905:(n,e,t)=>{t.d(e,{Zo:()=>p,kt:()=>_});var a=t(7294);function l(n,e,t){return e in n?Object.defineProperty(n,e,{value:t,enumerable:!0,configurable:!0,writable:!0}):n[e]=t,n}function r(n,e){var t=Object.keys(n);if(Object.getOwnPropertySymbols){var a=Object.getOwnPropertySymbols(n);e&&(a=a.filter((function(e){return Object.getOwnPropertyDescriptor(n,e).enumerable}))),t.push.apply(t,a)}return t}function i(n){for(var e=1;e=0||(l[t]=n[t]);return l}(n,e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(n);for(a=0;a=0||Object.prototype.propertyIsEnumerable.call(n,t)&&(l[t]=n[t])}return l}var s=a.createContext({}),m=function(n){var e=a.useContext(s),t=e;return n&&(t="function"==typeof n?n(e):i(i({},e),n)),t},p=function(n){var e=m(n.components);return a.createElement(s.Provider,{value:e},n.children)},d="mdxType",c={inlineCode:"code",wrapper:function(n){var e=n.children;return a.createElement(a.Fragment,{},e)}},u=a.forwardRef((function(n,e){var t=n.components,l=n.mdxType,r=n.originalType,s=n.parentName,p=o(n,["components","mdxType","originalType","parentName"]),d=m(t),u=l,_=d["".concat(s,".").concat(u)]||d[u]||c[u]||r;return t?a.createElement(_,i(i({ref:e},p),{},{components:t})):a.createElement(_,i({ref:e},p))}));function _(n,e){var t=arguments,l=e&&e.mdxType;if("string"==typeof n||l){var r=t.length,i=new Array(r);i[0]=u;var o={};for(var s in e)hasOwnProperty.call(e,s)&&(o[s]=e[s]);o.originalType=n,o[d]="string"==typeof n?n:l,i[1]=o;for(var m=2;m{t.r(e),t.d(e,{assets:()=>s,contentTitle:()=>i,default:()=>c,frontMatter:()=>r,metadata:()=>o,toc:()=>m});var a=t(7462),l=(t(7294),t(3905));const r={title:"6. Multi Models",description:"",sidebar_position:6,contributors:["Jongseob Jeon"]},i=void 0,o={unversionedId:"api-deployment/seldon-children",id:"api-deployment/seldon-children",title:"6. Multi Models",description:"",source:"@site/docs/api-deployment/seldon-children.md",sourceDirName:"api-deployment",slug:"/api-deployment/seldon-children",permalink:"/docs/api-deployment/seldon-children",draft:!1,editUrl:"https://github.com/mlops-for-all/mlops-for-all.github.io/tree/main/docs/api-deployment/seldon-children.md",tags:[],version:"current",lastUpdatedBy:"Aiden-Jeon",lastUpdatedAt:1706783082,formattedLastUpdatedAt:"2024\ub144 2\uc6d4 1\uc77c",sidebarPosition:6,frontMatter:{title:"6. Multi Models",description:"",sidebar_position:6,contributors:["Jongseob Jeon"]},sidebar:"tutorialSidebar",previous:{title:"5. Model from MLflow",permalink:"/docs/api-deployment/seldon-mlflow"},next:{title:"1. Python \uac00\uc0c1\ud658\uacbd \uc124\uce58",permalink:"/docs/appendix/pyenv"}},s={},m=[{value:"Multi Models",id:"multi-models",level:2},{value:"Pipeline",id:"pipeline",level:2}],p={toc:m},d="wrapper";function c(n){let{components:e,...r}=n;return(0,l.kt)(d,(0,a.Z)({},p,r,{components:e,mdxType:"MDXLayout"}),(0,l.kt)("h2",{id:"multi-models"},"Multi Models"),(0,l.kt)("p",null,"\uc55e\uc11c \uc124\uba85\ud588\ub358 \ubc29\ubc95\ub4e4\uc740 \ubaa8\ub450 \ub2e8\uc77c \ubaa8\ub378\uc744 \ub300\uc0c1\uc73c\ub85c \ud588\uc2b5\ub2c8\ub2e4.",(0,l.kt)("br",{parentName:"p"}),"\n","\uc774\ubc88 \ud398\uc774\uc9c0\uc5d0\uc11c\ub294 \uc5ec\ub7ec \uac1c\uc758 \ubaa8\ub378\uc744 \uc5f0\uacb0\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574\uc11c \uc54c\uc544\ubd05\ub2c8\ub2e4."),(0,l.kt)("h2",{id:"pipeline"},"Pipeline"),(0,l.kt)("p",null,"\uc6b0\uc120 \ubaa8\ub378\uc744 2\uac1c\ub97c \uc0dd\uc131\ud558\ub294 \ud30c\uc774\ud504\ub77c\uc778\uc744 \uc791\uc131\ud558\uaca0\uc2b5\ub2c8\ub2e4."),(0,l.kt)("p",null,"\ubaa8\ub378\uc740 \uc55e\uc11c \uc0ac\uc6a9\ud55c SVC \ubaa8\ub378\uc5d0 StandardScaler\ub97c \ucd94\uac00\ud558\uace0 \uc800\uc7a5\ud558\ub3c4\ub85d \ud558\uaca0\uc2b5\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-python"},'from functools import partial\n\nimport kfp\nfrom kfp.components import InputPath, OutputPath, create_component_from_func\n\n\n@partial(\n create_component_from_func,\n packages_to_install=["pandas", "scikit-learn"],\n)\ndef load_iris_data(\n data_path: OutputPath("csv"),\n target_path: OutputPath("csv"),\n):\n import pandas as pd\n from sklearn.datasets import load_iris\n\n iris = load_iris()\n\n data = pd.DataFrame(iris["data"], columns=iris["feature_names"])\n target = pd.DataFrame(iris["target"], columns=["target"])\n\n data.to_csv(data_path, index=False)\n target.to_csv(target_path, index=False)\n\n@partial(\n create_component_from_func,\n packages_to_install=["dill", "pandas", "scikit-learn", "mlflow"],\n)\ndef train_scaler_from_csv(\n data_path: InputPath("csv"),\n scaled_data_path: OutputPath("csv"),\n model_path: OutputPath("dill"),\n input_example_path: OutputPath("dill"),\n signature_path: OutputPath("dill"),\n conda_env_path: OutputPath("dill"),\n):\n import dill\n import pandas as pd\n from sklearn.preprocessing import StandardScaler\n\n from mlflow.models.signature import infer_signature\n from mlflow.utils.environment import _mlflow_conda_env\n\n data = pd.read_csv(data_path)\n\n scaler = StandardScaler()\n scaled_data = scaler.fit_transform(data)\n scaled_data = pd.DataFrame(scaled_data, columns=data.columns, index=data.index)\n\n scaled_data.to_csv(scaled_data_path, index=False)\n\n with open(model_path, mode="wb") as file_writer:\n dill.dump(scaler, file_writer)\n\n input_example = data.sample(1)\n with open(input_example_path, "wb") as file_writer:\n dill.dump(input_example, file_writer)\n\n signature = infer_signature(data, scaler.transform(data))\n with open(signature_path, "wb") as file_writer:\n dill.dump(signature, file_writer)\n\n conda_env = _mlflow_conda_env(\n additional_pip_deps=["scikit-learn"],\n install_mlflow=False\n )\n with open(conda_env_path, "wb") as file_writer:\n dill.dump(conda_env, file_writer)\n\n\n@partial(\n create_component_from_func,\n packages_to_install=["dill", "pandas", "scikit-learn", "mlflow"],\n)\ndef train_svc_from_csv(\n train_data_path: InputPath("csv"),\n train_target_path: InputPath("csv"),\n model_path: OutputPath("dill"),\n input_example_path: OutputPath("dill"),\n signature_path: OutputPath("dill"),\n conda_env_path: OutputPath("dill"),\n kernel: str,\n):\n import dill\n import pandas as pd\n from sklearn.svm import SVC\n\n from mlflow.models.signature import infer_signature\n from mlflow.utils.environment import _mlflow_conda_env\n\n train_data = pd.read_csv(train_data_path)\n train_target = pd.read_csv(train_target_path)\n\n clf = SVC(kernel=kernel)\n clf.fit(train_data, train_target)\n\n with open(model_path, mode="wb") as file_writer:\n dill.dump(clf, file_writer)\n\n input_example = train_data.sample(1)\n with open(input_example_path, "wb") as file_writer:\n dill.dump(input_example, file_writer)\n\n signature = infer_signature(train_data, clf.predict(train_data))\n with open(signature_path, "wb") as file_writer:\n dill.dump(signature, file_writer)\n\n conda_env = _mlflow_conda_env(\n additional_pip_deps=["scikit-learn"],\n install_mlflow=False\n )\n with open(conda_env_path, "wb") as file_writer:\n dill.dump(conda_env, file_writer)\n\n\n@partial(\n create_component_from_func,\n packages_to_install=["dill", "pandas", "scikit-learn", "mlflow", "boto3"],\n)\ndef upload_sklearn_model_to_mlflow(\n model_name: str,\n model_path: InputPath("dill"),\n input_example_path: InputPath("dill"),\n signature_path: InputPath("dill"),\n conda_env_path: InputPath("dill"),\n):\n import os\n import dill\n from mlflow.sklearn import save_model\n \n from mlflow.tracking.client import MlflowClient\n\n os.environ["MLFLOW_S3_ENDPOINT_URL"] = "http://minio-service.kubeflow.svc:9000"\n os.environ["AWS_ACCESS_KEY_ID"] = "minio"\n os.environ["AWS_SECRET_ACCESS_KEY"] = "minio123"\n\n client = MlflowClient("http://mlflow-server-service.mlflow-system.svc:5000")\n\n with open(model_path, mode="rb") as file_reader:\n clf = dill.load(file_reader)\n\n with open(input_example_path, "rb") as file_reader:\n input_example = dill.load(file_reader)\n\n with open(signature_path, "rb") as file_reader:\n signature = dill.load(file_reader)\n\n with open(conda_env_path, "rb") as file_reader:\n conda_env = dill.load(file_reader)\n save_model(\n sk_model=clf,\n path=model_name,\n serialization_format="cloudpickle",\n conda_env=conda_env,\n signature=signature,\n input_example=input_example,\n )\n run = client.create_run(experiment_id="0")\n client.log_artifact(run.info.run_id, model_name)\n\n\nfrom kfp.dsl import pipeline\n\n\n@pipeline(name="multi_model_pipeline")\ndef multi_model_pipeline(kernel: str = "rbf"):\n iris_data = load_iris_data()\n scaled_data = train_scaler_from_csv(data=iris_data.outputs["data"])\n _ = upload_sklearn_model_to_mlflow(\n model_name="scaler",\n model=scaled_data.outputs["model"],\n input_example=scaled_data.outputs["input_example"],\n signature=scaled_data.outputs["signature"],\n conda_env=scaled_data.outputs["conda_env"],\n )\n model = train_svc_from_csv(\n train_data=scaled_data.outputs["scaled_data"],\n train_target=iris_data.outputs["target"],\n kernel=kernel,\n )\n _ = upload_sklearn_model_to_mlflow(\n model_name="svc",\n model=model.outputs["model"],\n input_example=model.outputs["input_example"],\n signature=model.outputs["signature"],\n conda_env=model.outputs["conda_env"],\n )\n\n\nif __name__ == "__main__":\n kfp.compiler.Compiler().compile(multi_model_pipeline, "multi_model_pipeline.yaml")\n\n')),(0,l.kt)("p",null,"\ud30c\uc774\ud504\ub77c\uc778\uc744 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model-provision-location\n emptyDir: {}\n\n initContainers:\n - name: scaler-initializer\n image: gcr.io/kfserving/storage-initializer:v0.4.0\n args:\n - "s3://mlflow/mlflow/artifacts/0/7f445015a0e94519b003d316478766ef/artifacts/scaler"\n - "/mnt/models"\n volumeMounts:\n - mountPath: /mnt/models\n name: model-provision-location\n envFrom:\n - secretRef:\n name: seldon-init-container-secret\n - name: svc-initializer\n image: gcr.io/kfserving/storage-initializer:v0.4.0\n args:\n - "s3://mlflow/mlflow/artifacts/0/87eb168e76264b39a24b0e5ca0fe922b/artifacts/svc"\n - "/mnt/models"\n volumeMounts:\n - mountPath: /mnt/models\n name: model-provision-location\n envFrom:\n - secretRef:\n name: seldon-init-container-secret\n\n containers:\n - name: scaler\n image: seldonio/mlflowserver:1.8.0-dev\n volumeMounts:\n - mountPath: /mnt/models\n name: model-provision-location\n readOnly: true\n securityContext:\n privileged: true\n runAsUser: 0\n runAsGroup: 0\n - name: svc\n image: seldonio/mlflowserver:1.8.0-dev\n volumeMounts:\n - mountPath: /mnt/models\n name: model-provision-location\n readOnly: true\n securityContext:\n privileged: true\n runAsUser: 0\n runAsGroup: 0\n\n graph:\n name: scaler\n type: MODEL\n parameters:\n - name: model_uri\n type: STRING\n value: "/mnt/models"\n - name: predict_method\n type: STRING\n value: "transform"\n children:\n - name: svc\n type: MODEL\n parameters:\n - name: model_uri\n type: STRING\n value: "/mnt/models"\n')),(0,l.kt)("p",null,"\ubaa8\ub378\uc774 \ub450 \uac1c\uac00 \ub418\uc5c8\uc73c\ubbc0\ub85c \uac01 \ubaa8\ub378\uc758 initContainer\uc640 container\ub97c \uc815\uc758\ud574\uc8fc\uc5b4\uc57c \ud569\ub2c8\ub2e4.\n\uc774 \ud544\ub4dc\ub294 \uc785\ub825\uac12\uc744 array\ub85c \ubc1b\uc73c\uba70 \uc21c\uc11c\ub294 \uad00\uacc4\uc5c6\uc2b5\ub2c8\ub2e4."),(0,l.kt)("p",null,"\ubaa8\ub378\uc774 \uc2e4\ud589\ud558\ub294 \uc21c\uc11c\ub294 graph\uc5d0\uc11c 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\ubcf4\uaca0\uc2b5\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},'cat < multi-model.yaml\napiVersion: machinelearning.seldon.io/v1\nkind: SeldonDeployment\nmetadata:\n name: multi-model-example\n namespace: kubeflow-user-example-com\nspec:\n name: model\n predictors:\n - name: model\n\n componentSpecs:\n - spec:\n volumes:\n - name: model-provision-location\n emptyDir: {}\n\n initContainers:\n - name: scaler-initializer\n image: gcr.io/kfserving/storage-initializer:v0.4.0\n args:\n - "s3://mlflow/mlflow/artifacts/0/7f445015a0e94519b003d316478766ef/artifacts/scaler"\n - "/mnt/models"\n volumeMounts:\n - mountPath: /mnt/models\n name: model-provision-location\n envFrom:\n - secretRef:\n name: seldon-init-container-secret\n - name: svc-initializer\n image: gcr.io/kfserving/storage-initializer:v0.4.0\n args:\n - "s3://mlflow/mlflow/artifacts/0/87eb168e76264b39a24b0e5ca0fe922b/artifacts/svc"\n - "/mnt/models"\n volumeMounts:\n - mountPath: /mnt/models\n name: model-provision-location\n envFrom:\n - secretRef:\n name: seldon-init-container-secret\n\n containers:\n - name: scaler\n image: ghcr.io/mlops-for-all/mlflowserver\n volumeMounts:\n - mountPath: /mnt/models\n name: model-provision-location\n readOnly: true\n securityContext:\n privileged: true\n runAsUser: 0\n runAsGroup: 0\n - name: svc\n image: ghcr.io/mlops-for-all/mlflowserver\n volumeMounts:\n - mountPath: /mnt/models\n name: model-provision-location\n readOnly: true\n securityContext:\n privileged: true\n runAsUser: 0\n runAsGroup: 0\n\n graph:\n name: scaler\n type: MODEL\n parameters:\n - name: model_uri\n type: STRING\n value: "/mnt/models"\n - name: predict_method\n type: STRING\n value: "transform"\n children:\n - name: svc\n type: MODEL\n parameters:\n - name: model_uri\n type: STRING\n value: "/mnt/models"\nEOF\n')),(0,l.kt)("p",null,"\ub2e4\uc74c \uba85\ub839\uc5b4\ub97c \ud1b5\ud574 API\ub97c \uc0dd\uc131\ud569\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"kubectl apply -f multi-model.yaml\n")),(0,l.kt)("p",null,"\uc815\uc0c1\uc801\uc73c\ub85c \uc218\ud589\ub418\uba74 \ub2e4\uc74c\uacfc \uac19\uc774 \ucd9c\ub825\ub429\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"seldondeployment.machinelearning.seldon.io/multi-model-example created\n")),(0,l.kt)("p",null,"\uc815\uc0c1\uc801\uc73c\ub85c \uc0dd\uc131\ub410\ub294\uc9c0 \ud655\uc778\ud569\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"kubectl get po -n kubeflow-user-example-com | grep multi-model-example\n")),(0,l.kt)("p",null,"\uc815\uc0c1\uc801\uc73c\ub85c \uc0dd\uc131\ub418\uba74 \ub2e4\uc74c\uacfc \ube44\uc2b7\ud55c pod\uc774 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Multi Models",description:"",sidebar_position:6,contributors:["Jongseob Jeon"]},i=void 0,o={unversionedId:"api-deployment/seldon-children",id:"api-deployment/seldon-children",title:"6. Multi Models",description:"",source:"@site/docs/api-deployment/seldon-children.md",sourceDirName:"api-deployment",slug:"/api-deployment/seldon-children",permalink:"/docs/api-deployment/seldon-children",draft:!1,editUrl:"https://github.com/mlops-for-all/mlops-for-all.github.io/tree/main/docs/api-deployment/seldon-children.md",tags:[],version:"current",lastUpdatedBy:"conqrean",lastUpdatedAt:1705274857,formattedLastUpdatedAt:"2024\ub144 1\uc6d4 14\uc77c",sidebarPosition:6,frontMatter:{title:"6. Multi Models",description:"",sidebar_position:6,contributors:["Jongseob Jeon"]},sidebar:"tutorialSidebar",previous:{title:"5. Model from MLflow",permalink:"/docs/api-deployment/seldon-mlflow"},next:{title:"1. Python \uac00\uc0c1\ud658\uacbd \uc124\uce58",permalink:"/docs/appendix/pyenv"}},s={},m=[{value:"Multi Models",id:"multi-models",level:2},{value:"Pipeline",id:"pipeline",level:2}],p={toc:m},d="wrapper";function c(n){let{components:e,...r}=n;return(0,l.kt)(d,(0,a.Z)({},p,r,{components:e,mdxType:"MDXLayout"}),(0,l.kt)("h2",{id:"multi-models"},"Multi Models"),(0,l.kt)("p",null,"\uc55e\uc11c \uc124\uba85\ud588\ub358 \ubc29\ubc95\ub4e4\uc740 \ubaa8\ub450 \ub2e8\uc77c \ubaa8\ub378\uc744 \ub300\uc0c1\uc73c\ub85c \ud588\uc2b5\ub2c8\ub2e4.",(0,l.kt)("br",{parentName:"p"}),"\n","\uc774\ubc88 \ud398\uc774\uc9c0\uc5d0\uc11c\ub294 \uc5ec\ub7ec \uac1c\uc758 \ubaa8\ub378\uc744 \uc5f0\uacb0\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574\uc11c \uc54c\uc544\ubd05\ub2c8\ub2e4."),(0,l.kt)("h2",{id:"pipeline"},"Pipeline"),(0,l.kt)("p",null,"\uc6b0\uc120 \ubaa8\ub378\uc744 2\uac1c\ub97c \uc0dd\uc131\ud558\ub294 \ud30c\uc774\ud504\ub77c\uc778\uc744 \uc791\uc131\ud558\uaca0\uc2b5\ub2c8\ub2e4."),(0,l.kt)("p",null,"\ubaa8\ub378\uc740 \uc55e\uc11c \uc0ac\uc6a9\ud55c SVC \ubaa8\ub378\uc5d0 StandardScaler\ub97c \ucd94\uac00\ud558\uace0 \uc800\uc7a5\ud558\ub3c4\ub85d \ud558\uaca0\uc2b5\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-python"},'from functools import partial\n\nimport kfp\nfrom kfp.components import InputPath, OutputPath, create_component_from_func\n\n\n@partial(\n create_component_from_func,\n packages_to_install=["pandas", "scikit-learn"],\n)\ndef load_iris_data(\n data_path: OutputPath("csv"),\n target_path: OutputPath("csv"),\n):\n import pandas as pd\n from sklearn.datasets import load_iris\n\n iris = load_iris()\n\n data = pd.DataFrame(iris["data"], columns=iris["feature_names"])\n target = pd.DataFrame(iris["target"], columns=["target"])\n\n data.to_csv(data_path, index=False)\n target.to_csv(target_path, index=False)\n\n@partial(\n create_component_from_func,\n packages_to_install=["dill", "pandas", "scikit-learn", "mlflow"],\n)\ndef train_scaler_from_csv(\n data_path: InputPath("csv"),\n scaled_data_path: OutputPath("csv"),\n model_path: OutputPath("dill"),\n input_example_path: OutputPath("dill"),\n signature_path: OutputPath("dill"),\n conda_env_path: OutputPath("dill"),\n):\n import dill\n import pandas as pd\n from sklearn.preprocessing import StandardScaler\n\n from mlflow.models.signature import infer_signature\n from mlflow.utils.environment import _mlflow_conda_env\n\n data = pd.read_csv(data_path)\n\n scaler = StandardScaler()\n scaled_data = scaler.fit_transform(data)\n scaled_data = pd.DataFrame(scaled_data, columns=data.columns, index=data.index)\n\n scaled_data.to_csv(scaled_data_path, index=False)\n\n with open(model_path, mode="wb") as file_writer:\n dill.dump(scaler, file_writer)\n\n input_example = data.sample(1)\n with open(input_example_path, "wb") as file_writer:\n dill.dump(input_example, file_writer)\n\n signature = infer_signature(data, scaler.transform(data))\n with open(signature_path, "wb") as file_writer:\n dill.dump(signature, file_writer)\n\n conda_env = _mlflow_conda_env(\n additional_pip_deps=["scikit-learn"],\n install_mlflow=False\n )\n with open(conda_env_path, "wb") as file_writer:\n dill.dump(conda_env, file_writer)\n\n\n@partial(\n create_component_from_func,\n packages_to_install=["dill", "pandas", "scikit-learn", "mlflow"],\n)\ndef train_svc_from_csv(\n train_data_path: InputPath("csv"),\n train_target_path: InputPath("csv"),\n model_path: OutputPath("dill"),\n input_example_path: OutputPath("dill"),\n signature_path: OutputPath("dill"),\n conda_env_path: OutputPath("dill"),\n kernel: str,\n):\n import dill\n import pandas as pd\n from sklearn.svm import SVC\n\n from mlflow.models.signature import infer_signature\n from mlflow.utils.environment import _mlflow_conda_env\n\n train_data = pd.read_csv(train_data_path)\n train_target = pd.read_csv(train_target_path)\n\n clf = SVC(kernel=kernel)\n clf.fit(train_data, train_target)\n\n with open(model_path, mode="wb") as file_writer:\n dill.dump(clf, file_writer)\n\n input_example = train_data.sample(1)\n with open(input_example_path, "wb") as file_writer:\n dill.dump(input_example, file_writer)\n\n signature = infer_signature(train_data, clf.predict(train_data))\n with open(signature_path, "wb") as file_writer:\n dill.dump(signature, file_writer)\n\n conda_env = _mlflow_conda_env(\n additional_pip_deps=["scikit-learn"],\n install_mlflow=False\n )\n with open(conda_env_path, "wb") as file_writer:\n dill.dump(conda_env, file_writer)\n\n\n@partial(\n create_component_from_func,\n packages_to_install=["dill", "pandas", "scikit-learn", "mlflow", "boto3"],\n)\ndef upload_sklearn_model_to_mlflow(\n model_name: str,\n model_path: InputPath("dill"),\n input_example_path: InputPath("dill"),\n signature_path: InputPath("dill"),\n conda_env_path: InputPath("dill"),\n):\n import os\n import dill\n from mlflow.sklearn import save_model\n \n from mlflow.tracking.client import MlflowClient\n\n os.environ["MLFLOW_S3_ENDPOINT_URL"] = "http://minio-service.kubeflow.svc:9000"\n os.environ["AWS_ACCESS_KEY_ID"] = "minio"\n os.environ["AWS_SECRET_ACCESS_KEY"] = "minio123"\n\n client = MlflowClient("http://mlflow-server-service.mlflow-system.svc:5000")\n\n with open(model_path, mode="rb") as file_reader:\n clf = dill.load(file_reader)\n\n with open(input_example_path, "rb") as file_reader:\n input_example = dill.load(file_reader)\n\n with open(signature_path, "rb") as file_reader:\n signature = dill.load(file_reader)\n\n with open(conda_env_path, "rb") as file_reader:\n conda_env = dill.load(file_reader)\n save_model(\n sk_model=clf,\n path=model_name,\n serialization_format="cloudpickle",\n conda_env=conda_env,\n signature=signature,\n input_example=input_example,\n )\n run = client.create_run(experiment_id="0")\n client.log_artifact(run.info.run_id, model_name)\n\n\nfrom kfp.dsl import pipeline\n\n\n@pipeline(name="multi_model_pipeline")\ndef multi_model_pipeline(kernel: str = "rbf"):\n iris_data = load_iris_data()\n scaled_data = train_scaler_from_csv(data=iris_data.outputs["data"])\n _ = upload_sklearn_model_to_mlflow(\n model_name="scaler",\n model=scaled_data.outputs["model"],\n input_example=scaled_data.outputs["input_example"],\n signature=scaled_data.outputs["signature"],\n conda_env=scaled_data.outputs["conda_env"],\n )\n model = train_svc_from_csv(\n train_data=scaled_data.outputs["scaled_data"],\n train_target=iris_data.outputs["target"],\n kernel=kernel,\n )\n _ = upload_sklearn_model_to_mlflow(\n model_name="svc",\n model=model.outputs["model"],\n input_example=model.outputs["input_example"],\n signature=model.outputs["signature"],\n conda_env=model.outputs["conda_env"],\n )\n\n\nif __name__ == "__main__":\n kfp.compiler.Compiler().compile(multi_model_pipeline, "multi_model_pipeline.yaml")\n\n')),(0,l.kt)("p",null,"\ud30c\uc774\ud504\ub77c\uc778\uc744 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model-provision-location\n emptyDir: {}\n\n initContainers:\n - name: scaler-initializer\n image: gcr.io/kfserving/storage-initializer:v0.4.0\n args:\n - "s3://mlflow/mlflow/artifacts/0/7f445015a0e94519b003d316478766ef/artifacts/scaler"\n - "/mnt/models"\n volumeMounts:\n - mountPath: /mnt/models\n name: model-provision-location\n envFrom:\n - secretRef:\n name: seldon-init-container-secret\n - name: svc-initializer\n image: gcr.io/kfserving/storage-initializer:v0.4.0\n args:\n - "s3://mlflow/mlflow/artifacts/0/87eb168e76264b39a24b0e5ca0fe922b/artifacts/svc"\n - "/mnt/models"\n volumeMounts:\n - mountPath: /mnt/models\n name: model-provision-location\n envFrom:\n - secretRef:\n name: seldon-init-container-secret\n\n containers:\n - name: scaler\n image: seldonio/mlflowserver:1.8.0-dev\n volumeMounts:\n - mountPath: /mnt/models\n name: model-provision-location\n readOnly: true\n securityContext:\n privileged: true\n runAsUser: 0\n runAsGroup: 0\n - name: svc\n image: seldonio/mlflowserver:1.8.0-dev\n volumeMounts:\n - mountPath: /mnt/models\n name: model-provision-location\n readOnly: true\n securityContext:\n privileged: true\n runAsUser: 0\n runAsGroup: 0\n\n graph:\n name: scaler\n type: MODEL\n parameters:\n - name: model_uri\n type: STRING\n value: "/mnt/models"\n - name: predict_method\n type: STRING\n value: "transform"\n children:\n - name: svc\n type: MODEL\n parameters:\n - name: model_uri\n type: STRING\n value: "/mnt/models"\n')),(0,l.kt)("p",null,"\ubaa8\ub378\uc774 \ub450 \uac1c\uac00 \ub418\uc5c8\uc73c\ubbc0\ub85c \uac01 \ubaa8\ub378\uc758 initContainer\uc640 container\ub97c \uc815\uc758\ud574\uc8fc\uc5b4\uc57c \ud569\ub2c8\ub2e4.\n\uc774 \ud544\ub4dc\ub294 \uc785\ub825\uac12\uc744 array\ub85c \ubc1b\uc73c\uba70 \uc21c\uc11c\ub294 \uad00\uacc4\uc5c6\uc2b5\ub2c8\ub2e4."),(0,l.kt)("p",null,"\ubaa8\ub378\uc774 \uc2e4\ud589\ud558\ub294 \uc21c\uc11c\ub294 graph\uc5d0\uc11c 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/mnt/models\n name: model-provision-location\n envFrom:\n - secretRef:\n name: seldon-init-container-secret\n\n containers:\n - name: scaler\n image: ghcr.io/mlops-for-all/mlflowserver\n volumeMounts:\n - mountPath: /mnt/models\n name: model-provision-location\n readOnly: true\n securityContext:\n privileged: true\n runAsUser: 0\n runAsGroup: 0\n - name: svc\n image: ghcr.io/mlops-for-all/mlflowserver\n volumeMounts:\n - mountPath: /mnt/models\n name: model-provision-location\n readOnly: true\n securityContext:\n privileged: true\n runAsUser: 0\n runAsGroup: 0\n\n graph:\n name: scaler\n type: MODEL\n parameters:\n - name: model_uri\n type: STRING\n value: "/mnt/models"\n - name: predict_method\n type: STRING\n value: "transform"\n children:\n - name: svc\n type: MODEL\n parameters:\n - name: model_uri\n type: STRING\n value: "/mnt/models"\nEOF\n')),(0,l.kt)("p",null,"\ub2e4\uc74c \uba85\ub839\uc5b4\ub97c \ud1b5\ud574 API\ub97c 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\ub429\ub2c8\ub2e4."),(0,p.kt)("pre",null,(0,p.kt)("code",{parentName:"pre",className:"language-python"},'from functools import partial\n\nimport kfp\nfrom kfp.components import create_component_from_func, OutputPath\nfrom kfp.dsl import pipeline\n\n\n@partial(\n create_component_from_func,\n packages_to_install=["matplotlib"],\n)\ndef plot_linear(mlpipeline_ui_metadata: OutputPath("UI_Metadata")):\n import base64\n import json\n from io import BytesIO\n\n import matplotlib.pyplot as plt\n\n plt.plot([1, 2, 3], [1, 2, 3])\n\n tmpfile = BytesIO()\n plt.savefig(tmpfile, format="png")\n encoded = base64.b64encode(tmpfile.getvalue()).decode("utf-8")\n\n html = f""\n metadata = {\n "outputs": [\n {\n "type": "web-app",\n "storage": "inline",\n "source": html,\n },\n ],\n }\n with open(mlpipeline_ui_metadata, "w") as html_writer:\n json.dump(metadata, html_writer)\n\n\n@pipeline(name="plot_pipeline")\ndef plot_pipeline():\n plot_linear()\n\n\nif __name__ == "__main__":\n 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/tmp/outputs/mlpipeline_ui_metadata/data]\n command:\n - sh\n - -c\n - (PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location\n \'matplotlib\' || PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet\n --no-warn-script-location \'matplotlib\' --user) && "$0" "$@"\n - sh\n - -ec\n - |\n program_path=$(mktemp)\n printf "%s" "$0" > "$program_path"\n python3 -u "$program_path" "$@"\n - |\n def _make_parent_dirs_and_return_path(file_path: str):\n import os\n os.makedirs(os.path.dirname(file_path), exist_ok=True)\n return file_path\n def plot_linear(mlpipeline_ui_metadata):\n import base64\n import json\n from io import BytesIO\n import matplotlib.pyplot as plt\n plt.plot([1, 2, 3], [1, 2, 3])\n tmpfile = BytesIO()\n plt.savefig(tmpfile, format="png")\n encoded = base64.b64encode(tmpfile.getvalue()).decode("utf-8")\n html = f""\n metadata = {\n "outputs": [\n {\n "type": "web-app",\n "storage": "inline",\n "source": html,\n },\n ],\n }\n with open(mlpipeline_ui_metadata, "w") as html_writer:\n json.dump(metadata, html_writer)\n\n import argparse\n _parser = argparse.ArgumentParser(prog=\'Plot linear\', description=\'\')\n _parser.add_argument("--mlpipeline-ui-metadata", dest="mlpipeline_ui_metadata", type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)\n _parsed_args = vars(_parser.parse_args())\n _outputs = plot_linear(**_parsed_args)\n image: python:3.7\n outputs:\n artifacts:\n - {name: mlpipeline-ui-metadata, path: /tmp/outputs/mlpipeline_ui_metadata/data}\n metadata:\n labels:\n pipelines.kubeflow.org/kfp_sdk_version: 1.8.9\n pipelines.kubeflow.org/pipeline-sdk-type: kfp\n pipelines.kubeflow.org/enable_caching: "true"\n annotations: {pipelines.kubeflow.org/component_spec: \'{"implementation": {"container":\n {"args": ["--mlpipeline-ui-metadata", {"outputPath": "mlpipeline_ui_metadata"}],\n "command": ["sh", "-c", "(PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip\n install --quiet --no-warn-script-location \'\'matplotlib\'\' || PIP_DISABLE_PIP_VERSION_CHECK=1\n python3 -m pip install --quiet --no-warn-script-location \'\'matplotlib\'\'\n --user) && \\"$0\\" \\"$@\\"", "sh", "-ec", "program_path=$(mktemp)\\nprintf\n \\"%s\\" \\"$0\\" > \\"$program_path\\"\\npython3 -u \\"$program_path\\" \\"$@\\"\\n",\n "def _make_parent_dirs_and_return_path(file_path: str):\\n import os\\n os.makedirs(os.path.dirname(file_path),\n exist_ok=True)\\n return file_path\\n\\ndef plot_linear(mlpipeline_ui_metadata):\\n import\n base64\\n import json\\n from io import BytesIO\\n\\n import matplotlib.pyplot\n as plt\\n\\n plt.plot([1, 2, 3], [1, 2, 3])\\n\\n tmpfile = BytesIO()\\n plt.savefig(tmpfile,\n format=\\"png\\")\\n encoded = base64.b64encode(tmpfile.getvalue()).decode(\\"utf-8\\")\\n\\n html\n = f\\"\\"\\n metadata = {\\n \\"outputs\\":\n [\\n {\\n \\"type\\": \\"web-app\\",\\n \\"storage\\":\n \\"inline\\",\\n \\"source\\": html,\\n },\\n ],\\n }\\n with\n open(mlpipeline_ui_metadata, \\"w\\") as html_writer:\\n json.dump(metadata,\n html_writer)\\n\\nimport argparse\\n_parser = argparse.ArgumentParser(prog=\'\'Plot\n linear\'\', description=\'\'\'\')\\n_parser.add_argument(\\"--mlpipeline-ui-metadata\\",\n dest=\\"mlpipeline_ui_metadata\\", type=_make_parent_dirs_and_return_path,\n required=True, default=argparse.SUPPRESS)\\n_parsed_args = vars(_parser.parse_args())\\n\\n_outputs\n = plot_linear(**_parsed_args)\\n"], "image": "python:3.7"}}, "name": "Plot\n linear", "outputs": [{"name": "mlpipeline_ui_metadata", "type": "UI_Metadata"}]}\',\n pipelines.kubeflow.org/component_ref: \'{}\'}\n - name: plot-pipeline\n dag:\n tasks:\n - {name: plot-linear, template: plot-linear}\n arguments:\n parameters: []\n serviceAccountName: pipeline-runner\n')))),(0,p.kt)("p",null,"\uc2e4\ud589 \ud6c4 Visualization\uc744 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create_component_from_func, OutputPath\nfrom kfp.dsl import pipeline\n\n\n@create_component_from_func\ndef print_and_return_number(number: int) -> int:\n print(number)\n return number\n\n@create_component_from_func\ndef sum_and_print_numbers(number_1: int, number_2: int) -> int:\n sum_number = number_1 + number_2\n print(sum_number)\n return sum_number\n\n@create_component_from_func\ndef show_metric_of_sum(\n number: int,\n mlpipeline_metrics_path: OutputPath("Metrics"),\n ):\n import json\n metrics = {\n "metrics": [\n {\n "name": "sum_value",\n "numberValue": number,\n },\n ],\n }\n with open(mlpipeline_metrics_path, "w") as f:\n json.dump(metrics, f)\n\n@pipeline(name="example_pipeline")\ndef example_pipeline(number_1: int, number_2: int):\n number_1_result = print_and_return_number(number_1)\n number_2_result = print_and_return_number(number_2)\n sum_result = sum_and_print_numbers(\n number_1=number_1_result.output, number_2=number_2_result.output\n )\n 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\ub429\ub2c8\ub2e4."),(0,p.kt)("pre",null,(0,p.kt)("code",{parentName:"pre",className:"language-python"},'from functools import partial\n\nimport kfp\nfrom kfp.components import create_component_from_func, OutputPath\nfrom kfp.dsl import pipeline\n\n\n@partial(\n create_component_from_func,\n packages_to_install=["matplotlib"],\n)\ndef plot_linear(mlpipeline_ui_metadata: OutputPath("UI_Metadata")):\n import base64\n import json\n from io import BytesIO\n\n import matplotlib.pyplot as plt\n\n plt.plot([1, 2, 3], [1, 2, 3])\n\n tmpfile = BytesIO()\n plt.savefig(tmpfile, format="png")\n encoded = base64.b64encode(tmpfile.getvalue()).decode("utf-8")\n\n html = f""\n metadata = {\n "outputs": [\n {\n "type": "web-app",\n "storage": "inline",\n "source": html,\n },\n ],\n }\n with open(mlpipeline_ui_metadata, "w") as html_writer:\n json.dump(metadata, html_writer)\n\n\n@pipeline(name="plot_pipeline")\ndef plot_pipeline():\n plot_linear()\n\n\nif __name__ == "__main__":\n 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/tmp/outputs/mlpipeline_ui_metadata/data]\n command:\n - sh\n - -c\n - (PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location\n \'matplotlib\' || PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet\n --no-warn-script-location \'matplotlib\' --user) && "$0" "$@"\n - sh\n - -ec\n - |\n program_path=$(mktemp)\n printf "%s" "$0" > "$program_path"\n python3 -u "$program_path" "$@"\n - |\n def _make_parent_dirs_and_return_path(file_path: str):\n import os\n os.makedirs(os.path.dirname(file_path), exist_ok=True)\n return file_path\n def plot_linear(mlpipeline_ui_metadata):\n import base64\n import json\n from io import BytesIO\n import matplotlib.pyplot as plt\n plt.plot([1, 2, 3], [1, 2, 3])\n tmpfile = BytesIO()\n plt.savefig(tmpfile, format="png")\n encoded = base64.b64encode(tmpfile.getvalue()).decode("utf-8")\n html = f""\n metadata = {\n "outputs": [\n {\n "type": "web-app",\n "storage": "inline",\n "source": html,\n },\n ],\n }\n with open(mlpipeline_ui_metadata, "w") as html_writer:\n json.dump(metadata, html_writer)\n\n import argparse\n _parser = argparse.ArgumentParser(prog=\'Plot linear\', description=\'\')\n _parser.add_argument("--mlpipeline-ui-metadata", dest="mlpipeline_ui_metadata", type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)\n _parsed_args = vars(_parser.parse_args())\n _outputs = plot_linear(**_parsed_args)\n image: python:3.7\n outputs:\n artifacts:\n - {name: mlpipeline-ui-metadata, path: /tmp/outputs/mlpipeline_ui_metadata/data}\n metadata:\n labels:\n pipelines.kubeflow.org/kfp_sdk_version: 1.8.9\n pipelines.kubeflow.org/pipeline-sdk-type: kfp\n pipelines.kubeflow.org/enable_caching: "true"\n annotations: {pipelines.kubeflow.org/component_spec: \'{"implementation": {"container":\n {"args": ["--mlpipeline-ui-metadata", {"outputPath": "mlpipeline_ui_metadata"}],\n "command": ["sh", "-c", "(PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip\n install --quiet --no-warn-script-location \'\'matplotlib\'\' || PIP_DISABLE_PIP_VERSION_CHECK=1\n python3 -m pip install --quiet --no-warn-script-location \'\'matplotlib\'\'\n --user) && \\"$0\\" \\"$@\\"", "sh", "-ec", "program_path=$(mktemp)\\nprintf\n \\"%s\\" \\"$0\\" > \\"$program_path\\"\\npython3 -u \\"$program_path\\" \\"$@\\"\\n",\n "def _make_parent_dirs_and_return_path(file_path: str):\\n import os\\n os.makedirs(os.path.dirname(file_path),\n exist_ok=True)\\n return file_path\\n\\ndef plot_linear(mlpipeline_ui_metadata):\\n import\n base64\\n import json\\n from io import BytesIO\\n\\n import matplotlib.pyplot\n as plt\\n\\n plt.plot([1, 2, 3], [1, 2, 3])\\n\\n tmpfile = BytesIO()\\n plt.savefig(tmpfile,\n format=\\"png\\")\\n encoded = base64.b64encode(tmpfile.getvalue()).decode(\\"utf-8\\")\\n\\n html\n = f\\"\\"\\n metadata = {\\n \\"outputs\\":\n [\\n {\\n \\"type\\": \\"web-app\\",\\n \\"storage\\":\n \\"inline\\",\\n \\"source\\": html,\\n },\\n ],\\n }\\n with\n open(mlpipeline_ui_metadata, \\"w\\") as html_writer:\\n json.dump(metadata,\n html_writer)\\n\\nimport argparse\\n_parser = argparse.ArgumentParser(prog=\'\'Plot\n linear\'\', description=\'\'\'\')\\n_parser.add_argument(\\"--mlpipeline-ui-metadata\\",\n dest=\\"mlpipeline_ui_metadata\\", type=_make_parent_dirs_and_return_path,\n required=True, default=argparse.SUPPRESS)\\n_parsed_args = vars(_parser.parse_args())\\n\\n_outputs\n = plot_linear(**_parsed_args)\\n"], "image": "python:3.7"}}, "name": "Plot\n linear", "outputs": [{"name": "mlpipeline_ui_metadata", "type": "UI_Metadata"}]}\',\n pipelines.kubeflow.org/component_ref: \'{}\'}\n - name: plot-pipeline\n dag:\n tasks:\n - {name: plot-linear, template: plot-linear}\n arguments:\n parameters: []\n serviceAccountName: pipeline-runner\n')))),(0,p.kt)("p",null,"\uc2e4\ud589 \ud6c4 Visualization\uc744 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create_component_from_func, OutputPath\nfrom kfp.dsl import pipeline\n\n\n@create_component_from_func\ndef print_and_return_number(number: int) -> int:\n print(number)\n return number\n\n@create_component_from_func\ndef sum_and_print_numbers(number_1: int, number_2: int) -> int:\n sum_number = number_1 + number_2\n print(sum_number)\n return sum_number\n\n@create_component_from_func\ndef show_metric_of_sum(\n number: int,\n mlpipeline_metrics_path: OutputPath("Metrics"),\n ):\n import json\n metrics = {\n "metrics": [\n {\n "name": "sum_value",\n "numberValue": number,\n },\n ],\n }\n with open(mlpipeline_metrics_path, "w") as f:\n json.dump(metrics, f)\n\n@pipeline(name="example_pipeline")\ndef example_pipeline(number_1: int, number_2: int):\n number_1_result = print_and_return_number(number_1)\n number_2_result = print_and_return_number(number_2)\n sum_result = sum_and_print_numbers(\n number_1=number_1_result.output, number_2=number_2_result.output\n )\n 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packages_to_install=["dill", "pandas", "scikit-learn", "mlflow"],\n)\ndef train_scaler_from_csv(\n data_path: InputPath("csv"),\n scaled_data_path: OutputPath("csv"),\n model_path: OutputPath("dill"),\n input_example_path: OutputPath("dill"),\n signature_path: OutputPath("dill"),\n conda_env_path: OutputPath("dill"),\n):\n import dill\n import pandas as pd\n from sklearn.preprocessing import StandardScaler\n\n from mlflow.models.signature import infer_signature\n from mlflow.utils.environment import _mlflow_conda_env\n\n data = pd.read_csv(data_path)\n\n scaler = StandardScaler()\n scaled_data = scaler.fit_transform(data)\n scaled_data = pd.DataFrame(scaled_data, columns=data.columns, index=data.index)\n\n scaled_data.to_csv(scaled_data_path, index=False)\n\n with open(model_path, mode="wb") as file_writer:\n dill.dump(scaler, file_writer)\n\n input_example = data.sample(1)\n with open(input_example_path, "wb") as file_writer:\n dill.dump(input_example, file_writer)\n\n signature = infer_signature(data, scaler.transform(data))\n with open(signature_path, "wb") as file_writer:\n dill.dump(signature, file_writer)\n\n conda_env = _mlflow_conda_env(\n additional_pip_deps=["scikit-learn"],\n install_mlflow=False\n )\n with open(conda_env_path, "wb") as file_writer:\n dill.dump(conda_env, file_writer)\n\n\n@partial(\n create_component_from_func,\n packages_to_install=["dill", "pandas", "scikit-learn", "mlflow"],\n)\ndef train_svc_from_csv(\n train_data_path: InputPath("csv"),\n train_target_path: InputPath("csv"),\n model_path: OutputPath("dill"),\n input_example_path: OutputPath("dill"),\n signature_path: OutputPath("dill"),\n conda_env_path: OutputPath("dill"),\n kernel: str,\n):\n import dill\n import pandas as pd\n from sklearn.svm import SVC\n\n from mlflow.models.signature import infer_signature\n from mlflow.utils.environment import _mlflow_conda_env\n\n train_data = pd.read_csv(train_data_path)\n train_target = pd.read_csv(train_target_path)\n\n clf = SVC(kernel=kernel)\n clf.fit(train_data, train_target)\n\n with open(model_path, mode="wb") as file_writer:\n dill.dump(clf, file_writer)\n\n input_example = train_data.sample(1)\n with open(input_example_path, "wb") as file_writer:\n dill.dump(input_example, file_writer)\n\n signature = infer_signature(train_data, clf.predict(train_data))\n with open(signature_path, "wb") as file_writer:\n dill.dump(signature, file_writer)\n\n conda_env = _mlflow_conda_env(\n additional_pip_deps=["scikit-learn"],\n install_mlflow=False\n )\n with open(conda_env_path, "wb") as file_writer:\n dill.dump(conda_env, file_writer)\n\n\n@partial(\n create_component_from_func,\n packages_to_install=["dill", "pandas", "scikit-learn", "mlflow", "boto3"],\n)\ndef upload_sklearn_model_to_mlflow(\n model_name: str,\n model_path: InputPath("dill"),\n input_example_path: InputPath("dill"),\n signature_path: InputPath("dill"),\n conda_env_path: InputPath("dill"),\n):\n import os\n import dill\n from mlflow.sklearn import save_model\n \n from mlflow.tracking.client import MlflowClient\n\n os.environ["MLFLOW_S3_ENDPOINT_URL"] = "http://minio-service.kubeflow.svc:9000"\n os.environ["AWS_ACCESS_KEY_ID"] = "minio"\n os.environ["AWS_SECRET_ACCESS_KEY"] = "minio123"\n\n client = MlflowClient("http://mlflow-server-service.mlflow-system.svc:5000")\n\n with open(model_path, mode="rb") as file_reader:\n clf = dill.load(file_reader)\n\n with open(input_example_path, "rb") as file_reader:\n input_example = dill.load(file_reader)\n\n with open(signature_path, "rb") as file_reader:\n signature = dill.load(file_reader)\n\n with open(conda_env_path, "rb") as file_reader:\n conda_env = dill.load(file_reader)\n save_model(\n sk_model=clf,\n path=model_name,\n serialization_format="cloudpickle",\n conda_env=conda_env,\n signature=signature,\n input_example=input_example,\n )\n run = client.create_run(experiment_id="0")\n client.log_artifact(run.info.run_id, model_name)\n\n\nfrom kfp.dsl import pipeline\n\n\n@pipeline(name="multi_model_pipeline")\ndef multi_model_pipeline(kernel: str = "rbf"):\n iris_data = load_iris_data()\n scaled_data = train_scaler_from_csv(data=iris_data.outputs["data"])\n _ = upload_sklearn_model_to_mlflow(\n model_name="scaler",\n model=scaled_data.outputs["model"],\n input_example=scaled_data.outputs["input_example"],\n signature=scaled_data.outputs["signature"],\n conda_env=scaled_data.outputs["conda_env"],\n )\n model = train_svc_from_csv(\n train_data=scaled_data.outputs["scaled_data"],\n train_target=iris_data.outputs["target"],\n kernel=kernel,\n )\n _ = upload_sklearn_model_to_mlflow(\n model_name="svc",\n model=model.outputs["model"],\n input_example=model.outputs["input_example"],\n signature=model.outputs["signature"],\n conda_env=model.outputs["conda_env"],\n )\n\n\nif __name__ == "__main__":\n kfp.compiler.Compiler().compile(multi_model_pipeline, "multi_model_pipeline.yaml")\n\n')),(0,l.kt)("p",null,"\ud30c\uc774\ud504\ub77c\uc778\uc744 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model-provision-location\n emptyDir: {}\n\n initContainers:\n - name: scaler-initializer\n image: gcr.io/kfserving/storage-initializer:v0.4.0\n args:\n - "s3://mlflow/mlflow/artifacts/0/7f445015a0e94519b003d316478766ef/artifacts/scaler"\n - "/mnt/models"\n volumeMounts:\n - mountPath: /mnt/models\n name: model-provision-location\n envFrom:\n - secretRef:\n name: seldon-init-container-secret\n - name: svc-initializer\n image: gcr.io/kfserving/storage-initializer:v0.4.0\n args:\n - "s3://mlflow/mlflow/artifacts/0/87eb168e76264b39a24b0e5ca0fe922b/artifacts/svc"\n - "/mnt/models"\n volumeMounts:\n - mountPath: /mnt/models\n name: model-provision-location\n envFrom:\n - secretRef:\n name: seldon-init-container-secret\n\n containers:\n - name: scaler\n image: seldonio/mlflowserver:1.8.0-dev\n volumeMounts:\n - mountPath: /mnt/models\n name: model-provision-location\n readOnly: true\n securityContext:\n privileged: true\n runAsUser: 0\n runAsGroup: 0\n - name: svc\n image: seldonio/mlflowserver:1.8.0-dev\n volumeMounts:\n - mountPath: /mnt/models\n name: model-provision-location\n readOnly: true\n securityContext:\n privileged: true\n runAsUser: 0\n runAsGroup: 0\n\n graph:\n name: scaler\n type: MODEL\n parameters:\n - name: model_uri\n type: STRING\n value: "/mnt/models"\n - name: predict_method\n type: STRING\n value: "transform"\n children:\n - name: svc\n type: MODEL\n parameters:\n - name: model_uri\n type: STRING\n value: "/mnt/models"\n')),(0,l.kt)("p",null,"\ubaa8\ub378\uc774 \ub450 \uac1c\uac00 \ub418\uc5c8\uc73c\ubbc0\ub85c \uac01 \ubaa8\ub378\uc758 initContainer\uc640 container\ub97c \uc815\uc758\ud574\uc8fc\uc5b4\uc57c \ud569\ub2c8\ub2e4.\n\uc774 \ud544\ub4dc\ub294 \uc785\ub825\uac12\uc744 array\ub85c \ubc1b\uc73c\uba70 \uc21c\uc11c\ub294 \uad00\uacc4\uc5c6\uc2b5\ub2c8\ub2e4."),(0,l.kt)("p",null,"\ubaa8\ub378\uc774 \uc2e4\ud589\ud558\ub294 \uc21c\uc11c\ub294 graph\uc5d0\uc11c 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\ubcf4\uaca0\uc2b5\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},'cat < multi-model.yaml\napiVersion: machinelearning.seldon.io/v1\nkind: SeldonDeployment\nmetadata:\n name: multi-model-example\n namespace: kubeflow-user-example-com\nspec:\n name: model\n predictors:\n - name: model\n\n componentSpecs:\n - spec:\n volumes:\n - name: model-provision-location\n emptyDir: {}\n\n initContainers:\n - name: scaler-initializer\n image: gcr.io/kfserving/storage-initializer:v0.4.0\n args:\n - "s3://mlflow/mlflow/artifacts/0/7f445015a0e94519b003d316478766ef/artifacts/scaler"\n - "/mnt/models"\n volumeMounts:\n - mountPath: /mnt/models\n name: model-provision-location\n envFrom:\n - secretRef:\n name: seldon-init-container-secret\n - name: svc-initializer\n image: gcr.io/kfserving/storage-initializer:v0.4.0\n args:\n - "s3://mlflow/mlflow/artifacts/0/87eb168e76264b39a24b0e5ca0fe922b/artifacts/svc"\n - "/mnt/models"\n volumeMounts:\n - mountPath: /mnt/models\n name: model-provision-location\n envFrom:\n - secretRef:\n name: seldon-init-container-secret\n\n containers:\n - name: scaler\n image: ghcr.io/mlops-for-all/mlflowserver\n volumeMounts:\n - mountPath: /mnt/models\n name: model-provision-location\n readOnly: true\n securityContext:\n privileged: true\n runAsUser: 0\n runAsGroup: 0\n - name: svc\n image: ghcr.io/mlops-for-all/mlflowserver\n volumeMounts:\n - mountPath: /mnt/models\n name: model-provision-location\n readOnly: true\n securityContext:\n privileged: true\n runAsUser: 0\n runAsGroup: 0\n\n graph:\n name: scaler\n type: MODEL\n parameters:\n - name: model_uri\n type: STRING\n value: "/mnt/models"\n - name: predict_method\n type: STRING\n value: "transform"\n children:\n - name: svc\n type: MODEL\n parameters:\n - name: model_uri\n type: STRING\n value: "/mnt/models"\nEOF\n')),(0,l.kt)("p",null,"\ub2e4\uc74c \uba85\ub839\uc5b4\ub97c \ud1b5\ud574 API\ub97c 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\uc791\uc131\ud558\uaca0\uc2b5\ub2c8\ub2e4."),(0,l.kt)("p",null,"\ubaa8\ub378\uc740 \uc55e\uc11c \uc0ac\uc6a9\ud55c SVC \ubaa8\ub378\uc5d0 StandardScaler\ub97c \ucd94\uac00\ud558\uace0 \uc800\uc7a5\ud558\ub3c4\ub85d \ud558\uaca0\uc2b5\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-python"},'from functools import partial\n\nimport kfp\nfrom kfp.components import InputPath, OutputPath, create_component_from_func\n\n\n@partial(\n create_component_from_func,\n packages_to_install=["pandas", "scikit-learn"],\n)\ndef load_iris_data(\n data_path: OutputPath("csv"),\n target_path: OutputPath("csv"),\n):\n import pandas as pd\n from sklearn.datasets import load_iris\n\n iris = load_iris()\n\n data = pd.DataFrame(iris["data"], columns=iris["feature_names"])\n target = pd.DataFrame(iris["target"], columns=["target"])\n\n data.to_csv(data_path, index=False)\n target.to_csv(target_path, index=False)\n\n@partial(\n create_component_from_func,\n packages_to_install=["dill", "pandas", "scikit-learn", "mlflow"],\n)\ndef train_scaler_from_csv(\n data_path: InputPath("csv"),\n scaled_data_path: OutputPath("csv"),\n model_path: OutputPath("dill"),\n input_example_path: OutputPath("dill"),\n signature_path: OutputPath("dill"),\n conda_env_path: OutputPath("dill"),\n):\n import dill\n import pandas as pd\n from sklearn.preprocessing import StandardScaler\n\n from mlflow.models.signature import infer_signature\n from mlflow.utils.environment import _mlflow_conda_env\n\n data = pd.read_csv(data_path)\n\n scaler = StandardScaler()\n scaled_data = scaler.fit_transform(data)\n scaled_data = pd.DataFrame(scaled_data, columns=data.columns, index=data.index)\n\n scaled_data.to_csv(scaled_data_path, index=False)\n\n with open(model_path, mode="wb") as file_writer:\n dill.dump(scaler, file_writer)\n\n input_example = data.sample(1)\n with open(input_example_path, "wb") as file_writer:\n dill.dump(input_example, file_writer)\n\n signature = infer_signature(data, scaler.transform(data))\n with open(signature_path, "wb") as file_writer:\n dill.dump(signature, file_writer)\n\n conda_env = _mlflow_conda_env(\n additional_pip_deps=["scikit-learn"],\n install_mlflow=False\n )\n with open(conda_env_path, "wb") as file_writer:\n dill.dump(conda_env, file_writer)\n\n\n@partial(\n create_component_from_func,\n packages_to_install=["dill", "pandas", "scikit-learn", "mlflow"],\n)\ndef train_svc_from_csv(\n train_data_path: InputPath("csv"),\n train_target_path: InputPath("csv"),\n model_path: OutputPath("dill"),\n input_example_path: OutputPath("dill"),\n signature_path: OutputPath("dill"),\n conda_env_path: OutputPath("dill"),\n kernel: str,\n):\n import dill\n import pandas as pd\n from sklearn.svm import SVC\n\n from mlflow.models.signature import infer_signature\n from mlflow.utils.environment import _mlflow_conda_env\n\n train_data = pd.read_csv(train_data_path)\n train_target = pd.read_csv(train_target_path)\n\n clf = SVC(kernel=kernel)\n clf.fit(train_data, train_target)\n\n with open(model_path, mode="wb") as file_writer:\n dill.dump(clf, file_writer)\n\n input_example = train_data.sample(1)\n with open(input_example_path, "wb") as file_writer:\n dill.dump(input_example, file_writer)\n\n signature = infer_signature(train_data, clf.predict(train_data))\n with open(signature_path, "wb") as file_writer:\n dill.dump(signature, file_writer)\n\n conda_env = _mlflow_conda_env(\n additional_pip_deps=["scikit-learn"],\n install_mlflow=False\n )\n with open(conda_env_path, "wb") as file_writer:\n dill.dump(conda_env, file_writer)\n\n\n@partial(\n create_component_from_func,\n packages_to_install=["dill", "pandas", "scikit-learn", "mlflow", "boto3"],\n)\ndef upload_sklearn_model_to_mlflow(\n model_name: str,\n model_path: InputPath("dill"),\n input_example_path: InputPath("dill"),\n signature_path: InputPath("dill"),\n conda_env_path: InputPath("dill"),\n):\n import os\n import dill\n from mlflow.sklearn import save_model\n \n from mlflow.tracking.client import MlflowClient\n\n os.environ["MLFLOW_S3_ENDPOINT_URL"] = "http://minio-service.kubeflow.svc:9000"\n os.environ["AWS_ACCESS_KEY_ID"] = "minio"\n os.environ["AWS_SECRET_ACCESS_KEY"] = "minio123"\n\n client = MlflowClient("http://mlflow-server-service.mlflow-system.svc:5000")\n\n with open(model_path, mode="rb") as file_reader:\n clf = dill.load(file_reader)\n\n with open(input_example_path, "rb") as file_reader:\n input_example = dill.load(file_reader)\n\n with open(signature_path, "rb") as file_reader:\n signature = dill.load(file_reader)\n\n with open(conda_env_path, "rb") as file_reader:\n conda_env = dill.load(file_reader)\n save_model(\n sk_model=clf,\n path=model_name,\n serialization_format="cloudpickle",\n conda_env=conda_env,\n signature=signature,\n input_example=input_example,\n )\n run = client.create_run(experiment_id="0")\n client.log_artifact(run.info.run_id, model_name)\n\n\nfrom kfp.dsl import pipeline\n\n\n@pipeline(name="multi_model_pipeline")\ndef multi_model_pipeline(kernel: str = "rbf"):\n iris_data = load_iris_data()\n scaled_data = train_scaler_from_csv(data=iris_data.outputs["data"])\n _ = upload_sklearn_model_to_mlflow(\n model_name="scaler",\n model=scaled_data.outputs["model"],\n input_example=scaled_data.outputs["input_example"],\n signature=scaled_data.outputs["signature"],\n conda_env=scaled_data.outputs["conda_env"],\n )\n model = train_svc_from_csv(\n train_data=scaled_data.outputs["scaled_data"],\n train_target=iris_data.outputs["target"],\n kernel=kernel,\n )\n _ = upload_sklearn_model_to_mlflow(\n model_name="svc",\n model=model.outputs["model"],\n input_example=model.outputs["input_example"],\n signature=model.outputs["signature"],\n conda_env=model.outputs["conda_env"],\n )\n\n\nif __name__ == "__main__":\n kfp.compiler.Compiler().compile(multi_model_pipeline, "multi_model_pipeline.yaml")\n\n')),(0,l.kt)("p",null,"\ud30c\uc774\ud504\ub77c\uc778\uc744 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model-provision-location\n emptyDir: {}\n\n initContainers:\n - name: scaler-initializer\n image: gcr.io/kfserving/storage-initializer:v0.4.0\n args:\n - "s3://mlflow/mlflow/artifacts/0/7f445015a0e94519b003d316478766ef/artifacts/scaler"\n - "/mnt/models"\n volumeMounts:\n - mountPath: /mnt/models\n name: model-provision-location\n envFrom:\n - secretRef:\n name: seldon-init-container-secret\n - name: svc-initializer\n image: gcr.io/kfserving/storage-initializer:v0.4.0\n args:\n - "s3://mlflow/mlflow/artifacts/0/87eb168e76264b39a24b0e5ca0fe922b/artifacts/svc"\n - "/mnt/models"\n volumeMounts:\n - mountPath: /mnt/models\n name: model-provision-location\n envFrom:\n - secretRef:\n name: seldon-init-container-secret\n\n containers:\n - name: scaler\n image: seldonio/mlflowserver:1.8.0-dev\n volumeMounts:\n - mountPath: /mnt/models\n name: model-provision-location\n readOnly: true\n securityContext:\n privileged: true\n runAsUser: 0\n runAsGroup: 0\n - name: svc\n image: seldonio/mlflowserver:1.8.0-dev\n volumeMounts:\n - mountPath: /mnt/models\n name: model-provision-location\n readOnly: true\n securityContext:\n privileged: true\n runAsUser: 0\n runAsGroup: 0\n\n graph:\n name: scaler\n type: MODEL\n parameters:\n - name: model_uri\n type: STRING\n value: "/mnt/models"\n - name: predict_method\n type: STRING\n value: "transform"\n children:\n - name: svc\n type: MODEL\n parameters:\n - name: model_uri\n type: STRING\n value: "/mnt/models"\n')),(0,l.kt)("p",null,"\ubaa8\ub378\uc774 \ub450 \uac1c\uac00 \ub418\uc5c8\uc73c\ubbc0\ub85c \uac01 \ubaa8\ub378\uc758 initContainer\uc640 container\ub97c \uc815\uc758\ud574\uc8fc\uc5b4\uc57c \ud569\ub2c8\ub2e4.\n\uc774 \ud544\ub4dc\ub294 \uc785\ub825\uac12\uc744 array\ub85c \ubc1b\uc73c\uba70 \uc21c\uc11c\ub294 \uad00\uacc4\uc5c6\uc2b5\ub2c8\ub2e4."),(0,l.kt)("p",null,"\ubaa8\ub378\uc774 \uc2e4\ud589\ud558\ub294 \uc21c\uc11c\ub294 graph\uc5d0\uc11c 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/mnt/models\n name: model-provision-location\n envFrom:\n - secretRef:\n name: seldon-init-container-secret\n\n containers:\n - name: scaler\n image: ghcr.io/mlops-for-all/mlflowserver\n volumeMounts:\n - mountPath: /mnt/models\n name: model-provision-location\n readOnly: true\n securityContext:\n privileged: true\n runAsUser: 0\n runAsGroup: 0\n - name: svc\n image: ghcr.io/mlops-for-all/mlflowserver\n volumeMounts:\n - mountPath: /mnt/models\n name: model-provision-location\n readOnly: true\n securityContext:\n privileged: true\n runAsUser: 0\n runAsGroup: 0\n\n graph:\n name: scaler\n type: MODEL\n parameters:\n - name: model_uri\n type: STRING\n value: "/mnt/models"\n - name: predict_method\n type: STRING\n value: "transform"\n children:\n - name: svc\n type: MODEL\n parameters:\n - name: model_uri\n type: STRING\n value: "/mnt/models"\nEOF\n')),(0,l.kt)("p",null,"\ub2e4\uc74c \uba85\ub839\uc5b4\ub97c \ud1b5\ud574 API\ub97c 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Components of MLOps",description:"Describe MLOps Components",sidebar_position:3,date:new Date("2021-12-03T00:00:00.000Z"),lastmod:new Date("2021-12-10T00:00:00.000Z"),contributors:["Youngcheol Jang"]},o=void 0,a={unversionedId:"introduction/component",id:"version-1.0/introduction/component",title:"3. Components of MLOps",description:"Describe MLOps Components",source:"@site/versioned_docs/version-1.0/introduction/component.md",sourceDirName:"introduction",slug:"/introduction/component",permalink:"/docs/1.0/introduction/component",draft:!1,editUrl:"https://github.com/mlops-for-all/mlops-for-all.github.io/tree/main/versioned_docs/version-1.0/introduction/component.md",tags:[],version:"1.0",lastUpdatedBy:"Aiden-Jeon",lastUpdatedAt:1706783082,formattedLastUpdatedAt:"2024\ub144 2\uc6d4 1\uc77c",sidebarPosition:3,frontMatter:{title:"3. Components of MLOps",description:"Describe MLOps Components",sidebar_position:3,date:"2021-12-03T00:00:00.000Z",lastmod:"2021-12-10T00:00:00.000Z",contributors:["Youngcheol Jang"]},sidebar:"tutorialSidebar",previous:{title:"2. Levels of MLOps",permalink:"/docs/1.0/introduction/levels"},next:{title:"4. Why Kubernetes?",permalink:"/docs/1.0/introduction/why_kubernetes"}},p={},u=[{value:"Practitioners guide to MLOps",id:"practitioners-guide-to-mlops",level:2},{value:"1. Experimentation",id:"1-experimentation",level:3},{value:"2. Data Processing",id:"2-data-processing",level:3},{value:"3. Model training",id:"3-model-training",level:3},{value:"4. Model evaluation",id:"4-model-evaluation",level:3},{value:"5. Model serving",id:"5-model-serving",level:3},{value:"6. Online experimentation",id:"6-online-experimentation",level:3},{value:"7. Model Monitoring",id:"7-model-monitoring",level:3},{value:"8. ML Pipeline",id:"8-ml-pipeline",level:3},{value:"9. Model Registry",id:"9-model-registry",level:3},{value:"10. Dataset and Feature Repository",id:"10-dataset-and-feature-repository",level:3},{value:"11. ML Metadata and Artifact Tracking",id:"11-ml-metadata-and-artifact-tracking",level:3}],s={toc:u},d="wrapper";function c(e){let{components:t,...l}=e;return(0,r.kt)(d,(0,i.Z)({},s,l,{components:t,mdxType:"MDXLayout"}),(0,r.kt)("h2",{id:"practitioners-guide-to-mlops"},"Practitioners guide to MLOps"),(0,r.kt)("p",null," 2021\ub144 5\uc6d4\uc5d0 \ubc1c\ud45c\ub41c \uad6c\uae00\uc758 ",(0,r.kt)("a",{parentName:"p",href:"https://services.google.com/fh/files/misc/practitioners_guide_to_mlops_whitepaper.pdf"},"white paper : Practitioners guide to MLOps: A framework for continuous delivery and automation of machine learning"),"\uc5d0\uc11c\ub294 MLOps\uc758 \ud575\uc2ec \uae30\ub2a5\ub4e4\ub85c \ub2e4\uc74c\uacfc \uac19\uc740 \uac83\ub4e4\uc744 \uc5b8\uae09\ud558\uc600\uc2b5\ub2c8\ub2e4."),(0,r.kt)("p",null,(0,r.kt)("img",{alt:"mlops-component",src:n(8037).Z,width:"2352",height:"1890"})),(0,r.kt)("p",null," \uac01 \uae30\ub2a5\uc774 \uc5b4\ub5a4 \uc5ed\ud560\uc744 \ud558\ub294\uc9c0 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4."),(0,r.kt)("h3",{id:"1-experimentation"},"1. Experimentation"),(0,r.kt)("p",null," \uc2e4\ud5d8(Experimentation)\uc740 \uba38\uc2e0\ub7ec\ub2dd \uc5d4\uc9c0\ub2c8\uc5b4\ub4e4\uc774 \ub370\uc774\ud130\ub97c \ubd84\uc11d\ud558\uace0, \ud504\ub85c\ud1a0\ud0c0\uc785 \ubaa8\ub378\uc744 \ub9cc\ub4e4\uba70 \ud559\uc2b5 \uae30\ub2a5\uc744 \uad6c\ud604\ud560 \uc218 \uc788\ub3c4\ub85d \ud558\ub294 \ub2e4\uc74c\uacfc \uac19\uc740 \uae30\ub2a5\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4."),(0,r.kt)("ul",null,(0,r.kt)("li",{parentName:"ul"},"\uae43(Git)\uacfc \uac19\uc740 \ubc84\uc804 \ucee8\ud2b8\ub864 \ub3c4\uad6c\uc640 \ud1b5\ud569\ub41c \ub178\ud2b8\ubd81(Jupyter Notebook) \ud658\uacbd \uc81c\uacf5"),(0,r.kt)("li",{parentName:"ul"},"\uc0ac\uc6a9\ud55c \ub370\uc774\ud130, \ud558\uc774\ud37c \ud30c\ub77c\ubbf8\ud130, \ud3c9\uac00 \uc9c0\ud45c\ub97c \ud3ec\ud568\ud55c \uc2e4\ud5d8 \ucd94\uc801 \uae30\ub2a5 \uc81c\uacf5"),(0,r.kt)("li",{parentName:"ul"},"\ub370\uc774\ud130\uc640 \ubaa8\ub378\uc5d0 \ub300\ud55c \ubd84\uc11d \ubc0f \uc2dc\uac01\ud654 \uae30\ub2a5 \uc81c\uacf5")),(0,r.kt)("h3",{id:"2-data-processing"},"2. 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Model training"),(0,r.kt)("p",null," \ubaa8\ub378 \ud559\uc2b5(Model training)\uc740 \ubaa8\ub378 \ud559\uc2b5\uc744 \uc704\ud55c \uc54c\uace0\ub9ac\uc998\uc744 \ud6a8\uc728\uc801\uc73c\ub85c \uc2e4\ud589\uc2dc\ucf1c\uc8fc\ub294 \ub2e4\uc74c\uacfc \uac19\uc740 \uae30\ub2a5\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4."),(0,r.kt)("ul",null,(0,r.kt)("li",{parentName:"ul"},"ML \ud504\ub808\uc784\uc6cc\ud06c\uc758 \uc2e4\ud589\uc744 \uc704\ud55c \ud658\uacbd \uc81c\uacf5"),(0,r.kt)("li",{parentName:"ul"},"\ub2e4\uc218\uc758 GPU / \ubd84\uc0b0 \ud559\uc2b5 \uc0ac\uc6a9\uc744 \uc704\ud55c \ubd84\uc0b0 \ud559\uc2b5 \ud658\uacbd \uc81c\uacf5"),(0,r.kt)("li",{parentName:"ul"},"\ud558\uc774\ud37c \ud30c\ub77c\ubbf8\ud130 \ud29c\ub2dd\uacfc \ucd5c\uc801\ud654 \uae30\ub2a5 \uc81c\uacf5")),(0,r.kt)("h3",{id:"4-model-evaluation"},"4. Model evaluation"),(0,r.kt)("p",null," \ubaa8\ub378 \ud3c9\uac00(Model evaluation)\ub294 \uc2e4\ud5d8 \ud658\uacbd\uacfc \uc0c1\uc6a9 \ud658\uacbd\uc5d0\uc11c \ub3d9\uc791\ud558\ub294 \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \uad00\ucc30\ud560 \uc218 \uc788\ub294 \ub2e4\uc74c\uacfc \uac19\uc740 \uae30\ub2a5\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4."),(0,r.kt)("ul",null,(0,r.kt)("li",{parentName:"ul"},"\ud3c9\uac00 \ub370\uc774\ud130\uc5d0 \ub300\ud55c \ubaa8\ub378 \uc131\ub2a5 \ud3c9\uac00 \uae30\ub2a5"),(0,r.kt)("li",{parentName:"ul"},"\uc11c\ub85c \ub2e4\ub978 \uc9c0\uc18d \ud559\uc2b5 \uc2e4\ud589 \uacb0\uacfc\uc5d0 \ub300\ud55c \uc608\uce21 \uc131\ub2a5 \ucd94\uc801"),(0,r.kt)("li",{parentName:"ul"},"\uc11c\ub85c \ub2e4\ub978 \ubaa8\ub378\uc758 \uc131\ub2a5 \ube44\uad50\uc640 \uc2dc\uac01\ud654"),(0,r.kt)("li",{parentName:"ul"},"\ud574\uc11d\ud560 \uc218 \uc788\ub294 AI \uae30\uc220\uc744 \uc774\uc6a9\ud55c \ubaa8\ub378 \ucd9c\ub825 \ud574\uc11d \uae30\ub2a5 \uc81c\uacf5")),(0,r.kt)("h3",{id:"5-model-serving"},"5. Model serving"),(0,r.kt)("p",null," \ubaa8\ub378 \uc11c\ube59(Model serving)\uc740 \uc0c1\uc6a9 \ud658\uacbd\uc5d0 \ubaa8\ub378\uc744 \ubc30\ud3ec\ud558\uace0 \uc11c\ube59\ud558\uae30 \uc704\ud55c \ub2e4\uc74c\uacfc \uac19\uc740 \uae30\ub2a5\ub4e4\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4."),(0,r.kt)("ul",null,(0,r.kt)("li",{parentName:"ul"},"\uc800 \uc9c0\uc5f0 \ucd94\ub860\uacfc \uace0\uac00\uc6a9\uc131 \ucd94\ub860 \uae30\ub2a5 \uc81c\uacf5"),(0,r.kt)("li",{parentName:"ul"},"\ub2e4\uc591\ud55c ML \ubaa8\ub378 \uc11c\ube59 \ud504\ub808\uc784\uc6cc\ud06c \uc9c0\uc6d0(Tensorflow Serving, TorchServe, NVIDIA Triton, Scikit-learn, XGGoost. etc)"),(0,r.kt)("li",{parentName:"ul"},"\ubcf5\uc7a1\ud55c \ud615\ud0dc\uc758 \ucd94\ub860 \ub8e8\ud2f4 \uae30\ub2a5 \uc81c\uacf5, \uc608\ub97c \ub4e4\uc5b4 \uc804\ucc98\ub9ac(preprocess) \ub610\ub294 \ud6c4\ucc98\ub9ac(postprocess) \uae30\ub2a5\uacfc \ucd5c\uc885 \uacb0\uacfc\ub97c \uc704\ud574 \ub2e4\uc218\uc758 \ubaa8\ub378\uc774 \uc0ac\uc6a9\ub418\ub294 \uacbd\uc6b0\ub97c \ub9d0\ud569\ub2c8\ub2e4."),(0,r.kt)("li",{parentName:"ul"},"\uc21c\uac04\uc801\uc73c\ub85c \uce58\uc19f\ub294 \ucd94\ub860 \uc694\uccad\uc744 \ucc98\ub9ac\ud558\uae30 \uc704\ud55c \uc624\ud1a0 \uc2a4\ucf00\uc77c\ub9c1(autoscaling) \uae30\ub2a5 \uc81c\uacf5"),(0,r.kt)("li",{parentName:"ul"},"\ucd94\ub860 \uc694\uccad\uacfc \ucd94\ub860 \uacb0\uacfc\uc5d0 \ub300\ud55c \ub85c\uae45 \uae30\ub2a5 \uc81c\uacf5")),(0,r.kt)("h3",{id:"6-online-experimentation"},"6. Online experimentation"),(0,r.kt)("p",null," \uc628\ub77c\uc778 \uc2e4\ud5d8(Online experimentation)\uc740 \uc0c8\ub85c\uc6b4 \ubaa8\ub378\uc774 \uc0dd\uc131\ub418\uc5c8\uc744 \ub54c, \uc774 \ubaa8\ub378\uc744 \ubc30\ud3ec\ud558\uba74 \uc5b4\ub290 \uc815\ub3c4\uc758 \uc131\ub2a5\uc744 \ubcf4\uc77c \uac83\uc778\uc9c0 \uac80\uc99d\ud558\ub294 \uae30\ub2a5\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4. \uc774 \uae30\ub2a5\uc740 \uc0c8 \ubaa8\ub378\uc744 \ubc30\ud3ec\ud558\ub294 \uac83\uae4c\uc9c0 \uc5f0\ub3d9\ud558\uae30 \uc704\ud574 \ubaa8\ub378 \uc800\uc7a5\uc18c(Model Registry)\uc640 \uc5f0\ub3d9\ub418\uc5b4\uc57c \ud569\ub2c8\ub2e4."),(0,r.kt)("ul",null,(0,r.kt)("li",{parentName:"ul"},"\uce74\ub098\ub9ac(canary) & \uc100\ub3c4(shadow) \ubc30\ud3ec \uae30\ub2a5 \uc81c\uacf5"),(0,r.kt)("li",{parentName:"ul"},"A/B \ud14c\uc2a4\ud2b8 \uae30\ub2a5 \uc81c\uacf5"),(0,r.kt)("li",{parentName:"ul"},"\uba40\ud2f0 \uc554\ub4dc \ubc34\ub527(Multi-armed bandit) \ud14c\uc2a4\ud2b8 \uae30\ub2a5 \uc81c\uacf5")),(0,r.kt)("h3",{id:"7-model-monitoring"},"7. Model Monitoring"),(0,r.kt)("p",null,"\ubaa8\ub378 \ubaa8\ub2c8\ud130\ub9c1(Model Monitoring)\uc740 \uc0c1\uc6a9 \ud658\uacbd\uc5d0 \ubc30\ud3ec\ub41c \ubaa8\ub378\uc774 \uc815\uc0c1\uc801\uc73c\ub85c \ub3d9\uc791\ud558\uace0 \uc788\ub294\uc9c0\ub97c \ubaa8\ub2c8\ud130\ub9c1\ud558\ub294 \uae30\ub2a5\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4 \ubaa8\ub378\uc758 \uc131\ub2a5\uc774 \ub5a8\uc5b4\uc838 \uc5c5\ub370\uc774\ud2b8\uac00 \ud544\uc694\ud55c\uc9c0\uc5d0 \ub300\ud55c \uc815\ubcf4 \ub4f1\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4."),(0,r.kt)("h3",{id:"8-ml-pipeline"},"8. ML Pipeline"),(0,r.kt)("p",null,"\uba38\uc2e0\ub7ec\ub2dd \ud30c\uc774\ud504\ub77c\uc778(ML Pipeline)\uc740 \uc0c1\uc6a9 \ud658\uacbd\uc5d0\uc11c \ubcf5\uc7a1\ud55c ML \ud559\uc2b5\uacfc \ucd94\ub860 \uc791\uc5c5\uc744 \uad6c\uc131\ud558\uace0 \uc81c\uc5b4\ud558\uace0 \uc790\ub3d9\ud654\ud558\uae30 \uc704\ud55c \ub2e4\uc74c\uacfc \uac19\uc740 \uae30\ub2a5\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4."),(0,r.kt)("ul",null,(0,r.kt)("li",{parentName:"ul"},"\ub2e4\uc591\ud55c \uc774\ubca4\ud2b8\ub97c \uc18c\uc2a4\ub97c \ud1b5\ud55c \ud30c\uc774\ud504\ub77c\uc778 \uc2e4\ud589 \uae30\ub2a5"),(0,r.kt)("li",{parentName:"ul"},"\ud30c\uc774\ud504\ub77c\uc778 \ud30c\ub77c\ubbf8\ud130\uc640 \uc0dd\uc131\ub418\ub294 \uc0b0\ucd9c\ubb3c \uad00\ub9ac\ub97c \uc704\ud55c \uba38\uc2e0\ub7ec\ub2dd \uba54\ud0c0\ub370\uc774\ud130 \ucd94\uc801\uacfc \uc5f0\ub3d9 \uae30\ub2a5"),(0,r.kt)("li",{parentName:"ul"},"\uc77c\ubc18\uc801\uc778 \uba38\uc2e0\ub7ec\ub2dd \uc791\uc5c5\uc744 \uc704\ud55c \ub0b4\uc7a5 \ucef4\ud3ec\ub10c\ud2b8 \uc9c0\uc6d0\uacfc \uc0ac\uc6a9\uc790\uac00 \uc9c1\uc811 \uad6c\ud604\ud55c \ucef4\ud3ec\ub10c\ud2b8\uc5d0 \ub300\ud55c \uc9c0\uc6d0 \uae30\ub2a5"),(0,r.kt)("li",{parentName:"ul"},"\uc11c\ub85c \ub2e4\ub978 \uc2e4\ud589 \ud658\uacbd \uc81c\uacf5 \uae30\ub2a5")),(0,r.kt)("h3",{id:"9-model-registry"},"9. 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Why Kubernetes?",description:"Reason for using k8s in MLOps",sidebar_position:4,date:"2021-12-03T00:00:00.000Z",lastmod:"2021-12-10T00:00:00.000Z",contributors:["Jaeyeon Kim"]},sidebar:"tutorialSidebar",previous:{title:"3. Components of MLOps",permalink:"/docs/1.0/introduction/component"},next:{title:"1. 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\ub2e4\uc591\ud55c \uad6c\uc131 \uc694\uc18c\ub4e4\uc774 \ud544\uc694\ud558\uc9c0\ub9cc, \uac01\uac01\uc758 \uad6c\uc131 \uc694\uc18c\ub4e4\uc774 \uc720\uae30\uc801\uc73c\ub85c \uc6b4\uc601\ub418\uae30 \uc704\ud574\uc11c\ub294 \uc778\ud504\ub77c \ub808\ubca8\uc5d0\uc11c \uc218\ub9ce\uc740 \uc774\uc288\ub97c \ud574\uacb0\ud574\uc57c \ud569\ub2c8\ub2e4.",(0,o.kt)("br",{parentName:"p"}),"\n","\uac04\ub2e8\ud558\uac8c\ub294 \uc218\ub9ce\uc740 \uba38\uc2e0\ub7ec\ub2dd \ubaa8\ub378\uc758 \ud559\uc2b5 \uc694\uccad\uc744 \ucc28\ub840\ub300\ub85c \uc2e4\ud589\ud558\ub294 \uac83, \ub2e4\ub978 \uc791\uc5c5 \uacf5\uac04\uc5d0\uc11c\ub3c4 \uac19\uc740 \uc2e4\ud589 \ud658\uacbd\uc744 \ubcf4\uc7a5\ud574\uc57c \ud558\ub294 \uac83, \ubc30\ud3ec\ub41c \uc11c\ube44\uc2a4\uc5d0 \uc7a5\uc560\uac00 \uc0dd\uacbc\uc744 \ub54c \ube60\ub974\uac8c \ub300\uc751\ud574\uc57c \ud558\ub294 \uac83 \ub4f1\uc758 \uc774\uc288 \ub4f1\uc744 \uc0dd\uac01\ud574\ubcfc \uc218 \uc788\uc2b5\ub2c8\ub2e4.",(0,o.kt)("br",{parentName:"p"}),"\n","\uc5ec\uae30\uc11c \ucee8\ud14c\uc774\ub108(Container)\uc640 \ucee8\ud14c\uc774\ub108 \uc624\ucf00\uc2a4\ud2b8\ub808\uc774\uc158 \uc2dc\uc2a4\ud15c(Container Orchestration System)\uc758 \ud544\uc694\uc131\uc774 \ub4f1\uc7a5\ud569\ub2c8\ub2e4."),(0,o.kt)("p",null,"\ucfe0\ubc84\ub124\ud2f0\uc2a4\uc640 \uac19\uc740 \ucee8\ud14c\uc774\ub108 \uc624\ucf00\uc2a4\ud2b8\ub808\uc774\uc158 \uc2dc\uc2a4\ud15c\uc744 \ub3c4\uc785\ud558\uba74 \uc2e4\ud589 \ud658\uacbd\uc758 \uaca9\ub9ac\uc640 \uad00\ub9ac\ub97c \ud6a8\uc728\uc801\uc73c\ub85c \uc218\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ucee8\ud14c\uc774\ub108 \uc624\ucf00\uc2a4\ud2b8\ub808\uc774\uc158 \uc2dc\uc2a4\ud15c\uc744 \ub3c4\uc785\ud55c\ub2e4\uba74, \uba38\uc2e0\ub7ec\ub2dd \ubaa8\ub378\uc744 \uac1c\ubc1c\ud558\uace0 \ubc30\ud3ec\ud558\ub294 \uacfc\uc815\uc5d0\uc11c \ub2e4\uc218\uc758 \uac1c\ubc1c\uc790\uac00 \uc18c\uc218\uc758 \ud074\ub7ec\uc2a4\ud130\ub97c \uacf5\uc720\ud558\uba74\uc11c ",(0,o.kt)("em",{parentName:"p"},"'1\ubc88 \ud074\ub7ec\uc2a4\ud130 \uc0ac\uc6a9 \uc911\uc774\uc2e0\uac00\uc694?', 'GPU \uc0ac\uc6a9 \uc911\uc774\ub358 \uc81c \ud504\ub85c\uc138\uc2a4 \ub204\uac00 \uc8fd\uc600\ub098\uc694?', '\ub204\uac00 \ud074\ub7ec\uc2a4\ud130\uc5d0 x \ud328\ud0a4\uc9c0 \uc5c5\ub370\uc774\ud2b8\ud588\ub098\uc694?'")," \uc640 \uac19\uc740 \uc0c1\ud669\uc744 \ubc29\uc9c0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,o.kt)("h2",{id:"container"},"Container"),(0,o.kt)("p",null,"\uadf8\ub807\ub2e4\uba74 \ucee8\ud14c\uc774\ub108\ub780 \ubb34\uc5c7\uc77c\uae4c\uc694? \ub9c8\uc774\ud06c\ub85c\uc18c\ud504\ud2b8\uc5d0\uc11c\ub294 \ucee8\ud14c\uc774\ub108\ub97c ",(0,o.kt)("a",{parentName:"p",href:"https://azure.microsoft.com/ko-kr/overview/what-is-a-container/"},"\ub2e4\uc74c"),"\uacfc \uac19\uc774 \uc815\uc758\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4."),(0,o.kt)("blockquote",null,(0,o.kt)("p",{parentName:"blockquote"},"\ucee8\ud14c\uc774\ub108\ub780 : \uc560\ud50c\ub9ac\ucf00\uc774\uc158\uc758 \ud45c\uc900\ud654\ub41c \uc774\uc2dd \uac00\ub2a5\ud55c \ud328\ud0a4\uc9d5")),(0,o.kt)("p",null,"\uadf8\ub7f0\ub370 \uc65c \uba38\uc2e0\ub7ec\ub2dd\uc5d0\uc11c \ucee8\ud14c\uc774\ub108\uac00 \ud544\uc694\ud560\uae4c\uc694? \uba38\uc2e0\ub7ec\ub2dd \ubaa8\ub378\ub4e4\uc740 \uc6b4\uc601\uccb4\uc81c\ub098 Python \uc2e4\ud589 \ud658\uacbd, \ud328\ud0a4\uc9c0 \ubc84\uc804 \ub4f1\uc5d0 \ub530\ub77c \ub2e4\ub974\uac8c \ub3d9\uc791\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.",(0,o.kt)("br",{parentName:"p"}),"\n","\uc774\ub97c \ubc29\uc9c0\ud558\uae30 \uc704\ud574\uc11c \uba38\uc2e0\ub7ec\ub2dd\uc5d0 \uc0ac\uc6a9\ub41c \uc18c\uc2a4 \ucf54\ub4dc\uc640 \ud568\uaed8 \uc885\uc18d\uc801\uc778 \uc2e4\ud589 \ud658\uacbd \uc804\uccb4\ub97c ",(0,o.kt)("strong",{parentName:"p"},"\ud558\ub098\ub85c \ubb36\uc5b4\uc11c(\ud328\ud0a4\uc9d5\ud574\uc11c)")," \uacf5\uc720\ud558\uace0 \uc2e4\ud589\ud558\ub294 \ub370 \ud65c\uc6a9\ud560 \uc218 \uc788\ub294 \uae30\uc220\uc774 \ucee8\ud14c\uc774\ub108\ub77c\uc774\uc81c\uc774\uc158(Containerization) \uae30\uc220\uc785\ub2c8\ub2e4.\n\uc774\ub807\uac8c \ud328\ud0a4\uc9d5\ub41c \ud615\ud0dc\ub97c \ucee8\ud14c\uc774\ub108 \uc774\ubbf8\uc9c0\ub77c\uace0 \ubd80\ub974\uba70, \ucee8\ud14c\uc774\ub108 \uc774\ubbf8\uc9c0\ub97c \uacf5\uc720\ud568\uc73c\ub85c\uc368 \uc0ac\uc6a9\uc790\ub4e4\uc740 \uc5b4\ub5a4 \uc2dc\uc2a4\ud15c\uc5d0\uc11c\ub4e0 \uac19\uc740 \uc2e4\ud589 \uacb0\uacfc\ub97c \ubcf4\uc7a5\ud560 \uc218 \uc788\uac8c \ub429\ub2c8\ub2e4.",(0,o.kt)("br",{parentName:"p"}),"\n","\uc989, \ub2e8\uc21c\ud788 Jupyter Notebook \ud30c\uc77c\uc774\ub098, \ubaa8\ub378\uc758 \uc18c\uc2a4 \ucf54\ub4dc\uc640 requirements.txt \ud30c\uc77c\uc744 \uacf5\uc720\ud558\ub294 \uac83\uc774 \uc544\ub2cc, \ubaa8\ub4e0 \uc2e4\ud589 \ud658\uacbd\uc774 \ub2f4\uae34 \ucee8\ud14c\uc774\ub108 \uc774\ubbf8\uc9c0\ub97c \uacf5\uc720\ud55c\ub2e4\uba74 ",(0,o.kt)("em",{parentName:"p"},'"\uc81c \ub178\ud2b8\ubd81\uc5d0\uc11c\ub294 \uc798 \ub418\ub294\ub370\uc694?"')," \uc640 \uac19\uc740 \uc0c1\ud669\uc744 \ud53c\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,o.kt)("p",null,'\ucee8\ud14c\uc774\ub108\ub97c \ucc98\uc74c \uc811\ud558\uc2dc\ub294 \ubd84\ub4e4\uc774 \ud754\ud788 \ud558\uc2dc\ub294 \uc624\ud574 \uc911 \ud558\ub098\ub294 "',(0,o.kt)("strong",{parentName:"p"},"\ucee8\ud14c\uc774\ub108 == \ub3c4\ucee4"),'"\ub77c\uace0 \ubc1b\uc544\ub4e4\uc774\ub294 \uac83\uc785\ub2c8\ub2e4.',(0,o.kt)("br",{parentName:"p"}),"\n","\ub3c4\ucee4\ub294 \ucee8\ud14c\uc774\ub108\uc640 \uac19\uc740 \uc758\ubbf8\ub97c \uc9c0\ub2c8\ub294 \uac1c\ub150\uc774 \uc544\ub2c8\ub77c, \ucee8\ud14c\uc774\ub108\ub97c \ub744\uc6b0\uac70\ub098, \ucee8\ud14c\uc774\ub108 \uc774\ubbf8\uc9c0\ub97c \ub9cc\ub4e4\uace0 \uacf5\uc720\ud558\ub294 \uac83\uacfc \uac19\uc774 \ucee8\ud14c\uc774\ub108\ub97c \ub354\uc6b1\ub354 \uc27d\uace0 \uc720\uc5f0\ud558\uac8c \uc0ac\uc6a9\ud560 \uc218 \uc788\ub294 \uae30\ub2a5\uc744 \uc81c\uacf5\ud574\uc8fc\ub294 \ub3c4\uad6c\uc785\ub2c8\ub2e4. \uc815\ub9ac\ud558\uc790\uba74 \ucee8\ud14c\uc774\ub108\ub294 \uac00\uc0c1\ud654 \uae30\uc220\uc774\uace0, \ub3c4\ucee4\ub294 \uac00\uc0c1\ud654 \uae30\uc220\uc758 \uad6c\ud604\uccb4\ub77c\uace0 \ub9d0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,o.kt)("p",null,"\ub2e4\ub9cc, \ub3c4\ucee4\ub294 \uc5ec\ub7ec \ucee8\ud14c\uc774\ub108 \uac00\uc0c1\ud654 \ub3c4\uad6c \uc911\uc5d0\uc11c \uc26c\uc6b4 \uc0ac\uc6a9\uc131\uacfc \ub192\uc740 \ud6a8\uc728\uc131\uc744 \ubc14\ud0d5\uc73c\ub85c \uac00\uc7a5 \ube60\ub974\uac8c \uc131\uc7a5\ud558\uc5ec \ub300\uc138\uac00 \ub418\uc5c8\uae30\uc5d0 \ucee8\ud14c\uc774\ub108\ud558\uba74 \ub3c4\ucee4\ub77c\ub294 \uc774\ubbf8\uc9c0\uac00 \uc790\ub3d9\uc73c\ub85c \ub5a0\uc624\ub974\uac8c \ub418\uc5c8\uc2b5\ub2c8\ub2e4. \uc774\ub807\uac8c \ucee8\ud14c\uc774\ub108\uc640 \ub3c4\ucee4 \uc0dd\ud0dc\uacc4\uac00 \ub300\uc138\uac00 \ub418\uae30\uae4c\uc9c0\ub294 \ub2e4\uc591\ud55c \uc774\uc720\uac00 \uc788\uc9c0\ub9cc, \uae30\uc220\uc801\uc73c\ub85c \uc790\uc138\ud55c \uc774\uc57c\uae30\ub294 ",(0,o.kt)("em",{parentName:"p"},"\ubaa8\ub450\uc758 MLOps"),"\uc758 \ubc94\uc704\ub97c \ub118\uc5b4\uc11c\uae30 \ub54c\ubb38\uc5d0 \ub2e4\ub8e8\uc9c0\ub294 \uc54a\uaca0\uc2b5\ub2c8\ub2e4."),(0,o.kt)("p",null,"\ucee8\ud14c\uc774\ub108 \ud639\uc740 \ub3c4\ucee4\ub97c \ucc98\uc74c \ub4e4\uc5b4\ubcf4\uc2dc\ub294 \ubd84\ub4e4\uc5d0\uac8c\ub294 ",(0,o.kt)("em",{parentName:"p"},"\ubaa8\ub450\uc758 MLOps"),"\uc758 \ub0b4\uc6a9\uc774 \ub2e4\uc18c \uc5b4\ub835\uac8c \ub290\uaef4\uc9c8 \uc218 \uc788\uc73c\ubbc0\ub85c, ",(0,o.kt)("a",{parentName:"p",href:"https://opentutorials.org/course/4781"},"\uc0dd\ud65c\ucf54\ub529"),", ",(0,o.kt)("a",{parentName:"p",href:"https://subicura.com/2017/01/19/docker-guide-for-beginners-1.html"},"subicura \ub2d8\uc758 \uac1c\uc778 \ube14\ub85c\uadf8 \uae00")," \ub4f1\uc758 \uc790\ub8cc\ub97c \uba3c\uc800 \uc0b4\ud3b4\ubcf4\ub294 \uac83\uc744 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pd.read_csv(train_target_path)\n\n clf = SVC(kernel=kernel)\n clf.fit(train_data, train_target)\n\n with open(model_path, mode="wb") as file_writer:\n dill.dump(clf, file_writer)\n')),(0,r.kt)("p",null,"InputPath\ub098 OutputPath\ub294 string\uc744 \uc785\ub825\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774 string\uc740 \uc785\ub825 \ub610\ub294 \ucd9c\ub825\ud558\ub824\uace0 \ud558\ub294 \ud30c\uc77c\uc758 \ud3ec\ub9f7\uc785\ub2c8\ub2e4.",(0,r.kt)("br",{parentName:"p"}),"\n","\uadf8\ub807\ub2e4\uace0 \uaf2d \uc774 \ud3ec\ub9f7\uc73c\ub85c \ud30c\uc77c \ud615\ud0dc\ub85c \uc800\uc7a5\uc774 \uac15\uc81c\ub418\ub294 \uac83\uc740 \uc544\ub2d9\ub2c8\ub2e4.",(0,r.kt)("br",{parentName:"p"}),"\n","\ub2e4\ub9cc \ud30c\uc774\ud504\ub77c\uc778\uc744 \ucef4\ud30c\uc77c\ud560 \ub54c \ucd5c\uc18c\ud55c\uc758 \ud0c0\uc785 \uccb4\ud06c\ub97c \uc704\ud55c \ub3c4\uc6b0\ubbf8 \uc5ed\ud560\uc744 \ud569\ub2c8\ub2e4.",(0,r.kt)("br",{parentName:"p"}),"\n","\ub9cc\uc57d \ud30c\uc77c \ud3ec\ub9f7\uc774 \uace0\uc815\ub418\uc9c0 \uc54a\ub294\ub2e4\uba74 \uc785\ub825\ud558\uc9c0 \uc54a\uc73c\uba74 \ub429\ub2c8\ub2e4 (\ud0c0\uc785 \ud78c\ud2b8 \uc5d0\uc11c ",(0,r.kt)("inlineCode",{parentName:"p"},"Any")," \uc640 \uac19\uc740 \uc5ed\ud560\uc744 \ud569\ub2c8\ub2e4)."),(0,r.kt)("h3",{id:"convert-to-kubeflow-format"},"Convert to Kubeflow Format"),(0,r.kt)("p",null,"\uc791\uc131\ud55c \ucef4\ud3ec\ub10c\ud2b8\ub97c kubeflow\uc5d0\uc11c \uc0ac\uc6a9\ud560 \uc218 \uc788\ub294 \ud3ec\ub9f7\uc73c\ub85c \ubcc0\ud658\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-python"},'from kfp.components import InputPath, OutputPath, create_component_from_func\n\n\n@create_component_from_func\ndef train_from_csv(\n train_data_path: InputPath("csv"),\n train_target_path: InputPath("csv"),\n model_path: OutputPath("dill"),\n kernel: str,\n):\n import dill\n import pandas as pd\n\n from sklearn.svm import SVC\n\n train_data = pd.read_csv(train_data_path)\n train_target = pd.read_csv(train_target_path)\n\n clf = SVC(kernel=kernel)\n clf.fit(train_data, train_target)\n\n with open(model_path, mode="wb") as file_writer:\n dill.dump(clf, file_writer)\n')),(0,r.kt)("h2",{id:"rule-to-use-inputpathoutputpath"},"Rule to use InputPath/OutputPath"),(0,r.kt)("p",null,"InputPath\ub098 OutputPath argument\ub294 \ud30c\uc774\ud504\ub77c\uc778\uc73c\ub85c \uc791\uc131\ud560 \ub54c \uc9c0\ucf1c\uc57c\ud558\ub294 \uaddc\uce59\uc774 \uc788\uc2b5\ub2c8\ub2e4."),(0,r.kt)("h3",{id:"load-data-component"},"Load Data Component"),(0,r.kt)("p",null,"\uc704\uc5d0\uc11c \uc791\uc131\ud55c \ucef4\ud3ec\ub10c\ud2b8\ub97c \uc2e4\ud589\ud558\uae30 \uc704\ud574\uc11c\ub294 \ub370\uc774\ud130\uac00 \ud544\uc694\ud558\ubbc0\ub85c \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud558\ub294 \ucef4\ud3ec\ub10c\ud2b8\ub97c \uc791\uc131\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-python"},'from functools import partial\n\nfrom kfp.components import InputPath, OutputPath, create_component_from_func\n\n\n@create_component_from_func\ndef load_iris_data(\n data_path: OutputPath("csv"),\n target_path: OutputPath("csv"),\n):\n import pandas as pd\n from sklearn.datasets import load_iris\n\n iris = load_iris()\n\n data = pd.DataFrame(iris["data"], columns=iris["feature_names"])\n target = pd.DataFrame(iris["target"], columns=["target"])\n\n data.to_csv(data_path, index=False)\n target.to_csv(target_path, index=False)\n')),(0,r.kt)("h3",{id:"write-pipeline"},"Write Pipeline"),(0,r.kt)("p",null,"\uc774\uc81c \ud30c\uc774\ud504\ub77c\uc778\uc744 \uc791\uc131\ud574 \ubcf4\ub3c4\ub85d \ud558\uaca0\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-python"},'from kfp.dsl import pipeline\n\n\n@pipeline(name="complex_pipeline")\ndef complex_pipeline(kernel: str):\n iris_data = load_iris_data()\n model = train_from_csv(\n train_data=iris_data.outputs["data"],\n 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pd.read_csv(train_target_path)\n\n clf = SVC(kernel=kernel)\n clf.fit(train_data, train_target)\n\n with open(model_path, mode="wb") as file_writer:\n dill.dump(clf, file_writer)\n')),(0,r.kt)("p",null,"InputPath\ub098 OutputPath\ub294 string\uc744 \uc785\ub825\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774 string\uc740 \uc785\ub825 \ub610\ub294 \ucd9c\ub825\ud558\ub824\uace0 \ud558\ub294 \ud30c\uc77c\uc758 \ud3ec\ub9f7\uc785\ub2c8\ub2e4.",(0,r.kt)("br",{parentName:"p"}),"\n","\uadf8\ub807\ub2e4\uace0 \uaf2d \uc774 \ud3ec\ub9f7\uc73c\ub85c \ud30c\uc77c \ud615\ud0dc\ub85c \uc800\uc7a5\uc774 \uac15\uc81c\ub418\ub294 \uac83\uc740 \uc544\ub2d9\ub2c8\ub2e4.",(0,r.kt)("br",{parentName:"p"}),"\n","\ub2e4\ub9cc \ud30c\uc774\ud504\ub77c\uc778\uc744 \ucef4\ud30c\uc77c\ud560 \ub54c \ucd5c\uc18c\ud55c\uc758 \ud0c0\uc785 \uccb4\ud06c\ub97c \uc704\ud55c \ub3c4\uc6b0\ubbf8 \uc5ed\ud560\uc744 \ud569\ub2c8\ub2e4.",(0,r.kt)("br",{parentName:"p"}),"\n","\ub9cc\uc57d \ud30c\uc77c \ud3ec\ub9f7\uc774 \uace0\uc815\ub418\uc9c0 \uc54a\ub294\ub2e4\uba74 \uc785\ub825\ud558\uc9c0 \uc54a\uc73c\uba74 \ub429\ub2c8\ub2e4 (\ud0c0\uc785 \ud78c\ud2b8 \uc5d0\uc11c ",(0,r.kt)("inlineCode",{parentName:"p"},"Any")," \uc640 \uac19\uc740 \uc5ed\ud560\uc744 \ud569\ub2c8\ub2e4)."),(0,r.kt)("h3",{id:"convert-to-kubeflow-format"},"Convert to Kubeflow Format"),(0,r.kt)("p",null,"\uc791\uc131\ud55c \ucef4\ud3ec\ub10c\ud2b8\ub97c kubeflow\uc5d0\uc11c \uc0ac\uc6a9\ud560 \uc218 \uc788\ub294 \ud3ec\ub9f7\uc73c\ub85c \ubcc0\ud658\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-python"},'from kfp.components import InputPath, OutputPath, create_component_from_func\n\n\n@create_component_from_func\ndef train_from_csv(\n train_data_path: InputPath("csv"),\n train_target_path: InputPath("csv"),\n model_path: OutputPath("dill"),\n kernel: str,\n):\n import dill\n import pandas as pd\n\n from sklearn.svm import SVC\n\n train_data = pd.read_csv(train_data_path)\n train_target = pd.read_csv(train_target_path)\n\n clf = SVC(kernel=kernel)\n clf.fit(train_data, train_target)\n\n with open(model_path, mode="wb") as file_writer:\n dill.dump(clf, file_writer)\n')),(0,r.kt)("h2",{id:"rule-to-use-inputpathoutputpath"},"Rule to use InputPath/OutputPath"),(0,r.kt)("p",null,"InputPath\ub098 OutputPath argument\ub294 \ud30c\uc774\ud504\ub77c\uc778\uc73c\ub85c \uc791\uc131\ud560 \ub54c \uc9c0\ucf1c\uc57c\ud558\ub294 \uaddc\uce59\uc774 \uc788\uc2b5\ub2c8\ub2e4."),(0,r.kt)("h3",{id:"load-data-component"},"Load Data Component"),(0,r.kt)("p",null,"\uc704\uc5d0\uc11c \uc791\uc131\ud55c \ucef4\ud3ec\ub10c\ud2b8\ub97c \uc2e4\ud589\ud558\uae30 \uc704\ud574\uc11c\ub294 \ub370\uc774\ud130\uac00 \ud544\uc694\ud558\ubbc0\ub85c \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud558\ub294 \ucef4\ud3ec\ub10c\ud2b8\ub97c \uc791\uc131\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-python"},'from functools import partial\n\nfrom kfp.components import InputPath, OutputPath, create_component_from_func\n\n\n@create_component_from_func\ndef load_iris_data(\n data_path: OutputPath("csv"),\n target_path: OutputPath("csv"),\n):\n import pandas as pd\n from sklearn.datasets import load_iris\n\n iris = load_iris()\n\n data = pd.DataFrame(iris["data"], columns=iris["feature_names"])\n target = pd.DataFrame(iris["target"], columns=["target"])\n\n data.to_csv(data_path, index=False)\n target.to_csv(target_path, index=False)\n')),(0,r.kt)("h3",{id:"write-pipeline"},"Write Pipeline"),(0,r.kt)("p",null,"\uc774\uc81c \ud30c\uc774\ud504\ub77c\uc778\uc744 \uc791\uc131\ud574 \ubcf4\ub3c4\ub85d \ud558\uaca0\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-python"},'from kfp.dsl import pipeline\n\n\n@pipeline(name="complex_pipeline")\ndef complex_pipeline(kernel: str):\n iris_data = load_iris_data()\n model = train_from_csv(\n train_data=iris_data.outputs["data"],\n 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use rbac.authorization.k8s.io/v1 Role\nW1206 17:01:36.029764 26635 warnings.go:70] rbac.authorization.k8s.io/v1beta1 RoleBinding is deprecated in v1.17+, unavailable in v1.22+; use rbac.authorization.k8s.io/v1 RoleBinding\nNAME: ambassador\nLAST DEPLOYED: Mon Dec 6 17:01:34 2021\nNAMESPACE: seldon-system\nSTATUS: deployed\nREVISION: 1\nNOTES:\n-------------------------------------------------------------------------------\n Congratulations! You've successfully installed Ambassador!\n\n-------------------------------------------------------------------------------\nTo get the IP address of Ambassador, run the following commands:\nNOTE: It may take a few minutes for the LoadBalancer IP to be available.\n You can watch the status of by running 'kubectl get svc -w --namespace seldon-system ambassador'\n\n On GKE/Azure:\n export SERVICE_IP=$(kubectl get svc --namespace seldon-system ambassador -o jsonpath='{.status.loadBalancer.ingress[0].ip}')\n\n On AWS:\n export SERVICE_IP=$(kubectl get svc --namespace seldon-system ambassador -o jsonpath='{.status.loadBalancer.ingress[0].hostname}')\n\n echo http://$SERVICE_IP:\n\nFor help, visit our Slack at http://a8r.io/Slack or view the documentation online at https://www.getambassador.io.\n")),(0,o.kt)("p",null,"seldon-system \uc5d0 4 \uac1c\uc758 pod \uac00 Running \uc774 \ub420 \ub54c\uae4c\uc9c0 \uae30\ub2e4\ub9bd\ub2c8\ub2e4."),(0,o.kt)("pre",null,(0,o.kt)("code",{parentName:"pre",className:"language-bash"},"kubectl get pod -n seldon-system\n")),(0,o.kt)("pre",null,(0,o.kt)("code",{parentName:"pre",className:"language-bash"},"ambassador-7f596c8b57-4s9xh 1/1 Running 0 7m15s\nambassador-7f596c8b57-dt6lr 1/1 Running 0 7m15s\nambassador-7f596c8b57-h5l6f 1/1 Running 0 7m15s\nambassador-agent-77bccdfcd5-d5jxj 1/1 Running 0 7m15s\n")),(0,o.kt)("h3",{id:"seldon-core---helm-install"},"Seldon-Core - Helm Install"),(0,o.kt)("p",null,"seldon-core-operator Chart 1.11.2 \ubc84\uc804\uc744 \uc124\uce58\ud569\ub2c8\ub2e4."),(0,o.kt)("pre",null,(0,o.kt)("code",{parentName:"pre",className:"language-bash"},"helm install seldon-core seldon-core-operator \\\n --repo https://storage.googleapis.com/seldon-charts \\\n --namespace seldon-system \\\n --set usageMetrics.enabled=true \\\n --set ambassador.enabled=true \\\n --version 1.11.2\n")),(0,o.kt)("p",null,"\ub2e4\uc74c\uacfc \uac19\uc740 \uba54\uc2dc\uc9c0\uac00 \ucd9c\ub825\ub418\uc5b4\uc57c \ud569\ub2c8\ub2e4."),(0,o.kt)("pre",null,(0,o.kt)("code",{parentName:"pre",className:"language-bash"},"\uc0dd\ub7b5...\n\nW1206 17:05:38.336391 28181 warnings.go:70] admissionregistration.k8s.io/v1beta1 ValidatingWebhookConfiguration is deprecated in v1.16+, unavailable in v1.22+; use admissionregistration.k8s.io/v1 ValidatingWebhookConfiguration\nNAME: seldon-core\nLAST DEPLOYED: Mon Dec 6 17:05:34 2021\nNAMESPACE: seldon-system\nSTATUS: deployed\nREVISION: 1\nTEST SUITE: None\n")),(0,o.kt)("p",null,"seldon-system namespace \uc5d0 1 \uac1c\uc758 seldon-controller-manager pod \uac00 Running \uc774 \ub420 \ub54c\uae4c\uc9c0 \uae30\ub2e4\ub9bd\ub2c8\ub2e4."),(0,o.kt)("pre",null,(0,o.kt)("code",{parentName:"pre",className:"language-bash"},"kubectl get pod -n seldon-system | grep seldon-controller\n")),(0,o.kt)("pre",null,(0,o.kt)("code",{parentName:"pre",className:"language-bash"},"seldon-controller-manager-8457b8b5c7-r2frm 1/1 Running 0 2m22s\n")),(0,o.kt)("h2",{id:"references"},"References"),(0,o.kt)("ul",null,(0,o.kt)("li",{parentName:"ul"},(0,o.kt)("a",{parentName:"li",href:"https://docs.seldon.io/projects/seldon-core/en/latest/examples/server_examples.html#examples-server-examples--page-root"},"Example Model Servers with Seldon"))))}m.isMDXComponent=!0}}]); \ No newline at end of file diff --git a/assets/js/15cf2139.bdaf37eb.js b/assets/js/6246222d.cc133d21.js similarity index 69% rename from assets/js/15cf2139.bdaf37eb.js rename to assets/js/6246222d.cc133d21.js index 1a9ff7be..e43f8dd1 100644 --- a/assets/js/15cf2139.bdaf37eb.js +++ b/assets/js/6246222d.cc133d21.js @@ -1 +1 @@ -"use strict";(self.webpackChunkv_2=self.webpackChunkv_2||[]).push([[9925],{3905:(e,n,t)=>{t.d(n,{Zo:()=>d,kt:()=>b});var a=t(7294);function o(e,n,t){return n in e?Object.defineProperty(e,n,{value:t,enumerable:!0,configurable:!0,writable:!0}):e[n]=t,e}function r(e,n){var t=Object.keys(e);if(Object.getOwnPropertySymbols){var a=Object.getOwnPropertySymbols(e);n&&(a=a.filter((function(n){return Object.getOwnPropertyDescriptor(e,n).enumerable}))),t.push.apply(t,a)}return t}function s(e){for(var n=1;n=0||(o[t]=e[t]);return o}(e,n);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);for(a=0;a=0||Object.prototype.propertyIsEnumerable.call(e,t)&&(o[t]=e[t])}return o}var i=a.createContext({}),p=function(e){var n=a.useContext(i),t=n;return e&&(t="function"==typeof e?e(n):s(s({},n),e)),t},d=function(e){var n=p(e.components);return a.createElement(i.Provider,{value:n},e.children)},c="mdxType",m={inlineCode:"code",wrapper:function(e){var n=e.children;return a.createElement(a.Fragment,{},n)}},u=a.forwardRef((function(e,n){var t=e.components,o=e.mdxType,r=e.originalType,i=e.parentName,d=l(e,["components","mdxType","originalType","parentName"]),c=p(t),u=o,b=c["".concat(i,".").concat(u)]||c[u]||m[u]||r;return t?a.createElement(b,s(s({ref:n},d),{},{components:t})):a.createElement(b,s({ref:n},d))}));function b(e,n){var t=arguments,o=n&&n.mdxType;if("string"==typeof e||o){var r=t.length,s=new Array(r);s[0]=u;var l={};for(var i in n)hasOwnProperty.call(n,i)&&(l[i]=n[i]);l.originalType=e,l[c]="string"==typeof e?e:o,s[1]=l;for(var p=2;p{t.r(n),t.d(n,{assets:()=>i,contentTitle:()=>s,default:()=>m,frontMatter:()=>r,metadata:()=>l,toc:()=>p});var a=t(7462),o=(t(7294),t(3905));const r={title:"3. Seldon-Core",description:"\uad6c\uc131\uc694\uc18c \uc124\uce58 - Seldon-Core",sidebar_position:3,date:new Date("2021-12-13T00:00:00.000Z"),lastmod:new Date("2021-12-13T00:00:00.000Z"),contributors:["Jaeyeon Kim"]},s=void 0,l={unversionedId:"setup-components/install-components-seldon",id:"setup-components/install-components-seldon",title:"3. Seldon-Core",description:"\uad6c\uc131\uc694\uc18c \uc124\uce58 - Seldon-Core",source:"@site/docs/setup-components/install-components-seldon.md",sourceDirName:"setup-components",slug:"/setup-components/install-components-seldon",permalink:"/docs/setup-components/install-components-seldon",draft:!1,editUrl:"https://github.com/mlops-for-all/mlops-for-all.github.io/tree/main/docs/setup-components/install-components-seldon.md",tags:[],version:"current",lastUpdatedBy:"conqrean",lastUpdatedAt:1705274857,formattedLastUpdatedAt:"2024\ub144 1\uc6d4 14\uc77c",sidebarPosition:3,frontMatter:{title:"3. Seldon-Core",description:"\uad6c\uc131\uc694\uc18c \uc124\uce58 - Seldon-Core",sidebar_position:3,date:"2021-12-13T00:00:00.000Z",lastmod:"2021-12-13T00:00:00.000Z",contributors:["Jaeyeon Kim"]},sidebar:"tutorialSidebar",previous:{title:"2. MLflow Tracking Server",permalink:"/docs/setup-components/install-components-mlflow"},next:{title:"4. Prometheus & Grafana",permalink:"/docs/setup-components/install-components-pg"}},i={},p=[{value:"Seldon-Core",id:"seldon-core",level:2},{value:"Selon-Core \uc124\uce58",id:"selon-core-\uc124\uce58",level:2},{value:"Ambassador - Helm Repository \ucd94\uac00",id:"ambassador---helm-repository-\ucd94\uac00",level:3},{value:"Ambassador - Helm Repository \uc5c5\ub370\uc774\ud2b8",id:"ambassador---helm-repository-\uc5c5\ub370\uc774\ud2b8",level:3},{value:"Ambassador - Helm Install",id:"ambassador---helm-install",level:3},{value:"Seldon-Core - Helm Install",id:"seldon-core---helm-install",level:3},{value:"References",id:"references",level:2}],d={toc:p},c="wrapper";function m(e){let{components:n,...t}=e;return(0,o.kt)(c,(0,a.Z)({},d,t,{components:n,mdxType:"MDXLayout"}),(0,o.kt)("h2",{id:"seldon-core"},"Seldon-Core"),(0,o.kt)("p",null,"Seldon-Core\ub294 \ucfe0\ubc84\ub124\ud2f0\uc2a4 \ud658\uacbd\uc5d0 \uc218\ub9ce\uc740 \uba38\uc2e0\ub7ec\ub2dd \ubaa8\ub378\uc744 \ubc30\ud3ec\ud558\uace0 \uad00\ub9ac\ud560 \uc218 \uc788\ub294 \uc624\ud508\uc18c\uc2a4 \ud504\ub808\uc784\uc6cc\ud06c \uc911 \ud558\ub098\uc785\ub2c8\ub2e4.",(0,o.kt)("br",{parentName:"p"}),"\n","\ub354 \uc790\uc138\ud55c \ub0b4\uc6a9\uc740 Seldon-Core \uc758 \uacf5\uc2dd ",(0,o.kt)("a",{parentName:"p",href:"https://www.seldon.io/tech/products/core/"},"\uc81c\ud488 \uc124\uba85 \ud398\uc774\uc9c0")," \uc640 ",(0,o.kt)("a",{parentName:"p",href:"https://github.com/SeldonIO/seldon-core"},"\uae43\ud5d9")," \uadf8\ub9ac\uace0 API Deployment \ud30c\ud2b8\ub97c \ucc38\uace0\ud574\uc8fc\uc2dc\uae30\ub97c \ubc14\ub78d\ub2c8\ub2e4."),(0,o.kt)("h2",{id:"selon-core-\uc124\uce58"},"Selon-Core \uc124\uce58"),(0,o.kt)("p",null,"Seldon-Core\ub97c \uc0ac\uc6a9\ud558\uae30 \uc704\ud574\uc11c\ub294 \ucfe0\ubc84\ub124\ud2f0\uc2a4\uc758 \uc778\uadf8\ub808\uc2a4(Ingress)\ub97c \ub2f4\ub2f9\ud558\ub294 Ambassador \uc640 Istio \uc640 \uac19\uc740 ",(0,o.kt)("a",{parentName:"p",href:"https://docs.seldon.io/projects/seldon-core/en/latest/workflow/install.html"},"\ubaa8\ub4c8\uc774 \ud544\uc694\ud569\ub2c8\ub2e4"),".",(0,o.kt)("br",{parentName:"p"}),"\n","Seldon-Core \uc5d0\uc11c\ub294 Ambassador \uc640 Istio \ub9cc\uc744 \uacf5\uc2dd\uc801\uc73c\ub85c \uc9c0\uc6d0\ud558\uba70, ",(0,o.kt)("em",{parentName:"p"},"\ubaa8\ub450\uc758 MLOps"),"\uc5d0\uc11c\ub294 Ambassador\ub97c \uc0ac\uc6a9\ud574 Seldon-core\ub97c \uc0ac\uc6a9\ud558\ubbc0\ub85c Ambassador\ub97c \uc124\uce58\ud558\uaca0\uc2b5\ub2c8\ub2e4."),(0,o.kt)("h3",{id:"ambassador---helm-repository-\ucd94\uac00"},"Ambassador - Helm Repository \ucd94\uac00"),(0,o.kt)("pre",null,(0,o.kt)("code",{parentName:"pre",className:"language-bash"},"helm repo add datawire https://www.getambassador.io\n")),(0,o.kt)("p",null,"\ub2e4\uc74c\uacfc \uac19\uc740 \uba54\uc2dc\uc9c0\uac00 \ucd9c\ub825\ub418\uba74 \uc815\uc0c1\uc801\uc73c\ub85c \ucd94\uac00\ub41c \uac83\uc744 \uc758\ubbf8\ud569\ub2c8\ub2e4."),(0,o.kt)("pre",null,(0,o.kt)("code",{parentName:"pre",className:"language-bash"},'"datawire" has been added to your repositories\n')),(0,o.kt)("h3",{id:"ambassador---helm-repository-\uc5c5\ub370\uc774\ud2b8"},"Ambassador - Helm Repository \uc5c5\ub370\uc774\ud2b8"),(0,o.kt)("pre",null,(0,o.kt)("code",{parentName:"pre",className:"language-bash"},"helm repo update\n")),(0,o.kt)("p",null,"\ub2e4\uc74c\uacfc \uac19\uc740 \uba54\uc2dc\uc9c0\uac00 \ucd9c\ub825\ub418\uba74 \uc815\uc0c1\uc801\uc73c\ub85c \uc5c5\ub370\uc774\ud2b8\ub41c \uac83\uc744 \uc758\ubbf8\ud569\ub2c8\ub2e4."),(0,o.kt)("pre",null,(0,o.kt)("code",{parentName:"pre",className:"language-bash"},'Hang tight while we grab the latest from your chart repositories...\n...Successfully got an update from the "datawire" chart repository\nUpdate Complete. \u2388Happy Helming!\u2388\n')),(0,o.kt)("h3",{id:"ambassador---helm-install"},"Ambassador - Helm Install"),(0,o.kt)("p",null,"ambassador Chart 6.9.3 \ubc84\uc804\uc744 \uc124\uce58\ud569\ub2c8\ub2e4."),(0,o.kt)("pre",null,(0,o.kt)("code",{parentName:"pre",className:"language-bash"},"helm install ambassador datawire/ambassador \\\n --namespace seldon-system \\\n --create-namespace \\\n --set image.repository=quay.io/datawire/ambassador \\\n --set enableAES=false \\\n --set crds.keep=false \\\n --version 6.9.3\n")),(0,o.kt)("p",null,"\ub2e4\uc74c\uacfc \uac19\uc740 \uba54\uc2dc\uc9c0\uac00 \ucd9c\ub825\ub418\uc5b4\uc57c \ud569\ub2c8\ub2e4."),(0,o.kt)("pre",null,(0,o.kt)("code",{parentName:"pre",className:"language-bash"},"\uc0dd\ub7b5...\n\nW1206 17:01:36.026326 26635 warnings.go:70] rbac.authorization.k8s.io/v1beta1 Role is deprecated in v1.17+, unavailable in v1.22+; use rbac.authorization.k8s.io/v1 Role\nW1206 17:01:36.029764 26635 warnings.go:70] rbac.authorization.k8s.io/v1beta1 RoleBinding is deprecated in v1.17+, unavailable in v1.22+; use rbac.authorization.k8s.io/v1 RoleBinding\nNAME: ambassador\nLAST DEPLOYED: Mon Dec 6 17:01:34 2021\nNAMESPACE: seldon-system\nSTATUS: deployed\nREVISION: 1\nNOTES:\n-------------------------------------------------------------------------------\n Congratulations! You've successfully installed Ambassador!\n\n-------------------------------------------------------------------------------\nTo get the IP address of Ambassador, run the following commands:\nNOTE: It may take a few minutes for the LoadBalancer IP to be available.\n You can watch the status of by running 'kubectl get svc -w --namespace seldon-system ambassador'\n\n On GKE/Azure:\n export SERVICE_IP=$(kubectl get svc --namespace seldon-system ambassador -o jsonpath='{.status.loadBalancer.ingress[0].ip}')\n\n On AWS:\n export SERVICE_IP=$(kubectl get svc --namespace seldon-system ambassador -o jsonpath='{.status.loadBalancer.ingress[0].hostname}')\n\n echo http://$SERVICE_IP:\n\nFor help, visit our Slack at http://a8r.io/Slack or view the documentation online at https://www.getambassador.io.\n")),(0,o.kt)("p",null,"seldon-system \uc5d0 4 \uac1c\uc758 pod \uac00 Running \uc774 \ub420 \ub54c\uae4c\uc9c0 \uae30\ub2e4\ub9bd\ub2c8\ub2e4."),(0,o.kt)("pre",null,(0,o.kt)("code",{parentName:"pre",className:"language-bash"},"kubectl get pod -n seldon-system\n")),(0,o.kt)("pre",null,(0,o.kt)("code",{parentName:"pre",className:"language-bash"},"ambassador-7f596c8b57-4s9xh 1/1 Running 0 7m15s\nambassador-7f596c8b57-dt6lr 1/1 Running 0 7m15s\nambassador-7f596c8b57-h5l6f 1/1 Running 0 7m15s\nambassador-agent-77bccdfcd5-d5jxj 1/1 Running 0 7m15s\n")),(0,o.kt)("h3",{id:"seldon-core---helm-install"},"Seldon-Core - Helm Install"),(0,o.kt)("p",null,"seldon-core-operator Chart 1.11.2 \ubc84\uc804\uc744 \uc124\uce58\ud569\ub2c8\ub2e4."),(0,o.kt)("pre",null,(0,o.kt)("code",{parentName:"pre",className:"language-bash"},"helm install seldon-core seldon-core-operator \\\n --repo https://storage.googleapis.com/seldon-charts \\\n --namespace seldon-system \\\n --set usageMetrics.enabled=true \\\n --set ambassador.enabled=true \\\n --version 1.11.2\n")),(0,o.kt)("p",null,"\ub2e4\uc74c\uacfc \uac19\uc740 \uba54\uc2dc\uc9c0\uac00 \ucd9c\ub825\ub418\uc5b4\uc57c \ud569\ub2c8\ub2e4."),(0,o.kt)("pre",null,(0,o.kt)("code",{parentName:"pre",className:"language-bash"},"\uc0dd\ub7b5...\n\nW1206 17:05:38.336391 28181 warnings.go:70] admissionregistration.k8s.io/v1beta1 ValidatingWebhookConfiguration is deprecated in v1.16+, unavailable in v1.22+; use admissionregistration.k8s.io/v1 ValidatingWebhookConfiguration\nNAME: seldon-core\nLAST DEPLOYED: Mon Dec 6 17:05:34 2021\nNAMESPACE: seldon-system\nSTATUS: deployed\nREVISION: 1\nTEST SUITE: None\n")),(0,o.kt)("p",null,"seldon-system namespace \uc5d0 1 \uac1c\uc758 seldon-controller-manager pod \uac00 Running \uc774 \ub420 \ub54c\uae4c\uc9c0 \uae30\ub2e4\ub9bd\ub2c8\ub2e4."),(0,o.kt)("pre",null,(0,o.kt)("code",{parentName:"pre",className:"language-bash"},"kubectl get pod -n seldon-system | grep seldon-controller\n")),(0,o.kt)("pre",null,(0,o.kt)("code",{parentName:"pre",className:"language-bash"},"seldon-controller-manager-8457b8b5c7-r2frm 1/1 Running 0 2m22s\n")),(0,o.kt)("h2",{id:"references"},"References"),(0,o.kt)("ul",null,(0,o.kt)("li",{parentName:"ul"},(0,o.kt)("a",{parentName:"li",href:"https://docs.seldon.io/projects/seldon-core/en/latest/examples/server_examples.html#examples-server-examples--page-root"},"Example Model Servers with Seldon"))))}m.isMDXComponent=!0}}]); \ No newline at end of file +"use strict";(self.webpackChunkv_2=self.webpackChunkv_2||[]).push([[728],{3905:(e,n,t)=>{t.d(n,{Zo:()=>d,kt:()=>b});var a=t(7294);function o(e,n,t){return n in e?Object.defineProperty(e,n,{value:t,enumerable:!0,configurable:!0,writable:!0}):e[n]=t,e}function s(e,n){var t=Object.keys(e);if(Object.getOwnPropertySymbols){var a=Object.getOwnPropertySymbols(e);n&&(a=a.filter((function(n){return Object.getOwnPropertyDescriptor(e,n).enumerable}))),t.push.apply(t,a)}return t}function r(e){for(var n=1;n=0||(o[t]=e[t]);return o}(e,n);if(Object.getOwnPropertySymbols){var s=Object.getOwnPropertySymbols(e);for(a=0;a=0||Object.prototype.propertyIsEnumerable.call(e,t)&&(o[t]=e[t])}return o}var i=a.createContext({}),p=function(e){var n=a.useContext(i),t=n;return e&&(t="function"==typeof e?e(n):r(r({},n),e)),t},d=function(e){var n=p(e.components);return a.createElement(i.Provider,{value:n},e.children)},c="mdxType",m={inlineCode:"code",wrapper:function(e){var n=e.children;return a.createElement(a.Fragment,{},n)}},u=a.forwardRef((function(e,n){var t=e.components,o=e.mdxType,s=e.originalType,i=e.parentName,d=l(e,["components","mdxType","originalType","parentName"]),c=p(t),u=o,b=c["".concat(i,".").concat(u)]||c[u]||m[u]||s;return t?a.createElement(b,r(r({ref:n},d),{},{components:t})):a.createElement(b,r({ref:n},d))}));function b(e,n){var t=arguments,o=n&&n.mdxType;if("string"==typeof e||o){var s=t.length,r=new Array(s);r[0]=u;var l={};for(var i in n)hasOwnProperty.call(n,i)&&(l[i]=n[i]);l.originalType=e,l[c]="string"==typeof e?e:o,r[1]=l;for(var p=2;p{t.r(n),t.d(n,{assets:()=>i,contentTitle:()=>r,default:()=>m,frontMatter:()=>s,metadata:()=>l,toc:()=>p});var a=t(7462),o=(t(7294),t(3905));const s={title:"3. Seldon-Core",description:"\uad6c\uc131\uc694\uc18c \uc124\uce58 - Seldon-Core",sidebar_position:3,date:new Date("2021-12-13T00:00:00.000Z"),lastmod:new Date("2021-12-13T00:00:00.000Z"),contributors:["Jaeyeon Kim"]},r=void 0,l={unversionedId:"setup-components/install-components-seldon",id:"version-1.0/setup-components/install-components-seldon",title:"3. Seldon-Core",description:"\uad6c\uc131\uc694\uc18c \uc124\uce58 - Seldon-Core",source:"@site/versioned_docs/version-1.0/setup-components/install-components-seldon.md",sourceDirName:"setup-components",slug:"/setup-components/install-components-seldon",permalink:"/docs/1.0/setup-components/install-components-seldon",draft:!1,editUrl:"https://github.com/mlops-for-all/mlops-for-all.github.io/tree/main/versioned_docs/version-1.0/setup-components/install-components-seldon.md",tags:[],version:"1.0",lastUpdatedBy:"Aiden-Jeon",lastUpdatedAt:1706783082,formattedLastUpdatedAt:"2024\ub144 2\uc6d4 1\uc77c",sidebarPosition:3,frontMatter:{title:"3. Seldon-Core",description:"\uad6c\uc131\uc694\uc18c \uc124\uce58 - Seldon-Core",sidebar_position:3,date:"2021-12-13T00:00:00.000Z",lastmod:"2021-12-13T00:00:00.000Z",contributors:["Jaeyeon Kim"]},sidebar:"tutorialSidebar",previous:{title:"2. MLflow Tracking Server",permalink:"/docs/1.0/setup-components/install-components-mlflow"},next:{title:"4. Prometheus & Grafana",permalink:"/docs/1.0/setup-components/install-components-pg"}},i={},p=[{value:"Seldon-Core",id:"seldon-core",level:2},{value:"Selon-Core \uc124\uce58",id:"selon-core-\uc124\uce58",level:2},{value:"Ambassador - Helm Repository \ucd94\uac00",id:"ambassador---helm-repository-\ucd94\uac00",level:3},{value:"Ambassador - Helm Repository \uc5c5\ub370\uc774\ud2b8",id:"ambassador---helm-repository-\uc5c5\ub370\uc774\ud2b8",level:3},{value:"Ambassador - Helm Install",id:"ambassador---helm-install",level:3},{value:"Seldon-Core - Helm Install",id:"seldon-core---helm-install",level:3},{value:"References",id:"references",level:2}],d={toc:p},c="wrapper";function m(e){let{components:n,...t}=e;return(0,o.kt)(c,(0,a.Z)({},d,t,{components:n,mdxType:"MDXLayout"}),(0,o.kt)("h2",{id:"seldon-core"},"Seldon-Core"),(0,o.kt)("p",null,"Seldon-Core\ub294 \ucfe0\ubc84\ub124\ud2f0\uc2a4 \ud658\uacbd\uc5d0 \uc218\ub9ce\uc740 \uba38\uc2e0\ub7ec\ub2dd \ubaa8\ub378\uc744 \ubc30\ud3ec\ud558\uace0 \uad00\ub9ac\ud560 \uc218 \uc788\ub294 \uc624\ud508\uc18c\uc2a4 \ud504\ub808\uc784\uc6cc\ud06c \uc911 \ud558\ub098\uc785\ub2c8\ub2e4.",(0,o.kt)("br",{parentName:"p"}),"\n","\ub354 \uc790\uc138\ud55c \ub0b4\uc6a9\uc740 Seldon-Core \uc758 \uacf5\uc2dd ",(0,o.kt)("a",{parentName:"p",href:"https://www.seldon.io/tech/products/core/"},"\uc81c\ud488 \uc124\uba85 \ud398\uc774\uc9c0")," \uc640 ",(0,o.kt)("a",{parentName:"p",href:"https://github.com/SeldonIO/seldon-core"},"\uae43\ud5d9")," \uadf8\ub9ac\uace0 API Deployment \ud30c\ud2b8\ub97c \ucc38\uace0\ud574\uc8fc\uc2dc\uae30\ub97c \ubc14\ub78d\ub2c8\ub2e4."),(0,o.kt)("h2",{id:"selon-core-\uc124\uce58"},"Selon-Core \uc124\uce58"),(0,o.kt)("p",null,"Seldon-Core\ub97c \uc0ac\uc6a9\ud558\uae30 \uc704\ud574\uc11c\ub294 \ucfe0\ubc84\ub124\ud2f0\uc2a4\uc758 \uc778\uadf8\ub808\uc2a4(Ingress)\ub97c \ub2f4\ub2f9\ud558\ub294 Ambassador \uc640 Istio \uc640 \uac19\uc740 ",(0,o.kt)("a",{parentName:"p",href:"https://docs.seldon.io/projects/seldon-core/en/latest/workflow/install.html"},"\ubaa8\ub4c8\uc774 \ud544\uc694\ud569\ub2c8\ub2e4"),".",(0,o.kt)("br",{parentName:"p"}),"\n","Seldon-Core \uc5d0\uc11c\ub294 Ambassador \uc640 Istio \ub9cc\uc744 \uacf5\uc2dd\uc801\uc73c\ub85c \uc9c0\uc6d0\ud558\uba70, ",(0,o.kt)("em",{parentName:"p"},"\ubaa8\ub450\uc758 MLOps"),"\uc5d0\uc11c\ub294 Ambassador\ub97c \uc0ac\uc6a9\ud574 Seldon-core\ub97c \uc0ac\uc6a9\ud558\ubbc0\ub85c Ambassador\ub97c \uc124\uce58\ud558\uaca0\uc2b5\ub2c8\ub2e4."),(0,o.kt)("h3",{id:"ambassador---helm-repository-\ucd94\uac00"},"Ambassador - Helm Repository \ucd94\uac00"),(0,o.kt)("pre",null,(0,o.kt)("code",{parentName:"pre",className:"language-bash"},"helm repo add datawire https://www.getambassador.io\n")),(0,o.kt)("p",null,"\ub2e4\uc74c\uacfc \uac19\uc740 \uba54\uc2dc\uc9c0\uac00 \ucd9c\ub825\ub418\uba74 \uc815\uc0c1\uc801\uc73c\ub85c \ucd94\uac00\ub41c \uac83\uc744 \uc758\ubbf8\ud569\ub2c8\ub2e4."),(0,o.kt)("pre",null,(0,o.kt)("code",{parentName:"pre",className:"language-bash"},'"datawire" has been added to your repositories\n')),(0,o.kt)("h3",{id:"ambassador---helm-repository-\uc5c5\ub370\uc774\ud2b8"},"Ambassador - Helm Repository \uc5c5\ub370\uc774\ud2b8"),(0,o.kt)("pre",null,(0,o.kt)("code",{parentName:"pre",className:"language-bash"},"helm repo update\n")),(0,o.kt)("p",null,"\ub2e4\uc74c\uacfc \uac19\uc740 \uba54\uc2dc\uc9c0\uac00 \ucd9c\ub825\ub418\uba74 \uc815\uc0c1\uc801\uc73c\ub85c \uc5c5\ub370\uc774\ud2b8\ub41c \uac83\uc744 \uc758\ubbf8\ud569\ub2c8\ub2e4."),(0,o.kt)("pre",null,(0,o.kt)("code",{parentName:"pre",className:"language-bash"},'Hang tight while we grab the latest from your chart repositories...\n...Successfully got an update from the "datawire" chart repository\nUpdate Complete. \u2388Happy Helming!\u2388\n')),(0,o.kt)("h3",{id:"ambassador---helm-install"},"Ambassador - Helm Install"),(0,o.kt)("p",null,"ambassador Chart 6.9.3 \ubc84\uc804\uc744 \uc124\uce58\ud569\ub2c8\ub2e4."),(0,o.kt)("pre",null,(0,o.kt)("code",{parentName:"pre",className:"language-bash"},"helm install ambassador datawire/ambassador \\\n --namespace seldon-system \\\n --create-namespace \\\n --set image.repository=quay.io/datawire/ambassador \\\n --set enableAES=false \\\n --set crds.keep=false \\\n --version 6.9.3\n")),(0,o.kt)("p",null,"\ub2e4\uc74c\uacfc \uac19\uc740 \uba54\uc2dc\uc9c0\uac00 \ucd9c\ub825\ub418\uc5b4\uc57c \ud569\ub2c8\ub2e4."),(0,o.kt)("pre",null,(0,o.kt)("code",{parentName:"pre",className:"language-bash"},"\uc0dd\ub7b5...\n\nW1206 17:01:36.026326 26635 warnings.go:70] rbac.authorization.k8s.io/v1beta1 Role is deprecated in v1.17+, unavailable in v1.22+; use rbac.authorization.k8s.io/v1 Role\nW1206 17:01:36.029764 26635 warnings.go:70] rbac.authorization.k8s.io/v1beta1 RoleBinding is deprecated in v1.17+, unavailable in v1.22+; use rbac.authorization.k8s.io/v1 RoleBinding\nNAME: ambassador\nLAST DEPLOYED: Mon Dec 6 17:01:34 2021\nNAMESPACE: seldon-system\nSTATUS: deployed\nREVISION: 1\nNOTES:\n-------------------------------------------------------------------------------\n Congratulations! You've successfully installed Ambassador!\n\n-------------------------------------------------------------------------------\nTo get the IP address of Ambassador, run the following commands:\nNOTE: It may take a few minutes for the LoadBalancer IP to be available.\n You can watch the status of by running 'kubectl get svc -w --namespace seldon-system ambassador'\n\n On GKE/Azure:\n export SERVICE_IP=$(kubectl get svc --namespace seldon-system ambassador -o jsonpath='{.status.loadBalancer.ingress[0].ip}')\n\n On AWS:\n export SERVICE_IP=$(kubectl get svc --namespace seldon-system ambassador -o jsonpath='{.status.loadBalancer.ingress[0].hostname}')\n\n echo http://$SERVICE_IP:\n\nFor help, visit our Slack at http://a8r.io/Slack or view the documentation online at https://www.getambassador.io.\n")),(0,o.kt)("p",null,"seldon-system \uc5d0 4 \uac1c\uc758 pod \uac00 Running \uc774 \ub420 \ub54c\uae4c\uc9c0 \uae30\ub2e4\ub9bd\ub2c8\ub2e4."),(0,o.kt)("pre",null,(0,o.kt)("code",{parentName:"pre",className:"language-bash"},"kubectl get pod -n seldon-system\n")),(0,o.kt)("pre",null,(0,o.kt)("code",{parentName:"pre",className:"language-bash"},"ambassador-7f596c8b57-4s9xh 1/1 Running 0 7m15s\nambassador-7f596c8b57-dt6lr 1/1 Running 0 7m15s\nambassador-7f596c8b57-h5l6f 1/1 Running 0 7m15s\nambassador-agent-77bccdfcd5-d5jxj 1/1 Running 0 7m15s\n")),(0,o.kt)("h3",{id:"seldon-core---helm-install"},"Seldon-Core - Helm Install"),(0,o.kt)("p",null,"seldon-core-operator Chart 1.11.2 \ubc84\uc804\uc744 \uc124\uce58\ud569\ub2c8\ub2e4."),(0,o.kt)("pre",null,(0,o.kt)("code",{parentName:"pre",className:"language-bash"},"helm install seldon-core seldon-core-operator \\\n --repo https://storage.googleapis.com/seldon-charts \\\n --namespace seldon-system \\\n --set usageMetrics.enabled=true \\\n --set ambassador.enabled=true \\\n --version 1.11.2\n")),(0,o.kt)("p",null,"\ub2e4\uc74c\uacfc \uac19\uc740 \uba54\uc2dc\uc9c0\uac00 \ucd9c\ub825\ub418\uc5b4\uc57c \ud569\ub2c8\ub2e4."),(0,o.kt)("pre",null,(0,o.kt)("code",{parentName:"pre",className:"language-bash"},"\uc0dd\ub7b5...\n\nW1206 17:05:38.336391 28181 warnings.go:70] admissionregistration.k8s.io/v1beta1 ValidatingWebhookConfiguration is deprecated in v1.16+, unavailable in v1.22+; use admissionregistration.k8s.io/v1 ValidatingWebhookConfiguration\nNAME: seldon-core\nLAST DEPLOYED: Mon Dec 6 17:05:34 2021\nNAMESPACE: seldon-system\nSTATUS: deployed\nREVISION: 1\nTEST SUITE: None\n")),(0,o.kt)("p",null,"seldon-system namespace \uc5d0 1 \uac1c\uc758 seldon-controller-manager pod \uac00 Running \uc774 \ub420 \ub54c\uae4c\uc9c0 \uae30\ub2e4\ub9bd\ub2c8\ub2e4."),(0,o.kt)("pre",null,(0,o.kt)("code",{parentName:"pre",className:"language-bash"},"kubectl get pod -n seldon-system | grep seldon-controller\n")),(0,o.kt)("pre",null,(0,o.kt)("code",{parentName:"pre",className:"language-bash"},"seldon-controller-manager-8457b8b5c7-r2frm 1/1 Running 0 2m22s\n")),(0,o.kt)("h2",{id:"references"},"References"),(0,o.kt)("ul",null,(0,o.kt)("li",{parentName:"ul"},(0,o.kt)("a",{parentName:"li",href:"https://docs.seldon.io/projects/seldon-core/en/latest/examples/server_examples.html#examples-server-examples--page-root"},"Example Model Servers with Seldon"))))}m.isMDXComponent=!0}}]); \ No newline at end of file diff --git a/assets/js/68f81397.b43c4224.js b/assets/js/68f81397.1bd7fb55.js similarity index 60% rename from assets/js/68f81397.b43c4224.js rename to assets/js/68f81397.1bd7fb55.js index 520a511d..19800c8b 100644 --- a/assets/js/68f81397.b43c4224.js +++ b/assets/js/68f81397.1bd7fb55.js @@ -1 +1 @@ -"use strict";(self.webpackChunkv_2=self.webpackChunkv_2||[]).push([[2603],{3905:(e,t,r)=>{r.d(t,{Zo:()=>u,kt:()=>f});var n=r(7294);function o(e,t,r){return t in e?Object.defineProperty(e,t,{value:r,enumerable:!0,configurable:!0,writable:!0}):e[t]=r,e}function a(e,t){var r=Object.keys(e);if(Object.getOwnPropertySymbols){var n=Object.getOwnPropertySymbols(e);t&&(n=n.filter((function(t){return Object.getOwnPropertyDescriptor(e,t).enumerable}))),r.push.apply(r,n)}return r}function i(e){for(var t=1;t=0||(o[r]=e[r]);return o}(e,t);if(Object.getOwnPropertySymbols){var a=Object.getOwnPropertySymbols(e);for(n=0;n=0||Object.prototype.propertyIsEnumerable.call(e,r)&&(o[r]=e[r])}return o}var l=n.createContext({}),p=function(e){var t=n.useContext(l),r=t;return e&&(r="function"==typeof e?e(t):i(i({},t),e)),r},u=function(e){var t=p(e.components);return n.createElement(l.Provider,{value:t},e.children)},c="mdxType",d={inlineCode:"code",wrapper:function(e){var t=e.children;return n.createElement(n.Fragment,{},t)}},m=n.forwardRef((function(e,t){var r=e.components,o=e.mdxType,a=e.originalType,l=e.parentName,u=s(e,["components","mdxType","originalType","parentName"]),c=p(r),m=o,f=c["".concat(l,".").concat(m)]||c[m]||d[m]||a;return r?n.createElement(f,i(i({ref:t},u),{},{components:r})):n.createElement(f,i({ref:t},u))}));function f(e,t){var r=arguments,o=t&&t.mdxType;if("string"==typeof e||o){var a=r.length,i=new Array(a);i[0]=m;var s={};for(var l in t)hasOwnProperty.call(t,l)&&(s[l]=t[l]);s.originalType=e,s[c]="string"==typeof e?e:o,i[1]=s;for(var p=2;p{r.r(t),r.d(t,{assets:()=>l,contentTitle:()=>i,default:()=>d,frontMatter:()=>a,metadata:()=>s,toc:()=>p});var n=r(7462),o=(r(7294),r(3905));const a={title:"5. 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image: ghcr.io/mlops-for-all/mlflowserver\n volumeMounts:\n - mountPath: /mnt/models\n name: model-provision-location\n readOnly: true\n securityContext:\n privileged: true\n runAsUser: 0\n runAsGroup: 0\n\n graph:\n name: model\n type: MODEL\n parameters:\n - name: model_uri\n type: STRING\n value: "/mnt/models"\n - name: xtype\n type: STRING\n value: "dataframe"\n children: []\nEOF\n')),(0,a.kt)("p",null,"seldon pod\uc744 \uc0dd\uc131\ud569\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-bash"},"kubectl apply -f seldon-mlflow.yaml\n\n")),(0,a.kt)("p",null,"\uc815\uc0c1\uc801\uc73c\ub85c \uc218\ud589\ub418\uba74 \ub2e4\uc74c\uacfc \uac19\uc774 \ucd9c\ub825\ub429\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-bash"},"seldondeployment.machinelearning.seldon.io/seldon-example created\n")),(0,a.kt)("p",null,"\uc774\uc81c pod\uc774 \uc815\uc0c1\uc801\uc73c\ub85c \ub730 \ub54c\uae4c\uc9c0 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"names": [\n "sepal length (cm)",\n "sepal width (cm)",\n "petal length (cm)",\n "petal width (cm)"\n ]\n }\n}\'\n')),(0,a.kt)("p",null,"\uc815\uc0c1\uc801\uc73c\ub85c \uc2e4\ud589\ub420 \uacbd\uc6b0 \ub2e4\uc74c\uacfc \uac19\uc740 \uacb0\uacfc\ub97c \ubc1b\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-bash"},'{"data":{"names":[],"ndarray":["Virginica"]},"meta":{"requestPath":{"model":"ghcr.io/mlops-for-all/mlflowserver:e141f57"}}}\n')))}d.isMDXComponent=!0},8764:(e,n,t)=>{t.d(n,{Z:()=>l});const l=t.p+"assets/images/seldon-mlflow-0-1d29992e36aa6ee88621e221794159d1.png"}}]); \ No newline at end of file diff --git a/assets/js/d3303310.07acda0b.js b/assets/js/6d8a40f9.31db8daf.js similarity index 89% rename from assets/js/d3303310.07acda0b.js rename to assets/js/6d8a40f9.31db8daf.js index 9f5b52bf..edde35e7 100644 --- a/assets/js/d3303310.07acda0b.js +++ b/assets/js/6d8a40f9.31db8daf.js @@ -1 +1 @@ -"use 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Nvidia-Device-Plugin",id:"4-nvidia-device-plugin",level:2}],u={toc:p},d="wrapper";function c(e){let{components:n,...t}=e;return(0,r.kt)(d,(0,a.Z)({},u,t,{components:n,mdxType:"MDXLayout"}),(0,r.kt)("p",null,"\ucfe0\ubc84\ub124\ud2f0\uc2a4 \ubc0f Kubeflow \ub4f1\uc5d0\uc11c GP \ub97c \uc0ac\uc6a9\ud558\uae30 \uc704\ud574\uc11c\ub294 \ub2e4\uc74c \uc791\uc5c5\uc774 \ud544\uc694\ud569\ub2c8\ub2e4."),(0,r.kt)("h2",{id:"1-install-nvidia-driver"},"1. Install NVIDIA Driver"),(0,r.kt)("p",null,(0,r.kt)("inlineCode",{parentName:"p"},"nvidia-smi")," \uc218\ud589 \uc2dc \ub2e4\uc74c\uacfc \uac19\uc740 \ud654\uba74\uc774 \ucd9c\ub825\ub41c\ub2e4\uba74 \uc774 \ub2e8\uacc4\ub294 \uc0dd\ub7b5\ud574 \uc8fc\uc2dc\uae30 \ubc14\ub78d\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"mlops@ubuntu:~$ nvidia-smi \n+-----------------------------------------------------------------------------+\n| NVIDIA-SMI 470.86 Driver Version: 470.86 CUDA Version: 11.4 |\n|-------------------------------+----------------------+----------------------+\n| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n| | | MIG M. |\n|===============================+======================+======================|\n| 0 NVIDIA GeForce ... Off | 00000000:01:00.0 Off | N/A |\n| 25% 32C P8 4W / 120W | 211MiB / 6078MiB | 0% Default |\n| | | N/A |\n+-------------------------------+----------------------+----------------------+\n| 1 NVIDIA GeForce ... Off | 00000000:02:00.0 Off | N/A |\n| 0% 34C P8 7W / 175W | 5MiB / 7982MiB | 0% Default |\n| | | N/A |\n+-------------------------------+----------------------+----------------------+\n \n+-----------------------------------------------------------------------------+\n| Processes: |\n| GPU GI CI PID Type Process name GPU Memory |\n| ID ID Usage |\n|=============================================================================|\n| 0 N/A N/A 1644 G /usr/lib/xorg/Xorg 198MiB |\n| 0 N/A N/A 1893 G /usr/bin/gnome-shell 10MiB |\n| 1 N/A N/A 1644 G /usr/lib/xorg/Xorg 4MiB |\n+-----------------------------------------------------------------------------+\n")),(0,r.kt)("p",null,(0,r.kt)("inlineCode",{parentName:"p"},"nvidia-smi"),"\uc758 \ucd9c\ub825 \uacb0\uacfc\uac00 \uc704\uc640 \uac19\uc9c0 \uc54a\ub2e4\uba74 \uc7a5\ucc29\ub41c GPU\uc5d0 \ub9de\ub294 nvidia driver\ub97c \uc124\uce58\ud574 \uc8fc\uc2dc\uae30 \ubc14\ub78d\ub2c8\ub2e4."),(0,r.kt)("p",null,"\ub9cc\uc57d nvidia driver\uc758 \uc124\uce58\uc5d0 \uc775\uc219\ud558\uc9c0 \uc54a\ub2e4\uba74 \uc544\ub798 \uba85\ub839\uc5b4\ub97c \ud1b5\ud574 \uc124\uce58\ud558\uc2dc\uae30 \ubc14\ub78d\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"sudo add-apt-repository ppa:graphics-drivers/ppa\nsudo apt update && sudo apt install -y ubuntu-drivers-common\nsudo ubuntu-drivers autoinstall\nsudo reboot\n")),(0,r.kt)("h2",{id:"2-nvidia-docker-\uc124\uce58"},"2. NVIDIA-Docker \uc124\uce58"),(0,r.kt)("p",null,"NVIDIA-Docker\ub97c \uc124\uce58\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | \\\n sudo apt-key add -\ndistribution=$(. /etc/os-release;echo $ID$VERSION_ID)\ncurl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list\nsudo apt-get update\nsudo apt-get install -y nvidia-docker2 &&\nsudo systemctl restart docker\n")),(0,r.kt)("p",null,"\uc815\uc0c1\uc801\uc73c\ub85c \uc124\uce58\ub418\uc5c8\ub294\uc9c0 \ud655\uc778\ud558\uae30 \uc704\ud574, GPU\ub97c \uc0ac\uc6a9\ud558\ub294 \ub3c4\ucee4 \ucee8\ud14c\uc774\ub108\ub97c \uc2e4\ud589\ud574\ubd05\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"sudo docker run --rm --gpus all nvidia/cuda:11.0-base nvidia-smi\n")),(0,r.kt)("p",null,"\ub2e4\uc74c\uacfc \uac19\uc740 \uba54\uc2dc\uc9c0\uac00 \ubcf4\uc774\uba74 \uc815\uc0c1\uc801\uc73c\ub85c \uc124\uce58\ub41c \uac83\uc744 \uc758\ubbf8\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"mlops@ubuntu:~$ sudo docker run --rm --gpus all nvidia/cuda:11.0-base nvidia-smi\n+-----------------------------------------------------------------------------+\n| NVIDIA-SMI 470.86 Driver Version: 470.86 CUDA Version: 11.4 |\n|-------------------------------+----------------------+----------------------+\n| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n| | | MIG M. |\n|===============================+======================+======================|\n| 0 NVIDIA GeForce ... Off | 00000000:01:00.0 Off | N/A |\n| 25% 32C P8 4W / 120W | 211MiB / 6078MiB | 0% Default |\n| | | N/A |\n+-------------------------------+----------------------+----------------------+\n| 1 NVIDIA GeForce ... Off | 00000000:02:00.0 Off | N/A |\n| 0% 34C P8 6W / 175W | 5MiB / 7982MiB | 0% Default |\n| | | N/A |\n+-------------------------------+----------------------+----------------------+\n \n+-----------------------------------------------------------------------------+\n| Processes: |\n| GPU GI CI PID Type Process name GPU Memory |\n| ID ID Usage |\n|=============================================================================|\n+-----------------------------------------------------------------------------+\n")),(0,r.kt)("h2",{id:"3-nvidia-docker\ub97c-default-container-runtime\uc73c\ub85c-\uc124\uc815"},"3. NVIDIA-Docker\ub97c Default Container Runtime\uc73c\ub85c \uc124\uc815"),(0,r.kt)("p",null,"\ucfe0\ubc84\ub124\ud2f0\uc2a4\ub294 \uae30\ubcf8\uc801\uc73c\ub85c Docker-CE\ub97c Default Container Runtime\uc73c\ub85c \uc0ac\uc6a9\ud569\ub2c8\ub2e4.\n\ub530\ub77c\uc11c, Docker Container \ub0b4\uc5d0\uc11c NVIDIA GPU\ub97c \uc0ac\uc6a9\ud558\uae30 \uc704\ud574\uc11c\ub294 NVIDIA-Docker \ub97c Container Runtime \uc73c\ub85c \uc0ac\uc6a9\ud558\uc5ec pod\ub97c \uc0dd\uc131\ud560 \uc218 \uc788\ub3c4\ub85d Default Runtime\uc744 \uc218\uc815\ud574 \uc8fc\uc5b4\uc57c \ud569\ub2c8\ub2e4."),(0,r.kt)("ol",null,(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("p",{parentName:"li"},(0,r.kt)("inlineCode",{parentName:"p"},"/etc/docker/daemon.json")," \ud30c\uc77c\uc744 \uc5f4\uc5b4 \ub2e4\uc74c\uacfc \uac19\uc774 \uc218\uc815\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},'sudo vi /etc/docker/daemon.json\n\n{\n "default-runtime": "nvidia",\n "runtimes": {\n "nvidia": {\n "path": "nvidia-container-runtime",\n "runtimeArgs": []\n }\n }\n}\n'))),(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("p",{parentName:"li"},"\ud30c\uc77c\uc774 \ubcc0\uacbd\ub41c \uac83\uc744 \ud655\uc778\ud55c \ud6c4, Docker\ub97c \uc7ac\uc2dc\uc791\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"sudo systemctl daemon-reload\nsudo service docker restart\n"))),(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("p",{parentName:"li"},"\ubcc0\uacbd \uc0ac\ud56d\uc774 \ubc18\uc601\ub418\uc5c8\ub294\uc9c0 \ud655\uc778\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"sudo docker info | grep nvidia\n")),(0,r.kt)("p",{parentName:"li"},"\ub2e4\uc74c\uacfc \uac19\uc740 \uba54\uc2dc\uc9c0\uac00 \ubcf4\uc774\uba74 \uc815\uc0c1\uc801\uc73c\ub85c \uc124\uce58\ub41c \uac83\uc744 \uc758\ubbf8\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"mlops@ubuntu:~$ docker info | grep nvidia\nRuntimes: io.containerd.runc.v2 io.containerd.runtime.v1.linux nvidia runc\nDefault Runtime: nvidia\n")))),(0,r.kt)("h2",{id:"4-nvidia-device-plugin"},"4. Nvidia-Device-Plugin"),(0,r.kt)("ol",null,(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("p",{parentName:"li"},"nvidia-device-plugin daemonset\uc744 \uc0dd\uc131\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"kubectl create -f https://raw.githubusercontent.com/NVIDIA/k8s-device-plugin/v0.10.0/nvidia-device-plugin.yml\n"))),(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("p",{parentName:"li"},"nvidia-device-plugin pod\uc774 RUNNING \uc0c1\ud0dc\ub85c \uc0dd\uc131\ub418\uc5c8\ub294\uc9c0 \ud655\uc778\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"kubectl get pod -n kube-system | grep nvidia\n")),(0,r.kt)("p",{parentName:"li"},"\ub2e4\uc74c\uacfc \uac19\uc740 \uacb0\uacfc\uac00 \ucd9c\ub825\ub418\uc5b4\uc57c \ud569\ub2c8\ub2e4."),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"kube-system nvidia-device-plugin-daemonset-nlqh2 1/1 Running 0 1h\n"))),(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("p",{parentName:"li"},"node \uc815\ubcf4\uc5d0 gpu\uac00 \uc0ac\uc6a9\uac00\ub2a5\ud558\ub3c4\ub85d \uc124\uc815\ub418\uc5c8\ub294\uc9c0 \ud655\uc778\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},'kubectl get nodes "-o=custom-columns=NAME:.metadata.name,GPU:.status.allocatable.nvidia\\.com/gpu"\n')),(0,r.kt)("p",{parentName:"li"},"\ub2e4\uc74c\uacfc \uac19\uc740 \uba54\uc2dc\uc9c0\uac00 \ubcf4\uc774\uba74 \uc815\uc0c1\uc801\uc73c\ub85c \uc124\uc815\ub41c \uac83\uc744 \uc758\ubbf8\ud569\ub2c8\ub2e4.",(0,r.kt)("br",{parentName:"p"}),"\n","(",(0,r.kt)("em",{parentName:"p"},"\ubaa8\ub450\uc758 MLOps")," \uc5d0\uc11c \uc2e4\uc2b5\uc744 \uc9c4\ud589\ud55c \ud074\ub7ec\uc2a4\ud130\ub294 2\uac1c\uc758 GPU\uac00 \uc788\uc5b4\uc11c 2\uac00 \ucd9c\ub825\ub429\ub2c8\ub2e4.\n\ubcf8\uc778\uc758 \ud074\ub7ec\uc2a4\ud130\uc758 GPU \uac1c\uc218\uc640 \ub9de\ub294 \uc22b\uc790\uac00 \ucd9c\ub825\ub41c\ub2e4\uba74 \ub429\ub2c8\ub2e4.)"),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"NAME GPU\nubuntu 2\n")))),(0,r.kt)("p",null,"\uc124\uc815\ub418\uc9c0 \uc54a\uc740 \uacbd\uc6b0, GPU\uc758 value\uac00 ",(0,r.kt)("inlineCode",{parentName:"p"},"")," \uc73c\ub85c \ud45c\uc2dc\ub429\ub2c8\ub2e4."))}c.isMDXComponent=!0}}]); \ No newline at end of file +"use strict";(self.webpackChunkv_2=self.webpackChunkv_2||[]).push([[4775],{3905:(e,n,t)=>{t.d(n,{Zo:()=>u,kt:()=>k});var a=t(7294);function r(e,n,t){return n in e?Object.defineProperty(e,n,{value:t,enumerable:!0,configurable:!0,writable:!0}):e[n]=t,e}function i(e,n){var t=Object.keys(e);if(Object.getOwnPropertySymbols){var a=Object.getOwnPropertySymbols(e);n&&(a=a.filter((function(n){return Object.getOwnPropertyDescriptor(e,n).enumerable}))),t.push.apply(t,a)}return t}function o(e){for(var n=1;n=0||(r[t]=e[t]);return r}(e,n);if(Object.getOwnPropertySymbols){var i=Object.getOwnPropertySymbols(e);for(a=0;a=0||Object.prototype.propertyIsEnumerable.call(e,t)&&(r[t]=e[t])}return r}var s=a.createContext({}),p=function(e){var n=a.useContext(s),t=n;return e&&(t="function"==typeof e?e(n):o(o({},n),e)),t},u=function(e){var n=p(e.components);return a.createElement(s.Provider,{value:n},e.children)},d="mdxType",c={inlineCode:"code",wrapper:function(e){var n=e.children;return a.createElement(a.Fragment,{},n)}},m=a.forwardRef((function(e,n){var t=e.components,r=e.mdxType,i=e.originalType,s=e.parentName,u=l(e,["components","mdxType","originalType","parentName"]),d=p(t),m=r,k=d["".concat(s,".").concat(m)]||d[m]||c[m]||i;return t?a.createElement(k,o(o({ref:n},u),{},{components:t})):a.createElement(k,o({ref:n},u))}));function k(e,n){var t=arguments,r=n&&n.mdxType;if("string"==typeof e||r){var i=t.length,o=new Array(i);o[0]=m;var l={};for(var s in n)hasOwnProperty.call(n,s)&&(l[s]=n[s]);l.originalType=e,l[d]="string"==typeof e?e:r,o[1]=l;for(var p=2;p{t.r(n),t.d(n,{assets:()=>s,contentTitle:()=>o,default:()=>c,frontMatter:()=>i,metadata:()=>l,toc:()=>p});var a=t(7462),r=(t(7294),t(3905));const i={title:"6. (Optional) Setup GPU",description:"Install nvidia docker, nvidia device plugin",sidebar_position:6,date:new Date("2021-12-13T00:00:00.000Z"),lastmod:new Date("2021-12-13T00:00:00.000Z"),contributors:["Jaeyeon Kim"]},o=void 0,l={unversionedId:"setup-kubernetes/setup-nvidia-gpu",id:"setup-kubernetes/setup-nvidia-gpu",title:"6. (Optional) Setup GPU",description:"Install nvidia docker, nvidia device plugin",source:"@site/docs/setup-kubernetes/setup-nvidia-gpu.md",sourceDirName:"setup-kubernetes",slug:"/setup-kubernetes/setup-nvidia-gpu",permalink:"/docs/setup-kubernetes/setup-nvidia-gpu",draft:!1,editUrl:"https://github.com/mlops-for-all/mlops-for-all.github.io/tree/main/docs/setup-kubernetes/setup-nvidia-gpu.md",tags:[],version:"current",lastUpdatedBy:"Aiden-Jeon",lastUpdatedAt:1706783082,formattedLastUpdatedAt:"2024\ub144 2\uc6d4 1\uc77c",sidebarPosition:6,frontMatter:{title:"6. 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Nvidia-Device-Plugin",id:"4-nvidia-device-plugin",level:2}],u={toc:p},d="wrapper";function c(e){let{components:n,...t}=e;return(0,r.kt)(d,(0,a.Z)({},u,t,{components:n,mdxType:"MDXLayout"}),(0,r.kt)("p",null,"\ucfe0\ubc84\ub124\ud2f0\uc2a4 \ubc0f Kubeflow \ub4f1\uc5d0\uc11c GP \ub97c \uc0ac\uc6a9\ud558\uae30 \uc704\ud574\uc11c\ub294 \ub2e4\uc74c \uc791\uc5c5\uc774 \ud544\uc694\ud569\ub2c8\ub2e4."),(0,r.kt)("h2",{id:"1-install-nvidia-driver"},"1. Install NVIDIA Driver"),(0,r.kt)("p",null,(0,r.kt)("inlineCode",{parentName:"p"},"nvidia-smi")," \uc218\ud589 \uc2dc \ub2e4\uc74c\uacfc \uac19\uc740 \ud654\uba74\uc774 \ucd9c\ub825\ub41c\ub2e4\uba74 \uc774 \ub2e8\uacc4\ub294 \uc0dd\ub7b5\ud574 \uc8fc\uc2dc\uae30 \ubc14\ub78d\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"mlops@ubuntu:~$ nvidia-smi \n+-----------------------------------------------------------------------------+\n| NVIDIA-SMI 470.86 Driver Version: 470.86 CUDA Version: 11.4 |\n|-------------------------------+----------------------+----------------------+\n| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n| | | MIG M. |\n|===============================+======================+======================|\n| 0 NVIDIA GeForce ... Off | 00000000:01:00.0 Off | N/A |\n| 25% 32C P8 4W / 120W | 211MiB / 6078MiB | 0% Default |\n| | | N/A |\n+-------------------------------+----------------------+----------------------+\n| 1 NVIDIA GeForce ... Off | 00000000:02:00.0 Off | N/A |\n| 0% 34C P8 7W / 175W | 5MiB / 7982MiB | 0% Default |\n| | | N/A |\n+-------------------------------+----------------------+----------------------+\n \n+-----------------------------------------------------------------------------+\n| Processes: |\n| GPU GI CI PID Type Process name GPU Memory |\n| ID ID Usage |\n|=============================================================================|\n| 0 N/A N/A 1644 G /usr/lib/xorg/Xorg 198MiB |\n| 0 N/A N/A 1893 G /usr/bin/gnome-shell 10MiB |\n| 1 N/A N/A 1644 G /usr/lib/xorg/Xorg 4MiB |\n+-----------------------------------------------------------------------------+\n")),(0,r.kt)("p",null,(0,r.kt)("inlineCode",{parentName:"p"},"nvidia-smi"),"\uc758 \ucd9c\ub825 \uacb0\uacfc\uac00 \uc704\uc640 \uac19\uc9c0 \uc54a\ub2e4\uba74 \uc7a5\ucc29\ub41c GPU\uc5d0 \ub9de\ub294 nvidia driver\ub97c \uc124\uce58\ud574 \uc8fc\uc2dc\uae30 \ubc14\ub78d\ub2c8\ub2e4."),(0,r.kt)("p",null,"\ub9cc\uc57d nvidia driver\uc758 \uc124\uce58\uc5d0 \uc775\uc219\ud558\uc9c0 \uc54a\ub2e4\uba74 \uc544\ub798 \uba85\ub839\uc5b4\ub97c \ud1b5\ud574 \uc124\uce58\ud558\uc2dc\uae30 \ubc14\ub78d\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"sudo add-apt-repository ppa:graphics-drivers/ppa\nsudo apt update && sudo apt install -y ubuntu-drivers-common\nsudo ubuntu-drivers autoinstall\nsudo reboot\n")),(0,r.kt)("h2",{id:"2-nvidia-docker-\uc124\uce58"},"2. NVIDIA-Docker \uc124\uce58"),(0,r.kt)("p",null,"NVIDIA-Docker\ub97c \uc124\uce58\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | \\\n sudo apt-key add -\ndistribution=$(. /etc/os-release;echo $ID$VERSION_ID)\ncurl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list\nsudo apt-get update\nsudo apt-get install -y nvidia-docker2 &&\nsudo systemctl restart docker\n")),(0,r.kt)("p",null,"\uc815\uc0c1\uc801\uc73c\ub85c \uc124\uce58\ub418\uc5c8\ub294\uc9c0 \ud655\uc778\ud558\uae30 \uc704\ud574, GPU\ub97c \uc0ac\uc6a9\ud558\ub294 \ub3c4\ucee4 \ucee8\ud14c\uc774\ub108\ub97c \uc2e4\ud589\ud574\ubd05\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"sudo docker run --rm --gpus all nvidia/cuda:11.0-base nvidia-smi\n")),(0,r.kt)("p",null,"\ub2e4\uc74c\uacfc \uac19\uc740 \uba54\uc2dc\uc9c0\uac00 \ubcf4\uc774\uba74 \uc815\uc0c1\uc801\uc73c\ub85c \uc124\uce58\ub41c \uac83\uc744 \uc758\ubbf8\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"mlops@ubuntu:~$ sudo docker run --rm --gpus all nvidia/cuda:11.0-base nvidia-smi\n+-----------------------------------------------------------------------------+\n| NVIDIA-SMI 470.86 Driver Version: 470.86 CUDA Version: 11.4 |\n|-------------------------------+----------------------+----------------------+\n| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n| | | MIG M. |\n|===============================+======================+======================|\n| 0 NVIDIA GeForce ... Off | 00000000:01:00.0 Off | N/A |\n| 25% 32C P8 4W / 120W | 211MiB / 6078MiB | 0% Default |\n| | | N/A |\n+-------------------------------+----------------------+----------------------+\n| 1 NVIDIA GeForce ... Off | 00000000:02:00.0 Off | N/A |\n| 0% 34C P8 6W / 175W | 5MiB / 7982MiB | 0% Default |\n| | | N/A |\n+-------------------------------+----------------------+----------------------+\n \n+-----------------------------------------------------------------------------+\n| Processes: |\n| GPU GI CI PID Type Process name GPU Memory |\n| ID ID Usage |\n|=============================================================================|\n+-----------------------------------------------------------------------------+\n")),(0,r.kt)("h2",{id:"3-nvidia-docker\ub97c-default-container-runtime\uc73c\ub85c-\uc124\uc815"},"3. NVIDIA-Docker\ub97c Default Container Runtime\uc73c\ub85c \uc124\uc815"),(0,r.kt)("p",null,"\ucfe0\ubc84\ub124\ud2f0\uc2a4\ub294 \uae30\ubcf8\uc801\uc73c\ub85c Docker-CE\ub97c Default Container Runtime\uc73c\ub85c \uc0ac\uc6a9\ud569\ub2c8\ub2e4.\n\ub530\ub77c\uc11c, Docker Container \ub0b4\uc5d0\uc11c NVIDIA GPU\ub97c \uc0ac\uc6a9\ud558\uae30 \uc704\ud574\uc11c\ub294 NVIDIA-Docker \ub97c Container Runtime \uc73c\ub85c \uc0ac\uc6a9\ud558\uc5ec pod\ub97c \uc0dd\uc131\ud560 \uc218 \uc788\ub3c4\ub85d Default Runtime\uc744 \uc218\uc815\ud574 \uc8fc\uc5b4\uc57c \ud569\ub2c8\ub2e4."),(0,r.kt)("ol",null,(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("p",{parentName:"li"},(0,r.kt)("inlineCode",{parentName:"p"},"/etc/docker/daemon.json")," \ud30c\uc77c\uc744 \uc5f4\uc5b4 \ub2e4\uc74c\uacfc \uac19\uc774 \uc218\uc815\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},'sudo vi /etc/docker/daemon.json\n\n{\n "default-runtime": "nvidia",\n "runtimes": {\n "nvidia": {\n "path": "nvidia-container-runtime",\n "runtimeArgs": []\n }\n }\n}\n'))),(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("p",{parentName:"li"},"\ud30c\uc77c\uc774 \ubcc0\uacbd\ub41c \uac83\uc744 \ud655\uc778\ud55c \ud6c4, Docker\ub97c \uc7ac\uc2dc\uc791\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"sudo systemctl daemon-reload\nsudo service docker restart\n"))),(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("p",{parentName:"li"},"\ubcc0\uacbd \uc0ac\ud56d\uc774 \ubc18\uc601\ub418\uc5c8\ub294\uc9c0 \ud655\uc778\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"sudo docker info | grep nvidia\n")),(0,r.kt)("p",{parentName:"li"},"\ub2e4\uc74c\uacfc \uac19\uc740 \uba54\uc2dc\uc9c0\uac00 \ubcf4\uc774\uba74 \uc815\uc0c1\uc801\uc73c\ub85c \uc124\uce58\ub41c \uac83\uc744 \uc758\ubbf8\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"mlops@ubuntu:~$ docker info | grep nvidia\nRuntimes: io.containerd.runc.v2 io.containerd.runtime.v1.linux nvidia runc\nDefault Runtime: nvidia\n")))),(0,r.kt)("h2",{id:"4-nvidia-device-plugin"},"4. Nvidia-Device-Plugin"),(0,r.kt)("ol",null,(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("p",{parentName:"li"},"nvidia-device-plugin daemonset\uc744 \uc0dd\uc131\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"kubectl create -f https://raw.githubusercontent.com/NVIDIA/k8s-device-plugin/v0.10.0/nvidia-device-plugin.yml\n"))),(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("p",{parentName:"li"},"nvidia-device-plugin pod\uc774 RUNNING \uc0c1\ud0dc\ub85c \uc0dd\uc131\ub418\uc5c8\ub294\uc9c0 \ud655\uc778\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"kubectl get pod -n kube-system | grep nvidia\n")),(0,r.kt)("p",{parentName:"li"},"\ub2e4\uc74c\uacfc \uac19\uc740 \uacb0\uacfc\uac00 \ucd9c\ub825\ub418\uc5b4\uc57c \ud569\ub2c8\ub2e4."),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"kube-system nvidia-device-plugin-daemonset-nlqh2 1/1 Running 0 1h\n"))),(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("p",{parentName:"li"},"node \uc815\ubcf4\uc5d0 gpu\uac00 \uc0ac\uc6a9\uac00\ub2a5\ud558\ub3c4\ub85d \uc124\uc815\ub418\uc5c8\ub294\uc9c0 \ud655\uc778\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},'kubectl get nodes "-o=custom-columns=NAME:.metadata.name,GPU:.status.allocatable.nvidia\\.com/gpu"\n')),(0,r.kt)("p",{parentName:"li"},"\ub2e4\uc74c\uacfc \uac19\uc740 \uba54\uc2dc\uc9c0\uac00 \ubcf4\uc774\uba74 \uc815\uc0c1\uc801\uc73c\ub85c \uc124\uc815\ub41c \uac83\uc744 \uc758\ubbf8\ud569\ub2c8\ub2e4.",(0,r.kt)("br",{parentName:"p"}),"\n","(",(0,r.kt)("em",{parentName:"p"},"\ubaa8\ub450\uc758 MLOps")," \uc5d0\uc11c \uc2e4\uc2b5\uc744 \uc9c4\ud589\ud55c \ud074\ub7ec\uc2a4\ud130\ub294 2\uac1c\uc758 GPU\uac00 \uc788\uc5b4\uc11c 2\uac00 \ucd9c\ub825\ub429\ub2c8\ub2e4.\n\ubcf8\uc778\uc758 \ud074\ub7ec\uc2a4\ud130\uc758 GPU \uac1c\uc218\uc640 \ub9de\ub294 \uc22b\uc790\uac00 \ucd9c\ub825\ub41c\ub2e4\uba74 \ub429\ub2c8\ub2e4.)"),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"NAME GPU\nubuntu 2\n")))),(0,r.kt)("p",null,"\uc124\uc815\ub418\uc9c0 \uc54a\uc740 \uacbd\uc6b0, GPU\uc758 value\uac00 ",(0,r.kt)("inlineCode",{parentName:"p"},"")," \uc73c\ub85c \ud45c\uc2dc\ub429\ub2c8\ub2e4."))}c.isMDXComponent=!0}}]); \ No newline at end of file diff --git a/assets/js/6d8a40f9.4c3545c6.js b/assets/js/6d8a40f9.4c3545c6.js deleted file mode 100644 index c852b184..00000000 --- a/assets/js/6d8a40f9.4c3545c6.js +++ /dev/null @@ -1 +0,0 @@ -"use strict";(self.webpackChunkv_2=self.webpackChunkv_2||[]).push([[4775],{3905:(e,n,t)=>{t.d(n,{Zo:()=>u,kt:()=>k});var a=t(7294);function r(e,n,t){return n in e?Object.defineProperty(e,n,{value:t,enumerable:!0,configurable:!0,writable:!0}):e[n]=t,e}function i(e,n){var t=Object.keys(e);if(Object.getOwnPropertySymbols){var a=Object.getOwnPropertySymbols(e);n&&(a=a.filter((function(n){return Object.getOwnPropertyDescriptor(e,n).enumerable}))),t.push.apply(t,a)}return t}function o(e){for(var n=1;n=0||(r[t]=e[t]);return r}(e,n);if(Object.getOwnPropertySymbols){var i=Object.getOwnPropertySymbols(e);for(a=0;a=0||Object.prototype.propertyIsEnumerable.call(e,t)&&(r[t]=e[t])}return r}var s=a.createContext({}),p=function(e){var n=a.useContext(s),t=n;return e&&(t="function"==typeof e?e(n):o(o({},n),e)),t},u=function(e){var n=p(e.components);return a.createElement(s.Provider,{value:n},e.children)},d="mdxType",c={inlineCode:"code",wrapper:function(e){var n=e.children;return a.createElement(a.Fragment,{},n)}},m=a.forwardRef((function(e,n){var t=e.components,r=e.mdxType,i=e.originalType,s=e.parentName,u=l(e,["components","mdxType","originalType","parentName"]),d=p(t),m=r,k=d["".concat(s,".").concat(m)]||d[m]||c[m]||i;return t?a.createElement(k,o(o({ref:n},u),{},{components:t})):a.createElement(k,o({ref:n},u))}));function k(e,n){var t=arguments,r=n&&n.mdxType;if("string"==typeof e||r){var i=t.length,o=new Array(i);o[0]=m;var l={};for(var s in n)hasOwnProperty.call(n,s)&&(l[s]=n[s]);l.originalType=e,l[d]="string"==typeof e?e:r,o[1]=l;for(var p=2;p{t.r(n),t.d(n,{assets:()=>s,contentTitle:()=>o,default:()=>c,frontMatter:()=>i,metadata:()=>l,toc:()=>p});var a=t(7462),r=(t(7294),t(3905));const i={title:"6. (Optional) Setup GPU",description:"Install nvidia docker, nvidia device plugin",sidebar_position:6,date:new Date("2021-12-13T00:00:00.000Z"),lastmod:new Date("2021-12-13T00:00:00.000Z"),contributors:["Jaeyeon Kim"]},o=void 0,l={unversionedId:"setup-kubernetes/setup-nvidia-gpu",id:"setup-kubernetes/setup-nvidia-gpu",title:"6. (Optional) Setup GPU",description:"Install nvidia docker, nvidia device plugin",source:"@site/docs/setup-kubernetes/setup-nvidia-gpu.md",sourceDirName:"setup-kubernetes",slug:"/setup-kubernetes/setup-nvidia-gpu",permalink:"/docs/setup-kubernetes/setup-nvidia-gpu",draft:!1,editUrl:"https://github.com/mlops-for-all/mlops-for-all.github.io/tree/main/docs/setup-kubernetes/setup-nvidia-gpu.md",tags:[],version:"current",lastUpdatedBy:"conqrean",lastUpdatedAt:1705274857,formattedLastUpdatedAt:"2024\ub144 1\uc6d4 14\uc77c",sidebarPosition:6,frontMatter:{title:"6. 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Nvidia-Device-Plugin",id:"4-nvidia-device-plugin",level:2}],u={toc:p},d="wrapper";function c(e){let{components:n,...t}=e;return(0,r.kt)(d,(0,a.Z)({},u,t,{components:n,mdxType:"MDXLayout"}),(0,r.kt)("p",null,"\ucfe0\ubc84\ub124\ud2f0\uc2a4 \ubc0f Kubeflow \ub4f1\uc5d0\uc11c GP \ub97c \uc0ac\uc6a9\ud558\uae30 \uc704\ud574\uc11c\ub294 \ub2e4\uc74c \uc791\uc5c5\uc774 \ud544\uc694\ud569\ub2c8\ub2e4."),(0,r.kt)("h2",{id:"1-install-nvidia-driver"},"1. Install NVIDIA Driver"),(0,r.kt)("p",null,(0,r.kt)("inlineCode",{parentName:"p"},"nvidia-smi")," \uc218\ud589 \uc2dc \ub2e4\uc74c\uacfc \uac19\uc740 \ud654\uba74\uc774 \ucd9c\ub825\ub41c\ub2e4\uba74 \uc774 \ub2e8\uacc4\ub294 \uc0dd\ub7b5\ud574 \uc8fc\uc2dc\uae30 \ubc14\ub78d\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"mlops@ubuntu:~$ nvidia-smi \n+-----------------------------------------------------------------------------+\n| NVIDIA-SMI 470.86 Driver Version: 470.86 CUDA Version: 11.4 |\n|-------------------------------+----------------------+----------------------+\n| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n| | | MIG M. |\n|===============================+======================+======================|\n| 0 NVIDIA GeForce ... Off | 00000000:01:00.0 Off | N/A |\n| 25% 32C P8 4W / 120W | 211MiB / 6078MiB | 0% Default |\n| | | N/A |\n+-------------------------------+----------------------+----------------------+\n| 1 NVIDIA GeForce ... Off | 00000000:02:00.0 Off | N/A |\n| 0% 34C P8 7W / 175W | 5MiB / 7982MiB | 0% Default |\n| | | N/A |\n+-------------------------------+----------------------+----------------------+\n \n+-----------------------------------------------------------------------------+\n| Processes: |\n| GPU GI CI PID Type Process name GPU Memory |\n| ID ID Usage |\n|=============================================================================|\n| 0 N/A N/A 1644 G /usr/lib/xorg/Xorg 198MiB |\n| 0 N/A N/A 1893 G /usr/bin/gnome-shell 10MiB |\n| 1 N/A N/A 1644 G /usr/lib/xorg/Xorg 4MiB |\n+-----------------------------------------------------------------------------+\n")),(0,r.kt)("p",null,(0,r.kt)("inlineCode",{parentName:"p"},"nvidia-smi"),"\uc758 \ucd9c\ub825 \uacb0\uacfc\uac00 \uc704\uc640 \uac19\uc9c0 \uc54a\ub2e4\uba74 \uc7a5\ucc29\ub41c GPU\uc5d0 \ub9de\ub294 nvidia driver\ub97c \uc124\uce58\ud574 \uc8fc\uc2dc\uae30 \ubc14\ub78d\ub2c8\ub2e4."),(0,r.kt)("p",null,"\ub9cc\uc57d nvidia driver\uc758 \uc124\uce58\uc5d0 \uc775\uc219\ud558\uc9c0 \uc54a\ub2e4\uba74 \uc544\ub798 \uba85\ub839\uc5b4\ub97c \ud1b5\ud574 \uc124\uce58\ud558\uc2dc\uae30 \ubc14\ub78d\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"sudo add-apt-repository ppa:graphics-drivers/ppa\nsudo apt update && sudo apt install -y ubuntu-drivers-common\nsudo ubuntu-drivers autoinstall\nsudo reboot\n")),(0,r.kt)("h2",{id:"2-nvidia-docker-\uc124\uce58"},"2. NVIDIA-Docker \uc124\uce58"),(0,r.kt)("p",null,"NVIDIA-Docker\ub97c \uc124\uce58\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | \\\n sudo apt-key add -\ndistribution=$(. /etc/os-release;echo $ID$VERSION_ID)\ncurl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list\nsudo apt-get update\nsudo apt-get install -y nvidia-docker2 &&\nsudo systemctl restart docker\n")),(0,r.kt)("p",null,"\uc815\uc0c1\uc801\uc73c\ub85c \uc124\uce58\ub418\uc5c8\ub294\uc9c0 \ud655\uc778\ud558\uae30 \uc704\ud574, GPU\ub97c \uc0ac\uc6a9\ud558\ub294 \ub3c4\ucee4 \ucee8\ud14c\uc774\ub108\ub97c \uc2e4\ud589\ud574\ubd05\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"sudo docker run --rm --gpus all nvidia/cuda:11.0-base nvidia-smi\n")),(0,r.kt)("p",null,"\ub2e4\uc74c\uacfc \uac19\uc740 \uba54\uc2dc\uc9c0\uac00 \ubcf4\uc774\uba74 \uc815\uc0c1\uc801\uc73c\ub85c \uc124\uce58\ub41c \uac83\uc744 \uc758\ubbf8\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"mlops@ubuntu:~$ sudo docker run --rm --gpus all nvidia/cuda:11.0-base nvidia-smi\n+-----------------------------------------------------------------------------+\n| NVIDIA-SMI 470.86 Driver Version: 470.86 CUDA Version: 11.4 |\n|-------------------------------+----------------------+----------------------+\n| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n| | | MIG M. |\n|===============================+======================+======================|\n| 0 NVIDIA GeForce ... Off | 00000000:01:00.0 Off | N/A |\n| 25% 32C P8 4W / 120W | 211MiB / 6078MiB | 0% Default |\n| | | N/A |\n+-------------------------------+----------------------+----------------------+\n| 1 NVIDIA GeForce ... Off | 00000000:02:00.0 Off | N/A |\n| 0% 34C P8 6W / 175W | 5MiB / 7982MiB | 0% Default |\n| | | N/A |\n+-------------------------------+----------------------+----------------------+\n \n+-----------------------------------------------------------------------------+\n| Processes: |\n| GPU GI CI PID Type Process name GPU Memory |\n| ID ID Usage |\n|=============================================================================|\n+-----------------------------------------------------------------------------+\n")),(0,r.kt)("h2",{id:"3-nvidia-docker\ub97c-default-container-runtime\uc73c\ub85c-\uc124\uc815"},"3. NVIDIA-Docker\ub97c Default Container Runtime\uc73c\ub85c \uc124\uc815"),(0,r.kt)("p",null,"\ucfe0\ubc84\ub124\ud2f0\uc2a4\ub294 \uae30\ubcf8\uc801\uc73c\ub85c Docker-CE\ub97c Default Container Runtime\uc73c\ub85c \uc0ac\uc6a9\ud569\ub2c8\ub2e4.\n\ub530\ub77c\uc11c, Docker Container \ub0b4\uc5d0\uc11c NVIDIA GPU\ub97c \uc0ac\uc6a9\ud558\uae30 \uc704\ud574\uc11c\ub294 NVIDIA-Docker \ub97c Container Runtime \uc73c\ub85c \uc0ac\uc6a9\ud558\uc5ec pod\ub97c \uc0dd\uc131\ud560 \uc218 \uc788\ub3c4\ub85d Default Runtime\uc744 \uc218\uc815\ud574 \uc8fc\uc5b4\uc57c \ud569\ub2c8\ub2e4."),(0,r.kt)("ol",null,(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("p",{parentName:"li"},(0,r.kt)("inlineCode",{parentName:"p"},"/etc/docker/daemon.json")," \ud30c\uc77c\uc744 \uc5f4\uc5b4 \ub2e4\uc74c\uacfc \uac19\uc774 \uc218\uc815\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},'sudo vi /etc/docker/daemon.json\n\n{\n "default-runtime": "nvidia",\n "runtimes": {\n "nvidia": {\n "path": "nvidia-container-runtime",\n "runtimeArgs": []\n }\n }\n}\n'))),(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("p",{parentName:"li"},"\ud30c\uc77c\uc774 \ubcc0\uacbd\ub41c \uac83\uc744 \ud655\uc778\ud55c \ud6c4, Docker\ub97c \uc7ac\uc2dc\uc791\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"sudo systemctl daemon-reload\nsudo service docker restart\n"))),(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("p",{parentName:"li"},"\ubcc0\uacbd \uc0ac\ud56d\uc774 \ubc18\uc601\ub418\uc5c8\ub294\uc9c0 \ud655\uc778\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"sudo docker info | grep nvidia\n")),(0,r.kt)("p",{parentName:"li"},"\ub2e4\uc74c\uacfc \uac19\uc740 \uba54\uc2dc\uc9c0\uac00 \ubcf4\uc774\uba74 \uc815\uc0c1\uc801\uc73c\ub85c \uc124\uce58\ub41c \uac83\uc744 \uc758\ubbf8\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"mlops@ubuntu:~$ docker info | grep nvidia\nRuntimes: io.containerd.runc.v2 io.containerd.runtime.v1.linux nvidia runc\nDefault Runtime: nvidia\n")))),(0,r.kt)("h2",{id:"4-nvidia-device-plugin"},"4. Nvidia-Device-Plugin"),(0,r.kt)("ol",null,(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("p",{parentName:"li"},"nvidia-device-plugin daemonset\uc744 \uc0dd\uc131\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"kubectl create -f https://raw.githubusercontent.com/NVIDIA/k8s-device-plugin/v0.10.0/nvidia-device-plugin.yml\n"))),(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("p",{parentName:"li"},"nvidia-device-plugin pod\uc774 RUNNING \uc0c1\ud0dc\ub85c \uc0dd\uc131\ub418\uc5c8\ub294\uc9c0 \ud655\uc778\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"kubectl get pod -n kube-system | grep nvidia\n")),(0,r.kt)("p",{parentName:"li"},"\ub2e4\uc74c\uacfc \uac19\uc740 \uacb0\uacfc\uac00 \ucd9c\ub825\ub418\uc5b4\uc57c \ud569\ub2c8\ub2e4."),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"kube-system nvidia-device-plugin-daemonset-nlqh2 1/1 Running 0 1h\n"))),(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("p",{parentName:"li"},"node \uc815\ubcf4\uc5d0 gpu\uac00 \uc0ac\uc6a9\uac00\ub2a5\ud558\ub3c4\ub85d \uc124\uc815\ub418\uc5c8\ub294\uc9c0 \ud655\uc778\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},'kubectl get nodes "-o=custom-columns=NAME:.metadata.name,GPU:.status.allocatable.nvidia\\.com/gpu"\n')),(0,r.kt)("p",{parentName:"li"},"\ub2e4\uc74c\uacfc \uac19\uc740 \uba54\uc2dc\uc9c0\uac00 \ubcf4\uc774\uba74 \uc815\uc0c1\uc801\uc73c\ub85c \uc124\uc815\ub41c \uac83\uc744 \uc758\ubbf8\ud569\ub2c8\ub2e4.",(0,r.kt)("br",{parentName:"p"}),"\n","(",(0,r.kt)("em",{parentName:"p"},"\ubaa8\ub450\uc758 MLOps")," \uc5d0\uc11c \uc2e4\uc2b5\uc744 \uc9c4\ud589\ud55c \ud074\ub7ec\uc2a4\ud130\ub294 2\uac1c\uc758 GPU\uac00 \uc788\uc5b4\uc11c 2\uac00 \ucd9c\ub825\ub429\ub2c8\ub2e4.\n\ubcf8\uc778\uc758 \ud074\ub7ec\uc2a4\ud130\uc758 GPU \uac1c\uc218\uc640 \ub9de\ub294 \uc22b\uc790\uac00 \ucd9c\ub825\ub41c\ub2e4\uba74 \ub429\ub2c8\ub2e4.)"),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"NAME GPU\nubuntu 2\n")))),(0,r.kt)("p",null,"\uc124\uc815\ub418\uc9c0 \uc54a\uc740 \uacbd\uc6b0, GPU\uc758 value\uac00 ",(0,r.kt)("inlineCode",{parentName:"p"},"")," \uc73c\ub85c \ud45c\uc2dc\ub429\ub2c8\ub2e4."))}c.isMDXComponent=!0}}]); \ No newline at end of file diff --git a/assets/js/6fb0e7c9.0f76b09a.js b/assets/js/6fb0e7c9.0f76b09a.js new file mode 100644 index 00000000..391e56d7 --- /dev/null +++ b/assets/js/6fb0e7c9.0f76b09a.js @@ -0,0 +1 @@ +"use strict";(self.webpackChunkv_2=self.webpackChunkv_2||[]).push([[5105],{3905:(t,e,n)=>{n.d(e,{Zo:()=>m,kt:()=>c});var r=n(7294);function a(t,e,n){return e in t?Object.defineProperty(t,e,{value:n,enumerable:!0,configurable:!0,writable:!0}):t[e]=n,t}function l(t,e){var n=Object.keys(t);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(t);e&&(r=r.filter((function(e){return Object.getOwnPropertyDescriptor(t,e).enumerable}))),n.push.apply(n,r)}return n}function o(t){for(var e=1;e=0||(a[n]=t[n]);return a}(t,e);if(Object.getOwnPropertySymbols){var l=Object.getOwnPropertySymbols(t);for(r=0;r=0||Object.prototype.propertyIsEnumerable.call(t,n)&&(a[n]=t[n])}return a}var i=r.createContext({}),u=function(t){var e=r.useContext(i),n=e;return t&&(n="function"==typeof t?t(e):o(o({},e),t)),n},m=function(t){var e=u(t.components);return r.createElement(i.Provider,{value:e},t.children)},s="mdxType",d={inlineCode:"code",wrapper:function(t){var e=t.children;return r.createElement(r.Fragment,{},e)}},k=r.forwardRef((function(t,e){var n=t.components,a=t.mdxType,l=t.originalType,i=t.parentName,m=p(t,["components","mdxType","originalType","parentName"]),s=u(n),k=a,c=s["".concat(i,".").concat(k)]||s[k]||d[k]||l;return n?r.createElement(c,o(o({ref:e},m),{},{components:n})):r.createElement(c,o({ref:e},m))}));function c(t,e){var n=arguments,a=e&&e.mdxType;if("string"==typeof t||a){var l=n.length,o=new Array(l);o[0]=k;var p={};for(var i in e)hasOwnProperty.call(e,i)&&(p[i]=e[i]);p.originalType=t,p[s]="string"==typeof t?t:a,o[1]=p;for(var u=2;u{n.r(e),n.d(e,{assets:()=>i,contentTitle:()=>o,default:()=>d,frontMatter:()=>l,metadata:()=>p,toc:()=>u});var r=n(7462),a=(n(7294),n(3905));const l={title:"1. 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Docker rmi",id:"10-docker-rmi",level:2},{value:"References",id:"references",level:2}],u={toc:i},c="wrapper";function k(e){let{components:n,...a}=e;return(0,r.kt)(c,(0,t.Z)({},u,a,{components:n,mdxType:"MDXLayout"}),(0,r.kt)("h2",{id:"1-\uc815\uc0c1-\uc124\uce58-\ud655\uc778"},"1. \uc815\uc0c1 \uc124\uce58 \ud655\uc778"),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"docker run hello-world\n")),(0,r.kt)("p",null,"\uc815\uc0c1\uc801\uc73c\ub85c \uc124\uce58\ub41c \uacbd\uc6b0 \ub2e4\uc74c\uacfc \uac19\uc740 \uba54\uc2dc\uc9c0\ub97c \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"Hello from Docker!\nThis message shows that your installation appears to be working correctly.\n....\n")),(0,r.kt)("p",null,(0,r.kt)("strong",{parentName:"p"},"(For ubuntu)")," sudo \uc5c6\uc774 \uc0ac\uc6a9\ud558\uace0 \uc2f6\ub2e4\uba74 \uc544\ub798 \uc0ac\uc774\ud2b8\ub97c \ucc38\uace0\ud569\ub2c8\ub2e4."),(0,r.kt)("ul",null,(0,r.kt)("li",{parentName:"ul"},(0,r.kt)("a",{parentName:"li",href:"https://docs.docker.com/engine/install/linux-postinstall/#manage-docker-as-a-non-root-user"},"https://docs.docker.com/engine/install/linux-postinstall/#manage-docker-as-a-non-root-user"))),(0,r.kt)("h2",{id:"2-docker-pull"},"2. Docker Pull"),(0,r.kt)("p",null,"docker image registry(\ub3c4\ucee4 \uc774\ubbf8\uc9c0\ub97c \uc800\uc7a5\ud558\uace0 \uacf5\uc720\ud560 \uc218 \uc788\ub294 \uc800\uc7a5\uc18c)\ub85c\ubd80\ud130 Docker image \ub97c \ub85c\uceec\uc5d0 \ub2e4\uc6b4\ub85c\ub4dc \ubc1b\ub294 \ucee4\ub9e8\ub4dc\uc785\ub2c8\ub2e4."),(0,r.kt)("p",null,"\uc544\ub798 \ucee4\ub9e8\ub4dc\ub97c \ud1b5\ud574 docker pull\uc5d0\uc11c \uc0ac\uc6a9 \uac00\ub2a5\ud55c argument\ub4e4\uc744 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"docker pull --help\n")),(0,r.kt)("p",null,"\uc815\uc0c1\uc801\uc73c\ub85c \uc218\ud589\ub418\uba74 \uc544\ub798\uc640 \uac19\uc774 \ucd9c\ub825\ub429\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"Usage: docker pull [OPTIONS] NAME[:TAG|@DIGEST]\n\nPull an image or a repository from a registry\n\nOptions:\n -a, --all-tags Download all tagged images in the repository\n --disable-content-trust Skip image verification (default true)\n --platform string Set platform if server is multi-platform capable\n -q, --quiet Suppress verbose output\n")),(0,r.kt)("p",null,"\uc5ec\uae30\uc11c \uc54c \uc218 \uc788\ub294 \uac83\uc740 \ubc14\ub85c docker pull\uc740 \ub450 \uac1c \ud0c0\uc785\uc758 argument\ub97c \ubc1b\ub294\ub2e4\ub294 \uac83\uc744 \uc54c \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,r.kt)("ol",null,(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("inlineCode",{parentName:"li"},"[OPTIONS]")),(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("inlineCode",{parentName:"li"},"NAME[:TAG|@DIGEST]"))),(0,r.kt)("p",null,"help\uc5d0\uc11c \ub098\uc628 ",(0,r.kt)("inlineCode",{parentName:"p"},"-a"),", -",(0,r.kt)("inlineCode",{parentName:"p"},"q")," \uc635\uc158\uc744 \uc0ac\uc6a9\ud558\uae30 \uc704\ud574\uc11c\ub294 NAME \uc55e\uc5d0\uc11c \uc0ac\uc6a9\ud574\uc57c \ud569\ub2c8\ub2e4."),(0,r.kt)("p",null,"\uc9c1\uc811 ",(0,r.kt)("inlineCode",{parentName:"p"},"ubuntu:18.04")," \uc774\ubbf8\uc9c0\ub97c pull \ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"docker pull ubuntu:18.04\n")),(0,r.kt)("p",null,"\uc704 \uba85\ub839\uc5b4\ub97c \ud574\uc11d\ud558\uba74 ",(0,r.kt)("inlineCode",{parentName:"p"},"ubuntu")," \ub77c\ub294 \uc774\ub984\uc744 \uac00\uc9c4 \uc774\ubbf8\uc9c0 \uc911 ",(0,r.kt)("inlineCode",{parentName:"p"},"18.04")," \ud0dc\uadf8\uac00 \ub2ec\ub824\uc788\ub294 \uc774\ubbf8\uc9c0\ub97c \uac00\uc838\uc624\ub77c\ub294 \ub73b\uc785\ub2c8\ub2e4."),(0,r.kt)("p",null,"\ub9cc\uc57d, \uc815\uc0c1\uc801\uc73c\ub85c \uc218\ud589\ub41c\ub2e4\uba74 \ub2e4\uc74c\uacfc \ube44\uc2b7\ud558\uac8c \ucd9c\ub825\ub429\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"18.04: Pulling from library/ubuntu\n20d796c36622: Pull complete \nDigest: sha256:42cd9143b6060261187a72716906187294b8b66653b50d70bc7a90ccade5c984\nStatus: Downloaded newer image for ubuntu:18.04\ndocker.io/library/ubuntu:18.04\n")),(0,r.kt)("p",null,"\uc704\uc758 \uba85\ub839\uc5b4\ub97c \uc218\ud589\ud558\uba74 ",(0,r.kt)("a",{parentName:"p",href:"http://docker.io/library/"},"docker.io/library")," \ub77c\ub294 \uc774\ub984\uc758 registry \uc5d0\uc11c ubuntu:18.04 \ub77c\ub294 image \ub97c \uc5ec\ub7ec\ubd84\uc758 \ub178\ud2b8\ubd81\uc5d0 \ub2e4\uc6b4\ub85c\ub4dc \ubc1b\uac8c\ub429\ub2c8\ub2e4."),(0,r.kt)("ul",null,(0,r.kt)("li",{parentName:"ul"},"\ucc38\uace0\uc0ac\ud56d",(0,r.kt)("ul",{parentName:"li"},(0,r.kt)("li",{parentName:"ul"},"\ucd94\ud6c4 ",(0,r.kt)("a",{parentName:"li",href:"http://docker.io"},"docker.io")," \ub098 public \ud55c docker hub \uc640 \uac19\uc740 registry \ub300\uc2e0\uc5d0, \ud2b9\uc815 ",(0,r.kt)("strong",{parentName:"li"},"private")," \ud55c registry \uc5d0\uc11c docker image \ub97c \uac00\uc838\uc640\uc57c \ud558\ub294 \uacbd\uc6b0\uc5d0\ub294, ",(0,r.kt)("a",{parentName:"li",href:"https://docs.docker.com/engine/reference/commandline/login/"},(0,r.kt)("inlineCode",{parentName:"a"},"docker login"))," \uc744 \ud1b5\ud574\uc11c \ud2b9\uc815 registry \ub97c \ubc14\ub77c\ubcf4\ub3c4\ub85d \ud55c \ub4a4, docker pull \uc744 \uc218\ud589\ud558\ub294 \ud615\ud0dc\ub85c \uc0ac\uc6a9\ud569\ub2c8\ub2e4. \ud639\uc740 insecure registry \ub97c \uc124\uc815\ud558\ub294 ",(0,r.kt)("a",{parentName:"li",href:"https://stackoverflow.com/questions/42211380/add-insecure-registry-to-docker"},"\ubc29\uc548"),"\ub3c4 \ud65c\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,r.kt)("li",{parentName:"ul"},"\ud3d0\uc1c4\ub9dd\uc5d0\uc11c docker image \ub97c ",(0,r.kt)("inlineCode",{parentName:"li"},".tar")," \ud30c\uc77c\uacfc \uac19\uc740 \ud615\ud0dc\ub85c \uc800\uc7a5\ud558\uace0 \uacf5\uc720\ud560 \uc218 \uc788\ub3c4\ub85d ",(0,r.kt)("a",{parentName:"li",href:"https://docs.docker.com/engine/reference/commandline/save/"},(0,r.kt)("inlineCode",{parentName:"a"},"docker save")),", ",(0,r.kt)("a",{parentName:"li",href:"https://docs.docker.com/engine/reference/commandline/load/"},(0,r.kt)("inlineCode",{parentName:"a"},"docker load"))," \uc640 \uac19\uc740 \uba85\ub839\uc5b4\ub3c4 \uc874\uc7ac\ud569\ub2c8\ub2e4.")))),(0,r.kt)("h2",{id:"3-docker-images"},"3. Docker images"),(0,r.kt)("p",null,"\ub85c\uceec\uc5d0 \uc874\uc7ac\ud558\ub294 docker image \ub9ac\uc2a4\ud2b8\ub97c \ucd9c\ub825\ud558\ub294 \ucee4\ub9e8\ub4dc\uc785\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"docker images --help\n")),(0,r.kt)("p",null,"docker images\uc5d0\uc11c \uc0ac\uc6a9\ud560 \uc218 \uc788\ub294 argument\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"Usage: docker images [OPTIONS] [REPOSITORY[:TAG]]\n\nList images\n\nOptions:\n -a, --all Show all images (default hides intermediate images)\n --digests Show digests\n -f, --filter filter Filter output based on conditions provided\n --format string Pretty-print images using a Go template\n --no-trunc Don't truncate output\n -q, --quiet Only show image IDs\n")),(0,r.kt)("p",null,"\uc544\ub798 \uba85\ub839\uc5b4\ub97c \uc774\uc6a9\ud574 \uc9c1\uc811 \uc2e4\ud589\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"docker images\n")),(0,r.kt)("p",null,"\ub9cc\uc57d \ub3c4\ucee4\ub97c \ucd5c\ucd08 \uc124\uce58 \ud6c4 \uc774 \uc2e4\uc2b5\uc744 \uc9c4\ud589\ud55c\ub2e4\uba74 \ub2e4\uc74c\uacfc \ube44\uc2b7\ud558\uac8c \ucd9c\ub825\ub429\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"REPOSITORY TAG IMAGE ID CREATED SIZE\nubuntu 18.04 29e70752d7b2 2 days ago 56.7MB\n")),(0,r.kt)("p",null,"\uc904 \uc218 \uc788\ub294 argument\uc911 ",(0,r.kt)("inlineCode",{parentName:"p"},"-q"),"\ub97c \uc0ac\uc6a9\ud558\uba74 ",(0,r.kt)("inlineCode",{parentName:"p"},"IMAGE ID")," \ub9cc \ucd9c\ub825\ub429\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"docker images -q\n")),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"29e70752d7b2\n")),(0,r.kt)("h2",{id:"4-docker-ps"},"4. Docker ps"),(0,r.kt)("p",null,"\ud604\uc7ac \uc2e4\ud589 \uc911\uc778 \ub3c4\ucee4 \ucee8\ud14c\uc774\ub108 \ub9ac\uc2a4\ud2b8\ub97c \ucd9c\ub825\ud558\ub294 \ucee4\ub9e8\ub4dc\uc785\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"docker ps --help\n")),(0,r.kt)("p",null,"docker ps\uc5d0\uc11c \uc0ac\uc6a9\ud560 \uc218 \uc788\ub294 argument\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"Usage: docker ps [OPTIONS]\n\nList containers\n\nOptions:\n -a, --all Show all containers (default shows just running)\n -f, --filter filter Filter output based on conditions provided\n --format string Pretty-print containers using a Go template\n -n, --last int Show n last created containers (includes all states) (default -1)\n -l, --latest Show the latest created container (includes all states)\n --no-trunc Don't truncate output\n -q, --quiet Only display container IDs\n -s, --size Display total file sizes\n")),(0,r.kt)("p",null,"\uc544\ub798 \uba85\ub839\uc5b4\ub97c \uc774\uc6a9\ud574 \uc9c1\uc811 \uc2e4\ud589\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"docker ps\n")),(0,r.kt)("p",null,"\ud604\uc7ac \uc2e4\ud589 \uc911\uc778 \ucee8\ud14c\uc774\ub108\uac00 \uc5c6\ub2e4\uba74 \ub2e4\uc74c\uacfc \uac19\uc774 \ub098\uc635\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES\n")),(0,r.kt)("p",null,"\ub9cc\uc57d \uc2e4\ud589\ub418\ub294 \ucee8\ud14c\uc774\ub108\uac00 \uc788\ub2e4\uba74 \ub2e4\uc74c\uacfc \ube44\uc2b7\ud558\uac8c \ub098\uc635\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},'CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES\nc1e8f5e89d8d ubuntu "sleep 3600" 13 seconds ago Up 12 seconds trusting_newton\n')),(0,r.kt)("h2",{id:"5-docker-run"},"5. Docker run"),(0,r.kt)("p",null,"\ub3c4\ucee4 \ucee8\ud14c\uc774\ub108\ub97c \uc2e4\ud589\uc2dc\ud0a4\ub294 \ucee4\ub9e8\ub4dc\uc785\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"docker run --help\n")),(0,r.kt)("p",null,"docker run\uc744 \uc2e4\ud589\ud558\ub294 \uba85\ub839\uc5b4\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"Usage: docker run [OPTIONS] IMAGE [COMMAND] [ARG...]\n\nRun a command in a new container\n")),(0,r.kt)("p",null,"\uc5ec\uae30\uc11c \uc6b0\ub9ac\uac00 \ud655\uc778\ud574\uc57c \ud558\ub294 \uac83\uc740 \ubc14\ub85c docker run\uc740 \uc138 \uac1c \ud0c0\uc785\uc758 argument\ub97c \ubc1b\ub294\ub2e4\ub294 \uac83\uc744 \uc54c \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,r.kt)("ol",null,(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("inlineCode",{parentName:"li"},"[OPTIONS]")),(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("inlineCode",{parentName:"li"},"[COMMAND]")),(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("inlineCode",{parentName:"li"},"[ARG...]"))),(0,r.kt)("p",null,"\uc9c1\uc811 \ub3c4\ucee4 \ucee8\ud14c\uc774\ub108\ub97c \uc2e4\ud589\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"## Usage: docker run [OPTIONS] IMAGE [COMMAND] [ARG...]\ndocker run -it --name demo1 ubuntu:18.04 /bin/bash\n")),(0,r.kt)("ul",null,(0,r.kt)("li",{parentName:"ul"},(0,r.kt)("inlineCode",{parentName:"li"},"-it")," : ",(0,r.kt)("inlineCode",{parentName:"li"},"-i")," \uc635\uc158 + ",(0,r.kt)("inlineCode",{parentName:"li"},"-t")," \uc635\uc158",(0,r.kt)("ul",{parentName:"li"},(0,r.kt)("li",{parentName:"ul"},"container \ub97c \uc2e4\ud589\uc2dc\ud0b4\uacfc \ub3d9\uc2dc\uc5d0 interactive \ud55c terminal \ub85c \uc811\uc18d\uc2dc\ucf1c\uc8fc\ub294 \uc635\uc158"))),(0,r.kt)("li",{parentName:"ul"},(0,r.kt)("inlineCode",{parentName:"li"},"--name")," : name",(0,r.kt)("ul",{parentName:"li"},(0,r.kt)("li",{parentName:"ul"},"\ucee8\ud14c\uc774\ub108 id \ub300\uc2e0, \uad6c\ubd84\ud558\uae30 \uc27d\ub3c4\ub85d \uc9c0\uc815\ud574\uc8fc\ub294 \uc774\ub984"))),(0,r.kt)("li",{parentName:"ul"},(0,r.kt)("inlineCode",{parentName:"li"},"/bin/bash"),(0,r.kt)("ul",{parentName:"li"},(0,r.kt)("li",{parentName:"ul"},"\ucee8\ud14c\uc774\ub108\ub97c \uc2e4\ud589\uc2dc\ud0b4\uacfc \ub3d9\uc2dc\uc5d0 \uc2e4\ud589\ud560 \ucee4\ub9e8\ub4dc\ub85c, ",(0,r.kt)("inlineCode",{parentName:"li"},"/bin/bash")," \ub294 bash \uc258\uc744 \uc5ec\ub294 \uac83\uc744 \uc758\ubbf8\ud569\ub2c8\ub2e4.")))),(0,r.kt)("p",null,"\uc2e4\ud589 \ud6c4 ",(0,r.kt)("inlineCode",{parentName:"p"},"exit")," \uba85\ub839\uc5b4\ub97c \ud1b5\ud574 \ucee8\ud14c\uc774\ub108\ub97c \uc885\ub8cc\ud569\ub2c8\ub2e4."),(0,r.kt)("p",null,"\uc774 \uc81c \uc55e\uc11c \ubc30\uc6e0\ub358 ",(0,r.kt)("inlineCode",{parentName:"p"},"docker ps")," \uba85\ub839\uc5b4\ub97c \uce58\uba74 \ub2e4\uc74c\uacfc \uac19\uc774 \ub098\uc635\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES\n")),(0,r.kt)("p",null,"\uc2e4\ud589\ub418\uace0 \uc788\ub294 \ucee8\ud14c\uc774\ub108\uac00 \ub098\uc628\ub2e4\uace0 \ud588\uc9c0\ub9cc \uc5b4\uc9f8\uc11c\uc778\uc9c0 \ubc29\uae08 \uc2e4\ud589\ud55c \ucee8\ud14c\uc774\ub108\uac00 \ubcf4\uc774\uc9c0 \uc54a\uc2b5\ub2c8\ub2e4.\n\uadf8 \uc774\uc720\ub294 ",(0,r.kt)("inlineCode",{parentName:"p"},"docker ps"),"\ub294 \uae30\ubcf8\uac12\uc73c\ub85c \ud604\uc7ac \uc2e4\ud589 \uc911\uc778 \ucee8\ud14c\uc774\ub108\ub97c \ubcf4\uc5ec\uc8fc\uae30 \ub54c\ubb38\uc785\ub2c8\ub2e4."),(0,r.kt)("p",null,"\ub9cc\uc57d \uc885\ub8cc\ub41c \ucee8\ud14c\uc774\ub108\ub4e4\ub3c4 \ubcf4\uace0 \uc2f6\ub2e4\uba74 ",(0,r.kt)("inlineCode",{parentName:"p"},"-a")," \uc635\uc158\uc744 \uc8fc\uc5b4\uc57c \ud569\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"docker ps -a\n")),(0,r.kt)("p",null,"\uadf8\ub7ec\uba74 \ub2e4\uc74c\uacfc \uac19\uc774 \uc885\ub8cc\ub41c \ucee8\ud14c\uc774\ub108 \ubaa9\ub85d\ub3c4 \ub098\uc635\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},'CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES\n4c1aa74a382a ubuntu:18.04 "/bin/bash" 2 minutes ago Exited (0) 2 minutes ago demo1\n')),(0,r.kt)("h2",{id:"6-docker-exec"},"6. Docker exec"),(0,r.kt)("p",null,"Docker \ucee8\ud14c\uc774\ub108 \ub0b4\ubd80\uc5d0\uc11c \uba85\ub839\uc744 \ub0b4\ub9ac\uac70\ub098, \ub0b4\ubd80\ub85c \uc811\uc18d\ud558\ub294 \ucee4\ub9e8\ub4dc\uc785\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"docker exec --help\n")),(0,r.kt)("p",null,"\uc608\ub97c \ub4e4\uc5b4\uc11c \ub2e4\uc74c\uacfc \uac19\uc740 \uba85\ub839\uc5b4\ub97c \uc2e4\ud589\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"docker run -d --name demo2 ubuntu:18.04 sleep 3600\n")),(0,r.kt)("p",null,"\uc5ec\uae30\uc11c ",(0,r.kt)("inlineCode",{parentName:"p"},"-d")," \uc635\uc158\uc740 \ub3c4\ucee4 \ucee8\ud14c\uc774\ub108\ub97c \ubc31\uadf8\ub77c\uc6b4\ub4dc\uc5d0\uc11c \uc2e4\ud589\uc2dc\ucf1c\uc11c, \ucee8\ud14c\uc774\ub108\uc5d0\uc11c \uc811\uc18d \uc885\ub8cc\ub97c \ud558\ub354\ub77c\ub3c4, \uacc4\uc18d \uc2e4\ud589 \uc911\uc774 \ub418\ub3c4\ub85d \ud558\ub294 \ucee4\ub9e8\ub4dc\uc785\ub2c8\ub2e4."),(0,r.kt)("p",null,(0,r.kt)("inlineCode",{parentName:"p"},"docker ps"),"\ub97c \ud1b5\ud574 \ud604\uc7ac \uc2e4\ud589\uc911\uc778\uc9c0 \ud655\uc778\ud569\ub2c8\ub2e4."),(0,r.kt)("p",null,"\ub2e4\uc74c\uacfc \uac19\uc774 \uc2e4\ud589 \uc911\uc784\uc744 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},'CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES\nfc88a83e90f0 ubuntu:18.04 "sleep 3600" 4 seconds ago Up 3 seconds demo2\n')),(0,r.kt)("p",null,"\uc774\uc81c ",(0,r.kt)("inlineCode",{parentName:"p"},"docker exec")," \uba85\ub839\uc5b4\ub97c \ud1b5\ud574\uc11c \uc2e4\ud589\uc911\uc778 \ub3c4\ucee4 \ucee8\ud14c\uc774\ub108\uc5d0 \uc811\uc18d\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"docker exec -it demo2 /bin/bash\n")),(0,r.kt)("p",null,"\uc774 \uc804\uc758 ",(0,r.kt)("inlineCode",{parentName:"p"},"docker run"),"\uacfc \ub3d9\uc77c\ud558\uac8c container \ub0b4\ubd80\uc5d0 \uc811\uc18d\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,r.kt)("p",null,(0,r.kt)("inlineCode",{parentName:"p"},"exit"),"\uc744 \ud1b5\ud574 \uc885\ub8cc\ud569\ub2c8\ub2e4."),(0,r.kt)("h2",{id:"7-docker-logs"},"7. 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the repository\n --disable-content-trust Skip image verification (default true)\n --platform string Set platform if server is multi-platform capable\n -q, --quiet Suppress verbose output\n")),(0,r.kt)("p",null,"\uc5ec\uae30\uc11c \uc54c \uc218 \uc788\ub294 \uac83\uc740 \ubc14\ub85c docker pull\uc740 \ub450 \uac1c \ud0c0\uc785\uc758 argument\ub97c \ubc1b\ub294\ub2e4\ub294 \uac83\uc744 \uc54c \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,r.kt)("ol",null,(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("inlineCode",{parentName:"li"},"[OPTIONS]")),(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("inlineCode",{parentName:"li"},"NAME[:TAG|@DIGEST]"))),(0,r.kt)("p",null,"help\uc5d0\uc11c \ub098\uc628 ",(0,r.kt)("inlineCode",{parentName:"p"},"-a"),", -",(0,r.kt)("inlineCode",{parentName:"p"},"q")," \uc635\uc158\uc744 \uc0ac\uc6a9\ud558\uae30 \uc704\ud574\uc11c\ub294 NAME \uc55e\uc5d0\uc11c \uc0ac\uc6a9\ud574\uc57c \ud569\ub2c8\ub2e4."),(0,r.kt)("p",null,"\uc9c1\uc811 ",(0,r.kt)("inlineCode",{parentName:"p"},"ubuntu:18.04")," \uc774\ubbf8\uc9c0\ub97c pull \ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"docker pull ubuntu:18.04\n")),(0,r.kt)("p",null,"\uc704 \uba85\ub839\uc5b4\ub97c \ud574\uc11d\ud558\uba74 ",(0,r.kt)("inlineCode",{parentName:"p"},"ubuntu")," \ub77c\ub294 \uc774\ub984\uc744 \uac00\uc9c4 \uc774\ubbf8\uc9c0 \uc911 ",(0,r.kt)("inlineCode",{parentName:"p"},"18.04")," \ud0dc\uadf8\uac00 \ub2ec\ub824\uc788\ub294 \uc774\ubbf8\uc9c0\ub97c \uac00\uc838\uc624\ub77c\ub294 \ub73b\uc785\ub2c8\ub2e4."),(0,r.kt)("p",null,"\ub9cc\uc57d, \uc815\uc0c1\uc801\uc73c\ub85c \uc218\ud589\ub41c\ub2e4\uba74 \ub2e4\uc74c\uacfc \ube44\uc2b7\ud558\uac8c \ucd9c\ub825\ub429\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"18.04: Pulling from library/ubuntu\n20d796c36622: Pull complete \nDigest: sha256:42cd9143b6060261187a72716906187294b8b66653b50d70bc7a90ccade5c984\nStatus: Downloaded newer image for ubuntu:18.04\ndocker.io/library/ubuntu:18.04\n")),(0,r.kt)("p",null,"\uc704\uc758 \uba85\ub839\uc5b4\ub97c \uc218\ud589\ud558\uba74 ",(0,r.kt)("a",{parentName:"p",href:"http://docker.io/library/"},"docker.io/library")," \ub77c\ub294 \uc774\ub984\uc758 registry \uc5d0\uc11c ubuntu:18.04 \ub77c\ub294 image \ub97c \uc5ec\ub7ec\ubd84\uc758 \ub178\ud2b8\ubd81\uc5d0 \ub2e4\uc6b4\ub85c\ub4dc \ubc1b\uac8c\ub429\ub2c8\ub2e4."),(0,r.kt)("ul",null,(0,r.kt)("li",{parentName:"ul"},"\ucc38\uace0\uc0ac\ud56d",(0,r.kt)("ul",{parentName:"li"},(0,r.kt)("li",{parentName:"ul"},"\ucd94\ud6c4 ",(0,r.kt)("a",{parentName:"li",href:"http://docker.io"},"docker.io")," \ub098 public \ud55c docker hub \uc640 \uac19\uc740 registry \ub300\uc2e0\uc5d0, \ud2b9\uc815 ",(0,r.kt)("strong",{parentName:"li"},"private")," \ud55c registry \uc5d0\uc11c docker image \ub97c \uac00\uc838\uc640\uc57c \ud558\ub294 \uacbd\uc6b0\uc5d0\ub294, ",(0,r.kt)("a",{parentName:"li",href:"https://docs.docker.com/engine/reference/commandline/login/"},(0,r.kt)("inlineCode",{parentName:"a"},"docker login"))," \uc744 \ud1b5\ud574\uc11c \ud2b9\uc815 registry \ub97c \ubc14\ub77c\ubcf4\ub3c4\ub85d \ud55c \ub4a4, docker pull \uc744 \uc218\ud589\ud558\ub294 \ud615\ud0dc\ub85c \uc0ac\uc6a9\ud569\ub2c8\ub2e4. \ud639\uc740 insecure registry \ub97c \uc124\uc815\ud558\ub294 ",(0,r.kt)("a",{parentName:"li",href:"https://stackoverflow.com/questions/42211380/add-insecure-registry-to-docker"},"\ubc29\uc548"),"\ub3c4 \ud65c\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,r.kt)("li",{parentName:"ul"},"\ud3d0\uc1c4\ub9dd\uc5d0\uc11c docker image \ub97c ",(0,r.kt)("inlineCode",{parentName:"li"},".tar")," \ud30c\uc77c\uacfc \uac19\uc740 \ud615\ud0dc\ub85c \uc800\uc7a5\ud558\uace0 \uacf5\uc720\ud560 \uc218 \uc788\ub3c4\ub85d ",(0,r.kt)("a",{parentName:"li",href:"https://docs.docker.com/engine/reference/commandline/save/"},(0,r.kt)("inlineCode",{parentName:"a"},"docker save")),", ",(0,r.kt)("a",{parentName:"li",href:"https://docs.docker.com/engine/reference/commandline/load/"},(0,r.kt)("inlineCode",{parentName:"a"},"docker load"))," \uc640 \uac19\uc740 \uba85\ub839\uc5b4\ub3c4 \uc874\uc7ac\ud569\ub2c8\ub2e4.")))),(0,r.kt)("h2",{id:"3-docker-images"},"3. Docker images"),(0,r.kt)("p",null,"\ub85c\uceec\uc5d0 \uc874\uc7ac\ud558\ub294 docker image \ub9ac\uc2a4\ud2b8\ub97c \ucd9c\ub825\ud558\ub294 \ucee4\ub9e8\ub4dc\uc785\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"docker images --help\n")),(0,r.kt)("p",null,"docker images\uc5d0\uc11c \uc0ac\uc6a9\ud560 \uc218 \uc788\ub294 argument\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"Usage: docker images [OPTIONS] [REPOSITORY[:TAG]]\n\nList images\n\nOptions:\n -a, --all Show all images (default hides intermediate images)\n --digests Show digests\n -f, --filter filter Filter output based on conditions provided\n --format string Pretty-print images using a Go template\n --no-trunc Don't truncate output\n -q, --quiet Only show image IDs\n")),(0,r.kt)("p",null,"\uc544\ub798 \uba85\ub839\uc5b4\ub97c \uc774\uc6a9\ud574 \uc9c1\uc811 \uc2e4\ud589\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"docker images\n")),(0,r.kt)("p",null,"\ub9cc\uc57d \ub3c4\ucee4\ub97c \ucd5c\ucd08 \uc124\uce58 \ud6c4 \uc774 \uc2e4\uc2b5\uc744 \uc9c4\ud589\ud55c\ub2e4\uba74 \ub2e4\uc74c\uacfc \ube44\uc2b7\ud558\uac8c \ucd9c\ub825\ub429\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"REPOSITORY TAG IMAGE ID CREATED SIZE\nubuntu 18.04 29e70752d7b2 2 days ago 56.7MB\n")),(0,r.kt)("p",null,"\uc904 \uc218 \uc788\ub294 argument\uc911 ",(0,r.kt)("inlineCode",{parentName:"p"},"-q"),"\ub97c \uc0ac\uc6a9\ud558\uba74 ",(0,r.kt)("inlineCode",{parentName:"p"},"IMAGE ID")," \ub9cc \ucd9c\ub825\ub429\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"docker images -q\n")),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"29e70752d7b2\n")),(0,r.kt)("h2",{id:"4-docker-ps"},"4. Docker ps"),(0,r.kt)("p",null,"\ud604\uc7ac \uc2e4\ud589 \uc911\uc778 \ub3c4\ucee4 \ucee8\ud14c\uc774\ub108 \ub9ac\uc2a4\ud2b8\ub97c \ucd9c\ub825\ud558\ub294 \ucee4\ub9e8\ub4dc\uc785\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"docker ps --help\n")),(0,r.kt)("p",null,"docker ps\uc5d0\uc11c \uc0ac\uc6a9\ud560 \uc218 \uc788\ub294 argument\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"Usage: docker ps [OPTIONS]\n\nList containers\n\nOptions:\n -a, --all Show all containers (default shows just running)\n -f, --filter filter Filter output based on conditions provided\n --format string Pretty-print containers using a Go template\n -n, --last int Show n last created containers (includes all states) (default -1)\n -l, --latest Show the latest created container (includes all states)\n --no-trunc Don't truncate output\n -q, --quiet Only display container IDs\n -s, --size Display total file sizes\n")),(0,r.kt)("p",null,"\uc544\ub798 \uba85\ub839\uc5b4\ub97c \uc774\uc6a9\ud574 \uc9c1\uc811 \uc2e4\ud589\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"docker ps\n")),(0,r.kt)("p",null,"\ud604\uc7ac \uc2e4\ud589 \uc911\uc778 \ucee8\ud14c\uc774\ub108\uac00 \uc5c6\ub2e4\uba74 \ub2e4\uc74c\uacfc \uac19\uc774 \ub098\uc635\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES\n")),(0,r.kt)("p",null,"\ub9cc\uc57d \uc2e4\ud589\ub418\ub294 \ucee8\ud14c\uc774\ub108\uac00 \uc788\ub2e4\uba74 \ub2e4\uc74c\uacfc \ube44\uc2b7\ud558\uac8c \ub098\uc635\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},'CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES\nc1e8f5e89d8d ubuntu "sleep 3600" 13 seconds ago Up 12 seconds trusting_newton\n')),(0,r.kt)("h2",{id:"5-docker-run"},"5. Docker run"),(0,r.kt)("p",null,"\ub3c4\ucee4 \ucee8\ud14c\uc774\ub108\ub97c \uc2e4\ud589\uc2dc\ud0a4\ub294 \ucee4\ub9e8\ub4dc\uc785\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"docker run --help\n")),(0,r.kt)("p",null,"docker run\uc744 \uc2e4\ud589\ud558\ub294 \uba85\ub839\uc5b4\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"Usage: docker run [OPTIONS] IMAGE [COMMAND] [ARG...]\n\nRun a command in a new container\n")),(0,r.kt)("p",null,"\uc5ec\uae30\uc11c \uc6b0\ub9ac\uac00 \ud655\uc778\ud574\uc57c \ud558\ub294 \uac83\uc740 \ubc14\ub85c docker run\uc740 \uc138 \uac1c \ud0c0\uc785\uc758 argument\ub97c \ubc1b\ub294\ub2e4\ub294 \uac83\uc744 \uc54c \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,r.kt)("ol",null,(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("inlineCode",{parentName:"li"},"[OPTIONS]")),(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("inlineCode",{parentName:"li"},"[COMMAND]")),(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("inlineCode",{parentName:"li"},"[ARG...]"))),(0,r.kt)("p",null,"\uc9c1\uc811 \ub3c4\ucee4 \ucee8\ud14c\uc774\ub108\ub97c \uc2e4\ud589\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"## Usage: docker run [OPTIONS] IMAGE [COMMAND] [ARG...]\ndocker run -it --name demo1 ubuntu:18.04 /bin/bash\n")),(0,r.kt)("ul",null,(0,r.kt)("li",{parentName:"ul"},(0,r.kt)("inlineCode",{parentName:"li"},"-it")," : ",(0,r.kt)("inlineCode",{parentName:"li"},"-i")," \uc635\uc158 + ",(0,r.kt)("inlineCode",{parentName:"li"},"-t")," \uc635\uc158",(0,r.kt)("ul",{parentName:"li"},(0,r.kt)("li",{parentName:"ul"},"container \ub97c \uc2e4\ud589\uc2dc\ud0b4\uacfc \ub3d9\uc2dc\uc5d0 interactive \ud55c terminal \ub85c \uc811\uc18d\uc2dc\ucf1c\uc8fc\ub294 \uc635\uc158"))),(0,r.kt)("li",{parentName:"ul"},(0,r.kt)("inlineCode",{parentName:"li"},"--name")," : name",(0,r.kt)("ul",{parentName:"li"},(0,r.kt)("li",{parentName:"ul"},"\ucee8\ud14c\uc774\ub108 id \ub300\uc2e0, \uad6c\ubd84\ud558\uae30 \uc27d\ub3c4\ub85d \uc9c0\uc815\ud574\uc8fc\ub294 \uc774\ub984"))),(0,r.kt)("li",{parentName:"ul"},(0,r.kt)("inlineCode",{parentName:"li"},"/bin/bash"),(0,r.kt)("ul",{parentName:"li"},(0,r.kt)("li",{parentName:"ul"},"\ucee8\ud14c\uc774\ub108\ub97c \uc2e4\ud589\uc2dc\ud0b4\uacfc \ub3d9\uc2dc\uc5d0 \uc2e4\ud589\ud560 \ucee4\ub9e8\ub4dc\ub85c, ",(0,r.kt)("inlineCode",{parentName:"li"},"/bin/bash")," \ub294 bash \uc258\uc744 \uc5ec\ub294 \uac83\uc744 \uc758\ubbf8\ud569\ub2c8\ub2e4.")))),(0,r.kt)("p",null,"\uc2e4\ud589 \ud6c4 ",(0,r.kt)("inlineCode",{parentName:"p"},"exit")," \uba85\ub839\uc5b4\ub97c \ud1b5\ud574 \ucee8\ud14c\uc774\ub108\ub97c \uc885\ub8cc\ud569\ub2c8\ub2e4."),(0,r.kt)("p",null,"\uc774 \uc81c \uc55e\uc11c \ubc30\uc6e0\ub358 ",(0,r.kt)("inlineCode",{parentName:"p"},"docker ps")," \uba85\ub839\uc5b4\ub97c \uce58\uba74 \ub2e4\uc74c\uacfc \uac19\uc774 \ub098\uc635\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES\n")),(0,r.kt)("p",null,"\uc2e4\ud589\ub418\uace0 \uc788\ub294 \ucee8\ud14c\uc774\ub108\uac00 \ub098\uc628\ub2e4\uace0 \ud588\uc9c0\ub9cc \uc5b4\uc9f8\uc11c\uc778\uc9c0 \ubc29\uae08 \uc2e4\ud589\ud55c \ucee8\ud14c\uc774\ub108\uac00 \ubcf4\uc774\uc9c0 \uc54a\uc2b5\ub2c8\ub2e4.\n\uadf8 \uc774\uc720\ub294 ",(0,r.kt)("inlineCode",{parentName:"p"},"docker ps"),"\ub294 \uae30\ubcf8\uac12\uc73c\ub85c \ud604\uc7ac \uc2e4\ud589 \uc911\uc778 \ucee8\ud14c\uc774\ub108\ub97c \ubcf4\uc5ec\uc8fc\uae30 \ub54c\ubb38\uc785\ub2c8\ub2e4."),(0,r.kt)("p",null,"\ub9cc\uc57d \uc885\ub8cc\ub41c \ucee8\ud14c\uc774\ub108\ub4e4\ub3c4 \ubcf4\uace0 \uc2f6\ub2e4\uba74 ",(0,r.kt)("inlineCode",{parentName:"p"},"-a")," \uc635\uc158\uc744 \uc8fc\uc5b4\uc57c \ud569\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"docker ps -a\n")),(0,r.kt)("p",null,"\uadf8\ub7ec\uba74 \ub2e4\uc74c\uacfc \uac19\uc774 \uc885\ub8cc\ub41c \ucee8\ud14c\uc774\ub108 \ubaa9\ub85d\ub3c4 \ub098\uc635\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},'CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES\n4c1aa74a382a ubuntu:18.04 "/bin/bash" 2 minutes ago Exited (0) 2 minutes ago demo1\n')),(0,r.kt)("h2",{id:"6-docker-exec"},"6. Docker exec"),(0,r.kt)("p",null,"Docker \ucee8\ud14c\uc774\ub108 \ub0b4\ubd80\uc5d0\uc11c \uba85\ub839\uc744 \ub0b4\ub9ac\uac70\ub098, \ub0b4\ubd80\ub85c \uc811\uc18d\ud558\ub294 \ucee4\ub9e8\ub4dc\uc785\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"docker exec --help\n")),(0,r.kt)("p",null,"\uc608\ub97c \ub4e4\uc5b4\uc11c \ub2e4\uc74c\uacfc \uac19\uc740 \uba85\ub839\uc5b4\ub97c \uc2e4\ud589\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"docker run -d --name demo2 ubuntu:18.04 sleep 3600\n")),(0,r.kt)("p",null,"\uc5ec\uae30\uc11c ",(0,r.kt)("inlineCode",{parentName:"p"},"-d")," \uc635\uc158\uc740 \ub3c4\ucee4 \ucee8\ud14c\uc774\ub108\ub97c \ubc31\uadf8\ub77c\uc6b4\ub4dc\uc5d0\uc11c \uc2e4\ud589\uc2dc\ucf1c\uc11c, \ucee8\ud14c\uc774\ub108\uc5d0\uc11c \uc811\uc18d \uc885\ub8cc\ub97c \ud558\ub354\ub77c\ub3c4, \uacc4\uc18d \uc2e4\ud589 \uc911\uc774 \ub418\ub3c4\ub85d \ud558\ub294 \ucee4\ub9e8\ub4dc\uc785\ub2c8\ub2e4."),(0,r.kt)("p",null,(0,r.kt)("inlineCode",{parentName:"p"},"docker ps"),"\ub97c \ud1b5\ud574 \ud604\uc7ac \uc2e4\ud589\uc911\uc778\uc9c0 \ud655\uc778\ud569\ub2c8\ub2e4."),(0,r.kt)("p",null,"\ub2e4\uc74c\uacfc \uac19\uc774 \uc2e4\ud589 \uc911\uc784\uc744 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},'CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES\nfc88a83e90f0 ubuntu:18.04 "sleep 3600" 4 seconds ago Up 3 seconds demo2\n')),(0,r.kt)("p",null,"\uc774\uc81c ",(0,r.kt)("inlineCode",{parentName:"p"},"docker exec")," \uba85\ub839\uc5b4\ub97c \ud1b5\ud574\uc11c \uc2e4\ud589\uc911\uc778 \ub3c4\ucee4 \ucee8\ud14c\uc774\ub108\uc5d0 \uc811\uc18d\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"docker exec -it demo2 /bin/bash\n")),(0,r.kt)("p",null,"\uc774 \uc804\uc758 ",(0,r.kt)("inlineCode",{parentName:"p"},"docker run"),"\uacfc \ub3d9\uc77c\ud558\uac8c container \ub0b4\ubd80\uc5d0 \uc811\uc18d\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,r.kt)("p",null,(0,r.kt)("inlineCode",{parentName:"p"},"exit"),"\uc744 \ud1b5\ud574 \uc885\ub8cc\ud569\ub2c8\ub2e4."),(0,r.kt)("h2",{id:"7-docker-logs"},"7. Docker logs"),(0,r.kt)("p",null,"\ub3c4\ucee4 \ucee8\ud14c\uc774\ub108\uc758 log\ub97c \ud655\uc778\ud558\ub294 \ucee4\ub9e8\ub4dc \uc785\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"docker logs --help\n")),(0,r.kt)("p",null,"\ub2e4\uc74c\uacfc \uac19\uc740 \ucee8\ud14c\uc774\ub108\ub97c \uc2e4\ud589\uc2dc\ud0a4\ub3c4\ub85d \ud558\uaca0\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},'docker run --name demo3 -d busybox sh -c "while true; do $(echo date); sleep 1; done"\n')),(0,r.kt)("p",null,"\uc704 \uba85\ub839\uc5b4\ub97c \ud1b5\ud574\uc11c test \ub77c\ub294 \uc774\ub984\uc758 busybox \ucee8\ud14c\uc774\ub108\ub97c \ubc31\uadf8\ub77c\uc6b4\ub4dc\uc5d0\uc11c \ub3c4\ucee4 \ucee8\ud14c\uc774\ub108\ub85c \uc2e4\ud589\ud558\uc5ec, 1\ucd08\uc5d0 \ud55c \ubc88\uc529 \ud604\uc7ac \uc2dc\uac04\uc744 \ucd9c\ub825\ud558\ub3c4\ub85d \ud588\uc2b5\ub2c8\ub2e4."),(0,r.kt)("p",null,"\uc774\uc81c \uc544\ub798 \uba85\ub839\uc5b4\ub97c \ud1b5\ud574 log\ub97c \ud655\uc778\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"docker logs demo3\n")),(0,r.kt)("p",null,"\uc815\uc0c1\uc801\uc73c\ub85c \uc218\ud589\ub418\uba74 \uc544\ub798\uc640 \ube44\uc2b7\ud558\uac8c \ub098\uc635\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"Sun Mar 6 11:06:49 UTC 2022\nSun Mar 6 11:06:50 UTC 2022\nSun Mar 6 11:06:51 UTC 2022\nSun Mar 6 11:06:52 UTC 2022\nSun Mar 6 11:06:53 UTC 2022\nSun Mar 6 11:06:54 UTC 2022\n")),(0,r.kt)("p",null,"\uadf8\ub7f0\ub370 \uc774\ub807\uac8c \uc0ac\uc6a9\ud560 \uacbd\uc6b0 \uc5ec\ud0dc\uae4c\uc9c0 \ucc0d\ud78c log \ubc16\uc5d0 \ud655\uc778\ud560 \uc218 \uc5c6\uc2b5\ub2c8\ub2e4.",(0,r.kt)("br",{parentName:"p"}),"\n","\uc774 \ub54c ",(0,r.kt)("inlineCode",{parentName:"p"},"-f")," \uc635\uc158\uc744 \uc774\uc6a9\ud574 \uacc4\uc18d watch \ud558\uba70 \ucd9c\ub825\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"docker logs demo3 -f \n")),(0,r.kt)("h2",{id:"8-docker-stop"},"8. 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file_writer)\n')),(0,r.kt)("p",null,"InputPath\ub098 OutputPath\ub294 string\uc744 \uc785\ub825\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774 string\uc740 \uc785\ub825 \ub610\ub294 \ucd9c\ub825\ud558\ub824\uace0 \ud558\ub294 \ud30c\uc77c\uc758 \ud3ec\ub9f7\uc785\ub2c8\ub2e4.",(0,r.kt)("br",{parentName:"p"}),"\n","\uadf8\ub807\ub2e4\uace0 \uaf2d \uc774 \ud3ec\ub9f7\uc73c\ub85c \ud30c\uc77c \ud615\ud0dc\ub85c \uc800\uc7a5\uc774 \uac15\uc81c\ub418\ub294 \uac83\uc740 \uc544\ub2d9\ub2c8\ub2e4.",(0,r.kt)("br",{parentName:"p"}),"\n","\ub2e4\ub9cc \ud30c\uc774\ud504\ub77c\uc778\uc744 \ucef4\ud30c\uc77c\ud560 \ub54c \ucd5c\uc18c\ud55c\uc758 \ud0c0\uc785 \uccb4\ud06c\ub97c \uc704\ud55c \ub3c4\uc6b0\ubbf8 \uc5ed\ud560\uc744 \ud569\ub2c8\ub2e4.",(0,r.kt)("br",{parentName:"p"}),"\n","\ub9cc\uc57d \ud30c\uc77c \ud3ec\ub9f7\uc774 \uace0\uc815\ub418\uc9c0 \uc54a\ub294\ub2e4\uba74 \uc785\ub825\ud558\uc9c0 \uc54a\uc73c\uba74 \ub429\ub2c8\ub2e4 (\ud0c0\uc785 \ud78c\ud2b8 \uc5d0\uc11c ",(0,r.kt)("inlineCode",{parentName:"p"},"Any")," \uc640 \uac19\uc740 \uc5ed\ud560\uc744 \ud569\ub2c8\ub2e4)."),(0,r.kt)("h3",{id:"convert-to-kubeflow-format"},"Convert to Kubeflow Format"),(0,r.kt)("p",null,"\uc791\uc131\ud55c \ucef4\ud3ec\ub10c\ud2b8\ub97c kubeflow\uc5d0\uc11c \uc0ac\uc6a9\ud560 \uc218 \uc788\ub294 \ud3ec\ub9f7\uc73c\ub85c \ubcc0\ud658\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-python"},'from kfp.components import InputPath, OutputPath, create_component_from_func\n\n\n@create_component_from_func\ndef train_from_csv(\n train_data_path: InputPath("csv"),\n train_target_path: InputPath("csv"),\n model_path: OutputPath("dill"),\n kernel: str,\n):\n import dill\n import pandas as pd\n\n from sklearn.svm import SVC\n\n train_data = pd.read_csv(train_data_path)\n train_target = pd.read_csv(train_target_path)\n\n clf = SVC(kernel=kernel)\n clf.fit(train_data, train_target)\n\n with open(model_path, mode="wb") as file_writer:\n dill.dump(clf, file_writer)\n')),(0,r.kt)("h2",{id:"rule-to-use-inputpathoutputpath"},"Rule to use InputPath/OutputPath"),(0,r.kt)("p",null,"InputPath\ub098 OutputPath argument\ub294 \ud30c\uc774\ud504\ub77c\uc778\uc73c\ub85c \uc791\uc131\ud560 \ub54c \uc9c0\ucf1c\uc57c\ud558\ub294 \uaddc\uce59\uc774 \uc788\uc2b5\ub2c8\ub2e4."),(0,r.kt)("h3",{id:"load-data-component"},"Load Data Component"),(0,r.kt)("p",null,"\uc704\uc5d0\uc11c \uc791\uc131\ud55c \ucef4\ud3ec\ub10c\ud2b8\ub97c \uc2e4\ud589\ud558\uae30 \uc704\ud574\uc11c\ub294 \ub370\uc774\ud130\uac00 \ud544\uc694\ud558\ubbc0\ub85c \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud558\ub294 \ucef4\ud3ec\ub10c\ud2b8\ub97c \uc791\uc131\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-python"},'from functools import partial\n\nfrom kfp.components import InputPath, OutputPath, create_component_from_func\n\n\n@create_component_from_func\ndef load_iris_data(\n data_path: OutputPath("csv"),\n target_path: OutputPath("csv"),\n):\n import pandas as pd\n from sklearn.datasets import load_iris\n\n iris = load_iris()\n\n data = pd.DataFrame(iris["data"], columns=iris["feature_names"])\n target = pd.DataFrame(iris["target"], columns=["target"])\n\n data.to_csv(data_path, index=False)\n target.to_csv(target_path, index=False)\n')),(0,r.kt)("h3",{id:"write-pipeline"},"Write Pipeline"),(0,r.kt)("p",null,"\uc774\uc81c \ud30c\uc774\ud504\ub77c\uc778\uc744 \uc791\uc131\ud574 \ubcf4\ub3c4\ub85d \ud558\uaca0\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-python"},'from kfp.dsl import pipeline\n\n\n@pipeline(name="complex_pipeline")\ndef complex_pipeline(kernel: str):\n iris_data = load_iris_data()\n model = train_from_csv(\n train_data=iris_data.outputs["data"],\n train_target=iris_data.outputs["target"],\n kernel=kernel,\n )\n')),(0,r.kt)("p",null,"\ud55c \uac00\uc9c0 \uc774\uc0c1\ud55c \uc810\uc744 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\uc774\uc0c1 \uc720\uc800\ub294 \uacbd\ub85c\ub97c \uc2e0\uacbd\uc4f0\uc9c0 \uc54a\uace0 \ucef4\ud3ec\ub10c\ud2b8\uac04\uc758 \uad00\uacc4\ub9cc \uc2e0\uacbd\uc4f0\uba74 \ub429\ub2c8\ub2e4."),(0,r.kt)("p",null,"\uc774\uc81c \uc774 \ub0b4\uc6a9\uc744 \ubc14\ud0d5\uc73c\ub85c \ub2e4\uc2dc \ucef4\ud3ec\ub10c\ud2b8 \ub798\ud37c\ub97c \uc791\uc131\ud558\uba74 \ub2e4\uc74c\uacfc \uac19\uc774 \ub429\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-python"},'from kfp.components import InputPath, OutputPath\n\ndef train_from_csv(\n train_data_path: InputPath("csv"),\n train_target_path: InputPath("csv"),\n model_path: OutputPath("dill"),\n kernel: str,\n):\n import dill\n import pandas as pd\n\n from sklearn.svm import SVC\n\n train_data = pd.read_csv(train_data_path)\n train_target = pd.read_csv(train_target_path)\n\n clf = SVC(kernel=kernel)\n clf.fit(train_data, train_target)\n\n with open(model_path, mode="wb") as file_writer:\n dill.dump(clf, 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",(0,r.kt)("inlineCode",{parentName:"p"},"Any")," \uc640 \uac19\uc740 \uc5ed\ud560\uc744 \ud569\ub2c8\ub2e4)."),(0,r.kt)("h3",{id:"convert-to-kubeflow-format"},"Convert to Kubeflow Format"),(0,r.kt)("p",null,"\uc791\uc131\ud55c \ucef4\ud3ec\ub10c\ud2b8\ub97c kubeflow\uc5d0\uc11c \uc0ac\uc6a9\ud560 \uc218 \uc788\ub294 \ud3ec\ub9f7\uc73c\ub85c \ubcc0\ud658\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-python"},'from kfp.components import InputPath, OutputPath, create_component_from_func\n\n\n@create_component_from_func\ndef train_from_csv(\n train_data_path: InputPath("csv"),\n train_target_path: InputPath("csv"),\n model_path: OutputPath("dill"),\n kernel: str,\n):\n import dill\n import pandas as pd\n\n from sklearn.svm import SVC\n\n train_data = pd.read_csv(train_data_path)\n train_target = pd.read_csv(train_target_path)\n\n clf = SVC(kernel=kernel)\n clf.fit(train_data, train_target)\n\n with open(model_path, mode="wb") as 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target_path: OutputPath("csv"),\n):\n import pandas as pd\n from sklearn.datasets import load_iris\n\n iris = load_iris()\n\n data = pd.DataFrame(iris["data"], columns=iris["feature_names"])\n target = pd.DataFrame(iris["target"], columns=["target"])\n\n data.to_csv(data_path, index=False)\n target.to_csv(target_path, index=False)\n')),(0,r.kt)("h3",{id:"write-pipeline"},"Write Pipeline"),(0,r.kt)("p",null,"\uc774\uc81c \ud30c\uc774\ud504\ub77c\uc778\uc744 \uc791\uc131\ud574 \ubcf4\ub3c4\ub85d \ud558\uaca0\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-python"},'from kfp.dsl import pipeline\n\n\n@pipeline(name="complex_pipeline")\ndef complex_pipeline(kernel: str):\n iris_data = load_iris_data()\n model = train_from_csv(\n train_data=iris_data.outputs["data"],\n train_target=iris_data.outputs["target"],\n kernel=kernel,\n )\n')),(0,r.kt)("p",null,"\ud55c \uac00\uc9c0 \uc774\uc0c1\ud55c \uc810\uc744 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The Docker daemon streamed that output to the Docker client, which sent it\n to your terminal.\n\nTo try something more ambitious, you can run an Ubuntu container with:\n$ docker run -it ubuntu bash\n\nShare images, automate workflows, and more with a free Docker ID:\nhttps://hub.docker.com/\n\nFor more examples and ideas, visit:\nhttps://docs.docker.com/get-started/\n')))),(0,r.kt)("h2",{id:"turn-off-swap-memory"},"Turn off Swap Memory"),(0,r.kt)("p",null,"kubelet \uc774 \uc815\uc0c1\uc801\uc73c\ub85c \ub3d9\uc791\ud558\uac8c \ud558\uae30 \uc704\ud574\uc11c\ub294 ",(0,r.kt)("strong",{parentName:"p"},"\ud074\ub7ec\uc2a4\ud130")," \ub178\ub4dc\uc5d0\uc11c swap\uc774\ub77c\uace0 \ubd88\ub9ac\ub294 \uac00\uc0c1\uba54\ubaa8\ub9ac\ub97c \uaebc \ub450\uc5b4\uc57c \ud569\ub2c8\ub2e4. \ub2e4\uc74c \uba85\ub839\uc5b4\ub97c \ud1b5\ud574 swap\uc744 \uaebc \ub461\ub2c8\ub2e4.",(0,r.kt)("br",{parentName:"p"}),"\n",(0,r.kt)("strong",{parentName:"p"},"(\ud074\ub7ec\uc2a4\ud130\uc640 \ud074\ub77c\uc774\uc5b8\ud2b8\ub97c \uac19\uc740 \ub370\uc2a4\ud06c\ud1b1\uc5d0\uc11c \uc0ac\uc6a9\ud560 \ub54c swap \uba54\ubaa8\ub9ac\ub97c \uc885\ub8cc\ud558\uba74 \uc18d\ub3c4\uc758 \uc800\ud558\uac00 \uc788\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4)")," "),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"sudo sed -i '/ swap / s/^\\(.*\\)$/#\\1/g' /etc/fstab\nsudo swapoff -a\n")),(0,r.kt)("h2",{id:"install-kubectl"},"Install Kubectl"),(0,r.kt)("p",null,"kubectl \uc740 \ucfe0\ubc84\ub124\ud2f0\uc2a4 \ud074\ub7ec\uc2a4\ud130\uc5d0 API\ub97c \uc694\uccad\ud560 \ub54c \uc0ac\uc6a9\ud558\ub294 \ud074\ub77c\uc774\uc5b8\ud2b8 \ud234\uc785\ub2c8\ub2e4. ",(0,r.kt)("strong",{parentName:"p"},"\ud074\ub77c\uc774\uc5b8\ud2b8")," \ub178\ub4dc\uc5d0 \uc124\uce58\ud574\ub450\uc5b4\uc57c \ud569\ub2c8\ub2e4."),(0,r.kt)("ol",null,(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("p",{parentName:"li"},"\ud604\uc7ac \ud3f4\ub354\uc5d0 kubectl v1.21.7 \ubc84\uc804\uc744 \ub2e4\uc6b4\ubc1b\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"curl -LO https://dl.k8s.io/release/v1.21.7/bin/linux/amd64/kubectl\n"))),(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("p",{parentName:"li"},"kubectl \uc744 \uc0ac\uc6a9\ud560 \uc218 \uc788\ub3c4\ub85d \ud30c\uc77c\uc758 \uad8c\ud55c\uacfc \uc704\uce58\ub97c \ubcc0\uacbd\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"sudo install -o root -g root -m 0755 kubectl /usr/local/bin/kubectl\n"))),(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("p",{parentName:"li"},"\uc815\uc0c1\uc801\uc73c\ub85c \uc124\uce58\ub418\uc5c8\ub294\uc9c0 \ud655\uc778\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"kubectl version --client\n")),(0,r.kt)("p",{parentName:"li"},"\ub2e4\uc74c\uacfc \uac19\uc740 \uba54\uc2dc\uc9c0\uac00 \ubcf4\uc774\uba74 \uc815\uc0c1\uc801\uc73c\ub85c \uc124\uce58\ub41c \uac83\uc744 \uc758\ubbf8\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},'Client Version: version.Info{Major:"1", Minor:"21", GitVersion:"v1.21.7", GitCommit:"1f86634ff08f37e54e8bfcd86bc90b61c98f84d4", GitTreeState:"clean", BuildDate:"2021-11-17T14:41:19Z", GoVersion:"go1.16.10", Compiler:"gc", Platform:"linux/amd64"}\n'))),(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("p",{parentName:"li"},"\uc5ec\ub7ec \uac1c\uc758 \ucfe0\ubc84\ub124\ud2f0\uc2a4 \ud074\ub7ec\uc2a4\ud130\ub97c \uc0ac\uc6a9\ud558\ub294 \uacbd\uc6b0, \uc5ec\ub7ec \uac1c\uc758 kubeconfig \ud30c\uc77c\uc744 \uad00\ub9ac\ud574\uc57c \ud558\ub294 \uacbd\uc6b0\uac00 \uc788\uc2b5\ub2c8\ub2e4.",(0,r.kt)("br",{parentName:"p"}),"\n","\uc5ec\ub7ec \uac1c\uc758 kubeconfig \ud30c\uc77c \ud639\uc740 \uc5ec\ub7ec \uac1c\uc758 kube-context\ub97c \ud6a8\uc728\uc801\uc73c\ub85c \uad00\ub9ac\ud558\ub294 \ubc29\ubc95\uc740 \ub2e4\uc74c\uacfc \uac19\uc740 \ubb38\uc11c\ub97c \ucc38\uace0\ud558\uc2dc\uae30 \ubc14\ub78d\ub2c8\ub2e4."),(0,r.kt)("ul",{parentName:"li"},(0,r.kt)("li",{parentName:"ul"},(0,r.kt)("a",{parentName:"li",href:"https://dev.to/aabiseverywhere/configuring-multiple-kubeconfig-on-your-machine-59eo"},"https://dev.to/aabiseverywhere/configuring-multiple-kubeconfig-on-your-machine-59eo")),(0,r.kt)("li",{parentName:"ul"},(0,r.kt)("a",{parentName:"li",href:"https://github.com/ahmetb/kubectx"},"https://github.com/ahmetb/kubectx"))))),(0,r.kt)("h2",{id:"references"},"References"),(0,r.kt)("ul",null,(0,r.kt)("li",{parentName:"ul"},(0,r.kt)("a",{parentName:"li",href:"https://docs.docker.com/engine/install/ubuntu/"},"Install Docker Engine on Ubuntu")),(0,r.kt)("li",{parentName:"ul"},(0,r.kt)("a",{parentName:"li",href:"https://kubernetes.io/ko/docs/tasks/tools/install-kubectl-linux/"},"\ub9ac\ub205\uc2a4\uc5d0 kubectl \uc124\uce58 \ubc0f \uc124\uc815"))))}m.isMDXComponent=!0}}]); 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Component - Debugging",description:"",sidebar_position:13,contributors:["Jongseob Jeon"]},o=void 0,i={unversionedId:"kubeflow/how-to-debug",id:"kubeflow/how-to-debug",title:"13. Component - Debugging",description:"",source:"@site/docs/kubeflow/how-to-debug.md",sourceDirName:"kubeflow",slug:"/kubeflow/how-to-debug",permalink:"/docs/kubeflow/how-to-debug",draft:!1,editUrl:"https://github.com/mlops-for-all/mlops-for-all.github.io/tree/main/docs/kubeflow/how-to-debug.md",tags:[],version:"current",lastUpdatedBy:"conqrean",lastUpdatedAt:1705274857,formattedLastUpdatedAt:"2024\ub144 1\uc6d4 14\uc77c",sidebarPosition:13,frontMatter:{title:"13. Component - Debugging",description:"",sidebar_position:13,contributors:["Jongseob Jeon"]},sidebar:"tutorialSidebar",previous:{title:"12. Component - MLFlow",permalink:"/docs/kubeflow/advanced-mlflow"},next:{title:"1. What is API Deployment?",permalink:"/docs/api-deployment/what-is-api-deployment"}},l={},d=[{value:"Debugging Pipeline",id:"debugging-pipeline",level:2},{value:"Failed Component",id:"failed-component",level:2}],s={toc:d},u="wrapper";function c(t){let{components:e,...p}=t;return(0,r.kt)(u,(0,a.Z)({},s,p,{components:e,mdxType:"MDXLayout"}),(0,r.kt)("h2",{id:"debugging-pipeline"},"Debugging Pipeline"),(0,r.kt)("p",null,"\uc774\ubc88 \ud398\uc774\uc9c0\uc5d0\uc11c\ub294 Kubeflow \ucef4\ud3ec\ub10c\ud2b8\ub97c \ub514\ubc84\uae45\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574\uc11c \uc54c\uc544\ubd05\ub2c8\ub2e4."),(0,r.kt)("h2",{id:"failed-component"},"Failed Component"),(0,r.kt)("p",null,"\uc774\ubc88 \ud398\uc774\uc9c0\uc5d0\uc11c\ub294 ",(0,r.kt)("a",{parentName:"p",href:"/docs/kubeflow/advanced-mlflow#mlflow-pipeline"},"Component - MLFlow")," \uc5d0\uc11c \uc774\uc6a9\ud55c \ud30c\uc774\ud504\ub77c\uc778\uc744 \uc870\uae08 \uc218\uc815\ud574\uc11c \uc0ac\uc6a9\ud569\ub2c8\ub2e4."),(0,r.kt)("p",null,"\uc6b0\uc120 \ucef4\ud3ec\ub10c\ud2b8\uac00 \uc2e4\ud328\ud558\ub3c4\ub85d \ud30c\uc774\ud504\ub77c\uc778\uc744 \ubcc0\uacbd\ud558\ub3c4\ub85d \ud558\uaca0\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-python"},'from functools import partial\n\nimport kfp\nfrom kfp.components import InputPath, OutputPath, create_component_from_func\nfrom kfp.dsl import pipeline\n\n\n@partial(\n create_component_from_func,\n packages_to_install=["pandas", "scikit-learn"],\n)\ndef load_iris_data(\n data_path: OutputPath("csv"),\n target_path: OutputPath("csv"),\n):\n import pandas as pd\n from sklearn.datasets import load_iris\n\n iris = load_iris()\n\n data = pd.DataFrame(iris["data"], columns=iris["feature_names"])\n target = pd.DataFrame(iris["target"], columns=["target"])\n \n data["sepal length (cm)"] = None\n data.to_csv(data_path, index=False)\n target.to_csv(target_path, index=False)\n\n\n@partial(\n create_component_from_func,\n packages_to_install=["pandas"],\n)\ndef drop_na_from_csv(\n data_path: InputPath("csv"),\n output_path: OutputPath("csv"),\n):\n import pandas as pd\n\n data = pd.read_csv(data_path)\n data = data.dropna()\n data.to_csv(output_path, index=False)\n\n\n@partial(\n create_component_from_func,\n packages_to_install=["dill", "pandas", "scikit-learn", "mlflow"],\n)\ndef train_from_csv(\n train_data_path: InputPath("csv"),\n train_target_path: InputPath("csv"),\n model_path: OutputPath("dill"),\n input_example_path: OutputPath("dill"),\n signature_path: OutputPath("dill"),\n conda_env_path: OutputPath("dill"),\n kernel: str,\n):\n import dill\n import pandas as pd\n from sklearn.svm import SVC\n\n from mlflow.models.signature import infer_signature\n from mlflow.utils.environment import _mlflow_conda_env\n\n train_data = pd.read_csv(train_data_path)\n train_target = pd.read_csv(train_target_path)\n\n clf = SVC(kernel=kernel)\n clf.fit(train_data, train_target)\n\n with open(model_path, mode="wb") as file_writer:\n dill.dump(clf, file_writer)\n\n input_example = train_data.sample(1)\n with open(input_example_path, "wb") as file_writer:\n dill.dump(input_example, file_writer)\n\n signature = infer_signature(train_data, clf.predict(train_data))\n with open(signature_path, "wb") as file_writer:\n dill.dump(signature, file_writer)\n\n conda_env = _mlflow_conda_env(\n additional_pip_deps=["dill", "pandas", "scikit-learn"]\n )\n with open(conda_env_path, "wb") as file_writer:\n dill.dump(conda_env, file_writer)\n\n\n\n@pipeline(name="debugging_pipeline")\ndef debugging_pipeline(kernel: str):\n iris_data = load_iris_data()\n drop_data = drop_na_from_csv(data=iris_data.outputs["data"])\n model = train_from_csv(\n train_data=drop_data.outputs["output"],\n train_target=iris_data.outputs["target"],\n kernel=kernel,\n )\n\nif __name__ == "__main__":\n kfp.compiler.Compiler().compile(debugging_pipeline, 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What is API Deployment?",permalink:"/docs/api-deployment/what-is-api-deployment"}},l={},d=[{value:"Debugging Pipeline",id:"debugging-pipeline",level:2},{value:"Failed Component",id:"failed-component",level:2}],s={toc:d},u="wrapper";function c(t){let{components:e,...p}=t;return(0,r.kt)(u,(0,a.Z)({},s,p,{components:e,mdxType:"MDXLayout"}),(0,r.kt)("h2",{id:"debugging-pipeline"},"Debugging Pipeline"),(0,r.kt)("p",null,"\uc774\ubc88 \ud398\uc774\uc9c0\uc5d0\uc11c\ub294 Kubeflow \ucef4\ud3ec\ub10c\ud2b8\ub97c \ub514\ubc84\uae45\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574\uc11c \uc54c\uc544\ubd05\ub2c8\ub2e4."),(0,r.kt)("h2",{id:"failed-component"},"Failed Component"),(0,r.kt)("p",null,"\uc774\ubc88 \ud398\uc774\uc9c0\uc5d0\uc11c\ub294 ",(0,r.kt)("a",{parentName:"p",href:"/docs/kubeflow/advanced-mlflow#mlflow-pipeline"},"Component - MLFlow")," \uc5d0\uc11c \uc774\uc6a9\ud55c \ud30c\uc774\ud504\ub77c\uc778\uc744 \uc870\uae08 \uc218\uc815\ud574\uc11c \uc0ac\uc6a9\ud569\ub2c8\ub2e4."),(0,r.kt)("p",null,"\uc6b0\uc120 \ucef4\ud3ec\ub10c\ud2b8\uac00 \uc2e4\ud328\ud558\ub3c4\ub85d \ud30c\uc774\ud504\ub77c\uc778\uc744 \ubcc0\uacbd\ud558\ub3c4\ub85d \ud558\uaca0\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-python"},'from functools import partial\n\nimport kfp\nfrom kfp.components import InputPath, OutputPath, create_component_from_func\nfrom kfp.dsl import pipeline\n\n\n@partial(\n create_component_from_func,\n packages_to_install=["pandas", "scikit-learn"],\n)\ndef load_iris_data(\n data_path: OutputPath("csv"),\n target_path: OutputPath("csv"),\n):\n import pandas as pd\n from sklearn.datasets import load_iris\n\n iris = load_iris()\n\n data = pd.DataFrame(iris["data"], columns=iris["feature_names"])\n target = pd.DataFrame(iris["target"], columns=["target"])\n \n data["sepal length (cm)"] = None\n data.to_csv(data_path, index=False)\n target.to_csv(target_path, index=False)\n\n\n@partial(\n create_component_from_func,\n packages_to_install=["pandas"],\n)\ndef drop_na_from_csv(\n data_path: InputPath("csv"),\n output_path: OutputPath("csv"),\n):\n import pandas as pd\n\n data = pd.read_csv(data_path)\n data = data.dropna()\n data.to_csv(output_path, index=False)\n\n\n@partial(\n create_component_from_func,\n packages_to_install=["dill", "pandas", "scikit-learn", "mlflow"],\n)\ndef train_from_csv(\n train_data_path: InputPath("csv"),\n train_target_path: InputPath("csv"),\n model_path: OutputPath("dill"),\n input_example_path: OutputPath("dill"),\n signature_path: OutputPath("dill"),\n conda_env_path: OutputPath("dill"),\n kernel: str,\n):\n import dill\n import pandas as pd\n from sklearn.svm import SVC\n\n from mlflow.models.signature import infer_signature\n from mlflow.utils.environment import _mlflow_conda_env\n\n train_data = pd.read_csv(train_data_path)\n train_target = pd.read_csv(train_target_path)\n\n clf = SVC(kernel=kernel)\n clf.fit(train_data, train_target)\n\n with open(model_path, mode="wb") as file_writer:\n dill.dump(clf, file_writer)\n\n input_example = train_data.sample(1)\n with open(input_example_path, "wb") as file_writer:\n dill.dump(input_example, file_writer)\n\n signature = infer_signature(train_data, clf.predict(train_data))\n with open(signature_path, "wb") as file_writer:\n dill.dump(signature, file_writer)\n\n conda_env = _mlflow_conda_env(\n additional_pip_deps=["dill", "pandas", "scikit-learn"]\n )\n with open(conda_env_path, "wb") as file_writer:\n dill.dump(conda_env, file_writer)\n\n\n\n@pipeline(name="debugging_pipeline")\ndef debugging_pipeline(kernel: str):\n iris_data = load_iris_data()\n drop_data = drop_na_from_csv(data=iris_data.outputs["data"])\n model = train_from_csv(\n train_data=drop_data.outputs["output"],\n train_target=iris_data.outputs["target"],\n kernel=kernel,\n )\n\nif __name__ == "__main__":\n kfp.compiler.Compiler().compile(debugging_pipeline, 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\uc815\ub82c\ud558\ub294 \uac83\uc744 \ucd94\ucc9c\ud569\ub2c8\ub2e4. (ex. ",(0,a.kt)("inlineCode",{parentName:"p"},"COPY src/ app/src/"),")"),(0,a.kt)("p",null,"\uadf8\ub807\uae30 \ub54c\ubb38\uc5d0 \ubc18\ub300\ub85c \ubcc0\uacbd\ub418\uc9c0 \uc54a\ub294 \ubd80\ubd84\uc740 \ucd5c\ub300\ud55c \uc55e\ucabd\uc73c\ub85c \uc815\ub82c\ud558\ub294\uac8c \uc88b\uc2b5\ub2c8\ub2e4."),(0,a.kt)("p",null,"\ub9cc\uc57d \uac70\uc758 ",(0,a.kt)("strong",{parentName:"p"},"\ubcc0\uacbd\ub418\uc9c0 \uc54a\uc9c0\ub9cc"),", \uc5ec\ub7ec \uacf3\uc5d0\uc11c ",(0,a.kt)("strong",{parentName:"p"},"\uc790\uc8fc")," \uc4f0\uc774\ub294 \ubd80\ubd84\uc744 \uacf5\ud1b5\ud654\ud560 \uc218\ub3c4 \uc788\uc2b5\ub2c8\ub2e4.\n\ud574\ub2f9 \uacf5\ud1b5\ubd80\ubd84\ub9cc \ubb36\uc5b4\uc11c \ubcc4\ub3c4\uc758 \uc774\ubbf8\uc9c0\ub294 \ubbf8\ub9ac \ub9cc\ub4e4\uc5b4\ub454 \ub2e4\uc74c, ",(0,a.kt)("strong",{parentName:"p"},"\ubca0\uc774\uc2a4 \uc774\ubbf8\uc9c0")," \ub85c \ud65c\uc6a9\ud558\ub294 \uac83\uc774 \uc88b\uc2b5\ub2c8\ub2e4."),(0,a.kt)("p",null,"\uc608\ub97c \ub4e4\uc5b4, \ub2e4\ub978 \uac74 \uac70\uc758 \ub611\uac19\uc740\ub370, tensorflow-cpu \ub97c \uc0ac\uc6a9\ud558\ub294 \uc774\ubbf8\uc9c0\uc640, tensorflow-gpu \ub97c \uc0ac\uc6a9\ud558\ub294 \ud658\uacbd\uc744 \ubd84\ub9ac\ud574\uc11c \uc774\ubbf8\uc9c0\ub85c \ub9cc\ub4e4\uace0 \uc2f6\uc740 \uacbd\uc6b0\uc5d0\ub294 \ub2e4\uc74c\uacfc \uac19\uc774 \ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.",(0,a.kt)("br",{parentName:"p"}),"\n","python \uacfc \uae30\ud0c0 \uae30\ubcf8\uc801\uc778 \ud328\ud0a4\uc9c0\uac00 \uc124\uce58\ub41c ",(0,a.kt)("a",{parentName:"p",href:"http://ghcr.io/makinarocks/python:3.8-base-cpu"},(0,a.kt)("inlineCode",{parentName:"a"},"ghcr.io/makinarocks/python:3.8-base"))," \ub97c \ub9cc\ub4e4\uc5b4\ub450\uace0, ",(0,a.kt)("strong",{parentName:"p"},"tensorflow cpu \ubc84\uc804\uacfc gpu \ubc84\uc804\uc774")," \uc124\uce58\ub41c \uc774\ubbf8\uc9c0 \uc0c8\ub85c \ub9cc\ub4e4\ub54c\ub294, \uc704\uc758 \uc774\ubbf8\uc9c0\ub97c ",(0,a.kt)("inlineCode",{parentName:"p"},"FROM")," \uc73c\ub85c \ubd88\ub7ec\uc628 \ub2e4\uc74c, tensorflow install \ud558\ub294 \ubd80\ubd84\ub9cc \ubcc4\ub3c4\ub85c \uc791\uc131\ud574\uc11c Dockerfile \uc744 2 \uac1c\ub85c \uad00\ub9ac\ud55c\ub2e4\uba74 \uac00\ub3c5\uc131\ub3c4 \uc88b\uace0 \ube4c\ub4dc \uc2dc\uac04\ub3c4 \uc904\uc77c \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,a.kt)("p",null,(0,a.kt)("strong",{parentName:"p"},"\ud569\uce60 \uc218 \uc788\ub294 Layer \ub294 \ud569\uce58\ub294 \uac83"),"\uc774 Old version \uc758 \ub3c4\ucee4\uc5d0\uc11c\ub294 \uc131\ub2a5 \ud5a5\uc0c1 \ud6a8\uacfc\ub97c \uc774\ub04c\uc5c8\uc2b5\ub2c8\ub2e4. \uc5ec\ub7ec\ubd84\uc758 \ub3c4\ucee4 \ucee8\ud14c\uc774\ub108\uac00 \uc5b4\ub5a4 \ub3c4\ucee4 \ubc84\uc804\uc5d0\uc11c \uc2e4\ud589\ub420 \uac83\uc778\uc9c0 \ubcf4\uc7a5\ud560 \uc218 \uc5c6\uc73c\uba70, ",(0,a.kt)("strong",{parentName:"p"},"\uac00\ub3c5\uc131"),"\uc744 \uc704\ud574\uc11c\ub3c4 \ud569\uce60 \uc218 \uc788\ub294 Layer \ub294 \uc801\uc808\ud788 \ud569\uce58\ub294 \uac83\uc774 \uc88b\uc2b5\ub2c8\ub2e4."),(0,a.kt)("p",null,"\uc608\ub97c \ub4e4\uba74, \ub2e4\uc74c\uacfc \uac19\uc774 \uc791\uc131\ub41c ",(0,a.kt)("inlineCode",{parentName:"p"},"Dockerfile"),"\uc774 \uc788\uc2b5\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-docker"},"# Bad Case\nRUN apt-get update\nRUN apt-get install build-essential -y\nRUN apt-get install curl -y\nRUN apt-get install jq -y\nRUN apt-get install git -y\n")),(0,a.kt)("p",null,"\uc774\ub97c \uc544\ub798\uc640 \uac19\uc774 \ud569\uccd0\uc11c \uc801\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-docker"},"# Better Case\nRUN apt-get update && \\\n apt-get install -y \\\n build-essential \\\n curl \\\n jq \\\n git\n")),(0,a.kt)("p",null,"\ud3b8\uc758\ub97c \uc704\ud574\uc11c\ub294 ",(0,a.kt)("inlineCode",{parentName:"p"},".dockerignore")," \ub3c4 \uc0ac\uc6a9\ud558\ub294\uac8c \uc88b\uc2b5\ub2c8\ub2e4.\n",(0,a.kt)("inlineCode",{parentName:"p"},".dockerignore"),"\ub294 ",(0,a.kt)("inlineCode",{parentName:"p"},".gitignore")," \uc640 \ube44\uc2b7\ud55c \uc5ed\ud560\uc744 \ud55c\ub2e4\uace0 \uc774\ud574\ud558\uba74 \ub429\ub2c8\ub2e4. (git add \ud560 \ub54c \uc81c\uc678\ud560 \uc218 \uc788\ub4ef\uc774, docker build \ud560 \ub54c \uc790\ub3d9\uc73c\ub85c \uc81c\uc678)"),(0,a.kt)("p",null,"\ub354 \ub9ce\uc740 \uc815\ubcf4\ub294 ",(0,a.kt)("a",{parentName:"p",href:"https://docs.docker.com/develop/develop-images/dockerfile_best-practices/"},"Docker \uacf5\uc2dd \ubb38\uc11c"),"\uc5d0\uc11c \ud655\uc778\ud558\uc2e4 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,a.kt)("h3",{id:"entrypoint-vs-cmd"},"ENTRYPOINT vs CMD"),(0,a.kt)("p",null,(0,a.kt)("inlineCode",{parentName:"p"},"ENTRYPOINT")," \uc640 ",(0,a.kt)("inlineCode",{parentName:"p"},"CMD")," \ub294 \ubaa8\ub450 \ucee8\ud14c\uc774\ub108\uc758 \uc2e4\ud589 \uc2dc\uc810\uc5d0\uc11c \uc5b4\ub5a4 \uba85\ub839\uc5b4\ub97c \uc2e4\ud589\uc2dc\ud0a4\uace0 \uc2f6\uc744 \ub54c \uc0ac\uc6a9\ud569\ub2c8\ub2e4.\n\uadf8\ub9ac\uace0 \uc774 \ub458 \uc911 \ud558\ub098\ub294 \ubc18\ub4dc\uc2dc \uc874\uc7ac\ud574\uc57c \ud569\ub2c8\ub2e4."),(0,a.kt)("ul",null,(0,a.kt)("li",{parentName:"ul"},(0,a.kt)("strong",{parentName:"li"},"\ucc28\uc774\uc810"),(0,a.kt)("ul",{parentName:"li"},(0,a.kt)("li",{parentName:"ul"},(0,a.kt)("inlineCode",{parentName:"li"},"CMD"),": docker run \uc744 \uc218\ud589\ud560 \ub54c, \uc27d\uac8c \ubcc0\uacbd\ud558\uc5ec \uc0ac\uc6a9\ud560 \uc218 \uc788\uc74c"),(0,a.kt)("li",{parentName:"ul"},(0,a.kt)("inlineCode",{parentName:"li"},"ENTRYPOINT"),": ",(0,a.kt)("inlineCode",{parentName:"li"},"--entrypoint")," \ub97c \uc0ac\uc6a9\ud574\uc57c \ubcc0\uacbd\ud560 \uc218 \uc788\uc74c")))),(0,a.kt)("p",null,(0,a.kt)("inlineCode",{parentName:"p"},"ENTRYPOINT")," \uc640 ",(0,a.kt)("inlineCode",{parentName:"p"},"CMD")," \uac00 \ud568\uaed8 \uc4f0\uc77c \ub54c\ub294 \ubcf4\ud1b5 ",(0,a.kt)("inlineCode",{parentName:"p"},"CMD"),"\ub294 ",(0,a.kt)("inlineCode",{parentName:"p"},"ENTRYPOINT")," \uc5d0\uc11c \uc801\uc740 \uba85\ub839\uc758 arguments(parameters) \ub97c \uc758\ubbf8\ud569\ub2c8\ub2e4."),(0,a.kt)("p",null,"\uc608\ub97c \ub4e4\uc5b4\uc11c \ub2e4\uc74c\uacfc \uac19\uc740 ",(0,a.kt)("inlineCode",{parentName:"p"},"Dockerfile")," \uc774 \uc788\uc2b5\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-docker"},'FROM ubuntu:latest\n\n# \uc544\ub798 4 \uac00\uc9c0 option \uc744 \ubc14\uafd4\uac00\uba70 \uc9c1\uc811 \ud14c\uc2a4\ud2b8\ud574\ubcf4\uc2dc\uba74 \uc774\ud574\ud558\uae30 \ud3b8\ud569\ub2c8\ub2e4.\n# \ub2e8, NO ENTRYPOINT \uc635\uc158\uc740 base image \uc778 ubuntu:latest \uc5d0 \uc774\ubbf8 \uc788\uc5b4\uc11c \ud14c\uc2a4\ud2b8\ud574\ubcfc \uc218\ub294 \uc5c6\uace0 \ub098\uba38\uc9c0 v2, 3, 5, 6, 8, 9, 11, 12 \ub97c \ud14c\uc2a4\ud2b8\ud574\ubcfc \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n# ENTRYPOINT echo "Hello ENTRYPOINT"\n# ENTRYPOINT ["echo", "Hello ENTRYPOINT"]\n# CMD echo "Hello CMD"\n# CMD ["echo", "Hello CMD"]\n')),(0,a.kt)("p",null,"\uc704\uc758 ",(0,a.kt)("inlineCode",{parentName:"p"},"Dockerfile"),"\uc5d0\uc11c \uc8fc\uc11d\uc73c\ub85c \ud45c\uc2dc\ub41c \ubd80\ubd84\ub4e4\uc744 \ud574\uc81c\ud558\uba70 \ube4c\ub4dc\ud558\uace0 \uc2e4\ud589\ud558\uba74 \ub2e4\uc74c\uacfc \uac19\uc740 \uacb0\uacfc\ub97c \uc5bb\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,a.kt)("table",null,(0,a.kt)("thead",{parentName:"table"},(0,a.kt)("tr",{parentName:"thead"},(0,a.kt)("th",{parentName:"tr",align:null}),(0,a.kt)("th",{parentName:"tr",align:null},"No ENTRYPOINT"),(0,a.kt)("th",{parentName:"tr",align:null},"ENTRYPOINT a b"),(0,a.kt)("th",{parentName:"tr",align:null},"ENTRYPOINT ",'["a", "b"]'))),(0,a.kt)("tbody",{parentName:"table"},(0,a.kt)("tr",{parentName:"tbody"},(0,a.kt)("td",{parentName:"tr",align:null},(0,a.kt)("strong",{parentName:"td"},"NO CMD")),(0,a.kt)("td",{parentName:"tr",align:null},"Error!"),(0,a.kt)("td",{parentName:"tr",align:null},"/bin/sh -c a b"),(0,a.kt)("td",{parentName:"tr",align:null},"a b")),(0,a.kt)("tr",{parentName:"tbody"},(0,a.kt)("td",{parentName:"tr",align:null},(0,a.kt)("strong",{parentName:"td"},"CMD ",'["x", "y"]')),(0,a.kt)("td",{parentName:"tr",align:null},"x y"),(0,a.kt)("td",{parentName:"tr",align:null},"/bin/sh -c a b"),(0,a.kt)("td",{parentName:"tr",align:null},"a b x y")),(0,a.kt)("tr",{parentName:"tbody"},(0,a.kt)("td",{parentName:"tr",align:null},(0,a.kt)("strong",{parentName:"td"},"CMD x y")),(0,a.kt)("td",{parentName:"tr",align:null},"/bin/sh -c x y"),(0,a.kt)("td",{parentName:"tr",align:null},"/bin/sh -c a b"),(0,a.kt)("td",{parentName:"tr",align:null},"a b /bin/sh -c x y")))),(0,a.kt)("ul",null,(0,a.kt)("li",{parentName:"ul"},"In Kubernetes pod",(0,a.kt)("ul",{parentName:"li"},(0,a.kt)("li",{parentName:"ul"},(0,a.kt)("inlineCode",{parentName:"li"},"ENTRYPOINT")," \u2192 command"),(0,a.kt)("li",{parentName:"ul"},(0,a.kt)("inlineCode",{parentName:"li"},"CMD")," \u2192 args")))),(0,a.kt)("h3",{id:"docker-tag-\uc774\ub984-\uc9d3\uae30"},"Docker tag \uc774\ub984 \uc9d3\uae30"),(0,a.kt)("p",null,"\ub3c4\ucee4 \uc774\ubbf8\uc9c0\uc758 tag \ub85c ",(0,a.kt)("strong",{parentName:"p"},"latest \ub294 \uc0ac\uc6a9\ud558\uc9c0 \uc54a\ub294 \uac83\uc744 \uad8c\uc7a5"),"\ud569\ub2c8\ub2e4.",(0,a.kt)("br",{parentName:"p"}),"\n","\uc774\uc720\ub294 latest \ub294 default tag name \uc774\ubbc0\ub85c ",(0,a.kt)("strong",{parentName:"p"},"\uc758\ub3c4\uce58 \uc54a\uac8c overwritten")," \ub418\ub294 \uacbd\uc6b0\uac00 \ub108\ubb34 \ub9ce\uc774 \ubc1c\uc0dd\ud558\uae30 \ub54c\ubb38\uc785\ub2c8\ub2e4."),(0,a.kt)("p",null,"\ud558\ub098\uc758 \uc774\ubbf8\uc9c0\ub294 \ud558\ub098\uc758 \ud0dc\uadf8\ub97c \uac00\uc9d0(",(0,a.kt)("strong",{parentName:"p"},"uniqueness"),")\uc744 \ubcf4\uc7a5\ud574\uc57c \ucd94\ud6c4 Production \ub2e8\uacc4\uc5d0\uc11c ",(0,a.kt)("strong",{parentName:"p"},"\ud611\uc5c5/\ub514\ubc84\uae45"),"\uc5d0 \uc6a9\uc774\ud569\ub2c8\ub2e4.",(0,a.kt)("br",{parentName:"p"}),"\n","\ub0b4\uc6a9\uc740 \ub2e4\ub974\uc9c0\ub9cc, \ub3d9\uc77c\ud55c tag \ub97c \uc0ac\uc6a9\ud558\uac8c \ub418\uba74 \ucd94\ud6c4 dangling image \ub85c \ucde8\uae09\ub418\uc5b4 \uad00\ub9ac\ud558\uae30 \uc5b4\ub824\uc6cc\uc9d1\ub2c8\ub2e4.",(0,a.kt)("br",{parentName:"p"}),"\n","dangling image\ub294 ",(0,a.kt)("inlineCode",{parentName:"p"},"docker images"),"\uc5d0\ub294 \ub098\uc624\uc9c0 \uc54a\uc9c0\ub9cc \uacc4\uc18d\ud574\uc11c \uc800\uc7a5\uc18c\ub97c \ucc28\uc9c0\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4."),(0,a.kt)("h3",{id:"etc"},"ETC"),(0,a.kt)("ol",null,(0,a.kt)("li",{parentName:"ol"},"log \ub4f1\uc758 \uc815\ubcf4\ub294 container \ub0b4\ubd80\uac00 \uc544\ub2cc \uacf3\uc5d0 \ub530\ub85c \uc800\uc7a5\ud569\ub2c8\ub2e4.\ncontainer \ub0b4\ubd80\uc5d0\uc11c write \ud55c data \ub294 \uc5b8\uc81c\ub4e0\uc9c0 \uc0ac\ub77c\uc9c8 \uc218 \uc788\uae30 \ub54c\ubb38\uc785\ub2c8\ub2e4."),(0,a.kt)("li",{parentName:"ol"},"secret \ud55c \uc815\ubcf4, \ud658\uacbd(dev/prod) dependent \ud55c \uc815\ubcf4 \ub4f1\uc740 Dockerfile \uc5d0 \uc9c1\uc811 \uc801\ub294 \uac8c \uc544\ub2c8\ub77c, env var \ub610\ub294 .env config file \uc744 \uc0ac\uc6a9\ud569\ub2c8\ub2e4."),(0,a.kt)("li",{parentName:"ol"},"Dockerfile ",(0,a.kt)("strong",{parentName:"li"},"linter")," \ub3c4 \uc874\uc7ac\ud558\ubbc0\ub85c, \ud611\uc5c5 \uc2dc\uc5d0\ub294 \ud65c\uc6a9\ud558\uba74 \uc88b\uc2b5\ub2c8\ub2e4.\n",(0,a.kt)("a",{parentName:"li",href:"https://github.com/hadolint/hadolint"},"https://github.com/hadolint/hadolint"))),(0,a.kt)("h2",{id:"docker-run-\uc758-\ub2e4\uc591\ud55c-\uc635\uc158"},"docker run \uc758 \ub2e4\uc591\ud55c \uc635\uc158"),(0,a.kt)("h3",{id:"docker-run-with-volume"},"docker run with volume"),(0,a.kt)("p",null,"Docker container \uc0ac\uc6a9 \uc2dc \ubd88\ud3b8\ud55c \uc810\uc774 \uc788\uc2b5\ub2c8\ub2e4.\n\ubc14\ub85c Docker\ub294 \uae30\ubcf8\uc801\uc73c\ub85c Docker ",(0,a.kt)("strong",{parentName:"p"},"container \ub0b4\ubd80\uc5d0\uc11c \uc791\uc5c5\ud55c \ubaa8\ub4e0 \uc0ac\ud56d\uc740 \uc800\uc7a5\ub418\uc9c0 \uc54a\uc2b5\ub2c8\ub2e4."),"\n\uc774\uc720\ub294 Docker container \ub294 \uac01\uac01 \uaca9\ub9ac\ub41c \ud30c\uc77c\uc2dc\uc2a4\ud15c\uc744 \uc0ac\uc6a9\ud569\ub2c8\ub2e4. \ub530\ub77c\uc11c, ",(0,a.kt)("strong",{parentName:"p"},"\uc5ec\ub7ec docker container \ub07c\ub9ac \ub370\uc774\ud130\ub97c \uacf5\uc720\ud558\uae30 \uc5b4\ub835\uc2b5\ub2c8\ub2e4.")),(0,a.kt)("p",null,"\uc774 \ubb38\uc81c\ub97c \ud574\uacb0\ud558\uae30 \uc704\ud574\uc11c Docker\uc5d0\uc11c \uc81c\uacf5\ud558\ub294 \ubc29\uc2dd\uc740 ",(0,a.kt)("strong",{parentName:"p"},"2 \uac00\uc9c0"),"\uac00 \uc788\uc2b5\ub2c8\ub2e4."),(0,a.kt)("p",null,(0,a.kt)("img",{alt:"storage.png",src:n(5940).Z,width:"501",height:"255"})),(0,a.kt)("h4",{id:"docker-volume"},"Docker volume"),(0,a.kt)("ul",null,(0,a.kt)("li",{parentName:"ul"},"docker cli \ub97c \uc0ac\uc6a9\ud574 ",(0,a.kt)("inlineCode",{parentName:"li"},"volume")," \uc774\ub77c\ub294 \ub9ac\uc18c\uc2a4\ub97c \uc9c1\uc811 \uad00\ub9ac"),(0,a.kt)("li",{parentName:"ul"},"host \uc5d0\uc11c Docker area(",(0,a.kt)("inlineCode",{parentName:"li"},"/var/lib/docker"),") \uc544\ub798\uc5d0 \ud2b9\uc815 \ub514\ub809\ud1a0\ub9ac\ub97c \uc0dd\uc131\ud55c \ub2e4\uc74c, \ud574\ub2f9 \uacbd\ub85c\ub97c docker container \uc5d0 mount")),(0,a.kt)("h4",{id:"bind-mount"},"Bind mount"),(0,a.kt)("ul",null,(0,a.kt)("li",{parentName:"ul"},"host \uc758 \ud2b9\uc815 \uacbd\ub85c\ub97c docker container \uc5d0 mount")),(0,a.kt)("h4",{id:"how-to-use"},"How to use?"),(0,a.kt)("p",null,"\uc0ac\uc6a9 \ubc29\uc2dd\uc740 ",(0,a.kt)("strong",{parentName:"p"},"\ub3d9\uc77c\ud55c \uc778\ud130\ud398\uc774\uc2a4"),"\ub85c ",(0,a.kt)("inlineCode",{parentName:"p"},"-v")," \uc635\uc158\uc744 \ud1b5\ud574 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.",(0,a.kt)("br",{parentName:"p"}),"\n","\ub2e4\ub9cc, volume \uc744 \uc0ac\uc6a9\ud560 \ub54c\uc5d0\ub294 ",(0,a.kt)("inlineCode",{parentName:"p"},"docker volume create"),", ",(0,a.kt)("inlineCode",{parentName:"p"},"docker volume ls"),", ",(0,a.kt)("inlineCode",{parentName:"p"},"docker volume rm")," \ub4f1\uc744 \uc218\ud589\ud558\uc5ec \uc9c1\uc811 \uad00\ub9ac\ud574\uc8fc\uc5b4\uc57c \ud569\ub2c8\ub2e4."),(0,a.kt)("ul",null,(0,a.kt)("li",{parentName:"ul"},(0,a.kt)("p",{parentName:"li"},"Docker volume"),(0,a.kt)("pre",{parentName:"li"},(0,a.kt)("code",{parentName:"pre",className:"language-bash"},"docker run \\\n -v my_volume:/app \\\n nginx:latest\n"))),(0,a.kt)("li",{parentName:"ul"},(0,a.kt)("p",{parentName:"li"},"Blind mount"),(0,a.kt)("pre",{parentName:"li"},(0,a.kt)("code",{parentName:"pre",className:"language-bash"},"docker run \\\n -v /home/user/some/path:/app \\\n nginx:latest\n")))),(0,a.kt)("p",null,"\ub85c\uceec\uc5d0\uc11c \uac1c\ubc1c\ud560 \ub54c\ub294 bind mount \uac00 \ud3b8\ud558\uae34 \ud558\uc9c0\ub9cc, \ud658\uacbd\uc744 \uae54\ub054\ud558\uac8c \uc720\uc9c0\ud558\uace0 \uc2f6\ub2e4\uba74 docker volume \uc744 \uc0ac\uc6a9\ud558\uc5ec 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\uc774\ubbf8\uc9c0\ub97c \ub9cc\ub4e4 \ub54c \uace0\ub824\ud574\uc57c \ub420 \uc810"),(0,a.kt)("p",null,"Dockerfile \uc744 \ud65c\uc6a9\ud558\uc5ec \ub3c4\ucee4 \uc774\ubbf8\uc9c0\ub97c \ub9cc\ub4e4 \ub54c\ub294 \uba85\ub839\uc5b4\uc758 ",(0,a.kt)("strong",{parentName:"p"},"\uc21c\uc11c"),"\uac00 \uc911\uc694\ud569\ub2c8\ub2e4.",(0,a.kt)("br",{parentName:"p"}),"\n","\uadf8 \uc774\uc720\ub294 \ub3c4\ucee4 \uc774\ubbf8\uc9c0\ub294 \uc5ec\ub7ec \uac1c\uc758 Read-Only Layer \ub85c \uad6c\uc131\ub418\uc5b4\uc788\uace0, \uc774\ubbf8\uc9c0\ub97c \ube4c\ub4dc\ud560 \ub54c \uc774\ubbf8 \uc874\uc7ac\ud558\ub294 \ub808\uc774\uc5b4\ub294 ",(0,a.kt)("strong",{parentName:"p"},"\uce90\uc2dc\ub418\uc5b4")," \uc7ac\uc0ac\uc6a9\ub418\uae30 \ub54c\ubb38\uc5d0, \uc774\ub97c \uc0dd\uac01\ud574\uc11c Dockerfile \uc744 \uad6c\uc131\ud55c\ub2e4\uba74 ",(0,a.kt)("strong",{parentName:"p"},"\ube4c\ub4dc \uc2dc\uac04\uc744 \uc904\uc77c \uc218 \uc788\uc2b5\ub2c8\ub2e4.")),(0,a.kt)("p",null,"Dockerfile\uc5d0\uc11c ",(0,a.kt)("inlineCode",{parentName:"p"},"RUN"),", ",(0,a.kt)("inlineCode",{parentName:"p"},"ADD"),", ",(0,a.kt)("inlineCode",{parentName:"p"},"COPY")," \uba85\ub839\uc5b4 \ud558\ub098\uac00 \ud558\ub098\uc758 \ub808\uc774\uc5b4\ub85c \uc800\uc7a5\ub429\ub2c8\ub2e4."),(0,a.kt)("p",null,"\uc608\ub97c \ub4e4\uc5b4\uc11c \ub2e4\uc74c\uacfc \uac19\uc740 ",(0,a.kt)("inlineCode",{parentName:"p"},"Dockerfile"),"\uc774 \uc788\uc2b5\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-docker"},"# Layer 1\nFROM ubuntu:latest\n\n# Layer 2\nRUN apt-get update && apt-get install python3 pip3 -y\n\n# Layer 3\nRUN pip3 install -U pip && pip3 install torch\n\n# Layer 4\nCOPY src/ src/\n\n# Layer 5\nCMD python src/app.py\n")),(0,a.kt)("p",null,"\uc704\uc758 ",(0,a.kt)("inlineCode",{parentName:"p"},"Dockerfile"),"\ub85c \ube4c\ub4dc\ub41c \uc774\ubbf8\uc9c0\ub97c ",(0,a.kt)("inlineCode",{parentName:"p"},"docker run -it app:latest /bin/bash")," \uba85\ub839\uc5b4\ub85c \uc2e4\ud589\ud558\uba74 \ub2e4\uc74c\uacfc \uac19\uc740 \ub808\uc774\uc5b4\ub85c \ud45c\ud604\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,a.kt)("p",null,(0,a.kt)("img",{alt:"layers.png",src:n(6045).Z,width:"1080",height:"612"})),(0,a.kt)("p",null,"\ucd5c\uc0c1\ub2e8\uc758 R/W Layer \ub294 \uc774\ubbf8\uc9c0\uc5d0 \uc601\ud5a5\uc744 \uc8fc\uc9c0 \uc54a\uc2b5\ub2c8\ub2e4. \uc989, \ucee8\ud14c\uc774\ub108 \ub0b4\ubd80\uc5d0\uc11c \uc791\uc5c5\ud55c \ub0b4\uc5ed\uc740 \ubaa8\ub450 \ud718\ubc1c\uc131\uc785\ub2c8\ub2e4."),(0,a.kt)("p",null,"\ud558\ub2e8\uc758 \ub808\uc774\uc5b4\uac00 \ubcc0\uacbd\ub418\uba74, \uadf8 \uc704\uc758 \ub808\uc774\uc5b4\ub294 \ubaa8\ub450 \uc0c8\ub85c \ube4c\ub4dc\ub429\ub2c8\ub2e4. \uadf8\ub798\uc11c Dockerfile \ub0b4\uc7a5 \uba85\ub839\uc5b4\uc758 \uc21c\uc11c\uac00 \uc911\uc694\ud569\ub2c8\ub2e4.",(0,a.kt)("br",{parentName:"p"}),"\n","\uc608\ub97c \ub4e4\uba74, ",(0,a.kt)("strong",{parentName:"p"},"\uc790\uc8fc \ubcc0\uacbd"),"\ub418\ub294 \ubd80\ubd84\uc740 ",(0,a.kt)("strong",{parentName:"p"},"\ucd5c\ub300\ud55c \ub4a4\ucabd\uc73c\ub85c")," \uc815\ub82c\ud558\ub294 \uac83\uc744 \ucd94\ucc9c\ud569\ub2c8\ub2e4. (ex. ",(0,a.kt)("inlineCode",{parentName:"p"},"COPY src/ app/src/"),")"),(0,a.kt)("p",null,"\uadf8\ub807\uae30 \ub54c\ubb38\uc5d0 \ubc18\ub300\ub85c \ubcc0\uacbd\ub418\uc9c0 \uc54a\ub294 \ubd80\ubd84\uc740 \ucd5c\ub300\ud55c \uc55e\ucabd\uc73c\ub85c \uc815\ub82c\ud558\ub294\uac8c \uc88b\uc2b5\ub2c8\ub2e4."),(0,a.kt)("p",null,"\ub9cc\uc57d \uac70\uc758 ",(0,a.kt)("strong",{parentName:"p"},"\ubcc0\uacbd\ub418\uc9c0 \uc54a\uc9c0\ub9cc"),", \uc5ec\ub7ec \uacf3\uc5d0\uc11c ",(0,a.kt)("strong",{parentName:"p"},"\uc790\uc8fc")," \uc4f0\uc774\ub294 \ubd80\ubd84\uc744 \uacf5\ud1b5\ud654\ud560 \uc218\ub3c4 \uc788\uc2b5\ub2c8\ub2e4.\n\ud574\ub2f9 \uacf5\ud1b5\ubd80\ubd84\ub9cc \ubb36\uc5b4\uc11c \ubcc4\ub3c4\uc758 \uc774\ubbf8\uc9c0\ub294 \ubbf8\ub9ac \ub9cc\ub4e4\uc5b4\ub454 \ub2e4\uc74c, ",(0,a.kt)("strong",{parentName:"p"},"\ubca0\uc774\uc2a4 \uc774\ubbf8\uc9c0")," \ub85c \ud65c\uc6a9\ud558\ub294 \uac83\uc774 \uc88b\uc2b5\ub2c8\ub2e4."),(0,a.kt)("p",null,"\uc608\ub97c \ub4e4\uc5b4, \ub2e4\ub978 \uac74 \uac70\uc758 \ub611\uac19\uc740\ub370, tensorflow-cpu \ub97c \uc0ac\uc6a9\ud558\ub294 \uc774\ubbf8\uc9c0\uc640, tensorflow-gpu \ub97c \uc0ac\uc6a9\ud558\ub294 \ud658\uacbd\uc744 \ubd84\ub9ac\ud574\uc11c \uc774\ubbf8\uc9c0\ub85c \ub9cc\ub4e4\uace0 \uc2f6\uc740 \uacbd\uc6b0\uc5d0\ub294 \ub2e4\uc74c\uacfc \uac19\uc774 \ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.",(0,a.kt)("br",{parentName:"p"}),"\n","python \uacfc \uae30\ud0c0 \uae30\ubcf8\uc801\uc778 \ud328\ud0a4\uc9c0\uac00 \uc124\uce58\ub41c ",(0,a.kt)("a",{parentName:"p",href:"http://ghcr.io/makinarocks/python:3.8-base-cpu"},(0,a.kt)("inlineCode",{parentName:"a"},"ghcr.io/makinarocks/python:3.8-base"))," \ub97c \ub9cc\ub4e4\uc5b4\ub450\uace0, ",(0,a.kt)("strong",{parentName:"p"},"tensorflow cpu \ubc84\uc804\uacfc gpu \ubc84\uc804\uc774")," \uc124\uce58\ub41c \uc774\ubbf8\uc9c0 \uc0c8\ub85c \ub9cc\ub4e4\ub54c\ub294, \uc704\uc758 \uc774\ubbf8\uc9c0\ub97c ",(0,a.kt)("inlineCode",{parentName:"p"},"FROM")," \uc73c\ub85c \ubd88\ub7ec\uc628 \ub2e4\uc74c, tensorflow install \ud558\ub294 \ubd80\ubd84\ub9cc \ubcc4\ub3c4\ub85c \uc791\uc131\ud574\uc11c Dockerfile \uc744 2 \uac1c\ub85c \uad00\ub9ac\ud55c\ub2e4\uba74 \uac00\ub3c5\uc131\ub3c4 \uc88b\uace0 \ube4c\ub4dc \uc2dc\uac04\ub3c4 \uc904\uc77c \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,a.kt)("p",null,(0,a.kt)("strong",{parentName:"p"},"\ud569\uce60 \uc218 \uc788\ub294 Layer \ub294 \ud569\uce58\ub294 \uac83"),"\uc774 Old version \uc758 \ub3c4\ucee4\uc5d0\uc11c\ub294 \uc131\ub2a5 \ud5a5\uc0c1 \ud6a8\uacfc\ub97c \uc774\ub04c\uc5c8\uc2b5\ub2c8\ub2e4. \uc5ec\ub7ec\ubd84\uc758 \ub3c4\ucee4 \ucee8\ud14c\uc774\ub108\uac00 \uc5b4\ub5a4 \ub3c4\ucee4 \ubc84\uc804\uc5d0\uc11c \uc2e4\ud589\ub420 \uac83\uc778\uc9c0 \ubcf4\uc7a5\ud560 \uc218 \uc5c6\uc73c\uba70, ",(0,a.kt)("strong",{parentName:"p"},"\uac00\ub3c5\uc131"),"\uc744 \uc704\ud574\uc11c\ub3c4 \ud569\uce60 \uc218 \uc788\ub294 Layer \ub294 \uc801\uc808\ud788 \ud569\uce58\ub294 \uac83\uc774 \uc88b\uc2b5\ub2c8\ub2e4."),(0,a.kt)("p",null,"\uc608\ub97c \ub4e4\uba74, \ub2e4\uc74c\uacfc \uac19\uc774 \uc791\uc131\ub41c ",(0,a.kt)("inlineCode",{parentName:"p"},"Dockerfile"),"\uc774 \uc788\uc2b5\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-docker"},"# Bad Case\nRUN apt-get update\nRUN apt-get install build-essential -y\nRUN apt-get install curl -y\nRUN apt-get install jq -y\nRUN apt-get install git -y\n")),(0,a.kt)("p",null,"\uc774\ub97c \uc544\ub798\uc640 \uac19\uc774 \ud569\uccd0\uc11c \uc801\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-docker"},"# Better Case\nRUN apt-get update && \\\n apt-get install -y \\\n build-essential \\\n curl \\\n jq \\\n git\n")),(0,a.kt)("p",null,"\ud3b8\uc758\ub97c \uc704\ud574\uc11c\ub294 ",(0,a.kt)("inlineCode",{parentName:"p"},".dockerignore")," \ub3c4 \uc0ac\uc6a9\ud558\ub294\uac8c \uc88b\uc2b5\ub2c8\ub2e4.\n",(0,a.kt)("inlineCode",{parentName:"p"},".dockerignore"),"\ub294 ",(0,a.kt)("inlineCode",{parentName:"p"},".gitignore")," \uc640 \ube44\uc2b7\ud55c \uc5ed\ud560\uc744 \ud55c\ub2e4\uace0 \uc774\ud574\ud558\uba74 \ub429\ub2c8\ub2e4. (git add \ud560 \ub54c \uc81c\uc678\ud560 \uc218 \uc788\ub4ef\uc774, docker build \ud560 \ub54c \uc790\ub3d9\uc73c\ub85c \uc81c\uc678)"),(0,a.kt)("p",null,"\ub354 \ub9ce\uc740 \uc815\ubcf4\ub294 ",(0,a.kt)("a",{parentName:"p",href:"https://docs.docker.com/develop/develop-images/dockerfile_best-practices/"},"Docker \uacf5\uc2dd \ubb38\uc11c"),"\uc5d0\uc11c \ud655\uc778\ud558\uc2e4 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,a.kt)("h3",{id:"entrypoint-vs-cmd"},"ENTRYPOINT vs CMD"),(0,a.kt)("p",null,(0,a.kt)("inlineCode",{parentName:"p"},"ENTRYPOINT")," \uc640 ",(0,a.kt)("inlineCode",{parentName:"p"},"CMD")," \ub294 \ubaa8\ub450 \ucee8\ud14c\uc774\ub108\uc758 \uc2e4\ud589 \uc2dc\uc810\uc5d0\uc11c \uc5b4\ub5a4 \uba85\ub839\uc5b4\ub97c \uc2e4\ud589\uc2dc\ud0a4\uace0 \uc2f6\uc744 \ub54c \uc0ac\uc6a9\ud569\ub2c8\ub2e4.\n\uadf8\ub9ac\uace0 \uc774 \ub458 \uc911 \ud558\ub098\ub294 \ubc18\ub4dc\uc2dc \uc874\uc7ac\ud574\uc57c \ud569\ub2c8\ub2e4."),(0,a.kt)("ul",null,(0,a.kt)("li",{parentName:"ul"},(0,a.kt)("strong",{parentName:"li"},"\ucc28\uc774\uc810"),(0,a.kt)("ul",{parentName:"li"},(0,a.kt)("li",{parentName:"ul"},(0,a.kt)("inlineCode",{parentName:"li"},"CMD"),": docker run \uc744 \uc218\ud589\ud560 \ub54c, \uc27d\uac8c \ubcc0\uacbd\ud558\uc5ec \uc0ac\uc6a9\ud560 \uc218 \uc788\uc74c"),(0,a.kt)("li",{parentName:"ul"},(0,a.kt)("inlineCode",{parentName:"li"},"ENTRYPOINT"),": ",(0,a.kt)("inlineCode",{parentName:"li"},"--entrypoint")," \ub97c \uc0ac\uc6a9\ud574\uc57c \ubcc0\uacbd\ud560 \uc218 \uc788\uc74c")))),(0,a.kt)("p",null,(0,a.kt)("inlineCode",{parentName:"p"},"ENTRYPOINT")," \uc640 ",(0,a.kt)("inlineCode",{parentName:"p"},"CMD")," \uac00 \ud568\uaed8 \uc4f0\uc77c \ub54c\ub294 \ubcf4\ud1b5 ",(0,a.kt)("inlineCode",{parentName:"p"},"CMD"),"\ub294 ",(0,a.kt)("inlineCode",{parentName:"p"},"ENTRYPOINT")," \uc5d0\uc11c \uc801\uc740 \uba85\ub839\uc758 arguments(parameters) \ub97c \uc758\ubbf8\ud569\ub2c8\ub2e4."),(0,a.kt)("p",null,"\uc608\ub97c \ub4e4\uc5b4\uc11c \ub2e4\uc74c\uacfc \uac19\uc740 ",(0,a.kt)("inlineCode",{parentName:"p"},"Dockerfile")," \uc774 \uc788\uc2b5\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-docker"},'FROM ubuntu:latest\n\n# \uc544\ub798 4 \uac00\uc9c0 option \uc744 \ubc14\uafd4\uac00\uba70 \uc9c1\uc811 \ud14c\uc2a4\ud2b8\ud574\ubcf4\uc2dc\uba74 \uc774\ud574\ud558\uae30 \ud3b8\ud569\ub2c8\ub2e4.\n# \ub2e8, NO ENTRYPOINT \uc635\uc158\uc740 base image \uc778 ubuntu:latest \uc5d0 \uc774\ubbf8 \uc788\uc5b4\uc11c \ud14c\uc2a4\ud2b8\ud574\ubcfc \uc218\ub294 \uc5c6\uace0 \ub098\uba38\uc9c0 v2, 3, 5, 6, 8, 9, 11, 12 \ub97c \ud14c\uc2a4\ud2b8\ud574\ubcfc \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n# ENTRYPOINT echo "Hello ENTRYPOINT"\n# ENTRYPOINT ["echo", "Hello ENTRYPOINT"]\n# CMD echo "Hello CMD"\n# CMD ["echo", "Hello CMD"]\n')),(0,a.kt)("p",null,"\uc704\uc758 ",(0,a.kt)("inlineCode",{parentName:"p"},"Dockerfile"),"\uc5d0\uc11c \uc8fc\uc11d\uc73c\ub85c \ud45c\uc2dc\ub41c \ubd80\ubd84\ub4e4\uc744 \ud574\uc81c\ud558\uba70 \ube4c\ub4dc\ud558\uace0 \uc2e4\ud589\ud558\uba74 \ub2e4\uc74c\uacfc \uac19\uc740 \uacb0\uacfc\ub97c \uc5bb\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,a.kt)("table",null,(0,a.kt)("thead",{parentName:"table"},(0,a.kt)("tr",{parentName:"thead"},(0,a.kt)("th",{parentName:"tr",align:null}),(0,a.kt)("th",{parentName:"tr",align:null},"No ENTRYPOINT"),(0,a.kt)("th",{parentName:"tr",align:null},"ENTRYPOINT a b"),(0,a.kt)("th",{parentName:"tr",align:null},"ENTRYPOINT ",'["a", "b"]'))),(0,a.kt)("tbody",{parentName:"table"},(0,a.kt)("tr",{parentName:"tbody"},(0,a.kt)("td",{parentName:"tr",align:null},(0,a.kt)("strong",{parentName:"td"},"NO CMD")),(0,a.kt)("td",{parentName:"tr",align:null},"Error!"),(0,a.kt)("td",{parentName:"tr",align:null},"/bin/sh -c a b"),(0,a.kt)("td",{parentName:"tr",align:null},"a b")),(0,a.kt)("tr",{parentName:"tbody"},(0,a.kt)("td",{parentName:"tr",align:null},(0,a.kt)("strong",{parentName:"td"},"CMD ",'["x", "y"]')),(0,a.kt)("td",{parentName:"tr",align:null},"x y"),(0,a.kt)("td",{parentName:"tr",align:null},"/bin/sh -c a b"),(0,a.kt)("td",{parentName:"tr",align:null},"a b x y")),(0,a.kt)("tr",{parentName:"tbody"},(0,a.kt)("td",{parentName:"tr",align:null},(0,a.kt)("strong",{parentName:"td"},"CMD x y")),(0,a.kt)("td",{parentName:"tr",align:null},"/bin/sh -c x y"),(0,a.kt)("td",{parentName:"tr",align:null},"/bin/sh -c a b"),(0,a.kt)("td",{parentName:"tr",align:null},"a b /bin/sh -c x y")))),(0,a.kt)("ul",null,(0,a.kt)("li",{parentName:"ul"},"In Kubernetes pod",(0,a.kt)("ul",{parentName:"li"},(0,a.kt)("li",{parentName:"ul"},(0,a.kt)("inlineCode",{parentName:"li"},"ENTRYPOINT")," \u2192 command"),(0,a.kt)("li",{parentName:"ul"},(0,a.kt)("inlineCode",{parentName:"li"},"CMD")," \u2192 args")))),(0,a.kt)("h3",{id:"docker-tag-\uc774\ub984-\uc9d3\uae30"},"Docker tag \uc774\ub984 \uc9d3\uae30"),(0,a.kt)("p",null,"\ub3c4\ucee4 \uc774\ubbf8\uc9c0\uc758 tag \ub85c ",(0,a.kt)("strong",{parentName:"p"},"latest \ub294 \uc0ac\uc6a9\ud558\uc9c0 \uc54a\ub294 \uac83\uc744 \uad8c\uc7a5"),"\ud569\ub2c8\ub2e4.",(0,a.kt)("br",{parentName:"p"}),"\n","\uc774\uc720\ub294 latest \ub294 default tag name \uc774\ubbc0\ub85c ",(0,a.kt)("strong",{parentName:"p"},"\uc758\ub3c4\uce58 \uc54a\uac8c overwritten")," \ub418\ub294 \uacbd\uc6b0\uac00 \ub108\ubb34 \ub9ce\uc774 \ubc1c\uc0dd\ud558\uae30 \ub54c\ubb38\uc785\ub2c8\ub2e4."),(0,a.kt)("p",null,"\ud558\ub098\uc758 \uc774\ubbf8\uc9c0\ub294 \ud558\ub098\uc758 \ud0dc\uadf8\ub97c \uac00\uc9d0(",(0,a.kt)("strong",{parentName:"p"},"uniqueness"),")\uc744 \ubcf4\uc7a5\ud574\uc57c \ucd94\ud6c4 Production \ub2e8\uacc4\uc5d0\uc11c ",(0,a.kt)("strong",{parentName:"p"},"\ud611\uc5c5/\ub514\ubc84\uae45"),"\uc5d0 \uc6a9\uc774\ud569\ub2c8\ub2e4.",(0,a.kt)("br",{parentName:"p"}),"\n","\ub0b4\uc6a9\uc740 \ub2e4\ub974\uc9c0\ub9cc, \ub3d9\uc77c\ud55c tag \ub97c \uc0ac\uc6a9\ud558\uac8c \ub418\uba74 \ucd94\ud6c4 dangling image \ub85c \ucde8\uae09\ub418\uc5b4 \uad00\ub9ac\ud558\uae30 \uc5b4\ub824\uc6cc\uc9d1\ub2c8\ub2e4.",(0,a.kt)("br",{parentName:"p"}),"\n","dangling image\ub294 ",(0,a.kt)("inlineCode",{parentName:"p"},"docker images"),"\uc5d0\ub294 \ub098\uc624\uc9c0 \uc54a\uc9c0\ub9cc \uacc4\uc18d\ud574\uc11c \uc800\uc7a5\uc18c\ub97c \ucc28\uc9c0\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4."),(0,a.kt)("h3",{id:"etc"},"ETC"),(0,a.kt)("ol",null,(0,a.kt)("li",{parentName:"ol"},"log \ub4f1\uc758 \uc815\ubcf4\ub294 container \ub0b4\ubd80\uac00 \uc544\ub2cc \uacf3\uc5d0 \ub530\ub85c \uc800\uc7a5\ud569\ub2c8\ub2e4.\ncontainer \ub0b4\ubd80\uc5d0\uc11c write \ud55c data \ub294 \uc5b8\uc81c\ub4e0\uc9c0 \uc0ac\ub77c\uc9c8 \uc218 \uc788\uae30 \ub54c\ubb38\uc785\ub2c8\ub2e4."),(0,a.kt)("li",{parentName:"ol"},"secret \ud55c \uc815\ubcf4, \ud658\uacbd(dev/prod) dependent \ud55c \uc815\ubcf4 \ub4f1\uc740 Dockerfile \uc5d0 \uc9c1\uc811 \uc801\ub294 \uac8c \uc544\ub2c8\ub77c, env var \ub610\ub294 .env config file \uc744 \uc0ac\uc6a9\ud569\ub2c8\ub2e4."),(0,a.kt)("li",{parentName:"ol"},"Dockerfile ",(0,a.kt)("strong",{parentName:"li"},"linter")," \ub3c4 \uc874\uc7ac\ud558\ubbc0\ub85c, \ud611\uc5c5 \uc2dc\uc5d0\ub294 \ud65c\uc6a9\ud558\uba74 \uc88b\uc2b5\ub2c8\ub2e4.\n",(0,a.kt)("a",{parentName:"li",href:"https://github.com/hadolint/hadolint"},"https://github.com/hadolint/hadolint"))),(0,a.kt)("h2",{id:"docker-run-\uc758-\ub2e4\uc591\ud55c-\uc635\uc158"},"docker run \uc758 \ub2e4\uc591\ud55c \uc635\uc158"),(0,a.kt)("h3",{id:"docker-run-with-volume"},"docker run with volume"),(0,a.kt)("p",null,"Docker container \uc0ac\uc6a9 \uc2dc \ubd88\ud3b8\ud55c \uc810\uc774 \uc788\uc2b5\ub2c8\ub2e4.\n\ubc14\ub85c Docker\ub294 \uae30\ubcf8\uc801\uc73c\ub85c Docker ",(0,a.kt)("strong",{parentName:"p"},"container \ub0b4\ubd80\uc5d0\uc11c \uc791\uc5c5\ud55c \ubaa8\ub4e0 \uc0ac\ud56d\uc740 \uc800\uc7a5\ub418\uc9c0 \uc54a\uc2b5\ub2c8\ub2e4."),"\n\uc774\uc720\ub294 Docker container \ub294 \uac01\uac01 \uaca9\ub9ac\ub41c \ud30c\uc77c\uc2dc\uc2a4\ud15c\uc744 \uc0ac\uc6a9\ud569\ub2c8\ub2e4. \ub530\ub77c\uc11c, ",(0,a.kt)("strong",{parentName:"p"},"\uc5ec\ub7ec docker container \ub07c\ub9ac \ub370\uc774\ud130\ub97c \uacf5\uc720\ud558\uae30 \uc5b4\ub835\uc2b5\ub2c8\ub2e4.")),(0,a.kt)("p",null,"\uc774 \ubb38\uc81c\ub97c \ud574\uacb0\ud558\uae30 \uc704\ud574\uc11c Docker\uc5d0\uc11c \uc81c\uacf5\ud558\ub294 \ubc29\uc2dd\uc740 ",(0,a.kt)("strong",{parentName:"p"},"2 \uac00\uc9c0"),"\uac00 \uc788\uc2b5\ub2c8\ub2e4."),(0,a.kt)("p",null,(0,a.kt)("img",{alt:"storage.png",src:n(5940).Z,width:"501",height:"255"})),(0,a.kt)("h4",{id:"docker-volume"},"Docker volume"),(0,a.kt)("ul",null,(0,a.kt)("li",{parentName:"ul"},"docker cli \ub97c \uc0ac\uc6a9\ud574 ",(0,a.kt)("inlineCode",{parentName:"li"},"volume")," \uc774\ub77c\ub294 \ub9ac\uc18c\uc2a4\ub97c \uc9c1\uc811 \uad00\ub9ac"),(0,a.kt)("li",{parentName:"ul"},"host \uc5d0\uc11c Docker area(",(0,a.kt)("inlineCode",{parentName:"li"},"/var/lib/docker"),") \uc544\ub798\uc5d0 \ud2b9\uc815 \ub514\ub809\ud1a0\ub9ac\ub97c \uc0dd\uc131\ud55c \ub2e4\uc74c, \ud574\ub2f9 \uacbd\ub85c\ub97c docker container \uc5d0 mount")),(0,a.kt)("h4",{id:"bind-mount"},"Bind mount"),(0,a.kt)("ul",null,(0,a.kt)("li",{parentName:"ul"},"host \uc758 \ud2b9\uc815 \uacbd\ub85c\ub97c docker container \uc5d0 mount")),(0,a.kt)("h4",{id:"how-to-use"},"How to use?"),(0,a.kt)("p",null,"\uc0ac\uc6a9 \ubc29\uc2dd\uc740 ",(0,a.kt)("strong",{parentName:"p"},"\ub3d9\uc77c\ud55c \uc778\ud130\ud398\uc774\uc2a4"),"\ub85c ",(0,a.kt)("inlineCode",{parentName:"p"},"-v")," \uc635\uc158\uc744 \ud1b5\ud574 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.",(0,a.kt)("br",{parentName:"p"}),"\n","\ub2e4\ub9cc, volume \uc744 \uc0ac\uc6a9\ud560 \ub54c\uc5d0\ub294 ",(0,a.kt)("inlineCode",{parentName:"p"},"docker volume create"),", ",(0,a.kt)("inlineCode",{parentName:"p"},"docker volume ls"),", ",(0,a.kt)("inlineCode",{parentName:"p"},"docker volume rm")," \ub4f1\uc744 \uc218\ud589\ud558\uc5ec \uc9c1\uc811 \uad00\ub9ac\ud574\uc8fc\uc5b4\uc57c \ud569\ub2c8\ub2e4."),(0,a.kt)("ul",null,(0,a.kt)("li",{parentName:"ul"},(0,a.kt)("p",{parentName:"li"},"Docker volume"),(0,a.kt)("pre",{parentName:"li"},(0,a.kt)("code",{parentName:"pre",className:"language-bash"},"docker run \\\n -v my_volume:/app \\\n nginx:latest\n"))),(0,a.kt)("li",{parentName:"ul"},(0,a.kt)("p",{parentName:"li"},"Blind mount"),(0,a.kt)("pre",{parentName:"li"},(0,a.kt)("code",{parentName:"pre",className:"language-bash"},"docker run \\\n -v /home/user/some/path:/app \\\n nginx:latest\n")))),(0,a.kt)("p",null,"\ub85c\uceec\uc5d0\uc11c \uac1c\ubc1c\ud560 \ub54c\ub294 bind mount \uac00 \ud3b8\ud558\uae34 \ud558\uc9c0\ub9cc, \ud658\uacbd\uc744 \uae54\ub054\ud558\uac8c \uc720\uc9c0\ud558\uace0 \uc2f6\ub2e4\uba74 docker volume \uc744 \uc0ac\uc6a9\ud558\uc5ec create, rm \uc744 \uba85\uc2dc\uc801\uc73c\ub85c \uc218\ud589\ud558\ub294 \uac83\ub3c4 \ud558\ub098\uc758 \ubc29\ubc95\uc785\ub2c8\ub2e4."),(0,a.kt)("p",null,"\ucfe0\ubc84\ub124\ud2f0\uc2a4\uc5d0\uc11c \uc2a4\ud1a0\ub9ac\uc9c0\ub97c \uc81c\uacf5\ud558\ub294 \ubc29\uc2dd\ub3c4 \uacb0\uad6d docker \uc758 bind mount \ub97c \ud65c\uc6a9\ud558\uc5ec \uc81c\uacf5\ud569\ub2c8\ub2e4."),(0,a.kt)("h3",{id:"docker-run-with-resource-limit"},"docker run with resource limit"),(0,a.kt)("p",null,"\uae30\ubcf8\uc801\uc73c\ub85c docker container \ub294 ",(0,a.kt)("strong",{parentName:"p"},"host OS \uc758 cpu, memory \uc790\uc6d0\uc744 fully \uc0ac\uc6a9"),"\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ud558\uc9c0\ub9cc \uc774\ub807\uac8c \uc0ac\uc6a9\ud558\uac8c \ub418\uba74 host OS \uc758 \uc790\uc6d0 \uc0c1\ud669\uc5d0 \ub530\ub77c\uc11c ",(0,a.kt)("strong",{parentName:"p"},"OOM")," \ub4f1\uc758 \uc774\uc288\ub85c docker container \uac00 \ube44\uc815\uc0c1\uc801\uc73c\ub85c \uc885\ub8cc\ub418\ub294 \uc0c1\ud669\uc774 \ubc1c\uc0dd\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.",(0,a.kt)("br",{parentName:"p"}),"\n","\uc774\ub7f0 \ubb38\uc81c\ub97c \ub2e4\ub8e8\uae30 \uc704\ud574 ",(0,a.kt)("strong",{parentName:"p"},"docker container \uc2e4\ud589 \uc2dc, cpu \uc640 memory \uc758 \uc0ac\uc6a9\ub7c9 \uc81c\ud55c"),"\uc744 \uac78 \uc218 \uc788\ub294 ",(0,a.kt)("inlineCode",{parentName:"p"},"-m")," ",(0,a.kt)("a",{parentName:"p",href:"https://docs.docker.com/config/containers/resource_constraints/#limit-a-containers-access-to-memory"},"\uc635\uc158"),"\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-bash"},"docker run -d -m 512m --memory-reservation=256m --name 512-limit ubuntu sleep 3600\ndocker run -d -m 1g --memory-reservation=256m --name 1g-limit ubuntu sleep 3600\n")),(0,a.kt)("p",null,"\uc704\uc758 \ub3c4\ucee4\ub97c \uc2e4\ud589 \ud6c4 ",(0,a.kt)("inlineCode",{parentName:"p"},"docker stats")," \ucee4\ub9e8\ub4dc\ub97c \ud1b5\ud574 \uc0ac\uc6a9\ub7c9\uc744 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-bash"},"CONTAINER ID NAME CPU % MEM USAGE / LIMIT MEM % NET I/O BLOCK I/O PIDS\n4ea1258e2e09 1g-limit 0.00% 300KiB / 1GiB 0.03% 1kB / 0B 0B / 0B 1\n4edf94b9a3e5 512-limit 0.00% 296KiB / 512MiB 0.06% 1.11kB / 0B 0B / 0B 1\n")),(0,a.kt)("p",null,"\ucfe0\ubc84\ub124\ud2f0\uc2a4\uc5d0\uc11c pod \ub77c\ub294 \ub9ac\uc18c\uc2a4\uc5d0 cpu, memory \uc81c\ud55c\uc744 \uc904 \ub54c, \uc774 \ubc29\uc2dd\uc744 \ud65c\uc6a9\ud558\uc5ec \uc81c\uacf5\ud569\ub2c8\ub2e4."),(0,a.kt)("h3",{id:"docker-run-with-restart-policy"},"docker run with restart policy"),(0,a.kt)("p",null,"\ud2b9\uc815 \ucee8\ud14c\uc774\ub108\uac00 \uacc4\uc18d\ud574\uc11c running \uc0c1\ud0dc\ub97c \uc720\uc9c0\uc2dc\ucf1c\uc57c \ud558\ub294 \uacbd\uc6b0\uac00 \uc874\uc7ac\ud569\ub2c8\ub2e4. \uc774\ub7f0 \uacbd\uc6b0\ub97c \uc704\ud574\uc11c \ud574\ub2f9 \ucee8\ud14c\uc774\ub108\uac00 \uc885\ub8cc\ub418\uc790\ub9c8\uc790 \ubc14\ub85c \uc7ac\uc0dd\uc131\uc744 \uc2dc\ub3c4\ud560 \uc218 \uc788\ub294 ",(0,a.kt)("inlineCode",{parentName:"p"},"--restart=always")," \uc635\uc158\uc744 \uc81c\uacf5\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4."),(0,a.kt)("p",null,"\uc635\uc158 \uc785\ub825 \ud6c4 \ub3c4\ucee4\ub97c \uc2e4\ud589\ud569\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-bash"},"docker run --restart=always ubuntu\n")),(0,a.kt)("p",null,(0,a.kt)("inlineCode",{parentName:"p"},"watch -n1 docker ps"),"\ub97c \ud1b5\ud574 \uc7ac\uc2e4\ud589\uc774 \ub418\uace0 \uc788\ub294\uc9c0 \ud655\uc778\ud569\ub2c8\ub2e4.\n\uc815\uc0c1\uc801\uc73c\ub85c \uc218\ud589\ub418\uace0 \uc788\ub2e4\uba74 \ub2e4\uc74c\uacfc \uac19\uc774 STATUS\uc5d0 ",(0,a.kt)("inlineCode",{parentName:"p"},"Restarting (0)")," \uc774 \ucd9c\ub825\ub429\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-bash"},'CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES\na911850276e8 ubuntu "bash" 35 seconds ago Restarting (0) 6 seconds ago hungry_vaughan\n')),(0,a.kt)("ul",null,(0,a.kt)("li",{parentName:"ul"},(0,a.kt)("a",{parentName:"li",href:"https://docs.docker.com/engine/reference/commandline/run/#restart-policies---restart"},"https://docs.docker.com/engine/reference/commandline/run/#restart-policies---restart"),(0,a.kt)("ul",{parentName:"li"},(0,a.kt)("li",{parentName:"ul"},"on-failure with max retries"),(0,a.kt)("li",{parentName:"ul"},"always \ub4f1\uc758 \uc120\ud0dd\uc9c0 \uc81c\uacf5")))),(0,a.kt)("p",null,"\ucfe0\ubc84\ub124\ud2f0\uc2a4\uc5d0\uc11c job \uc774\ub77c\ub294 resource \uc758 restart \uc635\uc158\uc744 \uc904 \ub54c, \uc774 \ubc29\uc2dd\uc744 \ud65c\uc6a9\ud558\uc5ec \uc81c\uacf5\ud569\ub2c8\ub2e4."),(0,a.kt)("h3",{id:"docker-run-as-a-background-process"},"docker run as a background process"),(0,a.kt)("p",null,"\ub3c4\ucee4 \ucee8\ud14c\uc774\ub108\ub97c \uc2e4\ud589\ud560 \ub54c\ub294 \uae30\ubcf8\uc801\uc73c\ub85c foreground process \ub85c \uc2e4\ud589\ub429\ub2c8\ub2e4. \uc989, \ucee8\ud14c\uc774\ub108\ub97c \uc2e4\ud589\ud55c \ud130\ubbf8\ub110\uc774 \ud574\ub2f9 \ucee8\ud14c\uc774\ub108\uc5d0 \uc790\ub3d9\uc73c\ub85c attach \ub418\uc5b4 \uc788\uc5b4, \ub2e4\ub978 \uba85\ub839\uc744 \uc2e4\ud589\ud560 \uc218 \uc5c6\uc2b5\ub2c8\ub2e4."),(0,a.kt)("p",null,"\ub2e4\uc74c\uacfc \uac19\uc740 \uc608\uc2dc\ub97c \uc218\ud589\ud574\ubd05\ub2c8\ub2e4.",(0,a.kt)("br",{parentName:"p"}),"\n","\uc6b0\uc120 \ud130\ubbf8\ub110 2 \uac1c\ub97c \uc5f4\uc5b4, \ud558\ub098\uc758 \ud130\ubbf8\ub110\uc5d0\uc11c\ub294 ",(0,a.kt)("inlineCode",{parentName:"p"},"docker ps")," \ub97c \uc9c0\ucf1c\ubcf4\uace0, \ub2e4\ub978 \ud558\ub098\uc758 \ud130\ubbf8\ub110\uc5d0\uc11c\ub294 \ub2e4\uc74c\uacfc \uac19\uc740 \uba85\ub839\uc744 \ucc28\ub840\ub85c 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\uc785\ub2c8\ub2e4.",(0,p.kt)("br",{parentName:"p"}),"\n","\uc124\uc815\ud558\ub294 \ubc29\ubc95\uc740 \ud30c\uc774\ud504\ub77c\uc778\uc5d0\uc11c \ucef4\ud3ec\ub10c\ud2b8\uc5d0 \ub2e4\uc74c\uacfc \uac19\uc774 ",(0,p.kt)("inlineCode",{parentName:"p"},"set_display_name")," ",(0,p.kt)("a",{parentName:"p",href:"https://kubeflow-pipelines.readthedocs.io/en/latest/source/kfp.dsl.html#kfp.dsl.ContainerOp.set_display_name"},"attribute"),"\ub97c \uc774\uc6a9\ud558\uba74 \ub429\ub2c8\ub2e4."),(0,p.kt)("pre",null,(0,p.kt)("code",{parentName:"pre",className:"language-python"},'import kfp\nfrom kfp.components import create_component_from_func\nfrom kfp.dsl import pipeline\n\n\n@create_component_from_func\ndef print_and_return_number(number: int) -> int:\n print(number)\n return number\n\n\n@create_component_from_func\ndef sum_and_print_numbers(number_1: int, number_2: int):\n print(number_1 + number_2)\n\n\n@pipeline(name="example_pipeline")\ndef example_pipeline(number_1: int, number_2: int):\n number_1_result = print_and_return_number(number_1).set_display_name("This is number 1")\n number_2_result = print_and_return_number(number_2).set_display_name("This is number 2")\n sum_result = sum_and_print_numbers(\n number_1=number_1_result.output, number_2=number_2_result.output\n ).set_display_name("This is sum of number 1 and number 2")\n\n\nif __name__ == "__main__":\n kfp.compiler.Compiler().compile(example_pipeline, "example_pipeline.yaml")\n')),(0,p.kt)("p",null,"\uc774 \uc2a4\ud06c\ub9bd\ud2b8\ub97c \uc2e4\ud589\ud574\uc11c \ub098\uc628 ",(0,p.kt)("inlineCode",{parentName:"p"},"example_pipeline.yaml"),"\uc744 \ud655\uc778\ud558\uba74 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4."),(0,p.kt)("p",null,(0,p.kt)("details",null,(0,p.kt)("summary",null,"example_pipeline.yaml"),(0,p.kt)("pre",null,(0,p.kt)("code",{parentName:"pre",className:"language-bash"},'apiVersion: argoproj.io/v1alpha1\nkind: Workflow\nmetadata:\n generateName: example-pipeline-\n annotations: {pipelines.kubeflow.org/kfp_sdk_version: 1.8.9, pipelines.kubeflow.org/pipeline_compilation_time: \'2021-12-09T18:11:43.193190\',\n pipelines.kubeflow.org/pipeline_spec: \'{"inputs": [{"name": "number_1", "type":\n "Integer"}, {"name": "number_2", "type": "Integer"}], "name": "example_pipeline"}\'}\n labels: {pipelines.kubeflow.org/kfp_sdk_version: 1.8.9}\nspec:\n entrypoint: example-pipeline\n templates:\n - name: example-pipeline\n inputs:\n parameters:\n - {name: number_1}\n - {name: number_2}\n dag:\n tasks:\n - name: print-and-return-number\n template: print-and-return-number\n arguments:\n parameters:\n - {name: number_1, value: \'{{inputs.parameters.number_1}}\'}\n - name: print-and-return-number-2\n template: print-and-return-number-2\n arguments:\n parameters:\n - {name: number_2, value: \'{{inputs.parameters.number_2}}\'}\n - name: sum-and-print-numbers\n template: sum-and-print-numbers\n dependencies: [print-and-return-number, print-and-return-number-2]\n arguments:\n parameters:\n - {name: print-and-return-number-2-Output, value: \'{{tasks.print-and-return-number-2.outputs.parameters.print-and-return-number-2-Output}}\'}\n - {name: print-and-return-number-Output, value: \'{{tasks.print-and-return-number.outputs.parameters.print-and-return-number-Output}}\'}\n - name: print-and-return-number\n container:\n args: [--number, \'{{inputs.parameters.number_1}}\', \'----output-paths\', /tmp/outputs/Output/data]\n command:\n - sh\n - -ec\n - |\n program_path=$(mktemp)\n printf "%s" "$0" > "$program_path"\n python3 -u "$program_path" "$@"\n - |\n def print_and_return_number(number):\n print(number)\n return number\n\n def _serialize_int(int_value: int) -> str:\n if isinstance(int_value, str):\n return int_value\n if not isinstance(int_value, int):\n raise TypeError(\'Value "{}" has type "{}" instead of int.\'.format(\n str(int_value), str(type(int_value))))\n return str(int_value)\n\n import argparse\n _parser = argparse.ArgumentParser(prog=\'Print and return number\', description=\'\')\n _parser.add_argument("--number", dest="number", type=int, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("----output-paths", dest="_output_paths", type=str, nargs=1)\n _parsed_args = vars(_parser.parse_args())\n _output_files = _parsed_args.pop("_output_paths", [])\n\n _outputs = print_and_return_number(**_parsed_args)\n\n _outputs = [_outputs]\n\n _output_serializers = [\n _serialize_int,\n\n ]\n\n import os\n for idx, output_file in enumerate(_output_files):\n try:\n os.makedirs(os.path.dirname(output_file))\n except OSError:\n pass\n with open(output_file, \'w\') as f:\n f.write(_output_serializers[idx](_outputs[idx]))\n image: python:3.7\n inputs:\n parameters:\n - {name: number_1}\n outputs:\n parameters:\n - name: print-and-return-number-Output\n valueFrom: {path: /tmp/outputs/Output/data}\n artifacts:\n - {name: print-and-return-number-Output, path: /tmp/outputs/Output/data}\n metadata:\n annotations: {pipelines.kubeflow.org/task_display_name: This is number 1, pipelines.kubeflow.org/component_spec: \'{"implementation":\n {"container": {"args": ["--number", {"inputValue": "number"}, "----output-paths",\n {"outputPath": "Output"}], "command": ["sh", "-ec", "program_path=$(mktemp)\\nprintf\n \\"%s\\" \\"$0\\" > \\"$program_path\\"\\npython3 -u \\"$program_path\\" \\"$@\\"\\n",\n "def print_and_return_number(number):\\n print(number)\\n return number\\n\\ndef\n _serialize_int(int_value: int) -> str:\\n if isinstance(int_value, str):\\n return\n int_value\\n if not isinstance(int_value, int):\\n raise TypeError(\'\'Value\n \\"{}\\" has type \\"{}\\" instead of int.\'\'.format(\\n str(int_value),\n str(type(int_value))))\\n return str(int_value)\\n\\nimport argparse\\n_parser\n = argparse.ArgumentParser(prog=\'\'Print and return number\'\', description=\'\'\'\')\\n_parser.add_argument(\\"--number\\",\n dest=\\"number\\", type=int, required=True, default=argparse.SUPPRESS)\\n_parser.add_argument(\\"----output-paths\\",\n dest=\\"_output_paths\\", type=str, nargs=1)\\n_parsed_args = vars(_parser.parse_args())\\n_output_files\n = _parsed_args.pop(\\"_output_paths\\", [])\\n\\n_outputs = print_and_return_number(**_parsed_args)\\n\\n_outputs\n = [_outputs]\\n\\n_output_serializers = [\\n _serialize_int,\\n\\n]\\n\\nimport\n os\\nfor idx, output_file in enumerate(_output_files):\\n try:\\n os.makedirs(os.path.dirname(output_file))\\n except\n OSError:\\n pass\\n with open(output_file, \'\'w\'\') as f:\\n f.write(_output_serializers[idx](_outputs[idx]))\\n"],\n "image": "python:3.7"}}, "inputs": [{"name": "number", "type": "Integer"}],\n "name": "Print and return number", "outputs": [{"name": "Output", "type":\n "Integer"}]}\', pipelines.kubeflow.org/component_ref: \'{}\', pipelines.kubeflow.org/arguments.parameters: \'{"number":\n "{{inputs.parameters.number_1}}"}\'}\n labels:\n pipelines.kubeflow.org/kfp_sdk_version: 1.8.9\n pipelines.kubeflow.org/pipeline-sdk-type: kfp\n pipelines.kubeflow.org/enable_caching: "true"\n - name: print-and-return-number-2\n container:\n args: [--number, \'{{inputs.parameters.number_2}}\', \'----output-paths\', /tmp/outputs/Output/data]\n command:\n - sh\n - -ec\n - |\n program_path=$(mktemp)\n printf "%s" "$0" > "$program_path"\n python3 -u "$program_path" "$@"\n - |\n def print_and_return_number(number):\n print(number)\n return number\n\n def _serialize_int(int_value: int) -> str:\n if isinstance(int_value, str):\n return int_value\n if not isinstance(int_value, int):\n raise TypeError(\'Value "{}" has type "{}" instead of int.\'.format(\n str(int_value), str(type(int_value))))\n return str(int_value)\n\n import argparse\n _parser = argparse.ArgumentParser(prog=\'Print and return number\', description=\'\')\n _parser.add_argument("--number", dest="number", type=int, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("----output-paths", dest="_output_paths", type=str, nargs=1)\n _parsed_args = vars(_parser.parse_args())\n _output_files = _parsed_args.pop("_output_paths", [])\n\n _outputs = print_and_return_number(**_parsed_args)\n\n _outputs = [_outputs]\n\n _output_serializers = [\n _serialize_int,\n\n ]\n\n import os\n for idx, output_file in enumerate(_output_files):\n try:\n os.makedirs(os.path.dirname(output_file))\n except OSError:\n pass\n with open(output_file, \'w\') as f:\n f.write(_output_serializers[idx](_outputs[idx]))\n image: python:3.7\n inputs:\n parameters:\n - {name: number_2}\n outputs:\n parameters:\n - name: print-and-return-number-2-Output\n valueFrom: {path: /tmp/outputs/Output/data}\n artifacts:\n - {name: print-and-return-number-2-Output, path: /tmp/outputs/Output/data}\n metadata:\n annotations: {pipelines.kubeflow.org/task_display_name: This is number 2, pipelines.kubeflow.org/component_spec: \'{"implementation":\n {"container": {"args": ["--number", {"inputValue": "number"}, "----output-paths",\n {"outputPath": "Output"}], "command": ["sh", "-ec", "program_path=$(mktemp)\\nprintf\n \\"%s\\" \\"$0\\" > \\"$program_path\\"\\npython3 -u \\"$program_path\\" \\"$@\\"\\n",\n "def print_and_return_number(number):\\n print(number)\\n return number\\n\\ndef\n _serialize_int(int_value: int) -> str:\\n if isinstance(int_value, str):\\n return\n int_value\\n if not isinstance(int_value, int):\\n raise TypeError(\'\'Value\n \\"{}\\" has type \\"{}\\" instead of int.\'\'.format(\\n str(int_value),\n str(type(int_value))))\\n return str(int_value)\\n\\nimport argparse\\n_parser\n = argparse.ArgumentParser(prog=\'\'Print and return number\'\', description=\'\'\'\')\\n_parser.add_argument(\\"--number\\",\n dest=\\"number\\", type=int, required=True, default=argparse.SUPPRESS)\\n_parser.add_argument(\\"----output-paths\\",\n dest=\\"_output_paths\\", type=str, nargs=1)\\n_parsed_args = vars(_parser.parse_args())\\n_output_files\n = _parsed_args.pop(\\"_output_paths\\", [])\\n\\n_outputs = print_and_return_number(**_parsed_args)\\n\\n_outputs\n = [_outputs]\\n\\n_output_serializers = [\\n _serialize_int,\\n\\n]\\n\\nimport\n os\\nfor idx, output_file in enumerate(_output_files):\\n try:\\n os.makedirs(os.path.dirname(output_file))\\n except\n OSError:\\n pass\\n with open(output_file, \'\'w\'\') as f:\\n f.write(_output_serializers[idx](_outputs[idx]))\\n"],\n "image": "python:3.7"}}, "inputs": [{"name": "number", "type": "Integer"}],\n "name": "Print and return number", "outputs": [{"name": "Output", "type":\n "Integer"}]}\', pipelines.kubeflow.org/component_ref: \'{}\', pipelines.kubeflow.org/arguments.parameters: \'{"number":\n "{{inputs.parameters.number_2}}"}\'}\n labels:\n pipelines.kubeflow.org/kfp_sdk_version: 1.8.9\n pipelines.kubeflow.org/pipeline-sdk-type: kfp\n pipelines.kubeflow.org/enable_caching: "true"\n - name: sum-and-print-numbers\n container:\n args: [--number-1, \'{{inputs.parameters.print-and-return-number-Output}}\', --number-2,\n \'{{inputs.parameters.print-and-return-number-2-Output}}\']\n command:\n - sh\n - -ec\n - |\n program_path=$(mktemp)\n printf "%s" "$0" > "$program_path"\n python3 -u "$program_path" "$@"\n - |\n def sum_and_print_numbers(number_1, number_2):\n print(number_1 + number_2)\n\n import argparse\n _parser = argparse.ArgumentParser(prog=\'Sum and print numbers\', description=\'\')\n _parser.add_argument("--number-1", dest="number_1", type=int, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--number-2", dest="number_2", type=int, required=True, default=argparse.SUPPRESS)\n _parsed_args = vars(_parser.parse_args())\n\n _outputs = sum_and_print_numbers(**_parsed_args)\n image: python:3.7\n inputs:\n parameters:\n - {name: print-and-return-number-2-Output}\n - {name: print-and-return-number-Output}\n metadata:\n annotations: {pipelines.kubeflow.org/task_display_name: This is sum of number\n 1 and number 2, pipelines.kubeflow.org/component_spec: \'{"implementation":\n {"container": {"args": ["--number-1", {"inputValue": "number_1"}, "--number-2",\n {"inputValue": "number_2"}], "command": ["sh", "-ec", "program_path=$(mktemp)\\nprintf\n \\"%s\\" \\"$0\\" > \\"$program_path\\"\\npython3 -u \\"$program_path\\" \\"$@\\"\\n",\n "def sum_and_print_numbers(number_1, number_2):\\n print(number_1 + number_2)\\n\\nimport\n argparse\\n_parser = argparse.ArgumentParser(prog=\'\'Sum and print numbers\'\',\n description=\'\'\'\')\\n_parser.add_argument(\\"--number-1\\", dest=\\"number_1\\",\n type=int, required=True, default=argparse.SUPPRESS)\\n_parser.add_argument(\\"--number-2\\",\n dest=\\"number_2\\", type=int, required=True, default=argparse.SUPPRESS)\\n_parsed_args\n = vars(_parser.parse_args())\\n\\n_outputs = sum_and_print_numbers(**_parsed_args)\\n"],\n "image": "python:3.7"}}, "inputs": [{"name": "number_1", "type": "Integer"},\n {"name": "number_2", "type": "Integer"}], "name": "Sum and print numbers"}\',\n pipelines.kubeflow.org/component_ref: \'{}\', pipelines.kubeflow.org/arguments.parameters: \'{"number_1":\n "{{inputs.parameters.print-and-return-number-Output}}", "number_2": "{{inputs.parameters.print-and-return-number-2-Output}}"}\'}\n labels:\n pipelines.kubeflow.org/kfp_sdk_version: 1.8.9\n pipelines.kubeflow.org/pipeline-sdk-type: kfp\n pipelines.kubeflow.org/enable_caching: "true"\n arguments:\n parameters:\n - {name: number_1}\n - {name: number_2}\n serviceAccountName: pipeline-runner\n')))),(0,p.kt)("p",null,"\uc774 \uc804\uc758 \ud30c\uc77c\uacfc \ube44\uad50\ud558\uba74 ",(0,p.kt)("inlineCode",{parentName:"p"},"pipelines.kubeflow.org/task_display_name")," key\uac00 \uc0c8\ub85c \uc0dd\uc131\ub418\uc5c8\uc2b5\ub2c8\ub2e4."),(0,p.kt)("h3",{id:"ui-in-kubeflow"},"UI in Kubeflow"),(0,p.kt)("p",null,"\uc704\uc5d0\uc11c \ub9cc\ub4e0 \ud30c\uc77c\uc744 \uc774\uc6a9\ud574 \uc774\uc804\uc5d0 \uc0dd\uc131\ud55c ",(0,p.kt)("a",{parentName:"p",href:"/docs/kubeflow/basic-pipeline-upload#upload-pipeline-version"},"\ud30c\uc774\ud504\ub77c\uc778"),"\uc758 \ubc84\uc804\uc744 \uc62c\ub9ac\uaca0\uc2b5\ub2c8\ub2e4."),(0,p.kt)("p",null,(0,p.kt)("img",{alt:"adv-pipeline-0.png",src:r(9072).Z,width:"3360",height:"2100"})),(0,p.kt)("p",null,"\uadf8\ub7ec\uba74 \uc704\uc640 \uac19\uc774 \uc124\uc815\ud55c \uc774\ub984\uc774 \ub178\ucd9c\ub418\ub294 \uac83\uc744 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,p.kt)("h2",{id:"resources"},"Resources"),(0,p.kt)("h3",{id:"gpu"},"GPU"),(0,p.kt)("p",null,"\ud2b9\ubcc4\ud55c \uc124\uc815\uc774 \uc5c6\ub2e4\uba74 \ud30c\uc774\ud504\ub77c\uc778\uc740 \ucef4\ud3ec\ub10c\ud2b8\ub97c \ucfe0\ubc84\ub124\ud2f0\uc2a4 \ud30c\ub4dc(pod)\ub85c \uc2e4\ud589\ud560 \ub54c, \uae30\ubcf8 \ub9ac\uc18c\uc2a4 \uc2a4\ud399\uc73c\ub85c \uc2e4\ud589\ud558\uac8c \ub429\ub2c8\ub2e4.",(0,p.kt)("br",{parentName:"p"}),"\n","\ub9cc\uc57d GPU\ub97c \uc0ac\uc6a9\ud574 \ubaa8\ub378\uc744 \ud559\uc2b5\ud574\uc57c \ud560 \ub54c \ucfe0\ubc84\ub124\ud2f0\uc2a4\uc0c1\uc5d0\uc11c GPU\ub97c \ud560\ub2f9\ubc1b\uc9c0 \ubabb\ud574 \uc81c\ub300\ub85c \ud559\uc2b5\uc774 \uc774\ub8e8\uc5b4\uc9c0\uc9c0 \uc54a\uc2b5\ub2c8\ub2e4.",(0,p.kt)("br",{parentName:"p"}),"\n","\uc774\ub97c \uc704\ud574 ",(0,p.kt)("inlineCode",{parentName:"p"},"set_gpu_limit()")," ",(0,p.kt)("a",{parentName:"p",href:"https://kubeflow-pipelines.readthedocs.io/en/latest/source/kfp.dsl.html?highlight=set_gpu_limit#kfp.dsl.UserContainer.set_gpu_limit"},"attribute"),"\uc744 \uc774\uc6a9\ud574 \uc124\uc815\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,p.kt)("pre",null,(0,p.kt)("code",{parentName:"pre",className:"language-python"},'import kfp\nfrom kfp.components import create_component_from_func\nfrom kfp.dsl import pipeline\n\n\n@create_component_from_func\ndef print_and_return_number(number: int) -> int:\n print(number)\n return number\n\n\n@create_component_from_func\ndef sum_and_print_numbers(number_1: int, number_2: int):\n print(number_1 + number_2)\n\n\n@pipeline(name="example_pipeline")\ndef example_pipeline(number_1: int, number_2: int):\n number_1_result = print_and_return_number(number_1).set_display_name("This is number 1")\n number_2_result = print_and_return_number(number_2).set_display_name("This is number 2")\n sum_result = sum_and_print_numbers(\n number_1=number_1_result.output, number_2=number_2_result.output\n ).set_display_name("This is sum of number 1 and number 2").set_gpu_limit(1)\n\n\nif __name__ == "__main__":\n kfp.compiler.Compiler().compile(example_pipeline, "example_pipeline.yaml")\n')),(0,p.kt)("p",null,"\uc704\uc758 \uc2a4\ud06c\ub9bd\ud2b8\ub97c \uc2e4\ud589\ud558\uba74 \uc0dd\uc131\ub41c \ud30c\uc77c\uc5d0\uc11c ",(0,p.kt)("inlineCode",{parentName:"p"},"sum-and-print-numbers"),"\ub97c \uc790\uc138\ud788 \ubcf4\uba74 resources\uc5d0 ",(0,p.kt)("inlineCode",{parentName:"p"},"{nvidia.com/gpu: 1}")," \ub3c4 \ucd94\uac00\ub41c \uac83\uc744 \ubcfc \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\uc774\ub97c \ud1b5\ud574 GPU\ub97c \ud560\ub2f9\ubc1b\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,p.kt)("pre",null,(0,p.kt)("code",{parentName:"pre",className:"language-bash"},' - name: sum-and-print-numbers\n container:\n args: [--number-1, \'{{inputs.parameters.print-and-return-number-Output}}\', --number-2,\n \'{{inputs.parameters.print-and-return-number-2-Output}}\']\n command:\n - sh\n - -ec\n - |\n program_path=$(mktemp)\n printf "%s" "$0" > "$program_path"\n python3 -u "$program_path" "$@"\n - |\n def sum_and_print_numbers(number_1, number_2):\n print(number_1 + number_2)\n\n import argparse\n _parser = argparse.ArgumentParser(prog=\'Sum and print numbers\', description=\'\')\n _parser.add_argument("--number-1", dest="number_1", type=int, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--number-2", dest="number_2", type=int, required=True, default=argparse.SUPPRESS)\n _parsed_args = vars(_parser.parse_args())\n\n _outputs = sum_and_print_numbers(**_parsed_args)\n image: python:3.7\n resources:\n limits: {nvidia.com/gpu: 1}\n')),(0,p.kt)("h3",{id:"cpu"},"CPU"),(0,p.kt)("p",null,"cpu\uc758 \uac1c\uc218\ub97c \uc815\ud558\uae30 \uc704\ud574\uc11c \uc774\uc6a9\ud558\ub294 \ud568\uc218\ub294 ",(0,p.kt)("inlineCode",{parentName:"p"},".set_cpu_limit()")," ",(0,p.kt)("a",{parentName:"p",href:"https://kubeflow-pipelines.readthedocs.io/en/latest/source/kfp.dsl.html?highlight=set_gpu_limit#kfp.dsl.Sidecar.set_cpu_limit"},"attribute"),"\uc744 \uc774\uc6a9\ud574 \uc124\uc815\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.",(0,p.kt)("br",{parentName:"p"}),"\n","gpu\uc640\ub294 \ub2e4\ub978 \uc810\uc740 int\uac00 \uc544\ub2cc string\uc73c\ub85c \uc785\ub825\ud574\uc57c \ud55c\ub2e4\ub294 \uc810\uc785\ub2c8\ub2e4."),(0,p.kt)("pre",null,(0,p.kt)("code",{parentName:"pre",className:"language-python"},'import kfp\nfrom kfp.components import create_component_from_func\nfrom kfp.dsl import pipeline\n\n\n@create_component_from_func\ndef print_and_return_number(number: int) -> int:\n print(number)\n return number\n\n\n@create_component_from_func\ndef sum_and_print_numbers(number_1: int, number_2: int):\n print(number_1 + number_2)\n\n\n@pipeline(name="example_pipeline")\ndef example_pipeline(number_1: int, number_2: int):\n number_1_result = print_and_return_number(number_1).set_display_name("This is number 1")\n number_2_result = print_and_return_number(number_2).set_display_name("This is number 2")\n sum_result = sum_and_print_numbers(\n number_1=number_1_result.output, number_2=number_2_result.output\n ).set_display_name("This is sum of number 1 and number 2").set_gpu_limit(1).set_cpu_limit("16")\n\n\nif __name__ == "__main__":\n kfp.compiler.Compiler().compile(example_pipeline, "example_pipeline.yaml")\n')),(0,p.kt)("p",null,"\ubc14\ub010 \ubd80\ubd84\ub9cc \ud655\uc778\ud558\uba74 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4."),(0,p.kt)("pre",null,(0,p.kt)("code",{parentName:"pre",className:"language-bash"}," resources:\n limits: {nvidia.com/gpu: 1, cpu: '16'}\n")),(0,p.kt)("h3",{id:"memory"},"Memory"),(0,p.kt)("p",null,"\uba54\ubaa8\ub9ac\ub294 ",(0,p.kt)("inlineCode",{parentName:"p"},".set_memory_limit()")," ",(0,p.kt)("a",{parentName:"p",href:"https://kubeflow-pipelines.readthedocs.io/en/latest/source/kfp.dsl.html?highlight=set_gpu_limit#kfp.dsl.Sidecar.set_memory_limit"},"attribute"),"\uc744 \uc774\uc6a9\ud574 \uc124\uc815\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,p.kt)("pre",null,(0,p.kt)("code",{parentName:"pre",className:"language-python"},'import kfp\nfrom kfp.components import create_component_from_func\nfrom kfp.dsl import pipeline\n\n\n@create_component_from_func\ndef print_and_return_number(number: int) -> int:\n print(number)\n return number\n\n\n@create_component_from_func\ndef sum_and_print_numbers(number_1: int, number_2: int):\n print(number_1 + number_2)\n\n\n@pipeline(name="example_pipeline")\ndef example_pipeline(number_1: int, number_2: int):\n number_1_result = print_and_return_number(number_1).set_display_name("This is number 1")\n number_2_result = print_and_return_number(number_2).set_display_name("This is number 2")\n sum_result = sum_and_print_numbers(\n number_1=number_1_result.output, number_2=number_2_result.output\n ).set_display_name("This is sum of number 1 and number 2").set_gpu_limit(1).set_memory_limit("1G")\n\n\nif __name__ == "__main__":\n kfp.compiler.Compiler().compile(example_pipeline, "example_pipeline.yaml")\n\n')),(0,p.kt)("p",null,"\ubc14\ub010 \ubd80\ubd84\ub9cc \ud655\uc778\ud558\uba74 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4."),(0,p.kt)("pre",null,(0,p.kt)("code",{parentName:"pre",className:"language-bash"}," resources:\n limits: {nvidia.com/gpu: 1, memory: 1G}\n")))}l.isMDXComponent=!0},9072:(n,e,r)=>{r.d(e,{Z:()=>t});const 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m=2;m{r.r(e),r.d(e,{assets:()=>s,contentTitle:()=>u,default:()=>l,frontMatter:()=>a,metadata:()=>i,toc:()=>m});var t=r(7462),p=(r(7294),r(3905));const a={title:"10. 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\uc785\ub2c8\ub2e4.",(0,p.kt)("br",{parentName:"p"}),"\n","\uc124\uc815\ud558\ub294 \ubc29\ubc95\uc740 \ud30c\uc774\ud504\ub77c\uc778\uc5d0\uc11c \ucef4\ud3ec\ub10c\ud2b8\uc5d0 \ub2e4\uc74c\uacfc \uac19\uc774 ",(0,p.kt)("inlineCode",{parentName:"p"},"set_display_name")," ",(0,p.kt)("a",{parentName:"p",href:"https://kubeflow-pipelines.readthedocs.io/en/latest/source/kfp.dsl.html#kfp.dsl.ContainerOp.set_display_name"},"attribute"),"\ub97c \uc774\uc6a9\ud558\uba74 \ub429\ub2c8\ub2e4."),(0,p.kt)("pre",null,(0,p.kt)("code",{parentName:"pre",className:"language-python"},'import kfp\nfrom kfp.components import create_component_from_func\nfrom kfp.dsl import pipeline\n\n\n@create_component_from_func\ndef print_and_return_number(number: int) -> int:\n print(number)\n return number\n\n\n@create_component_from_func\ndef sum_and_print_numbers(number_1: int, number_2: int):\n print(number_1 + number_2)\n\n\n@pipeline(name="example_pipeline")\ndef example_pipeline(number_1: int, number_2: int):\n number_1_result = print_and_return_number(number_1).set_display_name("This is number 1")\n number_2_result = print_and_return_number(number_2).set_display_name("This is number 2")\n sum_result = sum_and_print_numbers(\n number_1=number_1_result.output, number_2=number_2_result.output\n ).set_display_name("This is sum of number 1 and number 2")\n\n\nif __name__ == "__main__":\n kfp.compiler.Compiler().compile(example_pipeline, "example_pipeline.yaml")\n')),(0,p.kt)("p",null,"\uc774 \uc2a4\ud06c\ub9bd\ud2b8\ub97c \uc2e4\ud589\ud574\uc11c \ub098\uc628 ",(0,p.kt)("inlineCode",{parentName:"p"},"example_pipeline.yaml"),"\uc744 \ud655\uc778\ud558\uba74 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4."),(0,p.kt)("p",null,(0,p.kt)("details",null,(0,p.kt)("summary",null,"example_pipeline.yaml"),(0,p.kt)("pre",null,(0,p.kt)("code",{parentName:"pre",className:"language-bash"},'apiVersion: argoproj.io/v1alpha1\nkind: Workflow\nmetadata:\n generateName: example-pipeline-\n annotations: {pipelines.kubeflow.org/kfp_sdk_version: 1.8.9, pipelines.kubeflow.org/pipeline_compilation_time: \'2021-12-09T18:11:43.193190\',\n pipelines.kubeflow.org/pipeline_spec: \'{"inputs": [{"name": "number_1", "type":\n "Integer"}, {"name": "number_2", "type": "Integer"}], "name": "example_pipeline"}\'}\n labels: {pipelines.kubeflow.org/kfp_sdk_version: 1.8.9}\nspec:\n entrypoint: example-pipeline\n templates:\n - name: example-pipeline\n inputs:\n parameters:\n - {name: number_1}\n - {name: number_2}\n dag:\n tasks:\n - name: print-and-return-number\n template: print-and-return-number\n arguments:\n parameters:\n - {name: number_1, value: \'{{inputs.parameters.number_1}}\'}\n - name: print-and-return-number-2\n template: print-and-return-number-2\n arguments:\n parameters:\n - {name: number_2, value: \'{{inputs.parameters.number_2}}\'}\n - name: sum-and-print-numbers\n template: sum-and-print-numbers\n dependencies: [print-and-return-number, print-and-return-number-2]\n arguments:\n parameters:\n - {name: print-and-return-number-2-Output, value: \'{{tasks.print-and-return-number-2.outputs.parameters.print-and-return-number-2-Output}}\'}\n - {name: print-and-return-number-Output, value: \'{{tasks.print-and-return-number.outputs.parameters.print-and-return-number-Output}}\'}\n - name: print-and-return-number\n container:\n args: [--number, \'{{inputs.parameters.number_1}}\', \'----output-paths\', /tmp/outputs/Output/data]\n command:\n - sh\n - -ec\n - |\n program_path=$(mktemp)\n printf "%s" "$0" > "$program_path"\n python3 -u "$program_path" "$@"\n - |\n def print_and_return_number(number):\n print(number)\n return number\n\n def _serialize_int(int_value: int) -> str:\n if isinstance(int_value, str):\n return int_value\n if not isinstance(int_value, int):\n raise TypeError(\'Value "{}" has type "{}" instead of int.\'.format(\n str(int_value), str(type(int_value))))\n return str(int_value)\n\n import argparse\n _parser = argparse.ArgumentParser(prog=\'Print and return number\', description=\'\')\n _parser.add_argument("--number", dest="number", type=int, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("----output-paths", dest="_output_paths", type=str, nargs=1)\n _parsed_args = vars(_parser.parse_args())\n _output_files = _parsed_args.pop("_output_paths", [])\n\n _outputs = print_and_return_number(**_parsed_args)\n\n _outputs = [_outputs]\n\n _output_serializers = [\n _serialize_int,\n\n ]\n\n import os\n for idx, output_file in enumerate(_output_files):\n try:\n os.makedirs(os.path.dirname(output_file))\n except OSError:\n pass\n with open(output_file, \'w\') as f:\n f.write(_output_serializers[idx](_outputs[idx]))\n image: python:3.7\n inputs:\n parameters:\n - {name: number_1}\n outputs:\n parameters:\n - name: print-and-return-number-Output\n valueFrom: {path: /tmp/outputs/Output/data}\n artifacts:\n - {name: print-and-return-number-Output, path: /tmp/outputs/Output/data}\n metadata:\n annotations: {pipelines.kubeflow.org/task_display_name: This is number 1, pipelines.kubeflow.org/component_spec: \'{"implementation":\n {"container": {"args": ["--number", {"inputValue": "number"}, "----output-paths",\n {"outputPath": "Output"}], "command": ["sh", "-ec", "program_path=$(mktemp)\\nprintf\n \\"%s\\" \\"$0\\" > \\"$program_path\\"\\npython3 -u \\"$program_path\\" \\"$@\\"\\n",\n "def print_and_return_number(number):\\n print(number)\\n return number\\n\\ndef\n _serialize_int(int_value: int) -> str:\\n if isinstance(int_value, str):\\n return\n int_value\\n if not isinstance(int_value, int):\\n raise TypeError(\'\'Value\n \\"{}\\" has type \\"{}\\" instead of int.\'\'.format(\\n str(int_value),\n str(type(int_value))))\\n return str(int_value)\\n\\nimport argparse\\n_parser\n = argparse.ArgumentParser(prog=\'\'Print and return number\'\', description=\'\'\'\')\\n_parser.add_argument(\\"--number\\",\n dest=\\"number\\", type=int, required=True, default=argparse.SUPPRESS)\\n_parser.add_argument(\\"----output-paths\\",\n dest=\\"_output_paths\\", type=str, nargs=1)\\n_parsed_args = vars(_parser.parse_args())\\n_output_files\n = _parsed_args.pop(\\"_output_paths\\", [])\\n\\n_outputs = print_and_return_number(**_parsed_args)\\n\\n_outputs\n = [_outputs]\\n\\n_output_serializers = [\\n _serialize_int,\\n\\n]\\n\\nimport\n os\\nfor idx, output_file in enumerate(_output_files):\\n try:\\n os.makedirs(os.path.dirname(output_file))\\n except\n OSError:\\n pass\\n with open(output_file, \'\'w\'\') as f:\\n f.write(_output_serializers[idx](_outputs[idx]))\\n"],\n "image": "python:3.7"}}, "inputs": [{"name": "number", "type": "Integer"}],\n "name": "Print and return number", "outputs": [{"name": "Output", "type":\n "Integer"}]}\', pipelines.kubeflow.org/component_ref: \'{}\', pipelines.kubeflow.org/arguments.parameters: \'{"number":\n "{{inputs.parameters.number_1}}"}\'}\n labels:\n pipelines.kubeflow.org/kfp_sdk_version: 1.8.9\n pipelines.kubeflow.org/pipeline-sdk-type: kfp\n pipelines.kubeflow.org/enable_caching: "true"\n - name: print-and-return-number-2\n container:\n args: [--number, \'{{inputs.parameters.number_2}}\', \'----output-paths\', /tmp/outputs/Output/data]\n command:\n - sh\n - -ec\n - |\n program_path=$(mktemp)\n printf "%s" "$0" > "$program_path"\n python3 -u "$program_path" "$@"\n - |\n def print_and_return_number(number):\n print(number)\n return number\n\n def _serialize_int(int_value: int) -> str:\n if isinstance(int_value, str):\n return int_value\n if not isinstance(int_value, int):\n raise TypeError(\'Value "{}" has type "{}" instead of int.\'.format(\n str(int_value), str(type(int_value))))\n return str(int_value)\n\n import argparse\n _parser = argparse.ArgumentParser(prog=\'Print and return number\', description=\'\')\n _parser.add_argument("--number", dest="number", type=int, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("----output-paths", dest="_output_paths", type=str, nargs=1)\n _parsed_args = vars(_parser.parse_args())\n _output_files = _parsed_args.pop("_output_paths", [])\n\n _outputs = print_and_return_number(**_parsed_args)\n\n _outputs = [_outputs]\n\n _output_serializers = [\n _serialize_int,\n\n ]\n\n import os\n for idx, output_file in enumerate(_output_files):\n try:\n os.makedirs(os.path.dirname(output_file))\n except OSError:\n pass\n with open(output_file, \'w\') as f:\n f.write(_output_serializers[idx](_outputs[idx]))\n image: python:3.7\n inputs:\n parameters:\n - {name: number_2}\n outputs:\n parameters:\n - name: print-and-return-number-2-Output\n valueFrom: {path: /tmp/outputs/Output/data}\n artifacts:\n - {name: print-and-return-number-2-Output, path: /tmp/outputs/Output/data}\n metadata:\n annotations: {pipelines.kubeflow.org/task_display_name: This is number 2, pipelines.kubeflow.org/component_spec: \'{"implementation":\n {"container": {"args": ["--number", {"inputValue": "number"}, "----output-paths",\n {"outputPath": "Output"}], "command": ["sh", "-ec", "program_path=$(mktemp)\\nprintf\n \\"%s\\" \\"$0\\" > \\"$program_path\\"\\npython3 -u \\"$program_path\\" \\"$@\\"\\n",\n "def print_and_return_number(number):\\n print(number)\\n return number\\n\\ndef\n _serialize_int(int_value: int) -> str:\\n if isinstance(int_value, str):\\n return\n int_value\\n if not isinstance(int_value, int):\\n raise TypeError(\'\'Value\n \\"{}\\" has type \\"{}\\" instead of int.\'\'.format(\\n str(int_value),\n str(type(int_value))))\\n return str(int_value)\\n\\nimport argparse\\n_parser\n = argparse.ArgumentParser(prog=\'\'Print and return number\'\', description=\'\'\'\')\\n_parser.add_argument(\\"--number\\",\n dest=\\"number\\", type=int, required=True, default=argparse.SUPPRESS)\\n_parser.add_argument(\\"----output-paths\\",\n dest=\\"_output_paths\\", type=str, nargs=1)\\n_parsed_args = vars(_parser.parse_args())\\n_output_files\n = _parsed_args.pop(\\"_output_paths\\", [])\\n\\n_outputs = print_and_return_number(**_parsed_args)\\n\\n_outputs\n = [_outputs]\\n\\n_output_serializers = [\\n _serialize_int,\\n\\n]\\n\\nimport\n os\\nfor idx, output_file in enumerate(_output_files):\\n try:\\n os.makedirs(os.path.dirname(output_file))\\n except\n OSError:\\n pass\\n with open(output_file, \'\'w\'\') as f:\\n f.write(_output_serializers[idx](_outputs[idx]))\\n"],\n "image": "python:3.7"}}, "inputs": [{"name": "number", "type": "Integer"}],\n "name": "Print and return number", "outputs": [{"name": "Output", "type":\n "Integer"}]}\', pipelines.kubeflow.org/component_ref: \'{}\', pipelines.kubeflow.org/arguments.parameters: \'{"number":\n "{{inputs.parameters.number_2}}"}\'}\n labels:\n pipelines.kubeflow.org/kfp_sdk_version: 1.8.9\n pipelines.kubeflow.org/pipeline-sdk-type: kfp\n pipelines.kubeflow.org/enable_caching: "true"\n - name: sum-and-print-numbers\n container:\n args: [--number-1, \'{{inputs.parameters.print-and-return-number-Output}}\', --number-2,\n \'{{inputs.parameters.print-and-return-number-2-Output}}\']\n command:\n - sh\n - -ec\n - |\n program_path=$(mktemp)\n printf "%s" "$0" > "$program_path"\n python3 -u "$program_path" "$@"\n - |\n def sum_and_print_numbers(number_1, number_2):\n print(number_1 + number_2)\n\n import argparse\n _parser = argparse.ArgumentParser(prog=\'Sum and print numbers\', description=\'\')\n _parser.add_argument("--number-1", dest="number_1", type=int, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--number-2", dest="number_2", type=int, required=True, default=argparse.SUPPRESS)\n _parsed_args = vars(_parser.parse_args())\n\n _outputs = sum_and_print_numbers(**_parsed_args)\n image: python:3.7\n inputs:\n parameters:\n - {name: print-and-return-number-2-Output}\n - {name: print-and-return-number-Output}\n metadata:\n annotations: {pipelines.kubeflow.org/task_display_name: This is sum of number\n 1 and number 2, pipelines.kubeflow.org/component_spec: \'{"implementation":\n {"container": {"args": ["--number-1", {"inputValue": "number_1"}, "--number-2",\n {"inputValue": "number_2"}], "command": ["sh", "-ec", "program_path=$(mktemp)\\nprintf\n \\"%s\\" \\"$0\\" > \\"$program_path\\"\\npython3 -u \\"$program_path\\" \\"$@\\"\\n",\n "def sum_and_print_numbers(number_1, number_2):\\n print(number_1 + number_2)\\n\\nimport\n argparse\\n_parser = argparse.ArgumentParser(prog=\'\'Sum and print numbers\'\',\n description=\'\'\'\')\\n_parser.add_argument(\\"--number-1\\", dest=\\"number_1\\",\n type=int, required=True, default=argparse.SUPPRESS)\\n_parser.add_argument(\\"--number-2\\",\n dest=\\"number_2\\", type=int, required=True, default=argparse.SUPPRESS)\\n_parsed_args\n = vars(_parser.parse_args())\\n\\n_outputs = sum_and_print_numbers(**_parsed_args)\\n"],\n "image": "python:3.7"}}, "inputs": [{"name": "number_1", "type": "Integer"},\n {"name": "number_2", "type": "Integer"}], "name": "Sum and print numbers"}\',\n pipelines.kubeflow.org/component_ref: \'{}\', pipelines.kubeflow.org/arguments.parameters: \'{"number_1":\n "{{inputs.parameters.print-and-return-number-Output}}", "number_2": "{{inputs.parameters.print-and-return-number-2-Output}}"}\'}\n labels:\n pipelines.kubeflow.org/kfp_sdk_version: 1.8.9\n pipelines.kubeflow.org/pipeline-sdk-type: kfp\n pipelines.kubeflow.org/enable_caching: "true"\n arguments:\n parameters:\n - {name: number_1}\n - {name: number_2}\n serviceAccountName: pipeline-runner\n')))),(0,p.kt)("p",null,"\uc774 \uc804\uc758 \ud30c\uc77c\uacfc \ube44\uad50\ud558\uba74 ",(0,p.kt)("inlineCode",{parentName:"p"},"pipelines.kubeflow.org/task_display_name")," key\uac00 \uc0c8\ub85c \uc0dd\uc131\ub418\uc5c8\uc2b5\ub2c8\ub2e4."),(0,p.kt)("h3",{id:"ui-in-kubeflow"},"UI in Kubeflow"),(0,p.kt)("p",null,"\uc704\uc5d0\uc11c \ub9cc\ub4e0 \ud30c\uc77c\uc744 \uc774\uc6a9\ud574 \uc774\uc804\uc5d0 \uc0dd\uc131\ud55c 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\ubaa8\ub378\uc744 \ud559\uc2b5\ud574\uc57c \ud560 \ub54c \ucfe0\ubc84\ub124\ud2f0\uc2a4\uc0c1\uc5d0\uc11c GPU\ub97c \ud560\ub2f9\ubc1b\uc9c0 \ubabb\ud574 \uc81c\ub300\ub85c \ud559\uc2b5\uc774 \uc774\ub8e8\uc5b4\uc9c0\uc9c0 \uc54a\uc2b5\ub2c8\ub2e4.",(0,p.kt)("br",{parentName:"p"}),"\n","\uc774\ub97c \uc704\ud574 ",(0,p.kt)("inlineCode",{parentName:"p"},"set_gpu_limit()")," ",(0,p.kt)("a",{parentName:"p",href:"https://kubeflow-pipelines.readthedocs.io/en/latest/source/kfp.dsl.html?highlight=set_gpu_limit#kfp.dsl.UserContainer.set_gpu_limit"},"attribute"),"\uc744 \uc774\uc6a9\ud574 \uc124\uc815\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,p.kt)("pre",null,(0,p.kt)("code",{parentName:"pre",className:"language-python"},'import kfp\nfrom kfp.components import create_component_from_func\nfrom kfp.dsl import pipeline\n\n\n@create_component_from_func\ndef print_and_return_number(number: int) -> int:\n print(number)\n return number\n\n\n@create_component_from_func\ndef sum_and_print_numbers(number_1: int, number_2: int):\n print(number_1 + number_2)\n\n\n@pipeline(name="example_pipeline")\ndef example_pipeline(number_1: int, number_2: int):\n number_1_result = print_and_return_number(number_1).set_display_name("This is number 1")\n number_2_result = print_and_return_number(number_2).set_display_name("This is number 2")\n sum_result = sum_and_print_numbers(\n number_1=number_1_result.output, number_2=number_2_result.output\n ).set_display_name("This is sum of number 1 and number 2").set_gpu_limit(1)\n\n\nif __name__ == "__main__":\n kfp.compiler.Compiler().compile(example_pipeline, "example_pipeline.yaml")\n')),(0,p.kt)("p",null,"\uc704\uc758 \uc2a4\ud06c\ub9bd\ud2b8\ub97c \uc2e4\ud589\ud558\uba74 \uc0dd\uc131\ub41c \ud30c\uc77c\uc5d0\uc11c ",(0,p.kt)("inlineCode",{parentName:"p"},"sum-and-print-numbers"),"\ub97c \uc790\uc138\ud788 \ubcf4\uba74 resources\uc5d0 ",(0,p.kt)("inlineCode",{parentName:"p"},"{nvidia.com/gpu: 1}")," \ub3c4 \ucd94\uac00\ub41c \uac83\uc744 \ubcfc \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\uc774\ub97c \ud1b5\ud574 GPU\ub97c \ud560\ub2f9\ubc1b\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,p.kt)("pre",null,(0,p.kt)("code",{parentName:"pre",className:"language-bash"},' - name: sum-and-print-numbers\n container:\n args: [--number-1, \'{{inputs.parameters.print-and-return-number-Output}}\', --number-2,\n \'{{inputs.parameters.print-and-return-number-2-Output}}\']\n command:\n - sh\n - -ec\n - |\n program_path=$(mktemp)\n printf "%s" "$0" > "$program_path"\n python3 -u "$program_path" "$@"\n - |\n def sum_and_print_numbers(number_1, number_2):\n print(number_1 + number_2)\n\n import argparse\n _parser = argparse.ArgumentParser(prog=\'Sum and print numbers\', description=\'\')\n _parser.add_argument("--number-1", dest="number_1", type=int, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--number-2", dest="number_2", type=int, required=True, default=argparse.SUPPRESS)\n _parsed_args = vars(_parser.parse_args())\n\n _outputs = sum_and_print_numbers(**_parsed_args)\n image: python:3.7\n resources:\n limits: {nvidia.com/gpu: 1}\n')),(0,p.kt)("h3",{id:"cpu"},"CPU"),(0,p.kt)("p",null,"cpu\uc758 \uac1c\uc218\ub97c \uc815\ud558\uae30 \uc704\ud574\uc11c \uc774\uc6a9\ud558\ub294 \ud568\uc218\ub294 ",(0,p.kt)("inlineCode",{parentName:"p"},".set_cpu_limit()")," ",(0,p.kt)("a",{parentName:"p",href:"https://kubeflow-pipelines.readthedocs.io/en/latest/source/kfp.dsl.html?highlight=set_gpu_limit#kfp.dsl.Sidecar.set_cpu_limit"},"attribute"),"\uc744 \uc774\uc6a9\ud574 \uc124\uc815\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.",(0,p.kt)("br",{parentName:"p"}),"\n","gpu\uc640\ub294 \ub2e4\ub978 \uc810\uc740 int\uac00 \uc544\ub2cc string\uc73c\ub85c \uc785\ub825\ud574\uc57c \ud55c\ub2e4\ub294 \uc810\uc785\ub2c8\ub2e4."),(0,p.kt)("pre",null,(0,p.kt)("code",{parentName:"pre",className:"language-python"},'import kfp\nfrom kfp.components import create_component_from_func\nfrom kfp.dsl import pipeline\n\n\n@create_component_from_func\ndef print_and_return_number(number: int) -> int:\n print(number)\n return number\n\n\n@create_component_from_func\ndef sum_and_print_numbers(number_1: int, number_2: int):\n print(number_1 + number_2)\n\n\n@pipeline(name="example_pipeline")\ndef example_pipeline(number_1: int, number_2: int):\n number_1_result = print_and_return_number(number_1).set_display_name("This is number 1")\n number_2_result = print_and_return_number(number_2).set_display_name("This is number 2")\n sum_result = sum_and_print_numbers(\n number_1=number_1_result.output, number_2=number_2_result.output\n ).set_display_name("This is sum of number 1 and number 2").set_gpu_limit(1).set_cpu_limit("16")\n\n\nif __name__ == "__main__":\n kfp.compiler.Compiler().compile(example_pipeline, "example_pipeline.yaml")\n')),(0,p.kt)("p",null,"\ubc14\ub010 \ubd80\ubd84\ub9cc \ud655\uc778\ud558\uba74 \ub2e4\uc74c\uacfc 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number_2)\n\n\n@pipeline(name="example_pipeline")\ndef example_pipeline(number_1: int, number_2: int):\n number_1_result = print_and_return_number(number_1).set_display_name("This is number 1")\n number_2_result = print_and_return_number(number_2).set_display_name("This is number 2")\n sum_result = sum_and_print_numbers(\n number_1=number_1_result.output, number_2=number_2_result.output\n ).set_display_name("This is sum of number 1 and number 2").set_gpu_limit(1).set_memory_limit("1G")\n\n\nif __name__ == "__main__":\n kfp.compiler.Compiler().compile(example_pipeline, "example_pipeline.yaml")\n\n')),(0,p.kt)("p",null,"\ubc14\ub010 \ubd80\ubd84\ub9cc \ud655\uc778\ud558\uba74 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4."),(0,p.kt)("pre",null,(0,p.kt)("code",{parentName:"pre",className:"language-bash"}," resources:\n limits: {nvidia.com/gpu: 1, memory: 1G}\n")))}l.isMDXComponent=!0},9072:(n,e,r)=>{r.d(e,{Z:()=>t});const 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server --disable traefik --disable servicelb --disable local-storage --docker\n")),(0,a.kt)("p",null,"k3s\ub97c \uc124\uce58 \ud6c4 k3s config\ub97c \ud655\uc778\ud569\ub2c8\ub2e4"),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-bash"},"sudo cat /etc/rancher/k3s/k3s.yaml\n")),(0,a.kt)("p",null,"\uc815\uc0c1\uc801\uc73c\ub85c \uc124\uce58\ub418\uba74 \ub2e4\uc74c\uacfc \uac19\uc740 \ud56d\ubaa9\uc774 \ucd9c\ub825\ub429\ub2c8\ub2e4.",(0,a.kt)("br",{parentName:"p"}),"\n","(\ubcf4\uc548 \ubb38\uc81c\uc640 \uad00\ub828\ub41c \ud0a4\ub4e4\uc740 <...>\ub85c \uac00\ub838\uc2b5\ub2c8\ub2e4.)"),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-bash"},"apiVersion: v1\nclusters:\n- cluster:\n certificate-authority-data:\n <...>\n server: https://127.0.0.1:6443\n name: default\ncontexts:\n- context:\n cluster: default\n user: default\n name: default\ncurrent-context: default\nkind: Config\npreferences: {}\nusers:\n- name: default\n user:\n client-certificate-data:\n <...>\n client-key-data:\n <...>\n")),(0,a.kt)("h2",{id:"2-\ucfe0\ubc84\ub124\ud2f0\uc2a4-\ud074\ub7ec\uc2a4\ud130-\uc14b\uc5c5"},"2. \ucfe0\ubc84\ub124\ud2f0\uc2a4 \ud074\ub7ec\uc2a4\ud130 \uc14b\uc5c5"),(0,a.kt)("p",null,"k3s config\ub97c \ud074\ub7ec\uc2a4\ud130\uc758 kubeconfig\ub85c \uc0ac\uc6a9\ud558\uae30 \uc704\ud574\uc11c \ubcf5\uc0ac\ud569\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-bash"},"mkdir .kube\nsudo cp /etc/rancher/k3s/k3s.yaml .kube/config\n")),(0,a.kt)("p",null,"\ubcf5\uc0ac\ub41c config \ud30c\uc77c\uc5d0 user\uac00 \uc811\uadfc\ud560 \uc218 \uc788\ub294 \uad8c\ud55c\uc744 \uc90d\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-bash"},"sudo chown $USER:$USER .kube/config\n")),(0,a.kt)("h2",{id:"3-\ucfe0\ubc84\ub124\ud2f0\uc2a4-\ud074\ub77c\uc774\uc5b8\ud2b8-\uc14b\uc5c5"},"3. \ucfe0\ubc84\ub124\ud2f0\uc2a4 \ud074\ub77c\uc774\uc5b8\ud2b8 \uc14b\uc5c5"),(0,a.kt)("p",null,"\uc774\uc81c \ud074\ub7ec\uc2a4\ud130\uc5d0\uc11c \uc124\uc815\ud55c kubeconfig\ub97c \ub85c\uceec\ub85c \uc774\ub3d9\ud569\ub2c8\ub2e4.\n\ub85c\uceec\uc5d0\uc11c\ub294 \uacbd\ub85c\ub97c ",(0,a.kt)("inlineCode",{parentName:"p"},"~/.kube/config"),"\ub85c \uc124\uc815\ud569\ub2c8\ub2e4."),(0,a.kt)("p",null,"\ucc98\uc74c \ubcf5\uc0ac\ud55c config \ud30c\uc77c\uc5d0\ub294 server ip\uac00 ",(0,a.kt)("inlineCode",{parentName:"p"},"https://127.0.0.1:6443")," \uc73c\ub85c \ub418\uc5b4 \uc788\uc2b5\ub2c8\ub2e4.",(0,a.kt)("br",{parentName:"p"}),"\n","\uc774 \uac12\uc744 \ud074\ub7ec\uc2a4\ud130\uc758 ip\uc5d0 \ub9de\uac8c \uc218\uc815\ud569\ub2c8\ub2e4.",(0,a.kt)("br",{parentName:"p"}),"\n","(\uc774\ubc88 \ud398\uc774\uc9c0\uc5d0\uc11c \uc0ac\uc6a9\ud558\ub294 \ud074\ub7ec\uc2a4\ud130\uc758 ip\uc5d0 \ub9de\ucdb0\uc11c ",(0,a.kt)("inlineCode",{parentName:"p"},"https://192.168.0.19:6443")," \uc73c\ub85c \uc218\uc815\ud588\uc2b5\ub2c8\ub2e4.)"),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-bash"},"apiVersion: v1\nclusters:\n- cluster:\n certificate-authority-data:\n <...>\n server: https://192.168.0.19:6443\n name: default\ncontexts:\n- context:\n cluster: default\n user: default\n name: default\ncurrent-context: default\nkind: Config\npreferences: {}\nusers:\n- name: default\n user:\n client-certificate-data:\n <...>\n client-key-data:\n <...>\n")),(0,a.kt)("h2",{id:"4-\ucfe0\ubc84\ub124\ud2f0\uc2a4-\uae30\ubcf8-\ubaa8\ub4c8-\uc124\uce58"},"4. \ucfe0\ubc84\ub124\ud2f0\uc2a4 \uae30\ubcf8 \ubaa8\ub4c8 \uc124\uce58"),(0,a.kt)("p",null,(0,a.kt)("a",{parentName:"p",href:"/docs/setup-kubernetes/install-kubernetes-module"},"Setup Kubernetes Modules"),"\uc744 \ucc38\uace0\ud558\uc5ec \ub2e4\uc74c \ucef4\ud3ec\ub10c\ud2b8\ub4e4\uc744 \uc124\uce58\ud574 \uc8fc\uc2dc\uae30 \ubc14\ub78d\ub2c8\ub2e4."),(0,a.kt)("ul",null,(0,a.kt)("li",{parentName:"ul"},"helm"),(0,a.kt)("li",{parentName:"ul"},"kustomize"),(0,a.kt)("li",{parentName:"ul"},"CSI plugin"),(0,a.kt)("li",{parentName:"ul"},"[Optional]"," nvidia-docker, nvidia-device-plugin")),(0,a.kt)("h2",{id:"5-\uc815\uc0c1-\uc124\uce58-\ud655\uc778"},"5. \uc815\uc0c1 \uc124\uce58 \ud655\uc778"),(0,a.kt)("p",null,"\ucd5c\uc885\uc801\uc73c\ub85c node\uac00 Ready \uc778\uc9c0, OS, Docker, Kubernetes \ubc84\uc804\uc744 \ud655\uc778\ud569\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-bash"},"kubectl get nodes -o wide\n")),(0,a.kt)("p",null,"\ub2e4\uc74c\uacfc \uac19\uc740 \uba54\uc2dc\uc9c0\uac00 \ubcf4\uc774\uba74 \uc815\uc0c1\uc801\uc73c\ub85c \uc124\uce58\ub41c \uac83\uc744 \uc758\ubbf8\ud569\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-bash"},"NAME STATUS ROLES AGE VERSION INTERNAL-IP EXTERNAL-IP OS-IMAGE KERNEL-VERSION CONTAINER-RUNTIME\nubuntu Ready control-plane,master 11m v1.21.7+k3s1 192.168.0.19 Ubuntu 20.04.3 LTS 5.4.0-91-generic docker://20.10.11\n")),(0,a.kt)("h2",{id:"6-references"},"6. 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Pipeline - Setting",permalink:"/docs/kubeflow/advanced-pipeline"}},o={},s=[{value:"Component Environment",id:"component-environment",level:2},{value:"Convert to Kubeflow Format",id:"convert-to-kubeflow-format",level:3},{value:"\ud328\ud0a4\uc9c0 \ucd94\uac00 \ubc29\ubc95",id:"\ud328\ud0a4\uc9c0-\ucd94\uac00-\ubc29\ubc95",level:2},{value:"1. base_image",id:"1-base_image",level:3},{value:"2. packages_to_install",id:"2-packages_to_install",level:3}],m={toc:s},d="wrapper";function _(n){let{components:e,...t}=n;return(0,r.kt)(d,(0,a.Z)({},m,t,{components:e,mdxType:"MDXLayout"}),(0,r.kt)("h2",{id:"component-environment"},"Component Environment"),(0,r.kt)("p",null,"\uc55e\uc11c ",(0,r.kt)("a",{parentName:"p",href:"/docs/kubeflow/advanced-component"},"8. Component - InputPath/OutputPath"),"\uc5d0\uc11c \uc791\uc131\ud55c \ud30c\uc774\ud504\ub77c\uc778\uc744 \uc2e4\ud589\ud558\uba74 \uc2e4\ud328\ud558\uac8c \ub429\ub2c8\ub2e4. \uc65c \uc2e4\ud328\ud558\ub294\uc9c0 \uc54c\uc544\ubcf4\uace0 \uc815\uc0c1\uc801\uc73c\ub85c \uc2e4\ud589\ub420 \uc218 \uc788\ub3c4\ub85d \uc218\uc815\ud569\ub2c8\ub2e4."),(0,r.kt)("h3",{id:"convert-to-kubeflow-format"},"Convert to Kubeflow Format"),(0,r.kt)("p",null,(0,r.kt)("a",{parentName:"p",href:"/docs/kubeflow/advanced-component#convert-to-kubeflow-format"},"\uc55e\uc5d0\uc11c \uc791\uc131\ud55c \ucef4\ud3ec\ub10c\ud2b8"),"\ub97c yaml\ud30c\uc77c\ub85c \ubcc0\ud658\ud558\ub3c4\ub85d \ud558\uaca0\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-python"},'from kfp.components import InputPath, OutputPath, create_component_from_func\n\n\n@create_component_from_func\ndef train_from_csv(\n train_data_path: InputPath("csv"),\n train_target_path: InputPath("csv"),\n model_path: OutputPath("dill"),\n kernel: str,\n):\n import dill\n import pandas as pd\n\n from sklearn.svm import SVC\n\n train_data = pd.read_csv(train_data_path)\n train_target = pd.read_csv(train_target_path)\n\n clf = SVC(kernel=kernel)\n clf.fit(train_data, train_target)\n\n with open(model_path, mode="wb") as file_writer:\n dill.dump(clf, file_writer)\n\n\nif __name__ == "__main__":\n train_from_csv.component_spec.save("train_from_csv.yaml")\n')),(0,r.kt)("p",null,"\uc704\uc758 \uc2a4\ud06c\ub9bd\ud2b8\ub97c \uc2e4\ud589\ud558\uba74 \ub2e4\uc74c\uacfc \uac19\uc740 ",(0,r.kt)("inlineCode",{parentName:"p"},"train_from_csv.yaml")," \ud30c\uc77c\uc744 \uc5bb\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},'name: Train from csv\ninputs:\n- {name: train_data, type: csv}\n- {name: train_target, type: csv}\n- {name: model, type: dill}\n- {name: kernel, type: String}\nimplementation:\n container:\n image: python:3.7\n command:\n - sh\n - -ec\n - |\n program_path=$(mktemp)\n printf "%s" "$0" > "$program_path"\n python3 -u "$program_path" "$@"\n - |\n def train_from_csv(\n train_data_path,\n train_target_path,\n model_path,\n kernel,\n ):\n import dill\n import pandas as pd\n\n from sklearn.svm import SVC\n\n train_data = pd.read_csv(train_data_path)\n train_target = pd.read_csv(train_target_path)\n\n clf = SVC(kernel=kernel)\n clf.fit(train_data, train_target)\n\n with open(model_path, mode="wb") as file_writer:\n dill.dump(clf, file_writer)\n\n import argparse\n _parser = argparse.ArgumentParser(prog=\'Train from csv\', description=\'\')\n _parser.add_argument("--train-data", dest="train_data_path", type=str, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--train-target", dest="train_target_path", type=str, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--model", dest="model_path", type=str, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--kernel", dest="kernel", type=str, required=True, default=argparse.SUPPRESS)\n _parsed_args = vars(_parser.parse_args())\n\n _outputs = train_from_csv(**_parsed_args)\n args:\n - --train-data\n - {inputPath: train_data}\n - --train-target\n - {inputPath: train_target}\n - --model\n - {inputPath: model}\n - --kernel\n - {inputValue: kernel}\n')),(0,r.kt)("p",null,"\uc55e\uc11c ",(0,r.kt)("a",{parentName:"p",href:"/docs/kubeflow/basic-component#convert-to-kubeflow-format"},"Basic Usage Component"),"\uc5d0\uc11c \uc124\uba85\ud55c \ub0b4\uc6a9\uc5d0 \ub530\ub974\uba74 \uc774 \ucef4\ud3ec\ub10c\ud2b8\ub294 \ub2e4\uc74c\uacfc \uac19\uc774 \uc2e4\ud589\ub429\ub2c8\ub2e4."),(0,r.kt)("ol",null,(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("inlineCode",{parentName:"li"},"docker pull python:3.7")),(0,r.kt)("li",{parentName:"ol"},"run ",(0,r.kt)("inlineCode",{parentName:"li"},"command"))),(0,r.kt)("p",null,"\ud558\uc9c0\ub9cc \uc704\uc5d0\uc11c \uc0dd\uc131\ub41c 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\uc788\uc744\uae4c\uc694?"),(0,r.kt)("h2",{id:"\ud328\ud0a4\uc9c0-\ucd94\uac00-\ubc29\ubc95"},"\ud328\ud0a4\uc9c0 \ucd94\uac00 \ubc29\ubc95"),(0,r.kt)("p",null,"Kubeflow\ub97c \ubcc0\ud658\ud558\ub294 \uacfc\uc815\uc5d0\uc11c \ub450 \uac00\uc9c0 \ubc29\ubc95\uc744 \ud1b5\ud574 \ud328\ud0a4\uc9c0\ub97c \ucd94\uac00\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,r.kt)("ol",null,(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("inlineCode",{parentName:"li"},"base_image")," \uc0ac\uc6a9"),(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("inlineCode",{parentName:"li"},"package_to_install")," \uc0ac\uc6a9")),(0,r.kt)("p",null,"\ucef4\ud3ec\ub10c\ud2b8\ub97c \ucef4\ud30c\uc77c\ud560 \ub54c \uc0ac\uc6a9\ud588\ub358 \ud568\uc218 ",(0,r.kt)("inlineCode",{parentName:"p"},"create_component_from_func")," \uac00 \uc5b4\ub5a4 argument\ub4e4\uc744 \ubc1b\uc744 \uc218 \uc788\ub294\uc9c0 \ud655\uc778\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"def create_component_from_func(\n func: Callable,\n output_component_file: Optional[str] = None,\n base_image: Optional[str] = None,\n packages_to_install: List[str] = None,\n annotations: Optional[Mapping[str, str]] = None,\n):\n")),(0,r.kt)("ul",null,(0,r.kt)("li",{parentName:"ul"},(0,r.kt)("inlineCode",{parentName:"li"},"func"),": \ucef4\ud3ec\ub10c\ud2b8\ub85c \ub9cc\ub4e4 \ucef4\ud3ec\ub10c\ud2b8 \ub798\ud37c \ud568\uc218"),(0,r.kt)("li",{parentName:"ul"},(0,r.kt)("inlineCode",{parentName:"li"},"base_image"),": \ucef4\ud3ec\ub10c\ud2b8 \ub798\ud37c\uac00 \uc2e4\ud589\ud560 \uc774\ubbf8\uc9c0"),(0,r.kt)("li",{parentName:"ul"},(0,r.kt)("inlineCode",{parentName:"li"},"packages_to_install"),": \ucef4\ud3ec\ub10c\ud2b8\uc5d0\uc11c \uc0ac\uc6a9\ud574\uc11c \ucd94\uac00\ub85c \uc124\uce58\ud574\uc57c \ud558\ub294 \ud328\ud0a4\uc9c0")),(0,r.kt)("h3",{id:"1-base_image"},"1. base_image"),(0,r.kt)("p",null,"\ucef4\ud3ec\ub10c\ud2b8\uac00 \uc2e4\ud589\ub418\ub294 \uc21c\uc11c\ub97c \uc880 \ub354 \uc790\uc138\ud788 \ub4e4\uc5ec\ub2e4\ubcf4\uba74 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4."),(0,r.kt)("ol",null,(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("inlineCode",{parentName:"li"},"docker pull base_image")),(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("inlineCode",{parentName:"li"},"pip install packages_to_install")),(0,r.kt)("li",{parentName:"ol"},"run ",(0,r.kt)("inlineCode",{parentName:"li"},"command"))),(0,r.kt)("p",null,"\ub9cc\uc57d \ucef4\ud3ec\ub10c\ud2b8\uac00 \uc0ac\uc6a9\ud558\ub294 base_image\uc5d0 \ud328\ud0a4\uc9c0\ub4e4\uc774 \uc804\ubd80 \uc124\uce58\ub418\uc5b4 \uc788\ub2e4\uba74 \ucd94\uac00\uc801\uc778 \ud328\ud0a4\uc9c0 \uc124\uce58 \uc5c6\uc774 \ubc14\ub85c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,r.kt)("p",null,"\uc608\ub97c \ub4e4\uc5b4, \uc774\ubc88 \ud398\uc774\uc9c0\uc5d0\uc11c\ub294 \ub2e4\uc74c\uacfc \uac19\uc740 Dockerfile\uc744 \uc791\uc131\ud558\uaca0\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-dockerfile"},"FROM python:3.7\n\nRUN pip install dill pandas scikit-learn\n")),(0,r.kt)("p",null,"\uc704\uc758 Dockerfile\uc744 \uc774\uc6a9\ud574 \uc774\ubbf8\uc9c0\ub97c \ube4c\ub4dc\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc2e4\uc2b5\uc5d0\uc11c \uc0ac\uc6a9\ud574\ubcfc \ub3c4\ucee4 \ud5c8\ube0c\ub294 ghcr\uc785\ub2c8\ub2e4.",(0,r.kt)("br",{parentName:"p"}),"\n","\uac01\uc790 \ud658\uacbd\uc5d0 \ub9de\ucd94\uc5b4\uc11c \ub3c4\ucee4 \ud5c8\ube0c\ub97c \uc120\ud0dd \ud6c4 \uc5c5\ub85c\ub4dc\ud558\uba74 \ub429\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"docker build . -f Dockerfile -t ghcr.io/mlops-for-all/base-image\ndocker push ghcr.io/mlops-for-all/base-image\n")),(0,r.kt)("p",null,"\uc774\uc81c base_image\ub97c \uc785\ub825\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-python"},'from functools import partial\nfrom kfp.components import InputPath, OutputPath, create_component_from_func\n\n@partial(\n create_component_from_func,\n base_image="ghcr.io/mlops-for-all/base-image:latest",\n)\ndef train_from_csv(\n train_data_path: InputPath("csv"),\n train_target_path: InputPath("csv"),\n model_path: OutputPath("dill"),\n kernel: str,\n):\n import dill\n import pandas as pd\n\n from sklearn.svm import SVC\n\n train_data = pd.read_csv(train_data_path)\n train_target = pd.read_csv(train_target_path)\n\n clf = SVC(kernel=kernel)\n clf.fit(train_data, train_target)\n\n with open(model_path, mode="wb") as file_writer:\n dill.dump(clf, file_writer)\n\nif __name__ == "__main__":\n train_from_csv.component_spec.save("train_from_csv.yaml")\n')),(0,r.kt)("p",null,"\uc774\uc81c \uc0dd\uc131\ub41c \ucef4\ud3ec\ub10c\ud2b8\ub97c \ucef4\ud30c\uc77c\ud558\uba74 \ub2e4\uc74c\uacfc \uac19\uc774 \ub098\uc635\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},'name: Train from csv\ninputs:\n- {name: train_data, type: csv}\n- {name: train_target, type: csv}\n- {name: kernel, type: String}\noutputs:\n- {name: model, type: dill}\nimplementation:\n container:\n image: ghcr.io/mlops-for-all/base-image:latest\n command:\n - sh\n - -ec\n - |\n program_path=$(mktemp)\n printf "%s" "$0" > "$program_path"\n python3 -u "$program_path" "$@"\n - |\n def _make_parent_dirs_and_return_path(file_path: str):\n import os\n os.makedirs(os.path.dirname(file_path), exist_ok=True)\n return file_path\n\n def train_from_csv(\n train_data_path,\n train_target_path,\n model_path,\n kernel,\n ):\n import dill\n import pandas as pd\n\n from sklearn.svm import SVC\n\n train_data = pd.read_csv(train_data_path)\n train_target = pd.read_csv(train_target_path)\n\n clf = SVC(kernel=kernel)\n clf.fit(train_data, train_target)\n\n with open(model_path, mode="wb") as file_writer:\n dill.dump(clf, file_writer)\n\n import argparse\n _parser = argparse.ArgumentParser(prog=\'Train from csv\', description=\'\')\n _parser.add_argument("--train-data", dest="train_data_path", type=str, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--train-target", dest="train_target_path", type=str, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--kernel", dest="kernel", type=str, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--model", dest="model_path", type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)\n _parsed_args = vars(_parser.parse_args())\n\n _outputs = train_from_csv(**_parsed_args)\n args:\n - --train-data\n - {inputPath: train_data}\n - --train-target\n - {inputPath: train_target}\n - --kernel\n - {inputValue: kernel}\n - --model\n - {outputPath: model}\n')),(0,r.kt)("p",null,"base_image\uac00 \uc6b0\ub9ac\uac00 \uc124\uc815\ud55c \uac12\uc73c\ub85c \ubc14\ub010 \uac83\uc744 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,r.kt)("h3",{id:"2-packages_to_install"},"2. packages_to_install"),(0,r.kt)("p",null,"\ud558\uc9c0\ub9cc \ud328\ud0a4\uc9c0\uac00 \ucd94\uac00\ub420 \ub54c\ub9c8\ub2e4 docker \uc774\ubbf8\uc9c0\ub97c \uacc4\uc18d\ud574\uc11c \uc0c8\ub85c \uc0dd\uc131\ud558\ub294 \uc791\uc5c5\uc740 \ub9ce\uc740 \uc2dc\uac04\uc774 \uc18c\uc694\ub429\ub2c8\ub2e4.\n\uc774 \ub54c, ",(0,r.kt)("inlineCode",{parentName:"p"},"packages_to_install")," argument \ub97c \uc0ac\uc6a9\ud558\uba74 \ud328\ud0a4\uc9c0\ub97c \ucee8\ud14c\uc774\ub108\uc5d0 \uc27d\uac8c \ucd94\uac00\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-python"},'from functools import partial\nfrom kfp.components import InputPath, OutputPath, create_component_from_func\n\n@partial(\n create_component_from_func,\n packages_to_install=["dill==0.3.4", "pandas==1.3.4", "scikit-learn==1.0.1"],\n)\ndef train_from_csv(\n train_data_path: InputPath("csv"),\n train_target_path: InputPath("csv"),\n model_path: OutputPath("dill"),\n kernel: str,\n):\n import dill\n import pandas as pd\n\n from sklearn.svm import SVC\n\n train_data = pd.read_csv(train_data_path)\n train_target = pd.read_csv(train_target_path)\n\n clf = SVC(kernel=kernel)\n clf.fit(train_data, train_target)\n\n with open(model_path, mode="wb") as file_writer:\n dill.dump(clf, file_writer)\n\nif __name__ == "__main__":\n train_from_csv.component_spec.save("train_from_csv.yaml")\n')),(0,r.kt)("p",null,"\uc2a4\ud06c\ub9bd\ud2b8\ub97c \uc2e4\ud589\ud558\uba74 \ub2e4\uc74c\uacfc \uac19\uc740 ",(0,r.kt)("inlineCode",{parentName:"p"},"train_from_csv.yaml")," \ud30c\uc77c\uc774 \uc0dd\uc131\ub429\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},'name: Train from csv\ninputs:\n- {name: train_data, type: csv}\n- {name: train_target, type: csv}\n- {name: kernel, type: String}\noutputs:\n- {name: model, type: dill}\nimplementation:\n container:\n image: python:3.7\n command:\n - sh\n - -c\n - (PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location\n \'dill==0.3.4\' \'pandas==1.3.4\' \'scikit-learn==1.0.1\' || PIP_DISABLE_PIP_VERSION_CHECK=1\n python3 -m pip install --quiet --no-warn-script-location \'dill==0.3.4\' \'pandas==1.3.4\'\n \'scikit-learn==1.0.1\' --user) && "$0" "$@"\n - sh\n - -ec\n - |\n program_path=$(mktemp)\n printf "%s" "$0" > "$program_path"\n python3 -u "$program_path" "$@"\n - |\n def _make_parent_dirs_and_return_path(file_path: str):\n import os\n os.makedirs(os.path.dirname(file_path), exist_ok=True)\n return file_path\n\n def train_from_csv(\n train_data_path,\n train_target_path,\n model_path,\n kernel,\n ):\n import dill\n import pandas as pd\n\n from sklearn.svm import SVC\n\n train_data = pd.read_csv(train_data_path)\n train_target = pd.read_csv(train_target_path)\n\n clf = SVC(kernel=kernel)\n clf.fit(train_data, train_target)\n\n with open(model_path, mode="wb") as file_writer:\n dill.dump(clf, file_writer)\n\n import argparse\n _parser = argparse.ArgumentParser(prog=\'Train from csv\', description=\'\')\n _parser.add_argument("--train-data", dest="train_data_path", type=str, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--train-target", dest="train_target_path", type=str, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--kernel", dest="kernel", type=str, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--model", dest="model_path", type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)\n _parsed_args = vars(_parser.parse_args())\n\n _outputs = train_from_csv(**_parsed_args)\n args:\n - --train-data\n - {inputPath: train_data}\n - --train-target\n - {inputPath: train_target}\n - --kernel\n - {inputValue: kernel}\n - --model\n - {outputPath: model}\n')),(0,r.kt)("p",null,"\uc704\uc5d0 \uc791\uc131\ud55c \ucef4\ud3ec\ub10c\ud2b8\uac00 \uc2e4\ud589\ub418\ub294 \uc21c\uc11c\ub97c \uc880 \ub354 \uc790\uc138\ud788 \ub4e4\uc5ec\ub2e4\ubcf4\uba74 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4."),(0,r.kt)("ol",null,(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("inlineCode",{parentName:"li"},"docker pull python:3.7")),(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("inlineCode",{parentName:"li"},"pip install dill==0.3.4 pandas==1.3.4 scikit-learn==1.0.1")),(0,r.kt)("li",{parentName:"ol"},"run ",(0,r.kt)("inlineCode",{parentName:"li"},"command"))),(0,r.kt)("p",null,"\uc0dd\uc131\ub41c yaml \ud30c\uc77c\uc744 \uc790\uc138\ud788 \ubcf4\uba74, \ub2e4\uc74c\uacfc \uac19\uc740 \uc904\uc774 \uc790\ub3d9\uc73c\ub85c \ucd94\uac00\ub418\uc5b4 \ud544\uc694\ud55c \ud328\ud0a4\uc9c0\uac00 \uc124\uce58\ub418\uae30 \ub54c\ubb38\uc5d0 \uc624\ub958 \uc5c6\uc774 \uc815\uc0c1\uc801\uc73c\ub85c \uc2e4\ud589\ub429\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"}," command:\n - sh\n - -c\n - (PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location\n 'dill==0.3.4' 'pandas==1.3.4' 'scikit-learn==1.0.1' || PIP_DISABLE_PIP_VERSION_CHECK=1\n python3 -m pip install --quiet --no-warn-script-location 'dill==0.3.4' 'pandas==1.3.4'\n 'scikit-learn==1.0.1' --user) && \"$0\" \"$@\"\n")))}_.isMDXComponent=!0}}]); \ No newline at end of file diff --git a/assets/js/a7958b24.f331f5cb.js b/assets/js/a7958b24.f331f5cb.js new file mode 100644 index 00000000..43176536 --- /dev/null +++ b/assets/js/a7958b24.f331f5cb.js @@ -0,0 +1 @@ +"use strict";(self.webpackChunkv_2=self.webpackChunkv_2||[]).push([[3255],{3905:(n,e,t)=>{t.d(e,{Zo:()=>m,kt:()=>c});var a=t(7294);function r(n,e,t){return e in n?Object.defineProperty(n,e,{value:t,enumerable:!0,configurable:!0,writable:!0}):n[e]=t,n}function p(n,e){var 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Component - Environment",description:"",sidebar_position:9,contributors:["Jongseob Jeon"]},i=void 0,l={unversionedId:"kubeflow/advanced-environment",id:"kubeflow/advanced-environment",title:"9. Component - Environment",description:"",source:"@site/docs/kubeflow/advanced-environment.md",sourceDirName:"kubeflow",slug:"/kubeflow/advanced-environment",permalink:"/docs/kubeflow/advanced-environment",draft:!1,editUrl:"https://github.com/mlops-for-all/mlops-for-all.github.io/tree/main/docs/kubeflow/advanced-environment.md",tags:[],version:"current",lastUpdatedBy:"Aiden-Jeon",lastUpdatedAt:1706783082,formattedLastUpdatedAt:"2024\ub144 2\uc6d4 1\uc77c",sidebarPosition:9,frontMatter:{title:"9. Component - Environment",description:"",sidebar_position:9,contributors:["Jongseob Jeon"]},sidebar:"tutorialSidebar",previous:{title:"8. Component - InputPath/OutputPath",permalink:"/docs/kubeflow/advanced-component"},next:{title:"10. Pipeline - Setting",permalink:"/docs/kubeflow/advanced-pipeline"}},o={},s=[{value:"Component Environment",id:"component-environment",level:2},{value:"Convert to Kubeflow Format",id:"convert-to-kubeflow-format",level:3},{value:"\ud328\ud0a4\uc9c0 \ucd94\uac00 \ubc29\ubc95",id:"\ud328\ud0a4\uc9c0-\ucd94\uac00-\ubc29\ubc95",level:2},{value:"1. base_image",id:"1-base_image",level:3},{value:"2. packages_to_install",id:"2-packages_to_install",level:3}],m={toc:s},d="wrapper";function _(n){let{components:e,...t}=n;return(0,r.kt)(d,(0,a.Z)({},m,t,{components:e,mdxType:"MDXLayout"}),(0,r.kt)("h2",{id:"component-environment"},"Component Environment"),(0,r.kt)("p",null,"\uc55e\uc11c ",(0,r.kt)("a",{parentName:"p",href:"/docs/kubeflow/advanced-component"},"8. 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model_path: OutputPath("dill"),\n kernel: str,\n):\n import dill\n import pandas as pd\n\n from sklearn.svm import SVC\n\n train_data = pd.read_csv(train_data_path)\n train_target = pd.read_csv(train_target_path)\n\n clf = SVC(kernel=kernel)\n clf.fit(train_data, train_target)\n\n with open(model_path, mode="wb") as file_writer:\n dill.dump(clf, file_writer)\n\n\nif __name__ == "__main__":\n train_from_csv.component_spec.save("train_from_csv.yaml")\n')),(0,r.kt)("p",null,"\uc704\uc758 \uc2a4\ud06c\ub9bd\ud2b8\ub97c \uc2e4\ud589\ud558\uba74 \ub2e4\uc74c\uacfc \uac19\uc740 ",(0,r.kt)("inlineCode",{parentName:"p"},"train_from_csv.yaml")," \ud30c\uc77c\uc744 \uc5bb\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},'name: Train from csv\ninputs:\n- {name: train_data, type: csv}\n- {name: train_target, type: csv}\n- {name: model, type: dill}\n- {name: kernel, type: String}\nimplementation:\n container:\n image: python:3.7\n command:\n - sh\n - -ec\n - |\n program_path=$(mktemp)\n printf "%s" "$0" > "$program_path"\n python3 -u "$program_path" "$@"\n - |\n def train_from_csv(\n train_data_path,\n train_target_path,\n model_path,\n kernel,\n ):\n import dill\n import pandas as pd\n\n from sklearn.svm import SVC\n\n train_data = pd.read_csv(train_data_path)\n train_target = pd.read_csv(train_target_path)\n\n clf = SVC(kernel=kernel)\n clf.fit(train_data, train_target)\n\n with open(model_path, mode="wb") as file_writer:\n dill.dump(clf, file_writer)\n\n import argparse\n _parser = argparse.ArgumentParser(prog=\'Train from csv\', description=\'\')\n _parser.add_argument("--train-data", dest="train_data_path", type=str, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--train-target", dest="train_target_path", type=str, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--model", dest="model_path", type=str, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--kernel", dest="kernel", type=str, required=True, default=argparse.SUPPRESS)\n _parsed_args = vars(_parser.parse_args())\n\n _outputs = train_from_csv(**_parsed_args)\n args:\n - --train-data\n - {inputPath: train_data}\n - --train-target\n - {inputPath: train_target}\n - --model\n - {inputPath: model}\n - --kernel\n - {inputValue: kernel}\n')),(0,r.kt)("p",null,"\uc55e\uc11c ",(0,r.kt)("a",{parentName:"p",href:"/docs/kubeflow/basic-component#convert-to-kubeflow-format"},"Basic Usage Component"),"\uc5d0\uc11c \uc124\uba85\ud55c \ub0b4\uc6a9\uc5d0 \ub530\ub974\uba74 \uc774 \ucef4\ud3ec\ub10c\ud2b8\ub294 \ub2e4\uc74c\uacfc \uac19\uc774 \uc2e4\ud589\ub429\ub2c8\ub2e4."),(0,r.kt)("ol",null,(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("inlineCode",{parentName:"li"},"docker pull python:3.7")),(0,r.kt)("li",{parentName:"ol"},"run ",(0,r.kt)("inlineCode",{parentName:"li"},"command"))),(0,r.kt)("p",null,"\ud558\uc9c0\ub9cc \uc704\uc5d0\uc11c \uc0dd\uc131\ub41c 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Dockerfile\uc744 \uc791\uc131\ud558\uaca0\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-dockerfile"},"FROM python:3.7\n\nRUN pip install dill pandas scikit-learn\n")),(0,r.kt)("p",null,"\uc704\uc758 Dockerfile\uc744 \uc774\uc6a9\ud574 \uc774\ubbf8\uc9c0\ub97c \ube4c\ub4dc\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc2e4\uc2b5\uc5d0\uc11c \uc0ac\uc6a9\ud574\ubcfc \ub3c4\ucee4 \ud5c8\ube0c\ub294 ghcr\uc785\ub2c8\ub2e4.",(0,r.kt)("br",{parentName:"p"}),"\n","\uac01\uc790 \ud658\uacbd\uc5d0 \ub9de\ucd94\uc5b4\uc11c \ub3c4\ucee4 \ud5c8\ube0c\ub97c \uc120\ud0dd \ud6c4 \uc5c5\ub85c\ub4dc\ud558\uba74 \ub429\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"docker build . -f Dockerfile -t ghcr.io/mlops-for-all/base-image\ndocker push ghcr.io/mlops-for-all/base-image\n")),(0,r.kt)("p",null,"\uc774\uc81c base_image\ub97c \uc785\ub825\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-python"},'from functools import partial\nfrom kfp.components import InputPath, OutputPath, create_component_from_func\n\n@partial(\n create_component_from_func,\n base_image="ghcr.io/mlops-for-all/base-image:latest",\n)\ndef train_from_csv(\n train_data_path: InputPath("csv"),\n train_target_path: InputPath("csv"),\n model_path: OutputPath("dill"),\n kernel: str,\n):\n import dill\n import pandas as pd\n\n from sklearn.svm import SVC\n\n train_data = pd.read_csv(train_data_path)\n train_target = pd.read_csv(train_target_path)\n\n clf = SVC(kernel=kernel)\n clf.fit(train_data, train_target)\n\n with open(model_path, mode="wb") as file_writer:\n dill.dump(clf, file_writer)\n\nif __name__ == "__main__":\n train_from_csv.component_spec.save("train_from_csv.yaml")\n')),(0,r.kt)("p",null,"\uc774\uc81c \uc0dd\uc131\ub41c \ucef4\ud3ec\ub10c\ud2b8\ub97c \ucef4\ud30c\uc77c\ud558\uba74 \ub2e4\uc74c\uacfc \uac19\uc774 \ub098\uc635\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},'name: Train from csv\ninputs:\n- {name: train_data, type: csv}\n- {name: train_target, type: csv}\n- {name: kernel, type: String}\noutputs:\n- {name: model, type: dill}\nimplementation:\n container:\n image: ghcr.io/mlops-for-all/base-image:latest\n command:\n - sh\n - -ec\n - |\n program_path=$(mktemp)\n printf "%s" "$0" > "$program_path"\n python3 -u "$program_path" "$@"\n - |\n def _make_parent_dirs_and_return_path(file_path: str):\n import os\n os.makedirs(os.path.dirname(file_path), exist_ok=True)\n return file_path\n\n def train_from_csv(\n train_data_path,\n train_target_path,\n model_path,\n kernel,\n ):\n import dill\n import pandas as pd\n\n from sklearn.svm import SVC\n\n train_data = pd.read_csv(train_data_path)\n train_target = pd.read_csv(train_target_path)\n\n clf = SVC(kernel=kernel)\n clf.fit(train_data, train_target)\n\n with open(model_path, mode="wb") as file_writer:\n dill.dump(clf, file_writer)\n\n import argparse\n _parser = argparse.ArgumentParser(prog=\'Train from csv\', description=\'\')\n _parser.add_argument("--train-data", dest="train_data_path", type=str, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--train-target", dest="train_target_path", type=str, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--kernel", dest="kernel", type=str, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--model", dest="model_path", type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)\n _parsed_args = vars(_parser.parse_args())\n\n _outputs = train_from_csv(**_parsed_args)\n args:\n - --train-data\n - {inputPath: train_data}\n - --train-target\n - {inputPath: train_target}\n - --kernel\n - {inputValue: kernel}\n - --model\n - {outputPath: model}\n')),(0,r.kt)("p",null,"base_image\uac00 \uc6b0\ub9ac\uac00 \uc124\uc815\ud55c 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"scikit-learn==1.0.1"],\n)\ndef train_from_csv(\n train_data_path: InputPath("csv"),\n train_target_path: InputPath("csv"),\n model_path: OutputPath("dill"),\n kernel: str,\n):\n import dill\n import pandas as pd\n\n from sklearn.svm import SVC\n\n train_data = pd.read_csv(train_data_path)\n train_target = pd.read_csv(train_target_path)\n\n clf = SVC(kernel=kernel)\n clf.fit(train_data, train_target)\n\n with open(model_path, mode="wb") as file_writer:\n dill.dump(clf, file_writer)\n\nif __name__ == "__main__":\n train_from_csv.component_spec.save("train_from_csv.yaml")\n')),(0,r.kt)("p",null,"\uc2a4\ud06c\ub9bd\ud2b8\ub97c \uc2e4\ud589\ud558\uba74 \ub2e4\uc74c\uacfc \uac19\uc740 ",(0,r.kt)("inlineCode",{parentName:"p"},"train_from_csv.yaml")," \ud30c\uc77c\uc774 \uc0dd\uc131\ub429\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},'name: Train from csv\ninputs:\n- {name: train_data, type: csv}\n- {name: train_target, type: csv}\n- {name: kernel, type: String}\noutputs:\n- {name: model, type: dill}\nimplementation:\n container:\n image: python:3.7\n command:\n - sh\n - -c\n - (PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location\n \'dill==0.3.4\' \'pandas==1.3.4\' \'scikit-learn==1.0.1\' || PIP_DISABLE_PIP_VERSION_CHECK=1\n python3 -m pip install --quiet --no-warn-script-location \'dill==0.3.4\' \'pandas==1.3.4\'\n \'scikit-learn==1.0.1\' --user) && "$0" "$@"\n - sh\n - -ec\n - |\n program_path=$(mktemp)\n printf "%s" "$0" > "$program_path"\n python3 -u "$program_path" "$@"\n - |\n def _make_parent_dirs_and_return_path(file_path: str):\n import os\n os.makedirs(os.path.dirname(file_path), exist_ok=True)\n return file_path\n\n def train_from_csv(\n train_data_path,\n train_target_path,\n model_path,\n kernel,\n ):\n import dill\n import pandas as pd\n\n from sklearn.svm import SVC\n\n train_data = pd.read_csv(train_data_path)\n train_target = pd.read_csv(train_target_path)\n\n clf = SVC(kernel=kernel)\n clf.fit(train_data, train_target)\n\n with open(model_path, mode="wb") as file_writer:\n dill.dump(clf, file_writer)\n\n import argparse\n _parser = argparse.ArgumentParser(prog=\'Train from csv\', description=\'\')\n _parser.add_argument("--train-data", dest="train_data_path", type=str, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--train-target", dest="train_target_path", type=str, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--kernel", dest="kernel", type=str, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--model", dest="model_path", type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)\n _parsed_args = vars(_parser.parse_args())\n\n _outputs = train_from_csv(**_parsed_args)\n args:\n - --train-data\n - {inputPath: train_data}\n - --train-target\n - {inputPath: train_target}\n - --kernel\n - {inputValue: kernel}\n - --model\n - {outputPath: model}\n')),(0,r.kt)("p",null,"\uc704\uc5d0 \uc791\uc131\ud55c \ucef4\ud3ec\ub10c\ud2b8\uac00 \uc2e4\ud589\ub418\ub294 \uc21c\uc11c\ub97c \uc880 \ub354 \uc790\uc138\ud788 \ub4e4\uc5ec\ub2e4\ubcf4\uba74 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4."),(0,r.kt)("ol",null,(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("inlineCode",{parentName:"li"},"docker pull python:3.7")),(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("inlineCode",{parentName:"li"},"pip install dill==0.3.4 pandas==1.3.4 scikit-learn==1.0.1")),(0,r.kt)("li",{parentName:"ol"},"run ",(0,r.kt)("inlineCode",{parentName:"li"},"command"))),(0,r.kt)("p",null,"\uc0dd\uc131\ub41c yaml \ud30c\uc77c\uc744 \uc790\uc138\ud788 \ubcf4\uba74, \ub2e4\uc74c\uacfc \uac19\uc740 \uc904\uc774 \uc790\ub3d9\uc73c\ub85c \ucd94\uac00\ub418\uc5b4 \ud544\uc694\ud55c \ud328\ud0a4\uc9c0\uac00 \uc124\uce58\ub418\uae30 \ub54c\ubb38\uc5d0 \uc624\ub958 \uc5c6\uc774 \uc815\uc0c1\uc801\uc73c\ub85c 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number_2)\n\n@pipeline(name="example_pipeline")\ndef example_pipeline(number_1: int, number_2: int):\n number_1_result = print_and_return_number(number_1)\n number_2_result = print_and_return_number(number_2)\n sum_result = sum_and_print_numbers(\n number_1=number_1_result.output, number_2=number_2_result.output\n )\n\nif __name__ == "__main__":\n kfp.compiler.Compiler().compile(example_pipeline, "example_pipeline.yaml")\n')),(0,p.kt)("p",null,"\ucef4\ud30c\uc77c\ub41c \uacb0\uacfc\ub97c \ubcf4\uba74 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4."),(0,p.kt)("details",null,(0,p.kt)("summary",null,"example_pipeline.yaml"),(0,p.kt)("pre",null,(0,p.kt)("code",{parentName:"pre",className:"language-bash"},'apiVersion: argoproj.io/v1alpha1\nkind: Workflow\nmetadata:\n generateName: example-pipeline-\n annotations: {pipelines.kubeflow.org/kfp_sdk_version: 1.6.3, pipelines.kubeflow.org/pipeline_compilation_time: \'2021-12-05T13:38:51.566777\',\n pipelines.kubeflow.org/pipeline_spec: \'{"inputs": 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print-and-return-number-Output, value: \'{{tasks.print-and-return-number.outputs.parameters.print-and-return-number-Output}}\'}\n - name: print-and-return-number\n container:\n args: [--number, \'{{inputs.parameters.number_1}}\', \'----output-paths\', /tmp/outputs/Output/data]\n command:\n - sh\n - -ec\n - |\n program_path=$(mktemp)\n printf "%s" "$0" > "$program_path"\n python3 -u "$program_path" "$@"\n - |\n def print_and_return_number(number):\n print(number)\n return number\n\n def _serialize_int(int_value: int) -> str:\n if isinstance(int_value, str):\n return int_value\n if not isinstance(int_value, int):\n raise TypeError(\'Value "{}" has type "{}" instead of int.\'.format(str(int_value), str(type(int_value))))\n return str(int_value)\n\n import argparse\n _parser = argparse.ArgumentParser(prog=\'Print and return number\', description=\'\')\n _parser.add_argument("--number", dest="number", type=int, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("----output-paths", dest="_output_paths", type=str, nargs=1)\n _parsed_args = vars(_parser.parse_args())\n _output_files = _parsed_args.pop("_output_paths", [])\n\n _outputs = print_and_return_number(**_parsed_args)\n\n _outputs = [_outputs]\n\n _output_serializers = [\n _serialize_int,\n\n ]\n\n import os\n for idx, output_file in enumerate(_output_files):\n try:\n os.makedirs(os.path.dirname(output_file))\n except OSError:\n pass\n with open(output_file, \'w\') as f:\n f.write(_output_serializers[idx](_outputs[idx]))\n image: python:3.7\n inputs:\n parameters:\n - {name: number_1}\n outputs:\n parameters:\n - name: print-and-return-number-Output\n valueFrom: {path: /tmp/outputs/Output/data}\n artifacts:\n - {name: print-and-return-number-Output, path: /tmp/outputs/Output/data}\n metadata:\n labels: {pipelines.kubeflow.org/kfp_sdk_version: 1.6.3, pipelines.kubeflow.org/pipeline-sdk-type: kfp}\n annotations: {pipelines.kubeflow.org/component_spec: \'{"implementation": {"container":\n {"args": ["--number", {"inputValue": "number"}, "----output-paths", {"outputPath":\n "Output"}], "command": ["sh", "-ec", "program_path=$(mktemp)\\nprintf \\"%s\\"\n \\"$0\\" > \\"$program_path\\"\\npython3 -u \\"$program_path\\" \\"$@\\"\\n", "def\n print_and_return_number(number):\\n print(number)\\n return number\\n\\ndef\n _serialize_int(int_value: int) -> str:\\n if isinstance(int_value, str):\\n return\n int_value\\n if not isinstance(int_value, int):\\n raise TypeError(\'\'Value\n \\"{}\\" has type \\"{}\\" instead of int.\'\'.format(str(int_value), str(type(int_value))))\\n return\n str(int_value)\\n\\nimport argparse\\n_parser = argparse.ArgumentParser(prog=\'\'Print\n and return number\'\', description=\'\'\'\')\\n_parser.add_argument(\\"--number\\",\n dest=\\"number\\", type=int, required=True, default=argparse.SUPPRESS)\\n_parser.add_argument(\\"----output-paths\\",\n dest=\\"_output_paths\\", type=str, nargs=1)\\n_parsed_args = vars(_parser.parse_args())\\n_output_files\n = _parsed_args.pop(\\"_output_paths\\", [])\\n\\n_outputs = print_and_return_number(**_parsed_args)\\n\\n_outputs\n = [_outputs]\\n\\n_output_serializers = [\\n _serialize_int,\\n\\n]\\n\\nimport\n os\\nfor idx, output_file in enumerate(_output_files):\\n try:\\n os.makedirs(os.path.dirname(output_file))\\n except\n OSError:\\n pass\\n with open(output_file, \'\'w\'\') as f:\\n f.write(_output_serializers[idx](_outputs[idx]))\\n"],\n "image": "python:3.7"}}, "inputs": [{"name": "number", "type": "Integer"}],\n "name": "Print and return number", "outputs": [{"name": "Output", "type":\n "Integer"}]}\', pipelines.kubeflow.org/component_ref: \'{}\', pipelines.kubeflow.org/arguments.parameters: \'{"number":\n "{{inputs.parameters.number_1}}"}\'}\n - name: print-and-return-number-2\n container:\n args: [--number, \'{{inputs.parameters.number_2}}\', \'----output-paths\', /tmp/outputs/Output/data]\n command:\n - sh\n - -ec\n - |\n program_path=$(mktemp)\n printf "%s" "$0" > "$program_path"\n python3 -u "$program_path" "$@"\n - |\n def print_and_return_number(number):\n print(number)\n return number\n\n def _serialize_int(int_value: int) -> str:\n if isinstance(int_value, str):\n return int_value\n if not isinstance(int_value, int):\n raise TypeError(\'Value "{}" has type "{}" instead of int.\'.format(str(int_value), str(type(int_value))))\n return str(int_value)\n\n import argparse\n _parser = argparse.ArgumentParser(prog=\'Print and return number\', description=\'\')\n _parser.add_argument("--number", dest="number", type=int, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("----output-paths", dest="_output_paths", type=str, nargs=1)\n _parsed_args = vars(_parser.parse_args())\n _output_files = _parsed_args.pop("_output_paths", [])\n\n _outputs = print_and_return_number(**_parsed_args)\n\n _outputs = [_outputs]\n\n _output_serializers = [\n _serialize_int,\n\n ]\n\n import os\n for idx, output_file in enumerate(_output_files):\n try:\n os.makedirs(os.path.dirname(output_file))\n except OSError:\n pass\n with open(output_file, \'w\') as f:\n f.write(_output_serializers[idx](_outputs[idx]))\n image: python:3.7\n inputs:\n parameters:\n - {name: number_2}\n outputs:\n parameters:\n - name: print-and-return-number-2-Output\n valueFrom: {path: /tmp/outputs/Output/data}\n artifacts:\n - {name: print-and-return-number-2-Output, path: /tmp/outputs/Output/data}\n metadata:\n labels: {pipelines.kubeflow.org/kfp_sdk_version: 1.6.3, pipelines.kubeflow.org/pipeline-sdk-type: kfp}\n annotations: {pipelines.kubeflow.org/component_spec: \'{"implementation": {"container":\n {"args": ["--number", {"inputValue": "number"}, "----output-paths", {"outputPath":\n "Output"}], "command": ["sh", "-ec", "program_path=$(mktemp)\\nprintf \\"%s\\"\n \\"$0\\" > \\"$program_path\\"\\npython3 -u \\"$program_path\\" \\"$@\\"\\n", "def\n print_and_return_number(number):\\n print(number)\\n return number\\n\\ndef\n _serialize_int(int_value: int) -> str:\\n if isinstance(int_value, str):\\n return\n int_value\\n if not isinstance(int_value, int):\\n raise TypeError(\'\'Value\n \\"{}\\" has type \\"{}\\" instead of int.\'\'.format(str(int_value), str(type(int_value))))\\n return\n str(int_value)\\n\\nimport argparse\\n_parser = argparse.ArgumentParser(prog=\'\'Print\n and return number\'\', description=\'\'\'\')\\n_parser.add_argument(\\"--number\\",\n dest=\\"number\\", type=int, required=True, default=argparse.SUPPRESS)\\n_parser.add_argument(\\"----output-paths\\",\n dest=\\"_output_paths\\", type=str, nargs=1)\\n_parsed_args = vars(_parser.parse_args())\\n_output_files\n = _parsed_args.pop(\\"_output_paths\\", [])\\n\\n_outputs = print_and_return_number(**_parsed_args)\\n\\n_outputs\n = [_outputs]\\n\\n_output_serializers = [\\n _serialize_int,\\n\\n]\\n\\nimport\n os\\nfor idx, output_file in enumerate(_output_files):\\n try:\\n os.makedirs(os.path.dirname(output_file))\\n except\n OSError:\\n pass\\n with open(output_file, \'\'w\'\') as f:\\n f.write(_output_serializers[idx](_outputs[idx]))\\n"],\n "image": "python:3.7"}}, "inputs": [{"name": "number", "type": "Integer"}],\n "name": "Print and return number", "outputs": [{"name": "Output", "type":\n "Integer"}]}\', pipelines.kubeflow.org/component_ref: \'{}\', pipelines.kubeflow.org/arguments.parameters: \'{"number":\n "{{inputs.parameters.number_2}}"}\'}\n - name: sum-and-print-numbers\n container:\n args: [--number-1, \'{{inputs.parameters.print-and-return-number-Output}}\', --number-2,\n \'{{inputs.parameters.print-and-return-number-2-Output}}\']\n command:\n - sh\n - -ec\n - |\n program_path=$(mktemp)\n printf "%s" "$0" > "$program_path"\n python3 -u "$program_path" "$@"\n - |\n def sum_and_print_numbers(number_1, number_2):\n print(number_1 + number_2)\n\n import argparse\n _parser = argparse.ArgumentParser(prog=\'Sum and print numbers\', description=\'\')\n _parser.add_argument("--number-1", 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[{"name": "number_1", "type":\n "Integer"}, {"name": "number_2", "type": "Integer"}], "name": "example_pipeline"}\'}\n labels: {pipelines.kubeflow.org/kfp_sdk_version: 1.6.3}\nspec:\n entrypoint: example-pipeline\n templates:\n - name: example-pipeline\n inputs:\n parameters:\n - {name: number_1}\n - {name: number_2}\n dag:\n tasks:\n - name: print-and-return-number\n template: print-and-return-number\n arguments:\n parameters:\n - {name: number_1, value: \'{{inputs.parameters.number_1}}\'}\n - name: print-and-return-number-2\n template: print-and-return-number-2\n arguments:\n parameters:\n - {name: number_2, value: \'{{inputs.parameters.number_2}}\'}\n - name: sum-and-print-numbers\n template: sum-and-print-numbers\n dependencies: [print-and-return-number, print-and-return-number-2]\n arguments:\n parameters:\n - {name: print-and-return-number-2-Output, value: \'{{tasks.print-and-return-number-2.outputs.parameters.print-and-return-number-2-Output}}\'}\n - {name: print-and-return-number-Output, value: \'{{tasks.print-and-return-number.outputs.parameters.print-and-return-number-Output}}\'}\n - name: print-and-return-number\n container:\n args: [--number, \'{{inputs.parameters.number_1}}\', \'----output-paths\', /tmp/outputs/Output/data]\n command:\n - sh\n - -ec\n - |\n program_path=$(mktemp)\n printf "%s" "$0" > "$program_path"\n python3 -u "$program_path" "$@"\n - |\n def print_and_return_number(number):\n print(number)\n return number\n\n def _serialize_int(int_value: int) -> str:\n if isinstance(int_value, str):\n return int_value\n if not isinstance(int_value, int):\n raise TypeError(\'Value "{}" has type "{}" instead of int.\'.format(str(int_value), str(type(int_value))))\n return str(int_value)\n\n import argparse\n _parser = argparse.ArgumentParser(prog=\'Print and return number\', description=\'\')\n _parser.add_argument("--number", dest="number", type=int, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("----output-paths", dest="_output_paths", type=str, nargs=1)\n _parsed_args = vars(_parser.parse_args())\n _output_files = _parsed_args.pop("_output_paths", [])\n\n _outputs = print_and_return_number(**_parsed_args)\n\n _outputs = [_outputs]\n\n _output_serializers = [\n _serialize_int,\n\n ]\n\n import os\n for idx, output_file in enumerate(_output_files):\n try:\n os.makedirs(os.path.dirname(output_file))\n except OSError:\n pass\n with open(output_file, \'w\') as f:\n f.write(_output_serializers[idx](_outputs[idx]))\n image: python:3.7\n inputs:\n parameters:\n - {name: number_1}\n outputs:\n parameters:\n - name: print-and-return-number-Output\n valueFrom: {path: /tmp/outputs/Output/data}\n artifacts:\n - {name: print-and-return-number-Output, path: /tmp/outputs/Output/data}\n metadata:\n labels: {pipelines.kubeflow.org/kfp_sdk_version: 1.6.3, pipelines.kubeflow.org/pipeline-sdk-type: kfp}\n annotations: {pipelines.kubeflow.org/component_spec: \'{"implementation": {"container":\n {"args": ["--number", {"inputValue": "number"}, "----output-paths", {"outputPath":\n "Output"}], "command": ["sh", "-ec", "program_path=$(mktemp)\\nprintf \\"%s\\"\n \\"$0\\" > \\"$program_path\\"\\npython3 -u \\"$program_path\\" \\"$@\\"\\n", "def\n print_and_return_number(number):\\n print(number)\\n return number\\n\\ndef\n _serialize_int(int_value: int) -> str:\\n if isinstance(int_value, str):\\n return\n int_value\\n if not isinstance(int_value, int):\\n raise TypeError(\'\'Value\n \\"{}\\" has type \\"{}\\" instead of int.\'\'.format(str(int_value), str(type(int_value))))\\n return\n str(int_value)\\n\\nimport argparse\\n_parser = argparse.ArgumentParser(prog=\'\'Print\n and return number\'\', description=\'\'\'\')\\n_parser.add_argument(\\"--number\\",\n dest=\\"number\\", type=int, required=True, default=argparse.SUPPRESS)\\n_parser.add_argument(\\"----output-paths\\",\n dest=\\"_output_paths\\", type=str, nargs=1)\\n_parsed_args = vars(_parser.parse_args())\\n_output_files\n = _parsed_args.pop(\\"_output_paths\\", [])\\n\\n_outputs = print_and_return_number(**_parsed_args)\\n\\n_outputs\n = [_outputs]\\n\\n_output_serializers = [\\n _serialize_int,\\n\\n]\\n\\nimport\n os\\nfor idx, output_file in enumerate(_output_files):\\n try:\\n os.makedirs(os.path.dirname(output_file))\\n except\n OSError:\\n pass\\n with open(output_file, \'\'w\'\') as f:\\n f.write(_output_serializers[idx](_outputs[idx]))\\n"],\n "image": "python:3.7"}}, "inputs": [{"name": "number", "type": "Integer"}],\n "name": "Print and return number", "outputs": [{"name": "Output", "type":\n "Integer"}]}\', pipelines.kubeflow.org/component_ref: \'{}\', pipelines.kubeflow.org/arguments.parameters: \'{"number":\n "{{inputs.parameters.number_1}}"}\'}\n - name: print-and-return-number-2\n container:\n args: [--number, \'{{inputs.parameters.number_2}}\', \'----output-paths\', /tmp/outputs/Output/data]\n command:\n - sh\n - -ec\n - |\n program_path=$(mktemp)\n printf "%s" "$0" > "$program_path"\n python3 -u "$program_path" "$@"\n - |\n def print_and_return_number(number):\n print(number)\n return number\n\n def _serialize_int(int_value: int) -> str:\n if isinstance(int_value, str):\n return int_value\n if not isinstance(int_value, int):\n raise TypeError(\'Value "{}" has type "{}" instead of int.\'.format(str(int_value), str(type(int_value))))\n return str(int_value)\n\n import argparse\n _parser = argparse.ArgumentParser(prog=\'Print and return number\', description=\'\')\n _parser.add_argument("--number", dest="number", type=int, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("----output-paths", dest="_output_paths", type=str, nargs=1)\n _parsed_args = vars(_parser.parse_args())\n _output_files = _parsed_args.pop("_output_paths", [])\n\n _outputs = print_and_return_number(**_parsed_args)\n\n _outputs = [_outputs]\n\n _output_serializers = [\n _serialize_int,\n\n ]\n\n import os\n for idx, output_file in enumerate(_output_files):\n try:\n os.makedirs(os.path.dirname(output_file))\n except OSError:\n pass\n with open(output_file, \'w\') as f:\n f.write(_output_serializers[idx](_outputs[idx]))\n image: python:3.7\n inputs:\n parameters:\n - {name: number_2}\n outputs:\n parameters:\n - name: print-and-return-number-2-Output\n valueFrom: {path: /tmp/outputs/Output/data}\n artifacts:\n - {name: print-and-return-number-2-Output, path: /tmp/outputs/Output/data}\n metadata:\n labels: {pipelines.kubeflow.org/kfp_sdk_version: 1.6.3, pipelines.kubeflow.org/pipeline-sdk-type: kfp}\n annotations: {pipelines.kubeflow.org/component_spec: \'{"implementation": {"container":\n {"args": ["--number", {"inputValue": "number"}, "----output-paths", {"outputPath":\n "Output"}], "command": ["sh", "-ec", "program_path=$(mktemp)\\nprintf \\"%s\\"\n \\"$0\\" > \\"$program_path\\"\\npython3 -u \\"$program_path\\" \\"$@\\"\\n", "def\n print_and_return_number(number):\\n print(number)\\n return number\\n\\ndef\n _serialize_int(int_value: int) -> str:\\n if isinstance(int_value, str):\\n return\n int_value\\n if not isinstance(int_value, int):\\n raise TypeError(\'\'Value\n \\"{}\\" has type \\"{}\\" instead of int.\'\'.format(str(int_value), str(type(int_value))))\\n return\n str(int_value)\\n\\nimport argparse\\n_parser = argparse.ArgumentParser(prog=\'\'Print\n and return number\'\', description=\'\'\'\')\\n_parser.add_argument(\\"--number\\",\n dest=\\"number\\", type=int, required=True, default=argparse.SUPPRESS)\\n_parser.add_argument(\\"----output-paths\\",\n dest=\\"_output_paths\\", type=str, nargs=1)\\n_parsed_args = vars(_parser.parse_args())\\n_output_files\n = _parsed_args.pop(\\"_output_paths\\", [])\\n\\n_outputs = print_and_return_number(**_parsed_args)\\n\\n_outputs\n = [_outputs]\\n\\n_output_serializers = [\\n _serialize_int,\\n\\n]\\n\\nimport\n os\\nfor idx, output_file in enumerate(_output_files):\\n try:\\n os.makedirs(os.path.dirname(output_file))\\n except\n OSError:\\n pass\\n with open(output_file, \'\'w\'\') as f:\\n f.write(_output_serializers[idx](_outputs[idx]))\\n"],\n "image": "python:3.7"}}, "inputs": [{"name": "number", "type": "Integer"}],\n "name": "Print and return number", "outputs": [{"name": "Output", "type":\n "Integer"}]}\', pipelines.kubeflow.org/component_ref: \'{}\', pipelines.kubeflow.org/arguments.parameters: \'{"number":\n "{{inputs.parameters.number_2}}"}\'}\n - name: sum-and-print-numbers\n container:\n args: [--number-1, \'{{inputs.parameters.print-and-return-number-Output}}\', --number-2,\n \'{{inputs.parameters.print-and-return-number-2-Output}}\']\n command:\n - sh\n - -ec\n - |\n program_path=$(mktemp)\n printf "%s" "$0" > "$program_path"\n python3 -u "$program_path" "$@"\n - |\n def sum_and_print_numbers(number_1, number_2):\n print(number_1 + number_2)\n\n import argparse\n _parser = argparse.ArgumentParser(prog=\'Sum and print numbers\', description=\'\')\n _parser.add_argument("--number-1", 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index=False)\n\n\n@partial(\n create_component_from_func,\n packages_to_install=["pandas"],\n)\ndef drop_na_from_csv(\n data_path: InputPath("csv"),\n output_path: OutputPath("csv"),\n):\n import pandas as pd\n\n data = pd.read_csv(data_path)\n data = data.dropna()\n data.to_csv(output_path, index=False)\n\n\n@partial(\n create_component_from_func,\n packages_to_install=["dill", "pandas", "scikit-learn", "mlflow"],\n)\ndef train_from_csv(\n train_data_path: InputPath("csv"),\n train_target_path: InputPath("csv"),\n model_path: OutputPath("dill"),\n input_example_path: OutputPath("dill"),\n signature_path: OutputPath("dill"),\n conda_env_path: OutputPath("dill"),\n kernel: str,\n):\n import dill\n import pandas as pd\n from sklearn.svm import SVC\n\n from mlflow.models.signature import infer_signature\n from mlflow.utils.environment import _mlflow_conda_env\n\n train_data = pd.read_csv(train_data_path)\n train_target = pd.read_csv(train_target_path)\n\n clf = SVC(kernel=kernel)\n 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\uc0ac\uc6a9\ud569\ub2c8\ub2e4."),(0,r.kt)("p",null,"\uc6b0\uc120 \ucef4\ud3ec\ub10c\ud2b8\uac00 \uc2e4\ud328\ud558\ub3c4\ub85d \ud30c\uc774\ud504\ub77c\uc778\uc744 \ubcc0\uacbd\ud558\ub3c4\ub85d \ud558\uaca0\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-python"},'from functools import partial\n\nimport kfp\nfrom kfp.components import InputPath, OutputPath, create_component_from_func\nfrom kfp.dsl import pipeline\n\n\n@partial(\n create_component_from_func,\n packages_to_install=["pandas", "scikit-learn"],\n)\ndef load_iris_data(\n data_path: OutputPath("csv"),\n target_path: OutputPath("csv"),\n):\n import pandas as pd\n from sklearn.datasets import load_iris\n\n iris = load_iris()\n\n data = pd.DataFrame(iris["data"], columns=iris["feature_names"])\n target = pd.DataFrame(iris["target"], columns=["target"])\n \n data["sepal length (cm)"] = None\n data.to_csv(data_path, index=False)\n target.to_csv(target_path, index=False)\n\n\n@partial(\n create_component_from_func,\n packages_to_install=["pandas"],\n)\ndef drop_na_from_csv(\n data_path: InputPath("csv"),\n output_path: OutputPath("csv"),\n):\n import pandas as pd\n\n data = pd.read_csv(data_path)\n data = data.dropna()\n data.to_csv(output_path, index=False)\n\n\n@partial(\n create_component_from_func,\n packages_to_install=["dill", "pandas", "scikit-learn", "mlflow"],\n)\ndef train_from_csv(\n train_data_path: InputPath("csv"),\n train_target_path: InputPath("csv"),\n model_path: OutputPath("dill"),\n input_example_path: OutputPath("dill"),\n signature_path: OutputPath("dill"),\n conda_env_path: OutputPath("dill"),\n kernel: str,\n):\n import dill\n import pandas as pd\n from sklearn.svm import SVC\n\n from mlflow.models.signature import infer_signature\n from mlflow.utils.environment import _mlflow_conda_env\n\n train_data = pd.read_csv(train_data_path)\n train_target = pd.read_csv(train_target_path)\n\n clf = SVC(kernel=kernel)\n 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Install Prerequisite",description:"Install docker",sidebar_position:3,date:new Date("2021-12-13T00:00:00.000Z"),lastmod:new Date("2023-09-29T00:00:00.000Z"),contributors:["Jaeyeon Kim","Jongsun Shinn","Sangwoo Shim","Minwook Je"]},l=void 0,s={unversionedId:"setup-kubernetes/install-prerequisite",id:"setup-kubernetes/install-prerequisite",title:"3. Install Prerequisite",description:"Install docker",source:"@site/docs/setup-kubernetes/install-prerequisite.md",sourceDirName:"setup-kubernetes",slug:"/setup-kubernetes/install-prerequisite",permalink:"/docs/setup-kubernetes/install-prerequisite",draft:!1,editUrl:"https://github.com/mlops-for-all/mlops-for-all.github.io/tree/main/docs/setup-kubernetes/install-prerequisite.md",tags:[],version:"current",lastUpdatedBy:"Aiden-Jeon",lastUpdatedAt:1706783082,formattedLastUpdatedAt:"2024\ub144 2\uc6d4 1\uc77c",sidebarPosition:3,frontMatter:{title:"3. Install Prerequisite",description:"Install docker",sidebar_position:3,date:"2021-12-13T00:00:00.000Z",lastmod:"2023-09-29T00:00:00.000Z",contributors:["Jaeyeon Kim","Jongsun Shinn","Sangwoo Shim","Minwook Je"]},sidebar:"tutorialSidebar",previous:{title:"2. Setup Kubernetes",permalink:"/docs/setup-kubernetes/kubernetes"},next:{title:"4.1. K3s",permalink:"/docs/setup-kubernetes/install-kubernetes/kubernetes-with-k3s"}},i={},p=[{value:"Install apt packages",id:"install-apt-packages",level:2},{value:"Install Docker",id:"install-docker",level:2},{value:"Turn off Swap Memory",id:"turn-off-swap-memory",level:2},{value:"Install Kubectl",id:"install-kubectl",level:2},{value:"References",id:"references",level:2}],u={toc:p},c="wrapper";function m(e){let{components:t,...a}=e;return(0,r.kt)(c,(0,n.Z)({},u,a,{components:t,mdxType:"MDXLayout"}),(0,r.kt)("p",null,"\uc774 \ud398\uc774\uc9c0\uc5d0\uc11c\ub294 \ucfe0\ubc84\ub124\ud2f0\uc2a4\ub97c \uc124\uce58\ud558\uae30\uc5d0 \uc55e\uc11c, ",(0,r.kt)("strong",{parentName:"p"},"\ud074\ub7ec\uc2a4\ud130"),"\uc640 ",(0,r.kt)("strong",{parentName:"p"},"\ud074\ub77c\uc774\uc5b8\ud2b8"),"\uc5d0 \uc124\uce58 \ud639\uc740 \uc124\uc815\ud574\ub450\uc5b4\uc57c \ud558\ub294 \ucef4\ud3ec\ub10c\ud2b8\ub4e4\uc5d0 \ub300\ud55c \ub9e4\ub274\uc5bc\uc744 \uc124\uba85\ud569\ub2c8\ub2e4."),(0,r.kt)("h2",{id:"install-apt-packages"},"Install apt packages"),(0,r.kt)("p",null,"\ucd94\ud6c4 \ud074\ub77c\uc774\uc5b8\ud2b8\uc640 \ud074\ub7ec\uc2a4\ud130\uc758 \uc6d0\ud65c\ud55c \ud1b5\uc2e0\uc744 \uc704\ud574\uc11c\ub294 Port-Forwarding\uc744 \uc218\ud589\ud574\uc57c \ud560 \uc77c\uc774 \uc788\uc2b5\ub2c8\ub2e4.\nPort-Forwarding\uc744 \uc704\ud574\uc11c\ub294 ",(0,r.kt)("strong",{parentName:"p"},"\ud074\ub7ec\uc2a4\ud130"),"\uc5d0 \ub2e4\uc74c \ud328\ud0a4\uc9c0\ub97c \uc124\uce58\ud574 \uc8fc\uc5b4\uc57c \ud569\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"sudo apt-get update\nsudo apt-get install -y socat\n")),(0,r.kt)("h2",{id:"install-docker"},"Install Docker"),(0,r.kt)("ol",null,(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("p",{parentName:"li"},"\ub3c4\ucee4 \uc124\uce58\uc5d0 \ud544\uc694\ud55c APT \ud328\ud0a4\uc9c0\ub4e4\uc744 \uc124\uce58\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"sudo apt-get update && sudo apt-get install -y ca-certificates curl gnupg lsb-release\n"))),(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("p",{parentName:"li"},"\ub3c4\ucee4\uc758 \uacf5\uc2dd GPG key\ub97c \ucd94\uac00\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo gpg --dearmor -o /usr/share/keyrings/docker-archive-keyring.gpg\n"))),(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("p",{parentName:"li"},"apt \ud328\ud0a4\uc9c0 \ub9e4\ub2c8\uc800\ub85c \ub3c4\ucee4\ub97c \uc124\uce58\ud560 \ub54c, stable Repository\uc5d0\uc11c \ubc1b\uc544\uc624\ub3c4\ub85d \uc124\uc815\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},'echo \\\n"deb [arch=$(dpkg --print-architecture) signed-by=/usr/share/keyrings/docker-archive-keyring.gpg] https://download.docker.com/linux/ubuntu \\\n$(lsb_release -cs) stable" | sudo tee /etc/apt/sources.list.d/docker.list > /dev/null\n'))),(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("p",{parentName:"li"},"\ud604\uc7ac \uc124\uce58\ud560 \uc218 \uc788\ub294 \ub3c4\ucee4 \ubc84\uc804\uc744 \ud655\uc778\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"sudo apt-get update && apt-cache madison docker-ce\n")),(0,r.kt)("p",{parentName:"li"},"\ucd9c\ub825\ub418\ub294 \ubc84\uc804 \uc911 ",(0,r.kt)("inlineCode",{parentName:"p"},"5:20.10.11~3-0~ubuntu-focal")," \ubc84\uc804\uc774 \uc788\ub294\uc9c0 \ud655\uc778\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"apt-cache madison docker-ce | grep 5:20.10.11~3-0~ubuntu-focal\n")),(0,r.kt)("p",{parentName:"li"},"\uc815\uc0c1\uc801\uc73c\ub85c \ucd94\uac00\uac00 \ub41c \uacbd\uc6b0 \ub2e4\uc74c\uacfc \uac19\uc774 \ucd9c\ub825\ub429\ub2c8\ub2e4."),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"docker-ce | 5:20.10.11~3-0~ubuntu-focal | https://download.docker.com/linux/ubuntu focal/stable amd64 Packages\n"))),(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("p",{parentName:"li"},(0,r.kt)("inlineCode",{parentName:"p"},"5:20.10.11~3-0~ubuntu-focal")," \ubc84\uc804\uc758 \ub3c4\ucee4\ub97c \uc124\uce58\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"sudo apt-get install -y containerd.io docker-ce=5:20.10.11~3-0~ubuntu-focal docker-ce-cli=5:20.10.11~3-0~ubuntu-focal\n"))),(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("p",{parentName:"li"},"\ub3c4\ucee4\uac00 \uc815\uc0c1\uc801\uc73c\ub85c \uc124\uce58\ub41c \uac83\uc744 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The Docker client contacted the Docker daemon.\n2. The Docker daemon pulled the "hello-world" image from the Docker Hub.\n (amd64)\n3. The Docker daemon created a new container from that image which runs the\n executable that produces the output you are currently reading.\n4. The Docker daemon streamed that output to the Docker client, which sent it\n to your terminal.\n\nTo try something more ambitious, you can run an Ubuntu container with:\n$ docker run -it ubuntu bash\n\nShare images, automate workflows, and more with a free Docker ID:\nhttps://hub.docker.com/\n\nFor more examples and ideas, visit:\nhttps://docs.docker.com/get-started/\n'))),(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("p",{parentName:"li"},"docker \uad00\ub828 command\ub97c sudo \ud0a4\uc6cc\ub4dc \uc5c6\uc774 \uc0ac\uc6a9\ud560 \uc218 \uc788\uac8c \ud558\ub3c4\ub85d \ub2e4\uc74c \uba85\ub839\uc5b4\ub97c \ud1b5\ud574 \uad8c\ud55c\uc744 \ucd94\uac00\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"sudo groupadd docker\nsudo usermod -aG docker $USER\nnewgrp docker\n"))),(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("p",{parentName:"li"},"sudo \ud0a4\uc6cc\ub4dc \uc5c6\uc774 docker command\ub97c \uc0ac\uc6a9\ud560 \uc218 \uc788\uac8c \ub41c \uac83\uc744 \ud655\uc778\ud558\uae30 \uc704\ud574, \ub2e4\uc2dc \ud55c\ubc88 docker run\uc744 \uc2e4\ud589\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"docker run hello-world\n")),(0,r.kt)("p",{parentName:"li"},"\uba85\ub839\uc5b4 \uc2e4\ud589 \ud6c4 \ub2e4\uc74c\uacfc \uac19\uc740 \uba54\uc2dc\uc9c0\uac00 \ubcf4\uc774\uba74 \uc815\uc0c1\uc801\uc73c\ub85c \uad8c\ud55c\uc774 \ucd94\uac00\ub41c \uac83\uc744 \uc758\ubbf8\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},'mlops@ubuntu:~$ docker run hello-world\n\nHello from Docker!\nThis message shows that your installation appears to be working correctly.\n\nTo generate this message, Docker took the following steps:\n1. The Docker client contacted the Docker daemon.\n2. The Docker daemon pulled the "hello-world" image from the Docker Hub.\n (amd64)\n3. The Docker daemon created a new container from that image which runs the\n executable that produces the output you are currently reading.\n4. The Docker daemon streamed that output to the Docker client, which sent it\n to your terminal.\n\nTo try something more ambitious, you can run an Ubuntu container with:\n$ docker run -it ubuntu bash\n\nShare images, automate workflows, and more with a free Docker ID:\nhttps://hub.docker.com/\n\nFor more examples and ideas, visit:\nhttps://docs.docker.com/get-started/\n')))),(0,r.kt)("h2",{id:"turn-off-swap-memory"},"Turn off Swap Memory"),(0,r.kt)("p",null,"kubelet \uc774 \uc815\uc0c1\uc801\uc73c\ub85c \ub3d9\uc791\ud558\uac8c \ud558\uae30 \uc704\ud574\uc11c\ub294 ",(0,r.kt)("strong",{parentName:"p"},"\ud074\ub7ec\uc2a4\ud130")," \ub178\ub4dc\uc5d0\uc11c swap\uc774\ub77c\uace0 \ubd88\ub9ac\ub294 \uac00\uc0c1\uba54\ubaa8\ub9ac\ub97c \uaebc \ub450\uc5b4\uc57c \ud569\ub2c8\ub2e4. \ub2e4\uc74c \uba85\ub839\uc5b4\ub97c \ud1b5\ud574 swap\uc744 \uaebc \ub461\ub2c8\ub2e4.",(0,r.kt)("br",{parentName:"p"}),"\n",(0,r.kt)("strong",{parentName:"p"},"(\ud074\ub7ec\uc2a4\ud130\uc640 \ud074\ub77c\uc774\uc5b8\ud2b8\ub97c \uac19\uc740 \ub370\uc2a4\ud06c\ud1b1\uc5d0\uc11c \uc0ac\uc6a9\ud560 \ub54c swap \uba54\ubaa8\ub9ac\ub97c \uc885\ub8cc\ud558\uba74 \uc18d\ub3c4\uc758 \uc800\ud558\uac00 \uc788\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4)")," "),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"sudo sed -i '/ swap / s/^\\(.*\\)$/#\\1/g' /etc/fstab\nsudo swapoff -a\n")),(0,r.kt)("h2",{id:"install-kubectl"},"Install Kubectl"),(0,r.kt)("p",null,"kubectl \uc740 \ucfe0\ubc84\ub124\ud2f0\uc2a4 \ud074\ub7ec\uc2a4\ud130\uc5d0 API\ub97c \uc694\uccad\ud560 \ub54c \uc0ac\uc6a9\ud558\ub294 \ud074\ub77c\uc774\uc5b8\ud2b8 \ud234\uc785\ub2c8\ub2e4. ",(0,r.kt)("strong",{parentName:"p"},"\ud074\ub77c\uc774\uc5b8\ud2b8")," \ub178\ub4dc\uc5d0 \uc124\uce58\ud574\ub450\uc5b4\uc57c \ud569\ub2c8\ub2e4."),(0,r.kt)("ol",null,(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("p",{parentName:"li"},"\ud604\uc7ac \ud3f4\ub354\uc5d0 kubectl v1.21.7 \ubc84\uc804\uc744 \ub2e4\uc6b4\ubc1b\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"curl -LO https://dl.k8s.io/release/v1.21.7/bin/linux/amd64/kubectl\n\n# Or if you use arm64\ncurl -LO https://dl.k8s.io/release/v1.21.7/bin/linux/arm64/kubectl\n")))),(0,r.kt)("ol",{start:2},(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("p",{parentName:"li"},"kubectl \uc744 \uc0ac\uc6a9\ud560 \uc218 \uc788\ub3c4\ub85d \ud30c\uc77c\uc758 \uad8c\ud55c\uacfc \uc704\uce58\ub97c \ubcc0\uacbd\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"sudo install -o root -g root -m 0755 kubectl /usr/local/bin/kubectl\n"))),(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("p",{parentName:"li"},"\uc815\uc0c1\uc801\uc73c\ub85c \uc124\uce58\ub418\uc5c8\ub294\uc9c0 \ud655\uc778\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"kubectl version --client\n")),(0,r.kt)("p",{parentName:"li"},"\ub2e4\uc74c\uacfc \uac19\uc740 \uba54\uc2dc\uc9c0\uac00 \ubcf4\uc774\uba74 \uc815\uc0c1\uc801\uc73c\ub85c \uc124\uce58\ub41c \uac83\uc744 \uc758\ubbf8\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},'Client Version: version.Info{Major:"1", Minor:"21", GitVersion:"v1.21.7", GitCommit:"1f86634ff08f37e54e8bfcd86bc90b61c98f84d4", GitTreeState:"clean", BuildDate:"2021-11-17T14:41:19Z", GoVersion:"go1.16.10", Compiler:"gc", Platform:"linux/amd64"}\n'))),(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("p",{parentName:"li"},"\uc5ec\ub7ec \uac1c\uc758 \ucfe0\ubc84\ub124\ud2f0\uc2a4 \ud074\ub7ec\uc2a4\ud130\ub97c \uc0ac\uc6a9\ud558\ub294 \uacbd\uc6b0, \uc5ec\ub7ec \uac1c\uc758 kubeconfig \ud30c\uc77c\uc744 \uad00\ub9ac\ud574\uc57c \ud558\ub294 \uacbd\uc6b0\uac00 \uc788\uc2b5\ub2c8\ub2e4.",(0,r.kt)("br",{parentName:"p"}),"\n","\uc5ec\ub7ec \uac1c\uc758 kubeconfig \ud30c\uc77c \ud639\uc740 \uc5ec\ub7ec \uac1c\uc758 kube-context\ub97c \ud6a8\uc728\uc801\uc73c\ub85c \uad00\ub9ac\ud558\ub294 \ubc29\ubc95\uc740 \ub2e4\uc74c\uacfc \uac19\uc740 \ubb38\uc11c\ub97c \ucc38\uace0\ud558\uc2dc\uae30 \ubc14\ub78d\ub2c8\ub2e4."),(0,r.kt)("ul",{parentName:"li"},(0,r.kt)("li",{parentName:"ul"},(0,r.kt)("a",{parentName:"li",href:"https://dev.to/aabiseverywhere/configuring-multiple-kubeconfig-on-your-machine-59eo"},"https://dev.to/aabiseverywhere/configuring-multiple-kubeconfig-on-your-machine-59eo")),(0,r.kt)("li",{parentName:"ul"},(0,r.kt)("a",{parentName:"li",href:"https://github.com/ahmetb/kubectx"},"https://github.com/ahmetb/kubectx"))))),(0,r.kt)("h2",{id:"references"},"References"),(0,r.kt)("ul",null,(0,r.kt)("li",{parentName:"ul"},(0,r.kt)("a",{parentName:"li",href:"https://docs.docker.com/engine/install/ubuntu/"},"Install Docker Engine on Ubuntu")),(0,r.kt)("li",{parentName:"ul"},(0,r.kt)("a",{parentName:"li",href:"https://kubernetes.io/ko/docs/tasks/tools/install-kubectl-linux/"},"\ub9ac\ub205\uc2a4\uc5d0 kubectl \uc124\uce58 \ubc0f \uc124\uc815"))))}m.isMDXComponent=!0}}]); \ No newline at end of file diff --git a/assets/js/b93cd888.e2f7150f.js b/assets/js/b93cd888.e2f7150f.js deleted file mode 100644 index 0fc1abf6..00000000 --- a/assets/js/b93cd888.e2f7150f.js +++ /dev/null @@ -1 +0,0 @@ -"use strict";(self.webpackChunkv_2=self.webpackChunkv_2||[]).push([[797],{3905:(e,t,a)=>{a.d(t,{Zo:()=>u,kt:()=>d});var n=a(7294);function r(e,t,a){return t in e?Object.defineProperty(e,t,{value:a,enumerable:!0,configurable:!0,writable:!0}):e[t]=a,e}function o(e,t){var a=Object.keys(e);if(Object.getOwnPropertySymbols){var n=Object.getOwnPropertySymbols(e);t&&(n=n.filter((function(t){return Object.getOwnPropertyDescriptor(e,t).enumerable}))),a.push.apply(a,n)}return a}function l(e){for(var t=1;t=0||(r[a]=e[a]);return r}(e,t);if(Object.getOwnPropertySymbols){var o=Object.getOwnPropertySymbols(e);for(n=0;n=0||Object.prototype.propertyIsEnumerable.call(e,a)&&(r[a]=e[a])}return r}var i=n.createContext({}),p=function(e){var t=n.useContext(i),a=t;return e&&(a="function"==typeof e?e(t):l(l({},t),e)),a},u=function(e){var t=p(e.components);return n.createElement(i.Provider,{value:t},e.children)},c="mdxType",m={inlineCode:"code",wrapper:function(e){var t=e.children;return n.createElement(n.Fragment,{},t)}},k=n.forwardRef((function(e,t){var a=e.components,r=e.mdxType,o=e.originalType,i=e.parentName,u=s(e,["components","mdxType","originalType","parentName"]),c=p(a),k=r,d=c["".concat(i,".").concat(k)]||c[k]||m[k]||o;return a?n.createElement(d,l(l({ref:t},u),{},{components:a})):n.createElement(d,l({ref:t},u))}));function d(e,t){var a=arguments,r=t&&t.mdxType;if("string"==typeof e||r){var o=a.length,l=new Array(o);l[0]=k;var s={};for(var i in t)hasOwnProperty.call(t,i)&&(s[i]=t[i]);s.originalType=e,s[c]="string"==typeof e?e:r,l[1]=s;for(var p=2;p{a.r(t),a.d(t,{assets:()=>i,contentTitle:()=>l,default:()=>m,frontMatter:()=>o,metadata:()=>s,toc:()=>p});var n=a(7462),r=(a(7294),a(3905));const o={title:"3. Install Prerequisite",description:"Install docker",sidebar_position:3,date:new Date("2021-12-13T00:00:00.000Z"),lastmod:new Date("2023-09-29T00:00:00.000Z"),contributors:["Jaeyeon Kim","Jongsun Shinn","Sangwoo Shim","Minwook Je"]},l=void 0,s={unversionedId:"setup-kubernetes/install-prerequisite",id:"setup-kubernetes/install-prerequisite",title:"3. Install Prerequisite",description:"Install docker",source:"@site/docs/setup-kubernetes/install-prerequisite.md",sourceDirName:"setup-kubernetes",slug:"/setup-kubernetes/install-prerequisite",permalink:"/docs/setup-kubernetes/install-prerequisite",draft:!1,editUrl:"https://github.com/mlops-for-all/mlops-for-all.github.io/tree/main/docs/setup-kubernetes/install-prerequisite.md",tags:[],version:"current",lastUpdatedBy:"conqrean",lastUpdatedAt:1705274857,formattedLastUpdatedAt:"2024\ub144 1\uc6d4 14\uc77c",sidebarPosition:3,frontMatter:{title:"3. Install Prerequisite",description:"Install docker",sidebar_position:3,date:"2021-12-13T00:00:00.000Z",lastmod:"2023-09-29T00:00:00.000Z",contributors:["Jaeyeon Kim","Jongsun Shinn","Sangwoo Shim","Minwook Je"]},sidebar:"tutorialSidebar",previous:{title:"2. Setup Kubernetes",permalink:"/docs/setup-kubernetes/kubernetes"},next:{title:"4.1. K3s",permalink:"/docs/setup-kubernetes/install-kubernetes/kubernetes-with-k3s"}},i={},p=[{value:"Install apt packages",id:"install-apt-packages",level:2},{value:"Install Docker",id:"install-docker",level:2},{value:"Turn off Swap Memory",id:"turn-off-swap-memory",level:2},{value:"Install Kubectl",id:"install-kubectl",level:2},{value:"References",id:"references",level:2}],u={toc:p},c="wrapper";function m(e){let{components:t,...a}=e;return(0,r.kt)(c,(0,n.Z)({},u,a,{components:t,mdxType:"MDXLayout"}),(0,r.kt)("p",null,"\uc774 \ud398\uc774\uc9c0\uc5d0\uc11c\ub294 \ucfe0\ubc84\ub124\ud2f0\uc2a4\ub97c \uc124\uce58\ud558\uae30\uc5d0 \uc55e\uc11c, ",(0,r.kt)("strong",{parentName:"p"},"\ud074\ub7ec\uc2a4\ud130"),"\uc640 ",(0,r.kt)("strong",{parentName:"p"},"\ud074\ub77c\uc774\uc5b8\ud2b8"),"\uc5d0 \uc124\uce58 \ud639\uc740 \uc124\uc815\ud574\ub450\uc5b4\uc57c \ud558\ub294 \ucef4\ud3ec\ub10c\ud2b8\ub4e4\uc5d0 \ub300\ud55c \ub9e4\ub274\uc5bc\uc744 \uc124\uba85\ud569\ub2c8\ub2e4."),(0,r.kt)("h2",{id:"install-apt-packages"},"Install apt packages"),(0,r.kt)("p",null,"\ucd94\ud6c4 \ud074\ub77c\uc774\uc5b8\ud2b8\uc640 \ud074\ub7ec\uc2a4\ud130\uc758 \uc6d0\ud65c\ud55c \ud1b5\uc2e0\uc744 \uc704\ud574\uc11c\ub294 Port-Forwarding\uc744 \uc218\ud589\ud574\uc57c \ud560 \uc77c\uc774 \uc788\uc2b5\ub2c8\ub2e4.\nPort-Forwarding\uc744 \uc704\ud574\uc11c\ub294 ",(0,r.kt)("strong",{parentName:"p"},"\ud074\ub7ec\uc2a4\ud130"),"\uc5d0 \ub2e4\uc74c \ud328\ud0a4\uc9c0\ub97c \uc124\uce58\ud574 \uc8fc\uc5b4\uc57c \ud569\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"sudo apt-get update\nsudo apt-get install -y socat\n")),(0,r.kt)("h2",{id:"install-docker"},"Install Docker"),(0,r.kt)("ol",null,(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("p",{parentName:"li"},"\ub3c4\ucee4 \uc124\uce58\uc5d0 \ud544\uc694\ud55c APT \ud328\ud0a4\uc9c0\ub4e4\uc744 \uc124\uce58\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"sudo apt-get update && sudo apt-get install -y ca-certificates curl gnupg lsb-release\n"))),(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("p",{parentName:"li"},"\ub3c4\ucee4\uc758 \uacf5\uc2dd GPG key\ub97c \ucd94\uac00\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo gpg --dearmor -o /usr/share/keyrings/docker-archive-keyring.gpg\n"))),(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("p",{parentName:"li"},"apt \ud328\ud0a4\uc9c0 \ub9e4\ub2c8\uc800\ub85c \ub3c4\ucee4\ub97c \uc124\uce58\ud560 \ub54c, stable Repository\uc5d0\uc11c \ubc1b\uc544\uc624\ub3c4\ub85d \uc124\uc815\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},'echo \\\n"deb [arch=$(dpkg --print-architecture) signed-by=/usr/share/keyrings/docker-archive-keyring.gpg] https://download.docker.com/linux/ubuntu \\\n$(lsb_release -cs) stable" | sudo tee /etc/apt/sources.list.d/docker.list > /dev/null\n'))),(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("p",{parentName:"li"},"\ud604\uc7ac \uc124\uce58\ud560 \uc218 \uc788\ub294 \ub3c4\ucee4 \ubc84\uc804\uc744 \ud655\uc778\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"sudo apt-get update && apt-cache madison docker-ce\n")),(0,r.kt)("p",{parentName:"li"},"\ucd9c\ub825\ub418\ub294 \ubc84\uc804 \uc911 ",(0,r.kt)("inlineCode",{parentName:"p"},"5:20.10.11~3-0~ubuntu-focal")," \ubc84\uc804\uc774 \uc788\ub294\uc9c0 \ud655\uc778\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"apt-cache madison docker-ce | grep 5:20.10.11~3-0~ubuntu-focal\n")),(0,r.kt)("p",{parentName:"li"},"\uc815\uc0c1\uc801\uc73c\ub85c \ucd94\uac00\uac00 \ub41c \uacbd\uc6b0 \ub2e4\uc74c\uacfc \uac19\uc774 \ucd9c\ub825\ub429\ub2c8\ub2e4."),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"docker-ce | 5:20.10.11~3-0~ubuntu-focal | https://download.docker.com/linux/ubuntu focal/stable amd64 Packages\n"))),(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("p",{parentName:"li"},(0,r.kt)("inlineCode",{parentName:"p"},"5:20.10.11~3-0~ubuntu-focal")," \ubc84\uc804\uc758 \ub3c4\ucee4\ub97c \uc124\uce58\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"sudo apt-get install -y containerd.io docker-ce=5:20.10.11~3-0~ubuntu-focal docker-ce-cli=5:20.10.11~3-0~ubuntu-focal\n"))),(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("p",{parentName:"li"},"\ub3c4\ucee4\uac00 \uc815\uc0c1\uc801\uc73c\ub85c \uc124\uce58\ub41c \uac83\uc744 \ud655\uc778\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"sudo docker run hello-world\n")),(0,r.kt)("p",{parentName:"li"},"\uba85\ub839\uc5b4 \uc2e4\ud589 \ud6c4 \ub2e4\uc74c\uacfc \uac19\uc740 \uba54\uc2dc\uc9c0\uac00 \ubcf4\uc774\uba74 \uc815\uc0c1\uc801\uc73c\ub85c \uc124\uce58\ub41c \uac83\uc744 \uc758\ubbf8\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},'mlops@ubuntu:~$ sudo docker run hello-world\n\nHello from Docker!\nThis message shows that your installation appears to be working correctly.\n\nTo generate this message, Docker took the following steps:\n1. The Docker client contacted the Docker daemon.\n2. The Docker daemon pulled the "hello-world" image from the Docker Hub.\n (amd64)\n3. The Docker daemon created a new container from that image which runs the\n executable that produces the output you are currently reading.\n4. The Docker daemon streamed that output to the Docker client, which sent it\n to your terminal.\n\nTo try something more ambitious, you can run an Ubuntu container with:\n$ docker run -it ubuntu bash\n\nShare images, automate workflows, and more with a free Docker ID:\nhttps://hub.docker.com/\n\nFor more examples and ideas, visit:\nhttps://docs.docker.com/get-started/\n'))),(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("p",{parentName:"li"},"docker \uad00\ub828 command\ub97c sudo \ud0a4\uc6cc\ub4dc \uc5c6\uc774 \uc0ac\uc6a9\ud560 \uc218 \uc788\uac8c \ud558\ub3c4\ub85d \ub2e4\uc74c \uba85\ub839\uc5b4\ub97c \ud1b5\ud574 \uad8c\ud55c\uc744 \ucd94\uac00\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"sudo groupadd docker\nsudo usermod -aG docker $USER\nnewgrp docker\n"))),(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("p",{parentName:"li"},"sudo \ud0a4\uc6cc\ub4dc \uc5c6\uc774 docker command\ub97c \uc0ac\uc6a9\ud560 \uc218 \uc788\uac8c \ub41c \uac83\uc744 \ud655\uc778\ud558\uae30 \uc704\ud574, \ub2e4\uc2dc \ud55c\ubc88 docker run\uc744 \uc2e4\ud589\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"docker run hello-world\n")),(0,r.kt)("p",{parentName:"li"},"\uba85\ub839\uc5b4 \uc2e4\ud589 \ud6c4 \ub2e4\uc74c\uacfc \uac19\uc740 \uba54\uc2dc\uc9c0\uac00 \ubcf4\uc774\uba74 \uc815\uc0c1\uc801\uc73c\ub85c \uad8c\ud55c\uc774 \ucd94\uac00\ub41c \uac83\uc744 \uc758\ubbf8\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},'mlops@ubuntu:~$ docker run hello-world\n\nHello from Docker!\nThis message shows that your installation appears to be working correctly.\n\nTo generate this message, Docker took the following steps:\n1. The Docker client contacted the Docker daemon.\n2. The Docker daemon pulled the "hello-world" image from the Docker Hub.\n (amd64)\n3. The Docker daemon created a new container from that image which runs the\n executable that produces the output you are currently reading.\n4. 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\uc18c\uc2a4\ucf54\ub4dc\ub9cc \ub123\uc5b4\ub450\ub294 \ud615\ud0dc\ub85c \ud65c\uc6a9\ud560 \uc218\uac00 \uc788\uc2b5\ub2c8\ub2e4.",(0,a.kt)("br",{parentName:"p"}),"\n","\uc774\ub7ec\ud55c \uacbd\uc6b0\uc5d0\ub294 ",(0,a.kt)("inlineCode",{parentName:"p"},"python:3.9"),", ",(0,a.kt)("inlineCode",{parentName:"p"},"python-3.9-alpine"),", ... \ub4f1\uc758 \uc798 \ub9cc\ub4e4\uc5b4\uc9c4 \uc774\ubbf8\uc9c0\ub97c \ubca0\uc774\uc2a4\ub85c \ud65c\uc6a9\ud569\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-docker"},"FROM [:] [AS ]\n\n# \uc608\uc2dc\nFROM ubuntu\nFROM ubuntu:18.04\nFROM nginx:latest AS ngx\n")),(0,a.kt)("h3",{id:"copy"},"COPY"),(0,a.kt)("p",null,(0,a.kt)("strong",{parentName:"p"},"host(\ub85c\uceec)\uc5d0\uc11c\uc758 ",(0,a.kt)("inlineCode",{parentName:"strong"},""))," \uacbd\ub85c\uc758 \ud30c\uc77c \ud639\uc740 \ub514\ub809\ud1a0\ub9ac\ub97c ",(0,a.kt)("strong",{parentName:"p"},"container \ub0b4\ubd80\uc5d0\uc11c\uc758 ",(0,a.kt)("inlineCode",{parentName:"strong"},""))," \uacbd\ub85c\uc5d0 \ubcf5\uc0ac\ud558\ub294 \uba85\ub839\uc5b4\uc785\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-docker"},"COPY ... \n\n# \uc608\uc2dc\nCOPY a.txt /some-directory/b.txt\nCOPY my-directory /some-directory-2\n")),(0,a.kt)("p",null,(0,a.kt)("inlineCode",{parentName:"p"},"ADD")," \ub294 ",(0,a.kt)("inlineCode",{parentName:"p"},"COPY")," \uc640 \ube44\uc2b7\ud558\uc9c0\ub9cc \ucd94\uac00\uc801\uc778 \uae30\ub2a5\uc744 \ud488\uace0 \uc788\uc2b5\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-docker"},"# 1 - \ud638\uc2a4\ud2b8\uc5d0 \uc555\ucd95\ub418\uc5b4\uc788\ub294 \ud30c\uc77c\uc744 \ud480\uba74\uc11c \ucee8\ud14c\uc774\ub108 \ub0b4\ubd80\ub85c copy \ud560 \uc218 \uc788\uc74c\nADD scripts.tar.gz /tmp\n# 2 - Remote URLs \uc5d0 \uc788\ub294 \ud30c\uc77c\uc744 \uc18c\uc2a4 \uacbd\ub85c\ub85c \uc9c0\uc815\ud560 \uc218 \uc788\uc74c\nADD http://www.example.com/script.sh /tmp\n\n# \uc704 \ub450 \uac00\uc9c0 \uae30\ub2a5\uc744 \uc0ac\uc6a9\ud558\uace0 \uc2f6\uc744 \uacbd\uc6b0\uc5d0\ub9cc COPY \ub300\uc2e0 ADD \ub97c \uc0ac\uc6a9\ud558\ub294 \uac83\uc744 \uad8c\uc7a5\n")),(0,a.kt)("h3",{id:"run"},"RUN"),(0,a.kt)("p",null,"\uba85\uc2dc\ud55c \ucee4\ub9e8\ub4dc\ub97c \ub3c4\ucee4 \ucee8\ud14c\uc774\ub108 \ub0b4\ubd80\uc5d0\uc11c \uc2e4\ud589\ud558\ub294 \uba85\ub839\uc5b4\uc785\ub2c8\ub2e4.",(0,a.kt)("br",{parentName:"p"}),"\n","\ub3c4\ucee4 \uc774\ubbf8\uc9c0\ub294 \ud574\ub2f9 \ucee4\ub9e8\ub4dc\ub4e4\uc774 \uc2e4\ud589\ub41c \uc0c1\ud0dc\ub97c \uc720\uc9c0\ud569\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-docker"},'RUN \nRUN ["executable-command", "parameter1", "parameter2"]\n\n# \uc608\uc2dc\nRUN pip install torch\nRUN pip install -r requirements.txt\n')),(0,a.kt)("h3",{id:"cmd"},"CMD"),(0,a.kt)("p",null,"\uba85\uc2dc\ud55c \ucee4\ub9e8\ub4dc\ub97c \ub3c4\ucee4 \ucee8\ud14c\uc774\ub108\uac00 ",(0,a.kt)("strong",{parentName:"p"},"\uc2dc\uc791\ub420 \ub54c"),", \uc2e4\ud589\ud558\ub294 \uac83\uc744 \uba85\uc2dc\ud558\ub294 \uba85\ub839\uc5b4\uc785\ub2c8\ub2e4.",(0,a.kt)("br",{parentName:"p"}),"\n","\ube44\uc2b7\ud55c \uc5ed\ud560\uc744 \ud558\ub294 \uba85\ub839\uc5b4\ub85c ",(0,a.kt)("strong",{parentName:"p"},"ENTRYPOINT")," \uac00 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \ub458\uc758 \ucc28\uc774\uc5d0 \ub300\ud574\uc11c\ub294 ",(0,a.kt)("strong",{parentName:"p"},"\ub4a4\uc5d0\uc11c")," \ub2e4\ub8f9\ub2c8\ub2e4.",(0,a.kt)("br",{parentName:"p"}),"\n","\ud558\ub098\uc758 \ub3c4\ucee4 \uc774\ubbf8\uc9c0\uc5d0\uc11c\ub294 \ud558\ub098\uc758 ",(0,a.kt)("strong",{parentName:"p"},"CMD")," \ub9cc \uc2e4\ud589\ud560 \uc218 \uc788\ub2e4\ub294 \uc810\uc5d0\uc11c ",(0,a.kt)("strong",{parentName:"p"},"RUN")," \uba85\ub839\uc5b4\uc640 \ub2e4\ub985\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-docker"},'CMD \nCMD ["executable-command", "parameter1", "parameter2"]\nCMD ["parameter1", "parameter2"] # ENTRYPOINT \uc640 \ud568\uaed8 \uc0ac\uc6a9\ub420 \ub54c\n\n# \uc608\uc2dc\nCMD python main.py\n')),(0,a.kt)("h3",{id:"workdir"},"WORKDIR"),(0,a.kt)("p",null,"\uc774\ud6c4 \ucd94\uac00\ub420 \uba85\ub839\uc5b4\ub97c \ucee8\ud14c\uc774\ub108 \ub0b4\uc758 \uc5b4\ub5a4 \ub514\ub809\ud1a0\ub9ac\uc5d0\uc11c \uc218\ud589\ud560 \uac83\uc778\uc9c0\ub97c \uba85\uc2dc\ud558\ub294 \uba85\ub839\uc5b4\uc785\ub2c8\ub2e4.",(0,a.kt)("br",{parentName:"p"}),"\n","\ub9cc\uc57d, \ud574\ub2f9 \ub514\ub809\ud1a0\ub9ac\uac00 \uc5c6\ub2e4\uba74 \uc0dd\uc131\ud569\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-docker"},"WORKDIR /path/to/workdir\n\n# \uc608\uc2dc\nWORKDIR /home/demo\nRUN pwd # /home/demo \uac00 \ucd9c\ub825\ub428\n")),(0,a.kt)("h3",{id:"env"},"ENV"),(0,a.kt)("p",null,"\ucee8\ud14c\uc774\ub108 \ub0b4\ubd80\uc5d0\uc11c \uc9c0\uc18d\uc801\uc73c\ub85c \uc0ac\uc6a9\ub420 environment variable \uc758 \uac12\uc744 \uc124\uc815\ud558\ub294 \uba85\ub839\uc5b4\uc785\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-docker"},"ENV \nENV =\n\n# \uc608\uc2dc\n# default \uc5b8\uc5b4 \uc124\uc815\nRUN locale-gen ko_KR.UTF-8\nENV LANG ko_KR.UTF-8\nENV LANGUAGE ko_KR.UTF-8\nENV LC_ALL ko_KR.UTF-8\n")),(0,a.kt)("h3",{id:"expose"},"EXPOSE"),(0,a.kt)("p",null,"\ucee8\ud14c\uc774\ub108\uc5d0\uc11c \ub6ab\uc5b4\uc904 \ud3ec\ud2b8/\ud504\ub85c\ud1a0\ucf5c\uc744 \uc9c0\uc815\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.",(0,a.kt)("br",{parentName:"p"}),"\n",(0,a.kt)("inlineCode",{parentName:"p"},"")," \uc744 \uc9c0\uc815\ud558\uc9c0 \uc54a\uc73c\uba74 TCP \uac00 \ub514\ud3f4\ud2b8\ub85c \uc124\uc815\ub429\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-docker"},"EXPOSE \nEXPOSE /\n\n# \uc608\uc2dc\nEXPOSE 8080\n")),(0,a.kt)("h2",{id:"3-\uac04\ub2e8\ud55c-dockerfile-\uc791\uc131\ud574\ubcf4\uae30"},"3. \uac04\ub2e8\ud55c Dockerfile \uc791\uc131\ud574\ubcf4\uae30"),(0,a.kt)("p",null,(0,a.kt)("inlineCode",{parentName:"p"},"vim Dockerfile")," \ud639\uc740 vscode \ub4f1 \ubcf8\uc778\uc774 \uc0ac\uc6a9\ud558\ub294 \ud3b8\uc9d1\uae30\ub85c ",(0,a.kt)("inlineCode",{parentName:"p"},"Dockerfile")," \uc744 \uc5f4\uc5b4 \ub2e4\uc74c\uacfc \uac19\uc774 \uc791\uc131\ud574\uc90d\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-docker"},"# base image \ub97c ubuntu 18.04 \ub85c \uc124\uc815\ud569\ub2c8\ub2e4.\nFROM ubuntu:18.04\n\n# apt-get update \uba85\ub839\uc744 \uc2e4\ud589\ud569\ub2c8\ub2e4.\nRUN apt-get update\n\n# TEST env var\uc758 \uac12\uc744 hello \ub85c \uc9c0\uc815\ud569\ub2c8\ub2e4.\nENV TEST hello\n\n# DOCKER CONTAINER \uac00 \uc2dc\uc791\ub420 \ub54c, \ud658\uacbd\ubcc0\uc218 TEST \uc758 \uac12\uc744 \ucd9c\ub825\ud569\ub2c8\ub2e4.\nCMD echo $TEST\n")),(0,a.kt)("h2",{id:"4-docker-build-from-dockerfile"},"4. Docker build from Dockerfile"),(0,a.kt)("p",null,(0,a.kt)("inlineCode",{parentName:"p"},"docker build")," \uba85\ub839\uc5b4\ub85c Dockerfile \ub85c\ubd80\ud130 Docker Image \ub97c \ub9cc\ub4e4\uc5b4\ubd05\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-bash"},"docker build --help\n")),(0,a.kt)("p",null,"Dockerfile \uc774 \uc788\ub294 \uacbd\ub85c\uc5d0\uc11c \ub2e4\uc74c \uba85\ub839\uc744 \uc2e4\ud589\ud569\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-bash"},"docker build -t my-image:v1.0.0 .\n")),(0,a.kt)("p",null,"\uc704 \ucee4\ub9e8\ub4dc\ub97c \uc124\uba85\ud558\uba74 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4."),(0,a.kt)("ul",null,(0,a.kt)("li",{parentName:"ul"},(0,a.kt)("inlineCode",{parentName:"li"},".")," : ",(0,a.kt)("strong",{parentName:"li"},"\ud604\uc7ac \uacbd\ub85c"),"\uc5d0 \uc788\ub294 Dockerfile \ub85c\ubd80\ud130"),(0,a.kt)("li",{parentName:"ul"},(0,a.kt)("inlineCode",{parentName:"li"},"-t")," : my-image \ub77c\ub294 ",(0,a.kt)("strong",{parentName:"li"},"\uc774\ub984"),"\uacfc v1.0.0 \uc774\ub77c\ub294 ",(0,a.kt)("strong",{parentName:"li"},"\ud0dc\uadf8"),"\ub85c ",(0,a.kt)("strong",{parentName:"li"},"\uc774\ubbf8\uc9c0"),"\ub97c"),(0,a.kt)("li",{parentName:"ul"},"\ube4c\ub4dc\ud558\uaca0\ub2e4\ub77c\ub294 \uba85\ub839\uc5b4")),(0,a.kt)("p",null,"\uc815\uc0c1\uc801\uc73c\ub85c \uc774\ubbf8\uc9c0 \ube4c\ub4dc\ub418\uc5c8\ub294\uc9c0 \ud655\uc778\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-bash"},"# grep : my-image \uac00 \uc788\ub294\uc9c0\ub97c \uc7a1\uc544\ub0b4\ub294 (grep) \ud558\ub294 \uba85\ub839\uc5b4\ndocker images | grep my-image\n")),(0,a.kt)("p",null,"\uc815\uc0c1\uc801\uc73c\ub85c \uc218\ud589\ub41c\ub2e4\uba74 \ub2e4\uc74c\uacfc \uac19\uc774 \ucd9c\ub825\ub429\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-bash"},"my-image v1.0.0 143114710b2d 3 seconds ago 87.9MB\n")),(0,a.kt)("h2",{id:"5-docker-run-from-dockerfile"},"5. Docker run from Dockerfile"),(0,a.kt)("p",null,"\uadf8\ub7fc \uc774\uc81c \ubc29\uae08 \ube4c\ub4dc\ud55c ",(0,a.kt)("inlineCode",{parentName:"p"},"my-image:v1.0.0")," \uc774\ubbf8\uc9c0\ub85c docker \ucee8\ud14c\uc774\ub108\ub97c ",(0,a.kt)("strong",{parentName:"p"},"run")," \ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-bash"},"docker run my-image:v1.0.0\n")),(0,a.kt)("p",null,"\uc815\uc0c1\uc801\uc73c\ub85c \uc218\ud589\ub41c\ub2e4\uba74 \ub2e4\uc74c\uacfc \uac19\uc774 \ub098\uc635\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-bash"},"hello\n")),(0,a.kt)("h2",{id:"6-docker-run-with-env"},"6. 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\uc18c\uc2a4\ucf54\ub4dc\ub9cc \ub123\uc5b4\ub450\ub294 \ud615\ud0dc\ub85c \ud65c\uc6a9\ud560 \uc218\uac00 \uc788\uc2b5\ub2c8\ub2e4.",(0,a.kt)("br",{parentName:"p"}),"\n","\uc774\ub7ec\ud55c \uacbd\uc6b0\uc5d0\ub294 ",(0,a.kt)("inlineCode",{parentName:"p"},"python:3.9"),", ",(0,a.kt)("inlineCode",{parentName:"p"},"python-3.9-alpine"),", ... \ub4f1\uc758 \uc798 \ub9cc\ub4e4\uc5b4\uc9c4 \uc774\ubbf8\uc9c0\ub97c \ubca0\uc774\uc2a4\ub85c \ud65c\uc6a9\ud569\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-docker"},"FROM [:] [AS ]\n\n# \uc608\uc2dc\nFROM ubuntu\nFROM ubuntu:18.04\nFROM nginx:latest AS ngx\n")),(0,a.kt)("h3",{id:"copy"},"COPY"),(0,a.kt)("p",null,(0,a.kt)("strong",{parentName:"p"},"host(\ub85c\uceec)\uc5d0\uc11c\uc758 ",(0,a.kt)("inlineCode",{parentName:"strong"},""))," \uacbd\ub85c\uc758 \ud30c\uc77c \ud639\uc740 \ub514\ub809\ud1a0\ub9ac\ub97c ",(0,a.kt)("strong",{parentName:"p"},"container \ub0b4\ubd80\uc5d0\uc11c\uc758 ",(0,a.kt)("inlineCode",{parentName:"strong"},""))," \uacbd\ub85c\uc5d0 \ubcf5\uc0ac\ud558\ub294 \uba85\ub839\uc5b4\uc785\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-docker"},"COPY ... \n\n# \uc608\uc2dc\nCOPY a.txt /some-directory/b.txt\nCOPY my-directory /some-directory-2\n")),(0,a.kt)("p",null,(0,a.kt)("inlineCode",{parentName:"p"},"ADD")," \ub294 ",(0,a.kt)("inlineCode",{parentName:"p"},"COPY")," \uc640 \ube44\uc2b7\ud558\uc9c0\ub9cc \ucd94\uac00\uc801\uc778 \uae30\ub2a5\uc744 \ud488\uace0 \uc788\uc2b5\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-docker"},"# 1 - \ud638\uc2a4\ud2b8\uc5d0 \uc555\ucd95\ub418\uc5b4\uc788\ub294 \ud30c\uc77c\uc744 \ud480\uba74\uc11c \ucee8\ud14c\uc774\ub108 \ub0b4\ubd80\ub85c copy \ud560 \uc218 \uc788\uc74c\nADD scripts.tar.gz /tmp\n# 2 - Remote URLs \uc5d0 \uc788\ub294 \ud30c\uc77c\uc744 \uc18c\uc2a4 \uacbd\ub85c\ub85c \uc9c0\uc815\ud560 \uc218 \uc788\uc74c\nADD http://www.example.com/script.sh /tmp\n\n# \uc704 \ub450 \uac00\uc9c0 \uae30\ub2a5\uc744 \uc0ac\uc6a9\ud558\uace0 \uc2f6\uc744 \uacbd\uc6b0\uc5d0\ub9cc COPY \ub300\uc2e0 ADD \ub97c \uc0ac\uc6a9\ud558\ub294 \uac83\uc744 \uad8c\uc7a5\n")),(0,a.kt)("h3",{id:"run"},"RUN"),(0,a.kt)("p",null,"\uba85\uc2dc\ud55c \ucee4\ub9e8\ub4dc\ub97c \ub3c4\ucee4 \ucee8\ud14c\uc774\ub108 \ub0b4\ubd80\uc5d0\uc11c \uc2e4\ud589\ud558\ub294 \uba85\ub839\uc5b4\uc785\ub2c8\ub2e4.",(0,a.kt)("br",{parentName:"p"}),"\n","\ub3c4\ucee4 \uc774\ubbf8\uc9c0\ub294 \ud574\ub2f9 \ucee4\ub9e8\ub4dc\ub4e4\uc774 \uc2e4\ud589\ub41c \uc0c1\ud0dc\ub97c \uc720\uc9c0\ud569\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-docker"},'RUN \nRUN ["executable-command", "parameter1", "parameter2"]\n\n# \uc608\uc2dc\nRUN pip install torch\nRUN pip install -r requirements.txt\n')),(0,a.kt)("h3",{id:"cmd"},"CMD"),(0,a.kt)("p",null,"\uba85\uc2dc\ud55c \ucee4\ub9e8\ub4dc\ub97c \ub3c4\ucee4 \ucee8\ud14c\uc774\ub108\uac00 ",(0,a.kt)("strong",{parentName:"p"},"\uc2dc\uc791\ub420 \ub54c"),", \uc2e4\ud589\ud558\ub294 \uac83\uc744 \uba85\uc2dc\ud558\ub294 \uba85\ub839\uc5b4\uc785\ub2c8\ub2e4.",(0,a.kt)("br",{parentName:"p"}),"\n","\ube44\uc2b7\ud55c \uc5ed\ud560\uc744 \ud558\ub294 \uba85\ub839\uc5b4\ub85c ",(0,a.kt)("strong",{parentName:"p"},"ENTRYPOINT")," \uac00 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \ub458\uc758 \ucc28\uc774\uc5d0 \ub300\ud574\uc11c\ub294 ",(0,a.kt)("strong",{parentName:"p"},"\ub4a4\uc5d0\uc11c")," \ub2e4\ub8f9\ub2c8\ub2e4.",(0,a.kt)("br",{parentName:"p"}),"\n","\ud558\ub098\uc758 \ub3c4\ucee4 \uc774\ubbf8\uc9c0\uc5d0\uc11c\ub294 \ud558\ub098\uc758 ",(0,a.kt)("strong",{parentName:"p"},"CMD")," \ub9cc \uc2e4\ud589\ud560 \uc218 \uc788\ub2e4\ub294 \uc810\uc5d0\uc11c ",(0,a.kt)("strong",{parentName:"p"},"RUN")," \uba85\ub839\uc5b4\uc640 \ub2e4\ub985\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-docker"},'CMD \nCMD ["executable-command", "parameter1", "parameter2"]\nCMD ["parameter1", "parameter2"] # ENTRYPOINT \uc640 \ud568\uaed8 \uc0ac\uc6a9\ub420 \ub54c\n\n# \uc608\uc2dc\nCMD python main.py\n')),(0,a.kt)("h3",{id:"workdir"},"WORKDIR"),(0,a.kt)("p",null,"\uc774\ud6c4 \ucd94\uac00\ub420 \uba85\ub839\uc5b4\ub97c \ucee8\ud14c\uc774\ub108 \ub0b4\uc758 \uc5b4\ub5a4 \ub514\ub809\ud1a0\ub9ac\uc5d0\uc11c \uc218\ud589\ud560 \uac83\uc778\uc9c0\ub97c \uba85\uc2dc\ud558\ub294 \uba85\ub839\uc5b4\uc785\ub2c8\ub2e4.",(0,a.kt)("br",{parentName:"p"}),"\n","\ub9cc\uc57d, \ud574\ub2f9 \ub514\ub809\ud1a0\ub9ac\uac00 \uc5c6\ub2e4\uba74 \uc0dd\uc131\ud569\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-docker"},"WORKDIR /path/to/workdir\n\n# \uc608\uc2dc\nWORKDIR /home/demo\nRUN pwd # /home/demo \uac00 \ucd9c\ub825\ub428\n")),(0,a.kt)("h3",{id:"env"},"ENV"),(0,a.kt)("p",null,"\ucee8\ud14c\uc774\ub108 \ub0b4\ubd80\uc5d0\uc11c \uc9c0\uc18d\uc801\uc73c\ub85c \uc0ac\uc6a9\ub420 environment variable \uc758 \uac12\uc744 \uc124\uc815\ud558\ub294 \uba85\ub839\uc5b4\uc785\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-docker"},"ENV \nENV =\n\n# \uc608\uc2dc\n# default \uc5b8\uc5b4 \uc124\uc815\nRUN locale-gen ko_KR.UTF-8\nENV LANG ko_KR.UTF-8\nENV LANGUAGE ko_KR.UTF-8\nENV LC_ALL ko_KR.UTF-8\n")),(0,a.kt)("h3",{id:"expose"},"EXPOSE"),(0,a.kt)("p",null,"\ucee8\ud14c\uc774\ub108\uc5d0\uc11c \ub6ab\uc5b4\uc904 \ud3ec\ud2b8/\ud504\ub85c\ud1a0\ucf5c\uc744 \uc9c0\uc815\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.",(0,a.kt)("br",{parentName:"p"}),"\n",(0,a.kt)("inlineCode",{parentName:"p"},"")," \uc744 \uc9c0\uc815\ud558\uc9c0 \uc54a\uc73c\uba74 TCP \uac00 \ub514\ud3f4\ud2b8\ub85c \uc124\uc815\ub429\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-docker"},"EXPOSE \nEXPOSE /\n\n# \uc608\uc2dc\nEXPOSE 8080\n")),(0,a.kt)("h2",{id:"3-\uac04\ub2e8\ud55c-dockerfile-\uc791\uc131\ud574\ubcf4\uae30"},"3. \uac04\ub2e8\ud55c Dockerfile \uc791\uc131\ud574\ubcf4\uae30"),(0,a.kt)("p",null,(0,a.kt)("inlineCode",{parentName:"p"},"vim Dockerfile")," \ud639\uc740 vscode \ub4f1 \ubcf8\uc778\uc774 \uc0ac\uc6a9\ud558\ub294 \ud3b8\uc9d1\uae30\ub85c ",(0,a.kt)("inlineCode",{parentName:"p"},"Dockerfile")," \uc744 \uc5f4\uc5b4 \ub2e4\uc74c\uacfc \uac19\uc774 \uc791\uc131\ud574\uc90d\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-docker"},"# base image \ub97c ubuntu 18.04 \ub85c \uc124\uc815\ud569\ub2c8\ub2e4.\nFROM ubuntu:18.04\n\n# apt-get update \uba85\ub839\uc744 \uc2e4\ud589\ud569\ub2c8\ub2e4.\nRUN apt-get update\n\n# TEST env var\uc758 \uac12\uc744 hello \ub85c \uc9c0\uc815\ud569\ub2c8\ub2e4.\nENV TEST hello\n\n# DOCKER CONTAINER \uac00 \uc2dc\uc791\ub420 \ub54c, \ud658\uacbd\ubcc0\uc218 TEST \uc758 \uac12\uc744 \ucd9c\ub825\ud569\ub2c8\ub2e4.\nCMD echo $TEST\n")),(0,a.kt)("h2",{id:"4-docker-build-from-dockerfile"},"4. Docker build from Dockerfile"),(0,a.kt)("p",null,(0,a.kt)("inlineCode",{parentName:"p"},"docker build")," \uba85\ub839\uc5b4\ub85c Dockerfile \ub85c\ubd80\ud130 Docker Image \ub97c \ub9cc\ub4e4\uc5b4\ubd05\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-bash"},"docker build --help\n")),(0,a.kt)("p",null,"Dockerfile \uc774 \uc788\ub294 \uacbd\ub85c\uc5d0\uc11c \ub2e4\uc74c \uba85\ub839\uc744 \uc2e4\ud589\ud569\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-bash"},"docker build -t my-image:v1.0.0 .\n")),(0,a.kt)("p",null,"\uc704 \ucee4\ub9e8\ub4dc\ub97c \uc124\uba85\ud558\uba74 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4."),(0,a.kt)("ul",null,(0,a.kt)("li",{parentName:"ul"},(0,a.kt)("inlineCode",{parentName:"li"},".")," : ",(0,a.kt)("strong",{parentName:"li"},"\ud604\uc7ac \uacbd\ub85c"),"\uc5d0 \uc788\ub294 Dockerfile \ub85c\ubd80\ud130"),(0,a.kt)("li",{parentName:"ul"},(0,a.kt)("inlineCode",{parentName:"li"},"-t")," : my-image \ub77c\ub294 ",(0,a.kt)("strong",{parentName:"li"},"\uc774\ub984"),"\uacfc v1.0.0 \uc774\ub77c\ub294 ",(0,a.kt)("strong",{parentName:"li"},"\ud0dc\uadf8"),"\ub85c ",(0,a.kt)("strong",{parentName:"li"},"\uc774\ubbf8\uc9c0"),"\ub97c"),(0,a.kt)("li",{parentName:"ul"},"\ube4c\ub4dc\ud558\uaca0\ub2e4\ub77c\ub294 \uba85\ub839\uc5b4")),(0,a.kt)("p",null,"\uc815\uc0c1\uc801\uc73c\ub85c \uc774\ubbf8\uc9c0 \ube4c\ub4dc\ub418\uc5c8\ub294\uc9c0 \ud655\uc778\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-bash"},"# grep : my-image \uac00 \uc788\ub294\uc9c0\ub97c \uc7a1\uc544\ub0b4\ub294 (grep) \ud558\ub294 \uba85\ub839\uc5b4\ndocker images | grep my-image\n")),(0,a.kt)("p",null,"\uc815\uc0c1\uc801\uc73c\ub85c \uc218\ud589\ub41c\ub2e4\uba74 \ub2e4\uc74c\uacfc \uac19\uc774 \ucd9c\ub825\ub429\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-bash"},"my-image v1.0.0 143114710b2d 3 seconds ago 87.9MB\n")),(0,a.kt)("h2",{id:"5-docker-run-from-dockerfile"},"5. Docker run from Dockerfile"),(0,a.kt)("p",null,"\uadf8\ub7fc \uc774\uc81c \ubc29\uae08 \ube4c\ub4dc\ud55c ",(0,a.kt)("inlineCode",{parentName:"p"},"my-image:v1.0.0")," \uc774\ubbf8\uc9c0\ub85c docker \ucee8\ud14c\uc774\ub108\ub97c ",(0,a.kt)("strong",{parentName:"p"},"run")," \ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-bash"},"docker run my-image:v1.0.0\n")),(0,a.kt)("p",null,"\uc815\uc0c1\uc801\uc73c\ub85c \uc218\ud589\ub41c\ub2e4\uba74 \ub2e4\uc74c\uacfc \uac19\uc774 \ub098\uc635\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-bash"},"hello\n")),(0,a.kt)("h2",{id:"6-docker-run-with-env"},"6. Docker run with env"),(0,a.kt)("p",null,"\uc774\ubc88\uc5d0\ub294 \ubc29\uae08 \ube4c\ub4dc\ud55c ",(0,a.kt)("inlineCode",{parentName:"p"},"my-image:v1.0.0")," \uc774\ubbf8\uc9c0\ub97c \uc2e4\ud589\ud558\ub294 \uc2dc\uc810\uc5d0, ",(0,a.kt)("inlineCode",{parentName:"p"},"TEST")," env var \uc758 \uac12\uc744 \ubcc0\uacbd\ud558\uc5ec docker \ucee8\ud14c\uc774\ub108\ub97c run \ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-bash"},"docker run -e TEST=bye my-image:v1.0.0\n")),(0,a.kt)("p",null,"\uc815\uc0c1\uc801\uc73c\ub85c \uc218\ud589\ub41c\ub2e4\uba74 \ub2e4\uc74c\uacfc \uac19\uc774 \ub098\uc635\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-bash"},"bye\n")))}d.isMDXComponent=!0}}]); \ No newline at end of file diff --git a/assets/js/c1115317.22c55ab3.js b/assets/js/c1115317.22c55ab3.js deleted file mode 100644 index 3c4db9ed..00000000 --- a/assets/js/c1115317.22c55ab3.js +++ 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\uc774\ubbf8\uc9c0 \uc798 \ub9cc\ub4e4\uae30",id:"\ub3c4\ucee4-\uc774\ubbf8\uc9c0-\uc798-\ub9cc\ub4e4\uae30",level:2},{value:"\ub3c4\ucee4 \uc774\ubbf8\uc9c0\ub97c \ub9cc\ub4e4 \ub54c \uace0\ub824\ud574\uc57c \ub420 \uc810",id:"\ub3c4\ucee4-\uc774\ubbf8\uc9c0\ub97c-\ub9cc\ub4e4-\ub54c-\uace0\ub824\ud574\uc57c-\ub420-\uc810",level:3},{value:"ENTRYPOINT vs CMD",id:"entrypoint-vs-cmd",level:3},{value:"Docker tag \uc774\ub984 \uc9d3\uae30",id:"docker-tag-\uc774\ub984-\uc9d3\uae30",level:3},{value:"ETC",id:"etc",level:3},{value:"docker run \uc758 \ub2e4\uc591\ud55c \uc635\uc158",id:"docker-run-\uc758-\ub2e4\uc591\ud55c-\uc635\uc158",level:2},{value:"docker run with volume",id:"docker-run-with-volume",level:3},{value:"Docker volume",id:"docker-volume",level:4},{value:"Bind mount",id:"bind-mount",level:4},{value:"How to use?",id:"how-to-use",level:4},{value:"docker run with resource limit",id:"docker-run-with-resource-limit",level:3},{value:"docker run with restart 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",(0,a.kt)("strong",{parentName:"p"},"\uc21c\uc11c"),"\uac00 \uc911\uc694\ud569\ub2c8\ub2e4.",(0,a.kt)("br",{parentName:"p"}),"\n","\uadf8 \uc774\uc720\ub294 \ub3c4\ucee4 \uc774\ubbf8\uc9c0\ub294 \uc5ec\ub7ec \uac1c\uc758 Read-Only Layer \ub85c \uad6c\uc131\ub418\uc5b4\uc788\uace0, \uc774\ubbf8\uc9c0\ub97c \ube4c\ub4dc\ud560 \ub54c \uc774\ubbf8 \uc874\uc7ac\ud558\ub294 \ub808\uc774\uc5b4\ub294 ",(0,a.kt)("strong",{parentName:"p"},"\uce90\uc2dc\ub418\uc5b4")," \uc7ac\uc0ac\uc6a9\ub418\uae30 \ub54c\ubb38\uc5d0, \uc774\ub97c \uc0dd\uac01\ud574\uc11c Dockerfile \uc744 \uad6c\uc131\ud55c\ub2e4\uba74 ",(0,a.kt)("strong",{parentName:"p"},"\ube4c\ub4dc \uc2dc\uac04\uc744 \uc904\uc77c \uc218 \uc788\uc2b5\ub2c8\ub2e4.")),(0,a.kt)("p",null,"Dockerfile\uc5d0\uc11c ",(0,a.kt)("inlineCode",{parentName:"p"},"RUN"),", ",(0,a.kt)("inlineCode",{parentName:"p"},"ADD"),", ",(0,a.kt)("inlineCode",{parentName:"p"},"COPY")," \uba85\ub839\uc5b4 \ud558\ub098\uac00 \ud558\ub098\uc758 \ub808\uc774\uc5b4\ub85c \uc800\uc7a5\ub429\ub2c8\ub2e4."),(0,a.kt)("p",null,"\uc608\ub97c \ub4e4\uc5b4\uc11c \ub2e4\uc74c\uacfc \uac19\uc740 ",(0,a.kt)("inlineCode",{parentName:"p"},"Dockerfile"),"\uc774 \uc788\uc2b5\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-docker"},"# Layer 1\nFROM ubuntu:latest\n\n# Layer 2\nRUN apt-get update && apt-get install python3 pip3 -y\n\n# Layer 3\nRUN pip3 install -U pip && pip3 install torch\n\n# Layer 4\nCOPY src/ src/\n\n# Layer 5\nCMD python src/app.py\n")),(0,a.kt)("p",null,"\uc704\uc758 ",(0,a.kt)("inlineCode",{parentName:"p"},"Dockerfile"),"\ub85c \ube4c\ub4dc\ub41c \uc774\ubbf8\uc9c0\ub97c ",(0,a.kt)("inlineCode",{parentName:"p"},"docker run -it app:latest /bin/bash")," \uba85\ub839\uc5b4\ub85c \uc2e4\ud589\ud558\uba74 \ub2e4\uc74c\uacfc \uac19\uc740 \ub808\uc774\uc5b4\ub85c \ud45c\ud604\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,a.kt)("p",null,(0,a.kt)("img",{alt:"layers.png",src:n(3116).Z,width:"1080",height:"612"})),(0,a.kt)("p",null,"\ucd5c\uc0c1\ub2e8\uc758 R/W Layer \ub294 \uc774\ubbf8\uc9c0\uc5d0 \uc601\ud5a5\uc744 \uc8fc\uc9c0 \uc54a\uc2b5\ub2c8\ub2e4. \uc989, \ucee8\ud14c\uc774\ub108 \ub0b4\ubd80\uc5d0\uc11c \uc791\uc5c5\ud55c \ub0b4\uc5ed\uc740 \ubaa8\ub450 \ud718\ubc1c\uc131\uc785\ub2c8\ub2e4."),(0,a.kt)("p",null,"\ud558\ub2e8\uc758 \ub808\uc774\uc5b4\uac00 \ubcc0\uacbd\ub418\uba74, \uadf8 \uc704\uc758 \ub808\uc774\uc5b4\ub294 \ubaa8\ub450 \uc0c8\ub85c \ube4c\ub4dc\ub429\ub2c8\ub2e4. \uadf8\ub798\uc11c Dockerfile \ub0b4\uc7a5 \uba85\ub839\uc5b4\uc758 \uc21c\uc11c\uac00 \uc911\uc694\ud569\ub2c8\ub2e4.",(0,a.kt)("br",{parentName:"p"}),"\n","\uc608\ub97c \ub4e4\uba74, ",(0,a.kt)("strong",{parentName:"p"},"\uc790\uc8fc \ubcc0\uacbd"),"\ub418\ub294 \ubd80\ubd84\uc740 ",(0,a.kt)("strong",{parentName:"p"},"\ucd5c\ub300\ud55c \ub4a4\ucabd\uc73c\ub85c")," \uc815\ub82c\ud558\ub294 \uac83\uc744 \ucd94\ucc9c\ud569\ub2c8\ub2e4. (ex. ",(0,a.kt)("inlineCode",{parentName:"p"},"COPY src/ app/src/"),")"),(0,a.kt)("p",null,"\uadf8\ub807\uae30 \ub54c\ubb38\uc5d0 \ubc18\ub300\ub85c \ubcc0\uacbd\ub418\uc9c0 \uc54a\ub294 \ubd80\ubd84\uc740 \ucd5c\ub300\ud55c \uc55e\ucabd\uc73c\ub85c \uc815\ub82c\ud558\ub294\uac8c \uc88b\uc2b5\ub2c8\ub2e4."),(0,a.kt)("p",null,"\ub9cc\uc57d \uac70\uc758 ",(0,a.kt)("strong",{parentName:"p"},"\ubcc0\uacbd\ub418\uc9c0 \uc54a\uc9c0\ub9cc"),", \uc5ec\ub7ec \uacf3\uc5d0\uc11c ",(0,a.kt)("strong",{parentName:"p"},"\uc790\uc8fc")," \uc4f0\uc774\ub294 \ubd80\ubd84\uc744 \uacf5\ud1b5\ud654\ud560 \uc218\ub3c4 \uc788\uc2b5\ub2c8\ub2e4.\n\ud574\ub2f9 \uacf5\ud1b5\ubd80\ubd84\ub9cc \ubb36\uc5b4\uc11c \ubcc4\ub3c4\uc758 \uc774\ubbf8\uc9c0\ub294 \ubbf8\ub9ac \ub9cc\ub4e4\uc5b4\ub454 \ub2e4\uc74c, ",(0,a.kt)("strong",{parentName:"p"},"\ubca0\uc774\uc2a4 \uc774\ubbf8\uc9c0")," \ub85c \ud65c\uc6a9\ud558\ub294 \uac83\uc774 \uc88b\uc2b5\ub2c8\ub2e4."),(0,a.kt)("p",null,"\uc608\ub97c \ub4e4\uc5b4, \ub2e4\ub978 \uac74 \uac70\uc758 \ub611\uac19\uc740\ub370, tensorflow-cpu \ub97c \uc0ac\uc6a9\ud558\ub294 \uc774\ubbf8\uc9c0\uc640, tensorflow-gpu \ub97c \uc0ac\uc6a9\ud558\ub294 \ud658\uacbd\uc744 \ubd84\ub9ac\ud574\uc11c \uc774\ubbf8\uc9c0\ub85c \ub9cc\ub4e4\uace0 \uc2f6\uc740 \uacbd\uc6b0\uc5d0\ub294 \ub2e4\uc74c\uacfc \uac19\uc774 \ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.",(0,a.kt)("br",{parentName:"p"}),"\n","python \uacfc \uae30\ud0c0 \uae30\ubcf8\uc801\uc778 \ud328\ud0a4\uc9c0\uac00 \uc124\uce58\ub41c ",(0,a.kt)("a",{parentName:"p",href:"http://ghcr.io/makinarocks/python:3.8-base-cpu"},(0,a.kt)("inlineCode",{parentName:"a"},"ghcr.io/makinarocks/python:3.8-base"))," \ub97c \ub9cc\ub4e4\uc5b4\ub450\uace0, ",(0,a.kt)("strong",{parentName:"p"},"tensorflow cpu \ubc84\uc804\uacfc gpu \ubc84\uc804\uc774")," \uc124\uce58\ub41c \uc774\ubbf8\uc9c0 \uc0c8\ub85c \ub9cc\ub4e4\ub54c\ub294, \uc704\uc758 \uc774\ubbf8\uc9c0\ub97c ",(0,a.kt)("inlineCode",{parentName:"p"},"FROM")," \uc73c\ub85c \ubd88\ub7ec\uc628 \ub2e4\uc74c, tensorflow install \ud558\ub294 \ubd80\ubd84\ub9cc \ubcc4\ub3c4\ub85c \uc791\uc131\ud574\uc11c Dockerfile \uc744 2 \uac1c\ub85c \uad00\ub9ac\ud55c\ub2e4\uba74 \uac00\ub3c5\uc131\ub3c4 \uc88b\uace0 \ube4c\ub4dc \uc2dc\uac04\ub3c4 \uc904\uc77c \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,a.kt)("p",null,(0,a.kt)("strong",{parentName:"p"},"\ud569\uce60 \uc218 \uc788\ub294 Layer \ub294 \ud569\uce58\ub294 \uac83"),"\uc774 Old version \uc758 \ub3c4\ucee4\uc5d0\uc11c\ub294 \uc131\ub2a5 \ud5a5\uc0c1 \ud6a8\uacfc\ub97c \uc774\ub04c\uc5c8\uc2b5\ub2c8\ub2e4. \uc5ec\ub7ec\ubd84\uc758 \ub3c4\ucee4 \ucee8\ud14c\uc774\ub108\uac00 \uc5b4\ub5a4 \ub3c4\ucee4 \ubc84\uc804\uc5d0\uc11c \uc2e4\ud589\ub420 \uac83\uc778\uc9c0 \ubcf4\uc7a5\ud560 \uc218 \uc5c6\uc73c\uba70, ",(0,a.kt)("strong",{parentName:"p"},"\uac00\ub3c5\uc131"),"\uc744 \uc704\ud574\uc11c\ub3c4 \ud569\uce60 \uc218 \uc788\ub294 Layer \ub294 \uc801\uc808\ud788 \ud569\uce58\ub294 \uac83\uc774 \uc88b\uc2b5\ub2c8\ub2e4."),(0,a.kt)("p",null,"\uc608\ub97c \ub4e4\uba74, \ub2e4\uc74c\uacfc \uac19\uc774 \uc791\uc131\ub41c ",(0,a.kt)("inlineCode",{parentName:"p"},"Dockerfile"),"\uc774 \uc788\uc2b5\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-docker"},"# Bad Case\nRUN apt-get update\nRUN apt-get install build-essential -y\nRUN apt-get install curl -y\nRUN apt-get install jq -y\nRUN apt-get install git -y\n")),(0,a.kt)("p",null,"\uc774\ub97c \uc544\ub798\uc640 \uac19\uc774 \ud569\uccd0\uc11c \uc801\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-docker"},"# Better Case\nRUN apt-get update && \\\n apt-get install -y \\\n build-essential \\\n curl \\\n jq \\\n git\n")),(0,a.kt)("p",null,"\ud3b8\uc758\ub97c \uc704\ud574\uc11c\ub294 ",(0,a.kt)("inlineCode",{parentName:"p"},".dockerignore")," \ub3c4 \uc0ac\uc6a9\ud558\ub294\uac8c \uc88b\uc2b5\ub2c8\ub2e4.\n",(0,a.kt)("inlineCode",{parentName:"p"},".dockerignore"),"\ub294 ",(0,a.kt)("inlineCode",{parentName:"p"},".gitignore")," \uc640 \ube44\uc2b7\ud55c \uc5ed\ud560\uc744 \ud55c\ub2e4\uace0 \uc774\ud574\ud558\uba74 \ub429\ub2c8\ub2e4. (git add \ud560 \ub54c \uc81c\uc678\ud560 \uc218 \uc788\ub4ef\uc774, docker build \ud560 \ub54c \uc790\ub3d9\uc73c\ub85c \uc81c\uc678)"),(0,a.kt)("p",null,"\ub354 \ub9ce\uc740 \uc815\ubcf4\ub294 ",(0,a.kt)("a",{parentName:"p",href:"https://docs.docker.com/develop/develop-images/dockerfile_best-practices/"},"Docker \uacf5\uc2dd \ubb38\uc11c"),"\uc5d0\uc11c \ud655\uc778\ud558\uc2e4 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,a.kt)("h3",{id:"entrypoint-vs-cmd"},"ENTRYPOINT vs CMD"),(0,a.kt)("p",null,(0,a.kt)("inlineCode",{parentName:"p"},"ENTRYPOINT")," \uc640 ",(0,a.kt)("inlineCode",{parentName:"p"},"CMD")," \ub294 \ubaa8\ub450 \ucee8\ud14c\uc774\ub108\uc758 \uc2e4\ud589 \uc2dc\uc810\uc5d0\uc11c \uc5b4\ub5a4 \uba85\ub839\uc5b4\ub97c \uc2e4\ud589\uc2dc\ud0a4\uace0 \uc2f6\uc744 \ub54c \uc0ac\uc6a9\ud569\ub2c8\ub2e4.\n\uadf8\ub9ac\uace0 \uc774 \ub458 \uc911 \ud558\ub098\ub294 \ubc18\ub4dc\uc2dc \uc874\uc7ac\ud574\uc57c \ud569\ub2c8\ub2e4."),(0,a.kt)("ul",null,(0,a.kt)("li",{parentName:"ul"},(0,a.kt)("strong",{parentName:"li"},"\ucc28\uc774\uc810"),(0,a.kt)("ul",{parentName:"li"},(0,a.kt)("li",{parentName:"ul"},(0,a.kt)("inlineCode",{parentName:"li"},"CMD"),": docker run \uc744 \uc218\ud589\ud560 \ub54c, \uc27d\uac8c \ubcc0\uacbd\ud558\uc5ec \uc0ac\uc6a9\ud560 \uc218 \uc788\uc74c"),(0,a.kt)("li",{parentName:"ul"},(0,a.kt)("inlineCode",{parentName:"li"},"ENTRYPOINT"),": ",(0,a.kt)("inlineCode",{parentName:"li"},"--entrypoint")," \ub97c \uc0ac\uc6a9\ud574\uc57c \ubcc0\uacbd\ud560 \uc218 \uc788\uc74c")))),(0,a.kt)("p",null,(0,a.kt)("inlineCode",{parentName:"p"},"ENTRYPOINT")," \uc640 ",(0,a.kt)("inlineCode",{parentName:"p"},"CMD")," \uac00 \ud568\uaed8 \uc4f0\uc77c \ub54c\ub294 \ubcf4\ud1b5 ",(0,a.kt)("inlineCode",{parentName:"p"},"CMD"),"\ub294 ",(0,a.kt)("inlineCode",{parentName:"p"},"ENTRYPOINT")," \uc5d0\uc11c \uc801\uc740 \uba85\ub839\uc758 arguments(parameters) \ub97c \uc758\ubbf8\ud569\ub2c8\ub2e4."),(0,a.kt)("p",null,"\uc608\ub97c \ub4e4\uc5b4\uc11c \ub2e4\uc74c\uacfc \uac19\uc740 ",(0,a.kt)("inlineCode",{parentName:"p"},"Dockerfile")," \uc774 \uc788\uc2b5\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-docker"},'FROM ubuntu:latest\n\n# \uc544\ub798 4 \uac00\uc9c0 option \uc744 \ubc14\uafd4\uac00\uba70 \uc9c1\uc811 \ud14c\uc2a4\ud2b8\ud574\ubcf4\uc2dc\uba74 \uc774\ud574\ud558\uae30 \ud3b8\ud569\ub2c8\ub2e4.\n# \ub2e8, NO ENTRYPOINT \uc635\uc158\uc740 base image \uc778 ubuntu:latest \uc5d0 \uc774\ubbf8 \uc788\uc5b4\uc11c \ud14c\uc2a4\ud2b8\ud574\ubcfc \uc218\ub294 \uc5c6\uace0 \ub098\uba38\uc9c0 v2, 3, 5, 6, 8, 9, 11, 12 \ub97c \ud14c\uc2a4\ud2b8\ud574\ubcfc \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n# ENTRYPOINT echo "Hello ENTRYPOINT"\n# ENTRYPOINT ["echo", "Hello ENTRYPOINT"]\n# CMD echo "Hello CMD"\n# CMD ["echo", "Hello CMD"]\n')),(0,a.kt)("p",null,"\uc704\uc758 ",(0,a.kt)("inlineCode",{parentName:"p"},"Dockerfile"),"\uc5d0\uc11c \uc8fc\uc11d\uc73c\ub85c \ud45c\uc2dc\ub41c \ubd80\ubd84\ub4e4\uc744 \ud574\uc81c\ud558\uba70 \ube4c\ub4dc\ud558\uace0 \uc2e4\ud589\ud558\uba74 \ub2e4\uc74c\uacfc \uac19\uc740 \uacb0\uacfc\ub97c \uc5bb\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,a.kt)("table",null,(0,a.kt)("thead",{parentName:"table"},(0,a.kt)("tr",{parentName:"thead"},(0,a.kt)("th",{parentName:"tr",align:null}),(0,a.kt)("th",{parentName:"tr",align:null},"No ENTRYPOINT"),(0,a.kt)("th",{parentName:"tr",align:null},"ENTRYPOINT a b"),(0,a.kt)("th",{parentName:"tr",align:null},"ENTRYPOINT ",'["a", "b"]'))),(0,a.kt)("tbody",{parentName:"table"},(0,a.kt)("tr",{parentName:"tbody"},(0,a.kt)("td",{parentName:"tr",align:null},(0,a.kt)("strong",{parentName:"td"},"NO CMD")),(0,a.kt)("td",{parentName:"tr",align:null},"Error!"),(0,a.kt)("td",{parentName:"tr",align:null},"/bin/sh -c a b"),(0,a.kt)("td",{parentName:"tr",align:null},"a b")),(0,a.kt)("tr",{parentName:"tbody"},(0,a.kt)("td",{parentName:"tr",align:null},(0,a.kt)("strong",{parentName:"td"},"CMD ",'["x", "y"]')),(0,a.kt)("td",{parentName:"tr",align:null},"x y"),(0,a.kt)("td",{parentName:"tr",align:null},"/bin/sh -c a b"),(0,a.kt)("td",{parentName:"tr",align:null},"a b x y")),(0,a.kt)("tr",{parentName:"tbody"},(0,a.kt)("td",{parentName:"tr",align:null},(0,a.kt)("strong",{parentName:"td"},"CMD x y")),(0,a.kt)("td",{parentName:"tr",align:null},"/bin/sh -c x y"),(0,a.kt)("td",{parentName:"tr",align:null},"/bin/sh -c a b"),(0,a.kt)("td",{parentName:"tr",align:null},"a b /bin/sh -c x y")))),(0,a.kt)("ul",null,(0,a.kt)("li",{parentName:"ul"},"In Kubernetes pod",(0,a.kt)("ul",{parentName:"li"},(0,a.kt)("li",{parentName:"ul"},(0,a.kt)("inlineCode",{parentName:"li"},"ENTRYPOINT")," \u2192 command"),(0,a.kt)("li",{parentName:"ul"},(0,a.kt)("inlineCode",{parentName:"li"},"CMD")," \u2192 args")))),(0,a.kt)("h3",{id:"docker-tag-\uc774\ub984-\uc9d3\uae30"},"Docker tag \uc774\ub984 \uc9d3\uae30"),(0,a.kt)("p",null,"\ub3c4\ucee4 \uc774\ubbf8\uc9c0\uc758 tag \ub85c ",(0,a.kt)("strong",{parentName:"p"},"latest \ub294 \uc0ac\uc6a9\ud558\uc9c0 \uc54a\ub294 \uac83\uc744 \uad8c\uc7a5"),"\ud569\ub2c8\ub2e4.",(0,a.kt)("br",{parentName:"p"}),"\n","\uc774\uc720\ub294 latest \ub294 default tag name \uc774\ubbc0\ub85c ",(0,a.kt)("strong",{parentName:"p"},"\uc758\ub3c4\uce58 \uc54a\uac8c overwritten")," \ub418\ub294 \uacbd\uc6b0\uac00 \ub108\ubb34 \ub9ce\uc774 \ubc1c\uc0dd\ud558\uae30 \ub54c\ubb38\uc785\ub2c8\ub2e4."),(0,a.kt)("p",null,"\ud558\ub098\uc758 \uc774\ubbf8\uc9c0\ub294 \ud558\ub098\uc758 \ud0dc\uadf8\ub97c \uac00\uc9d0(",(0,a.kt)("strong",{parentName:"p"},"uniqueness"),")\uc744 \ubcf4\uc7a5\ud574\uc57c \ucd94\ud6c4 Production \ub2e8\uacc4\uc5d0\uc11c ",(0,a.kt)("strong",{parentName:"p"},"\ud611\uc5c5/\ub514\ubc84\uae45"),"\uc5d0 \uc6a9\uc774\ud569\ub2c8\ub2e4.",(0,a.kt)("br",{parentName:"p"}),"\n","\ub0b4\uc6a9\uc740 \ub2e4\ub974\uc9c0\ub9cc, \ub3d9\uc77c\ud55c tag \ub97c \uc0ac\uc6a9\ud558\uac8c \ub418\uba74 \ucd94\ud6c4 dangling image \ub85c \ucde8\uae09\ub418\uc5b4 \uad00\ub9ac\ud558\uae30 \uc5b4\ub824\uc6cc\uc9d1\ub2c8\ub2e4.",(0,a.kt)("br",{parentName:"p"}),"\n","dangling image\ub294 ",(0,a.kt)("inlineCode",{parentName:"p"},"docker images"),"\uc5d0\ub294 \ub098\uc624\uc9c0 \uc54a\uc9c0\ub9cc \uacc4\uc18d\ud574\uc11c \uc800\uc7a5\uc18c\ub97c \ucc28\uc9c0\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4."),(0,a.kt)("h3",{id:"etc"},"ETC"),(0,a.kt)("ol",null,(0,a.kt)("li",{parentName:"ol"},"log \ub4f1\uc758 \uc815\ubcf4\ub294 container \ub0b4\ubd80\uac00 \uc544\ub2cc \uacf3\uc5d0 \ub530\ub85c \uc800\uc7a5\ud569\ub2c8\ub2e4.\ncontainer \ub0b4\ubd80\uc5d0\uc11c write \ud55c data \ub294 \uc5b8\uc81c\ub4e0\uc9c0 \uc0ac\ub77c\uc9c8 \uc218 \uc788\uae30 \ub54c\ubb38\uc785\ub2c8\ub2e4."),(0,a.kt)("li",{parentName:"ol"},"secret \ud55c \uc815\ubcf4, \ud658\uacbd(dev/prod) dependent \ud55c \uc815\ubcf4 \ub4f1\uc740 Dockerfile \uc5d0 \uc9c1\uc811 \uc801\ub294 \uac8c \uc544\ub2c8\ub77c, env var \ub610\ub294 .env config file \uc744 \uc0ac\uc6a9\ud569\ub2c8\ub2e4."),(0,a.kt)("li",{parentName:"ol"},"Dockerfile ",(0,a.kt)("strong",{parentName:"li"},"linter")," \ub3c4 \uc874\uc7ac\ud558\ubbc0\ub85c, \ud611\uc5c5 \uc2dc\uc5d0\ub294 \ud65c\uc6a9\ud558\uba74 \uc88b\uc2b5\ub2c8\ub2e4.\n",(0,a.kt)("a",{parentName:"li",href:"https://github.com/hadolint/hadolint"},"https://github.com/hadolint/hadolint"))),(0,a.kt)("h2",{id:"docker-run-\uc758-\ub2e4\uc591\ud55c-\uc635\uc158"},"docker run \uc758 \ub2e4\uc591\ud55c \uc635\uc158"),(0,a.kt)("h3",{id:"docker-run-with-volume"},"docker run with volume"),(0,a.kt)("p",null,"Docker container \uc0ac\uc6a9 \uc2dc \ubd88\ud3b8\ud55c \uc810\uc774 \uc788\uc2b5\ub2c8\ub2e4.\n\ubc14\ub85c Docker\ub294 \uae30\ubcf8\uc801\uc73c\ub85c Docker ",(0,a.kt)("strong",{parentName:"p"},"container \ub0b4\ubd80\uc5d0\uc11c \uc791\uc5c5\ud55c \ubaa8\ub4e0 \uc0ac\ud56d\uc740 \uc800\uc7a5\ub418\uc9c0 \uc54a\uc2b5\ub2c8\ub2e4."),"\n\uc774\uc720\ub294 Docker container \ub294 \uac01\uac01 \uaca9\ub9ac\ub41c \ud30c\uc77c\uc2dc\uc2a4\ud15c\uc744 \uc0ac\uc6a9\ud569\ub2c8\ub2e4. \ub530\ub77c\uc11c, ",(0,a.kt)("strong",{parentName:"p"},"\uc5ec\ub7ec docker container \ub07c\ub9ac \ub370\uc774\ud130\ub97c \uacf5\uc720\ud558\uae30 \uc5b4\ub835\uc2b5\ub2c8\ub2e4.")),(0,a.kt)("p",null,"\uc774 \ubb38\uc81c\ub97c \ud574\uacb0\ud558\uae30 \uc704\ud574\uc11c Docker\uc5d0\uc11c \uc81c\uacf5\ud558\ub294 \ubc29\uc2dd\uc740 ",(0,a.kt)("strong",{parentName:"p"},"2 \uac00\uc9c0"),"\uac00 \uc788\uc2b5\ub2c8\ub2e4."),(0,a.kt)("p",null,(0,a.kt)("img",{alt:"storage.png",src:n(7229).Z,width:"501",height:"255"})),(0,a.kt)("h4",{id:"docker-volume"},"Docker volume"),(0,a.kt)("ul",null,(0,a.kt)("li",{parentName:"ul"},"docker cli \ub97c \uc0ac\uc6a9\ud574 ",(0,a.kt)("inlineCode",{parentName:"li"},"volume")," \uc774\ub77c\ub294 \ub9ac\uc18c\uc2a4\ub97c \uc9c1\uc811 \uad00\ub9ac"),(0,a.kt)("li",{parentName:"ul"},"host \uc5d0\uc11c Docker area(",(0,a.kt)("inlineCode",{parentName:"li"},"/var/lib/docker"),") \uc544\ub798\uc5d0 \ud2b9\uc815 \ub514\ub809\ud1a0\ub9ac\ub97c \uc0dd\uc131\ud55c \ub2e4\uc74c, \ud574\ub2f9 \uacbd\ub85c\ub97c docker container \uc5d0 mount")),(0,a.kt)("h4",{id:"bind-mount"},"Bind mount"),(0,a.kt)("ul",null,(0,a.kt)("li",{parentName:"ul"},"host \uc758 \ud2b9\uc815 \uacbd\ub85c\ub97c docker container \uc5d0 mount")),(0,a.kt)("h4",{id:"how-to-use"},"How to use?"),(0,a.kt)("p",null,"\uc0ac\uc6a9 \ubc29\uc2dd\uc740 ",(0,a.kt)("strong",{parentName:"p"},"\ub3d9\uc77c\ud55c \uc778\ud130\ud398\uc774\uc2a4"),"\ub85c ",(0,a.kt)("inlineCode",{parentName:"p"},"-v")," \uc635\uc158\uc744 \ud1b5\ud574 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.",(0,a.kt)("br",{parentName:"p"}),"\n","\ub2e4\ub9cc, volume \uc744 \uc0ac\uc6a9\ud560 \ub54c\uc5d0\ub294 ",(0,a.kt)("inlineCode",{parentName:"p"},"docker volume create"),", ",(0,a.kt)("inlineCode",{parentName:"p"},"docker volume ls"),", ",(0,a.kt)("inlineCode",{parentName:"p"},"docker volume rm")," \ub4f1\uc744 \uc218\ud589\ud558\uc5ec \uc9c1\uc811 \uad00\ub9ac\ud574\uc8fc\uc5b4\uc57c \ud569\ub2c8\ub2e4."),(0,a.kt)("ul",null,(0,a.kt)("li",{parentName:"ul"},(0,a.kt)("p",{parentName:"li"},"Docker volume"),(0,a.kt)("pre",{parentName:"li"},(0,a.kt)("code",{parentName:"pre",className:"language-bash"},"docker run \\\n -v my_volume:/app \\\n nginx:latest\n"))),(0,a.kt)("li",{parentName:"ul"},(0,a.kt)("p",{parentName:"li"},"Blind mount"),(0,a.kt)("pre",{parentName:"li"},(0,a.kt)("code",{parentName:"pre",className:"language-bash"},"docker run \\\n -v /home/user/some/path:/app \\\n nginx:latest\n")))),(0,a.kt)("p",null,"\ub85c\uceec\uc5d0\uc11c \uac1c\ubc1c\ud560 \ub54c\ub294 bind mount \uac00 \ud3b8\ud558\uae34 \ud558\uc9c0\ub9cc, \ud658\uacbd\uc744 \uae54\ub054\ud558\uac8c \uc720\uc9c0\ud558\uace0 \uc2f6\ub2e4\uba74 docker volume \uc744 \uc0ac\uc6a9\ud558\uc5ec 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\ub9cc\ub4e4\uae30"),(0,a.kt)("h3",{id:"\ub3c4\ucee4-\uc774\ubbf8\uc9c0\ub97c-\ub9cc\ub4e4-\ub54c-\uace0\ub824\ud574\uc57c-\ub420-\uc810"},"\ub3c4\ucee4 \uc774\ubbf8\uc9c0\ub97c \ub9cc\ub4e4 \ub54c \uace0\ub824\ud574\uc57c \ub420 \uc810"),(0,a.kt)("p",null,"Dockerfile \uc744 \ud65c\uc6a9\ud558\uc5ec \ub3c4\ucee4 \uc774\ubbf8\uc9c0\ub97c \ub9cc\ub4e4 \ub54c\ub294 \uba85\ub839\uc5b4\uc758 ",(0,a.kt)("strong",{parentName:"p"},"\uc21c\uc11c"),"\uac00 \uc911\uc694\ud569\ub2c8\ub2e4.",(0,a.kt)("br",{parentName:"p"}),"\n","\uadf8 \uc774\uc720\ub294 \ub3c4\ucee4 \uc774\ubbf8\uc9c0\ub294 \uc5ec\ub7ec \uac1c\uc758 Read-Only Layer \ub85c \uad6c\uc131\ub418\uc5b4\uc788\uace0, \uc774\ubbf8\uc9c0\ub97c \ube4c\ub4dc\ud560 \ub54c \uc774\ubbf8 \uc874\uc7ac\ud558\ub294 \ub808\uc774\uc5b4\ub294 ",(0,a.kt)("strong",{parentName:"p"},"\uce90\uc2dc\ub418\uc5b4")," \uc7ac\uc0ac\uc6a9\ub418\uae30 \ub54c\ubb38\uc5d0, \uc774\ub97c \uc0dd\uac01\ud574\uc11c Dockerfile \uc744 \uad6c\uc131\ud55c\ub2e4\uba74 ",(0,a.kt)("strong",{parentName:"p"},"\ube4c\ub4dc \uc2dc\uac04\uc744 \uc904\uc77c \uc218 \uc788\uc2b5\ub2c8\ub2e4.")),(0,a.kt)("p",null,"Dockerfile\uc5d0\uc11c ",(0,a.kt)("inlineCode",{parentName:"p"},"RUN"),", ",(0,a.kt)("inlineCode",{parentName:"p"},"ADD"),", ",(0,a.kt)("inlineCode",{parentName:"p"},"COPY")," \uba85\ub839\uc5b4 \ud558\ub098\uac00 \ud558\ub098\uc758 \ub808\uc774\uc5b4\ub85c \uc800\uc7a5\ub429\ub2c8\ub2e4."),(0,a.kt)("p",null,"\uc608\ub97c \ub4e4\uc5b4\uc11c \ub2e4\uc74c\uacfc \uac19\uc740 ",(0,a.kt)("inlineCode",{parentName:"p"},"Dockerfile"),"\uc774 \uc788\uc2b5\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-docker"},"# Layer 1\nFROM ubuntu:latest\n\n# Layer 2\nRUN apt-get update && apt-get install python3 pip3 -y\n\n# Layer 3\nRUN pip3 install -U pip && pip3 install torch\n\n# Layer 4\nCOPY src/ src/\n\n# Layer 5\nCMD python src/app.py\n")),(0,a.kt)("p",null,"\uc704\uc758 ",(0,a.kt)("inlineCode",{parentName:"p"},"Dockerfile"),"\ub85c \ube4c\ub4dc\ub41c \uc774\ubbf8\uc9c0\ub97c ",(0,a.kt)("inlineCode",{parentName:"p"},"docker run -it app:latest /bin/bash")," \uba85\ub839\uc5b4\ub85c \uc2e4\ud589\ud558\uba74 \ub2e4\uc74c\uacfc \uac19\uc740 \ub808\uc774\uc5b4\ub85c \ud45c\ud604\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,a.kt)("p",null,(0,a.kt)("img",{alt:"layers.png",src:n(3116).Z,width:"1080",height:"612"})),(0,a.kt)("p",null,"\ucd5c\uc0c1\ub2e8\uc758 R/W Layer \ub294 \uc774\ubbf8\uc9c0\uc5d0 \uc601\ud5a5\uc744 \uc8fc\uc9c0 \uc54a\uc2b5\ub2c8\ub2e4. \uc989, \ucee8\ud14c\uc774\ub108 \ub0b4\ubd80\uc5d0\uc11c \uc791\uc5c5\ud55c \ub0b4\uc5ed\uc740 \ubaa8\ub450 \ud718\ubc1c\uc131\uc785\ub2c8\ub2e4."),(0,a.kt)("p",null,"\ud558\ub2e8\uc758 \ub808\uc774\uc5b4\uac00 \ubcc0\uacbd\ub418\uba74, \uadf8 \uc704\uc758 \ub808\uc774\uc5b4\ub294 \ubaa8\ub450 \uc0c8\ub85c \ube4c\ub4dc\ub429\ub2c8\ub2e4. \uadf8\ub798\uc11c Dockerfile \ub0b4\uc7a5 \uba85\ub839\uc5b4\uc758 \uc21c\uc11c\uac00 \uc911\uc694\ud569\ub2c8\ub2e4.",(0,a.kt)("br",{parentName:"p"}),"\n","\uc608\ub97c \ub4e4\uba74, ",(0,a.kt)("strong",{parentName:"p"},"\uc790\uc8fc \ubcc0\uacbd"),"\ub418\ub294 \ubd80\ubd84\uc740 ",(0,a.kt)("strong",{parentName:"p"},"\ucd5c\ub300\ud55c \ub4a4\ucabd\uc73c\ub85c")," \uc815\ub82c\ud558\ub294 \uac83\uc744 \ucd94\ucc9c\ud569\ub2c8\ub2e4. (ex. ",(0,a.kt)("inlineCode",{parentName:"p"},"COPY src/ app/src/"),")"),(0,a.kt)("p",null,"\uadf8\ub807\uae30 \ub54c\ubb38\uc5d0 \ubc18\ub300\ub85c \ubcc0\uacbd\ub418\uc9c0 \uc54a\ub294 \ubd80\ubd84\uc740 \ucd5c\ub300\ud55c \uc55e\ucabd\uc73c\ub85c \uc815\ub82c\ud558\ub294\uac8c \uc88b\uc2b5\ub2c8\ub2e4."),(0,a.kt)("p",null,"\ub9cc\uc57d \uac70\uc758 ",(0,a.kt)("strong",{parentName:"p"},"\ubcc0\uacbd\ub418\uc9c0 \uc54a\uc9c0\ub9cc"),", \uc5ec\ub7ec \uacf3\uc5d0\uc11c ",(0,a.kt)("strong",{parentName:"p"},"\uc790\uc8fc")," \uc4f0\uc774\ub294 \ubd80\ubd84\uc744 \uacf5\ud1b5\ud654\ud560 \uc218\ub3c4 \uc788\uc2b5\ub2c8\ub2e4.\n\ud574\ub2f9 \uacf5\ud1b5\ubd80\ubd84\ub9cc \ubb36\uc5b4\uc11c \ubcc4\ub3c4\uc758 \uc774\ubbf8\uc9c0\ub294 \ubbf8\ub9ac \ub9cc\ub4e4\uc5b4\ub454 \ub2e4\uc74c, ",(0,a.kt)("strong",{parentName:"p"},"\ubca0\uc774\uc2a4 \uc774\ubbf8\uc9c0")," \ub85c \ud65c\uc6a9\ud558\ub294 \uac83\uc774 \uc88b\uc2b5\ub2c8\ub2e4."),(0,a.kt)("p",null,"\uc608\ub97c \ub4e4\uc5b4, \ub2e4\ub978 \uac74 \uac70\uc758 \ub611\uac19\uc740\ub370, tensorflow-cpu \ub97c \uc0ac\uc6a9\ud558\ub294 \uc774\ubbf8\uc9c0\uc640, tensorflow-gpu \ub97c \uc0ac\uc6a9\ud558\ub294 \ud658\uacbd\uc744 \ubd84\ub9ac\ud574\uc11c \uc774\ubbf8\uc9c0\ub85c \ub9cc\ub4e4\uace0 \uc2f6\uc740 \uacbd\uc6b0\uc5d0\ub294 \ub2e4\uc74c\uacfc \uac19\uc774 \ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.",(0,a.kt)("br",{parentName:"p"}),"\n","python \uacfc \uae30\ud0c0 \uae30\ubcf8\uc801\uc778 \ud328\ud0a4\uc9c0\uac00 \uc124\uce58\ub41c ",(0,a.kt)("a",{parentName:"p",href:"http://ghcr.io/makinarocks/python:3.8-base-cpu"},(0,a.kt)("inlineCode",{parentName:"a"},"ghcr.io/makinarocks/python:3.8-base"))," \ub97c \ub9cc\ub4e4\uc5b4\ub450\uace0, ",(0,a.kt)("strong",{parentName:"p"},"tensorflow cpu \ubc84\uc804\uacfc gpu \ubc84\uc804\uc774")," \uc124\uce58\ub41c \uc774\ubbf8\uc9c0 \uc0c8\ub85c \ub9cc\ub4e4\ub54c\ub294, \uc704\uc758 \uc774\ubbf8\uc9c0\ub97c ",(0,a.kt)("inlineCode",{parentName:"p"},"FROM")," \uc73c\ub85c \ubd88\ub7ec\uc628 \ub2e4\uc74c, tensorflow install \ud558\ub294 \ubd80\ubd84\ub9cc \ubcc4\ub3c4\ub85c \uc791\uc131\ud574\uc11c Dockerfile \uc744 2 \uac1c\ub85c \uad00\ub9ac\ud55c\ub2e4\uba74 \uac00\ub3c5\uc131\ub3c4 \uc88b\uace0 \ube4c\ub4dc \uc2dc\uac04\ub3c4 \uc904\uc77c \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,a.kt)("p",null,(0,a.kt)("strong",{parentName:"p"},"\ud569\uce60 \uc218 \uc788\ub294 Layer \ub294 \ud569\uce58\ub294 \uac83"),"\uc774 Old version \uc758 \ub3c4\ucee4\uc5d0\uc11c\ub294 \uc131\ub2a5 \ud5a5\uc0c1 \ud6a8\uacfc\ub97c \uc774\ub04c\uc5c8\uc2b5\ub2c8\ub2e4. \uc5ec\ub7ec\ubd84\uc758 \ub3c4\ucee4 \ucee8\ud14c\uc774\ub108\uac00 \uc5b4\ub5a4 \ub3c4\ucee4 \ubc84\uc804\uc5d0\uc11c \uc2e4\ud589\ub420 \uac83\uc778\uc9c0 \ubcf4\uc7a5\ud560 \uc218 \uc5c6\uc73c\uba70, ",(0,a.kt)("strong",{parentName:"p"},"\uac00\ub3c5\uc131"),"\uc744 \uc704\ud574\uc11c\ub3c4 \ud569\uce60 \uc218 \uc788\ub294 Layer \ub294 \uc801\uc808\ud788 \ud569\uce58\ub294 \uac83\uc774 \uc88b\uc2b5\ub2c8\ub2e4."),(0,a.kt)("p",null,"\uc608\ub97c \ub4e4\uba74, \ub2e4\uc74c\uacfc \uac19\uc774 \uc791\uc131\ub41c ",(0,a.kt)("inlineCode",{parentName:"p"},"Dockerfile"),"\uc774 \uc788\uc2b5\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-docker"},"# Bad Case\nRUN apt-get update\nRUN apt-get install build-essential -y\nRUN apt-get install curl -y\nRUN apt-get install jq -y\nRUN apt-get install git -y\n")),(0,a.kt)("p",null,"\uc774\ub97c \uc544\ub798\uc640 \uac19\uc774 \ud569\uccd0\uc11c \uc801\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-docker"},"# Better Case\nRUN apt-get update && \\\n apt-get install -y \\\n build-essential \\\n curl \\\n jq \\\n git\n")),(0,a.kt)("p",null,"\ud3b8\uc758\ub97c \uc704\ud574\uc11c\ub294 ",(0,a.kt)("inlineCode",{parentName:"p"},".dockerignore")," \ub3c4 \uc0ac\uc6a9\ud558\ub294\uac8c \uc88b\uc2b5\ub2c8\ub2e4.\n",(0,a.kt)("inlineCode",{parentName:"p"},".dockerignore"),"\ub294 ",(0,a.kt)("inlineCode",{parentName:"p"},".gitignore")," \uc640 \ube44\uc2b7\ud55c \uc5ed\ud560\uc744 \ud55c\ub2e4\uace0 \uc774\ud574\ud558\uba74 \ub429\ub2c8\ub2e4. (git add \ud560 \ub54c \uc81c\uc678\ud560 \uc218 \uc788\ub4ef\uc774, docker build \ud560 \ub54c \uc790\ub3d9\uc73c\ub85c \uc81c\uc678)"),(0,a.kt)("p",null,"\ub354 \ub9ce\uc740 \uc815\ubcf4\ub294 ",(0,a.kt)("a",{parentName:"p",href:"https://docs.docker.com/develop/develop-images/dockerfile_best-practices/"},"Docker \uacf5\uc2dd \ubb38\uc11c"),"\uc5d0\uc11c \ud655\uc778\ud558\uc2e4 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,a.kt)("h3",{id:"entrypoint-vs-cmd"},"ENTRYPOINT vs CMD"),(0,a.kt)("p",null,(0,a.kt)("inlineCode",{parentName:"p"},"ENTRYPOINT")," \uc640 ",(0,a.kt)("inlineCode",{parentName:"p"},"CMD")," \ub294 \ubaa8\ub450 \ucee8\ud14c\uc774\ub108\uc758 \uc2e4\ud589 \uc2dc\uc810\uc5d0\uc11c \uc5b4\ub5a4 \uba85\ub839\uc5b4\ub97c \uc2e4\ud589\uc2dc\ud0a4\uace0 \uc2f6\uc744 \ub54c \uc0ac\uc6a9\ud569\ub2c8\ub2e4.\n\uadf8\ub9ac\uace0 \uc774 \ub458 \uc911 \ud558\ub098\ub294 \ubc18\ub4dc\uc2dc \uc874\uc7ac\ud574\uc57c \ud569\ub2c8\ub2e4."),(0,a.kt)("ul",null,(0,a.kt)("li",{parentName:"ul"},(0,a.kt)("strong",{parentName:"li"},"\ucc28\uc774\uc810"),(0,a.kt)("ul",{parentName:"li"},(0,a.kt)("li",{parentName:"ul"},(0,a.kt)("inlineCode",{parentName:"li"},"CMD"),": docker run \uc744 \uc218\ud589\ud560 \ub54c, \uc27d\uac8c \ubcc0\uacbd\ud558\uc5ec \uc0ac\uc6a9\ud560 \uc218 \uc788\uc74c"),(0,a.kt)("li",{parentName:"ul"},(0,a.kt)("inlineCode",{parentName:"li"},"ENTRYPOINT"),": ",(0,a.kt)("inlineCode",{parentName:"li"},"--entrypoint")," \ub97c \uc0ac\uc6a9\ud574\uc57c \ubcc0\uacbd\ud560 \uc218 \uc788\uc74c")))),(0,a.kt)("p",null,(0,a.kt)("inlineCode",{parentName:"p"},"ENTRYPOINT")," \uc640 ",(0,a.kt)("inlineCode",{parentName:"p"},"CMD")," \uac00 \ud568\uaed8 \uc4f0\uc77c \ub54c\ub294 \ubcf4\ud1b5 ",(0,a.kt)("inlineCode",{parentName:"p"},"CMD"),"\ub294 ",(0,a.kt)("inlineCode",{parentName:"p"},"ENTRYPOINT")," \uc5d0\uc11c \uc801\uc740 \uba85\ub839\uc758 arguments(parameters) \ub97c \uc758\ubbf8\ud569\ub2c8\ub2e4."),(0,a.kt)("p",null,"\uc608\ub97c \ub4e4\uc5b4\uc11c \ub2e4\uc74c\uacfc \uac19\uc740 ",(0,a.kt)("inlineCode",{parentName:"p"},"Dockerfile")," \uc774 \uc788\uc2b5\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-docker"},'FROM ubuntu:latest\n\n# \uc544\ub798 4 \uac00\uc9c0 option \uc744 \ubc14\uafd4\uac00\uba70 \uc9c1\uc811 \ud14c\uc2a4\ud2b8\ud574\ubcf4\uc2dc\uba74 \uc774\ud574\ud558\uae30 \ud3b8\ud569\ub2c8\ub2e4.\n# \ub2e8, NO ENTRYPOINT \uc635\uc158\uc740 base image \uc778 ubuntu:latest \uc5d0 \uc774\ubbf8 \uc788\uc5b4\uc11c \ud14c\uc2a4\ud2b8\ud574\ubcfc \uc218\ub294 \uc5c6\uace0 \ub098\uba38\uc9c0 v2, 3, 5, 6, 8, 9, 11, 12 \ub97c \ud14c\uc2a4\ud2b8\ud574\ubcfc \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n# ENTRYPOINT echo "Hello ENTRYPOINT"\n# ENTRYPOINT ["echo", "Hello ENTRYPOINT"]\n# CMD echo "Hello CMD"\n# CMD ["echo", "Hello CMD"]\n')),(0,a.kt)("p",null,"\uc704\uc758 ",(0,a.kt)("inlineCode",{parentName:"p"},"Dockerfile"),"\uc5d0\uc11c \uc8fc\uc11d\uc73c\ub85c \ud45c\uc2dc\ub41c \ubd80\ubd84\ub4e4\uc744 \ud574\uc81c\ud558\uba70 \ube4c\ub4dc\ud558\uace0 \uc2e4\ud589\ud558\uba74 \ub2e4\uc74c\uacfc \uac19\uc740 \uacb0\uacfc\ub97c \uc5bb\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,a.kt)("table",null,(0,a.kt)("thead",{parentName:"table"},(0,a.kt)("tr",{parentName:"thead"},(0,a.kt)("th",{parentName:"tr",align:null}),(0,a.kt)("th",{parentName:"tr",align:null},"No ENTRYPOINT"),(0,a.kt)("th",{parentName:"tr",align:null},"ENTRYPOINT a b"),(0,a.kt)("th",{parentName:"tr",align:null},"ENTRYPOINT ",'["a", "b"]'))),(0,a.kt)("tbody",{parentName:"table"},(0,a.kt)("tr",{parentName:"tbody"},(0,a.kt)("td",{parentName:"tr",align:null},(0,a.kt)("strong",{parentName:"td"},"NO CMD")),(0,a.kt)("td",{parentName:"tr",align:null},"Error!"),(0,a.kt)("td",{parentName:"tr",align:null},"/bin/sh -c a b"),(0,a.kt)("td",{parentName:"tr",align:null},"a b")),(0,a.kt)("tr",{parentName:"tbody"},(0,a.kt)("td",{parentName:"tr",align:null},(0,a.kt)("strong",{parentName:"td"},"CMD ",'["x", "y"]')),(0,a.kt)("td",{parentName:"tr",align:null},"x y"),(0,a.kt)("td",{parentName:"tr",align:null},"/bin/sh -c a b"),(0,a.kt)("td",{parentName:"tr",align:null},"a b x y")),(0,a.kt)("tr",{parentName:"tbody"},(0,a.kt)("td",{parentName:"tr",align:null},(0,a.kt)("strong",{parentName:"td"},"CMD x y")),(0,a.kt)("td",{parentName:"tr",align:null},"/bin/sh -c x y"),(0,a.kt)("td",{parentName:"tr",align:null},"/bin/sh -c a b"),(0,a.kt)("td",{parentName:"tr",align:null},"a b /bin/sh -c x y")))),(0,a.kt)("ul",null,(0,a.kt)("li",{parentName:"ul"},"In Kubernetes pod",(0,a.kt)("ul",{parentName:"li"},(0,a.kt)("li",{parentName:"ul"},(0,a.kt)("inlineCode",{parentName:"li"},"ENTRYPOINT")," \u2192 command"),(0,a.kt)("li",{parentName:"ul"},(0,a.kt)("inlineCode",{parentName:"li"},"CMD")," \u2192 args")))),(0,a.kt)("h3",{id:"docker-tag-\uc774\ub984-\uc9d3\uae30"},"Docker tag \uc774\ub984 \uc9d3\uae30"),(0,a.kt)("p",null,"\ub3c4\ucee4 \uc774\ubbf8\uc9c0\uc758 tag \ub85c ",(0,a.kt)("strong",{parentName:"p"},"latest \ub294 \uc0ac\uc6a9\ud558\uc9c0 \uc54a\ub294 \uac83\uc744 \uad8c\uc7a5"),"\ud569\ub2c8\ub2e4.",(0,a.kt)("br",{parentName:"p"}),"\n","\uc774\uc720\ub294 latest \ub294 default tag name \uc774\ubbc0\ub85c ",(0,a.kt)("strong",{parentName:"p"},"\uc758\ub3c4\uce58 \uc54a\uac8c overwritten")," \ub418\ub294 \uacbd\uc6b0\uac00 \ub108\ubb34 \ub9ce\uc774 \ubc1c\uc0dd\ud558\uae30 \ub54c\ubb38\uc785\ub2c8\ub2e4."),(0,a.kt)("p",null,"\ud558\ub098\uc758 \uc774\ubbf8\uc9c0\ub294 \ud558\ub098\uc758 \ud0dc\uadf8\ub97c \uac00\uc9d0(",(0,a.kt)("strong",{parentName:"p"},"uniqueness"),")\uc744 \ubcf4\uc7a5\ud574\uc57c \ucd94\ud6c4 Production \ub2e8\uacc4\uc5d0\uc11c ",(0,a.kt)("strong",{parentName:"p"},"\ud611\uc5c5/\ub514\ubc84\uae45"),"\uc5d0 \uc6a9\uc774\ud569\ub2c8\ub2e4.",(0,a.kt)("br",{parentName:"p"}),"\n","\ub0b4\uc6a9\uc740 \ub2e4\ub974\uc9c0\ub9cc, \ub3d9\uc77c\ud55c tag \ub97c \uc0ac\uc6a9\ud558\uac8c \ub418\uba74 \ucd94\ud6c4 dangling image \ub85c \ucde8\uae09\ub418\uc5b4 \uad00\ub9ac\ud558\uae30 \uc5b4\ub824\uc6cc\uc9d1\ub2c8\ub2e4.",(0,a.kt)("br",{parentName:"p"}),"\n","dangling image\ub294 ",(0,a.kt)("inlineCode",{parentName:"p"},"docker images"),"\uc5d0\ub294 \ub098\uc624\uc9c0 \uc54a\uc9c0\ub9cc \uacc4\uc18d\ud574\uc11c \uc800\uc7a5\uc18c\ub97c \ucc28\uc9c0\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4."),(0,a.kt)("h3",{id:"etc"},"ETC"),(0,a.kt)("ol",null,(0,a.kt)("li",{parentName:"ol"},"log \ub4f1\uc758 \uc815\ubcf4\ub294 container \ub0b4\ubd80\uac00 \uc544\ub2cc \uacf3\uc5d0 \ub530\ub85c \uc800\uc7a5\ud569\ub2c8\ub2e4.\ncontainer \ub0b4\ubd80\uc5d0\uc11c write \ud55c data \ub294 \uc5b8\uc81c\ub4e0\uc9c0 \uc0ac\ub77c\uc9c8 \uc218 \uc788\uae30 \ub54c\ubb38\uc785\ub2c8\ub2e4."),(0,a.kt)("li",{parentName:"ol"},"secret \ud55c \uc815\ubcf4, \ud658\uacbd(dev/prod) dependent \ud55c \uc815\ubcf4 \ub4f1\uc740 Dockerfile \uc5d0 \uc9c1\uc811 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\uc54a\uc2b5\ub2c8\ub2e4."),"\n\uc774\uc720\ub294 Docker container \ub294 \uac01\uac01 \uaca9\ub9ac\ub41c \ud30c\uc77c\uc2dc\uc2a4\ud15c\uc744 \uc0ac\uc6a9\ud569\ub2c8\ub2e4. \ub530\ub77c\uc11c, ",(0,a.kt)("strong",{parentName:"p"},"\uc5ec\ub7ec docker container \ub07c\ub9ac \ub370\uc774\ud130\ub97c \uacf5\uc720\ud558\uae30 \uc5b4\ub835\uc2b5\ub2c8\ub2e4.")),(0,a.kt)("p",null,"\uc774 \ubb38\uc81c\ub97c \ud574\uacb0\ud558\uae30 \uc704\ud574\uc11c Docker\uc5d0\uc11c \uc81c\uacf5\ud558\ub294 \ubc29\uc2dd\uc740 ",(0,a.kt)("strong",{parentName:"p"},"2 \uac00\uc9c0"),"\uac00 \uc788\uc2b5\ub2c8\ub2e4."),(0,a.kt)("p",null,(0,a.kt)("img",{alt:"storage.png",src:n(7229).Z,width:"501",height:"255"})),(0,a.kt)("h4",{id:"docker-volume"},"Docker volume"),(0,a.kt)("ul",null,(0,a.kt)("li",{parentName:"ul"},"docker cli \ub97c \uc0ac\uc6a9\ud574 ",(0,a.kt)("inlineCode",{parentName:"li"},"volume")," \uc774\ub77c\ub294 \ub9ac\uc18c\uc2a4\ub97c \uc9c1\uc811 \uad00\ub9ac"),(0,a.kt)("li",{parentName:"ul"},"host \uc5d0\uc11c Docker area(",(0,a.kt)("inlineCode",{parentName:"li"},"/var/lib/docker"),") \uc544\ub798\uc5d0 \ud2b9\uc815 \ub514\ub809\ud1a0\ub9ac\ub97c \uc0dd\uc131\ud55c \ub2e4\uc74c, \ud574\ub2f9 \uacbd\ub85c\ub97c docker container \uc5d0 mount")),(0,a.kt)("h4",{id:"bind-mount"},"Bind mount"),(0,a.kt)("ul",null,(0,a.kt)("li",{parentName:"ul"},"host \uc758 \ud2b9\uc815 \uacbd\ub85c\ub97c docker container \uc5d0 mount")),(0,a.kt)("h4",{id:"how-to-use"},"How to use?"),(0,a.kt)("p",null,"\uc0ac\uc6a9 \ubc29\uc2dd\uc740 ",(0,a.kt)("strong",{parentName:"p"},"\ub3d9\uc77c\ud55c \uc778\ud130\ud398\uc774\uc2a4"),"\ub85c ",(0,a.kt)("inlineCode",{parentName:"p"},"-v")," \uc635\uc158\uc744 \ud1b5\ud574 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.",(0,a.kt)("br",{parentName:"p"}),"\n","\ub2e4\ub9cc, volume \uc744 \uc0ac\uc6a9\ud560 \ub54c\uc5d0\ub294 ",(0,a.kt)("inlineCode",{parentName:"p"},"docker volume create"),", ",(0,a.kt)("inlineCode",{parentName:"p"},"docker volume ls"),", ",(0,a.kt)("inlineCode",{parentName:"p"},"docker volume rm")," \ub4f1\uc744 \uc218\ud589\ud558\uc5ec \uc9c1\uc811 \uad00\ub9ac\ud574\uc8fc\uc5b4\uc57c \ud569\ub2c8\ub2e4."),(0,a.kt)("ul",null,(0,a.kt)("li",{parentName:"ul"},(0,a.kt)("p",{parentName:"li"},"Docker volume"),(0,a.kt)("pre",{parentName:"li"},(0,a.kt)("code",{parentName:"pre",className:"language-bash"},"docker run \\\n -v my_volume:/app \\\n nginx:latest\n"))),(0,a.kt)("li",{parentName:"ul"},(0,a.kt)("p",{parentName:"li"},"Blind mount"),(0,a.kt)("pre",{parentName:"li"},(0,a.kt)("code",{parentName:"pre",className:"language-bash"},"docker run \\\n -v /home/user/some/path:/app \\\n nginx:latest\n")))),(0,a.kt)("p",null,"\ub85c\uceec\uc5d0\uc11c \uac1c\ubc1c\ud560 \ub54c\ub294 bind mount \uac00 \ud3b8\ud558\uae34 \ud558\uc9c0\ub9cc, \ud658\uacbd\uc744 \uae54\ub054\ud558\uac8c \uc720\uc9c0\ud558\uace0 \uc2f6\ub2e4\uba74 docker volume \uc744 \uc0ac\uc6a9\ud558\uc5ec create, rm \uc744 \uba85\uc2dc\uc801\uc73c\ub85c \uc218\ud589\ud558\ub294 \uac83\ub3c4 \ud558\ub098\uc758 \ubc29\ubc95\uc785\ub2c8\ub2e4."),(0,a.kt)("p",null,"\ucfe0\ubc84\ub124\ud2f0\uc2a4\uc5d0\uc11c \uc2a4\ud1a0\ub9ac\uc9c0\ub97c \uc81c\uacf5\ud558\ub294 \ubc29\uc2dd\ub3c4 \uacb0\uad6d docker \uc758 bind mount \ub97c \ud65c\uc6a9\ud558\uc5ec \uc81c\uacf5\ud569\ub2c8\ub2e4."),(0,a.kt)("h3",{id:"docker-run-with-resource-limit"},"docker run with resource limit"),(0,a.kt)("p",null,"\uae30\ubcf8\uc801\uc73c\ub85c docker container \ub294 ",(0,a.kt)("strong",{parentName:"p"},"host OS \uc758 cpu, memory \uc790\uc6d0\uc744 fully \uc0ac\uc6a9"),"\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ud558\uc9c0\ub9cc \uc774\ub807\uac8c \uc0ac\uc6a9\ud558\uac8c \ub418\uba74 host OS \uc758 \uc790\uc6d0 \uc0c1\ud669\uc5d0 \ub530\ub77c\uc11c ",(0,a.kt)("strong",{parentName:"p"},"OOM")," \ub4f1\uc758 \uc774\uc288\ub85c docker container \uac00 \ube44\uc815\uc0c1\uc801\uc73c\ub85c \uc885\ub8cc\ub418\ub294 \uc0c1\ud669\uc774 \ubc1c\uc0dd\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.",(0,a.kt)("br",{parentName:"p"}),"\n","\uc774\ub7f0 \ubb38\uc81c\ub97c \ub2e4\ub8e8\uae30 \uc704\ud574 ",(0,a.kt)("strong",{parentName:"p"},"docker container \uc2e4\ud589 \uc2dc, cpu \uc640 memory \uc758 \uc0ac\uc6a9\ub7c9 \uc81c\ud55c"),"\uc744 \uac78 \uc218 \uc788\ub294 ",(0,a.kt)("inlineCode",{parentName:"p"},"-m")," ",(0,a.kt)("a",{parentName:"p",href:"https://docs.docker.com/config/containers/resource_constraints/#limit-a-containers-access-to-memory"},"\uc635\uc158"),"\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-bash"},"docker run -d -m 512m --memory-reservation=256m --name 512-limit ubuntu sleep 3600\ndocker run -d -m 1g --memory-reservation=256m --name 1g-limit ubuntu sleep 3600\n")),(0,a.kt)("p",null,"\uc704\uc758 \ub3c4\ucee4\ub97c \uc2e4\ud589 \ud6c4 ",(0,a.kt)("inlineCode",{parentName:"p"},"docker stats")," \ucee4\ub9e8\ub4dc\ub97c \ud1b5\ud574 \uc0ac\uc6a9\ub7c9\uc744 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-bash"},"CONTAINER ID NAME CPU % MEM USAGE / LIMIT MEM % NET I/O BLOCK I/O PIDS\n4ea1258e2e09 1g-limit 0.00% 300KiB / 1GiB 0.03% 1kB / 0B 0B / 0B 1\n4edf94b9a3e5 512-limit 0.00% 296KiB / 512MiB 0.06% 1.11kB / 0B 0B / 0B 1\n")),(0,a.kt)("p",null,"\ucfe0\ubc84\ub124\ud2f0\uc2a4\uc5d0\uc11c pod \ub77c\ub294 \ub9ac\uc18c\uc2a4\uc5d0 cpu, memory \uc81c\ud55c\uc744 \uc904 \ub54c, \uc774 \ubc29\uc2dd\uc744 \ud65c\uc6a9\ud558\uc5ec \uc81c\uacf5\ud569\ub2c8\ub2e4."),(0,a.kt)("h3",{id:"docker-run-with-restart-policy"},"docker run with restart policy"),(0,a.kt)("p",null,"\ud2b9\uc815 \ucee8\ud14c\uc774\ub108\uac00 \uacc4\uc18d\ud574\uc11c running \uc0c1\ud0dc\ub97c \uc720\uc9c0\uc2dc\ucf1c\uc57c \ud558\ub294 \uacbd\uc6b0\uac00 \uc874\uc7ac\ud569\ub2c8\ub2e4. \uc774\ub7f0 \uacbd\uc6b0\ub97c \uc704\ud574\uc11c \ud574\ub2f9 \ucee8\ud14c\uc774\ub108\uac00 \uc885\ub8cc\ub418\uc790\ub9c8\uc790 \ubc14\ub85c \uc7ac\uc0dd\uc131\uc744 \uc2dc\ub3c4\ud560 \uc218 \uc788\ub294 ",(0,a.kt)("inlineCode",{parentName:"p"},"--restart=always")," \uc635\uc158\uc744 \uc81c\uacf5\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4."),(0,a.kt)("p",null,"\uc635\uc158 \uc785\ub825 \ud6c4 \ub3c4\ucee4\ub97c \uc2e4\ud589\ud569\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-bash"},"docker run --restart=always ubuntu\n")),(0,a.kt)("p",null,(0,a.kt)("inlineCode",{parentName:"p"},"watch -n1 docker ps"),"\ub97c \ud1b5\ud574 \uc7ac\uc2e4\ud589\uc774 \ub418\uace0 \uc788\ub294\uc9c0 \ud655\uc778\ud569\ub2c8\ub2e4.\n\uc815\uc0c1\uc801\uc73c\ub85c \uc218\ud589\ub418\uace0 \uc788\ub2e4\uba74 \ub2e4\uc74c\uacfc \uac19\uc774 STATUS\uc5d0 ",(0,a.kt)("inlineCode",{parentName:"p"},"Restarting (0)")," \uc774 \ucd9c\ub825\ub429\ub2c8\ub2e4."),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-bash"},'CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES\na911850276e8 ubuntu "bash" 35 seconds ago Restarting (0) 6 seconds ago hungry_vaughan\n')),(0,a.kt)("ul",null,(0,a.kt)("li",{parentName:"ul"},(0,a.kt)("a",{parentName:"li",href:"https://docs.docker.com/engine/reference/commandline/run/#restart-policies---restart"},"https://docs.docker.com/engine/reference/commandline/run/#restart-policies---restart"),(0,a.kt)("ul",{parentName:"li"},(0,a.kt)("li",{parentName:"ul"},"on-failure with max retries"),(0,a.kt)("li",{parentName:"ul"},"always \ub4f1\uc758 \uc120\ud0dd\uc9c0 \uc81c\uacf5")))),(0,a.kt)("p",null,"\ucfe0\ubc84\ub124\ud2f0\uc2a4\uc5d0\uc11c job \uc774\ub77c\ub294 resource \uc758 restart \uc635\uc158\uc744 \uc904 \ub54c, \uc774 \ubc29\uc2dd\uc744 \ud65c\uc6a9\ud558\uc5ec \uc81c\uacf5\ud569\ub2c8\ub2e4."),(0,a.kt)("h3",{id:"docker-run-as-a-background-process"},"docker run as a background process"),(0,a.kt)("p",null,"\ub3c4\ucee4 \ucee8\ud14c\uc774\ub108\ub97c \uc2e4\ud589\ud560 \ub54c\ub294 \uae30\ubcf8\uc801\uc73c\ub85c foreground process \ub85c \uc2e4\ud589\ub429\ub2c8\ub2e4. \uc989, \ucee8\ud14c\uc774\ub108\ub97c \uc2e4\ud589\ud55c \ud130\ubbf8\ub110\uc774 \ud574\ub2f9 \ucee8\ud14c\uc774\ub108\uc5d0 \uc790\ub3d9\uc73c\ub85c attach \ub418\uc5b4 \uc788\uc5b4, \ub2e4\ub978 \uba85\ub839\uc744 \uc2e4\ud589\ud560 \uc218 \uc5c6\uc2b5\ub2c8\ub2e4."),(0,a.kt)("p",null,"\ub2e4\uc74c\uacfc \uac19\uc740 \uc608\uc2dc\ub97c \uc218\ud589\ud574\ubd05\ub2c8\ub2e4.",(0,a.kt)("br",{parentName:"p"}),"\n","\uc6b0\uc120 \ud130\ubbf8\ub110 2 \uac1c\ub97c \uc5f4\uc5b4, \ud558\ub098\uc758 \ud130\ubbf8\ub110\uc5d0\uc11c\ub294 ",(0,a.kt)("inlineCode",{parentName:"p"},"docker ps")," \ub97c \uc9c0\ucf1c\ubcf4\uace0, \ub2e4\ub978 \ud558\ub098\uc758 \ud130\ubbf8\ub110\uc5d0\uc11c\ub294 \ub2e4\uc74c\uacfc \uac19\uc740 \uba85\ub839\uc744 \ucc28\ub840\ub85c 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\ud50c\ub7ab\ud3fc\uc5d0\uc11c\ub294 \uc790\uccb4\uc801\uc73c\ub85c \ub85c\ub4dc \ubca8\ub7f0\uc11c(Load Balancer)\ub97c \uc81c\uacf5\ud574 \uc8fc\uc9c0\ub9cc, \uc628\ud504\ub808\ubbf8\uc2a4 \ud074\ub7ec\uc2a4\ud130\uc5d0\uc11c\ub294 \ub85c\ub4dc \ubca8\ub7f0\uc2f1 \uae30\ub2a5\uc744 \uc81c\uacf5\ud558\ub294 \ubaa8\ub4c8\uc744 \ucd94\uac00\uc801\uc73c\ub85c \uc124\uce58\ud574\uc57c \ud569\ub2c8\ub2e4.",(0,l.kt)("br",{parentName:"p"}),"\n",(0,l.kt)("a",{parentName:"p",href:"https://metallb.universe.tf/"},"MetalLB"),"\ub294 \ubca0\uc5b4\uba54\ud0c8 \ud658\uacbd\uc5d0\uc11c \uc0ac\uc6a9\ud560 \uc218 \uc788\ub294 \ub85c\ub4dc \ubca8\ub7f0\uc11c\ub97c \uc81c\uacf5\ud558\ub294 \uc624\ud508\uc18c\uc2a4 \ud504\ub85c\uc81d\ud2b8 \uc785\ub2c8\ub2e4."),(0,l.kt)("h2",{id:"\uc694\uad6c\uc0ac\ud56d"},"\uc694\uad6c\uc0ac\ud56d"),(0,l.kt)("table",null,(0,l.kt)("thead",{parentName:"table"},(0,l.kt)("tr",{parentName:"thead"},(0,l.kt)("th",{parentName:"tr",align:null},"\uc694\uad6c \uc0ac\ud56d"),(0,l.kt)("th",{parentName:"tr",align:null},"\ubc84\uc804 \ubc0f \ub0b4\uc6a9"))),(0,l.kt)("tbody",{parentName:"table"},(0,l.kt)("tr",{parentName:"tbody"},(0,l.kt)("td",{parentName:"tr",align:null},"Kubernetes"),(0,l.kt)("td",{parentName:"tr",align:null},"\ub85c\ub4dc \ubca8\ub7f0\uc2f1 \uae30\ub2a5\uc774 \uc5c6\ub294 >= v1.13.0")),(0,l.kt)("tr",{parentName:"tbody"},(0,l.kt)("td",{parentName:"tr",align:null},(0,l.kt)("a",{parentName:"td",href:"https://metallb.universe.tf/installation/network-addons/"},"\ud638\ud658\uac00\ub2a5\ud55c \ub124\ud2b8\uc6cc\ud06c CNI")),(0,l.kt)("td",{parentName:"tr",align:null},"Calico, Canal, Cilium, Flannel, Kube-ovn, Kube-router, Weave Net")),(0,l.kt)("tr",{parentName:"tbody"},(0,l.kt)("td",{parentName:"tr",align:null},"IPv4 \uc8fc\uc18c"),(0,l.kt)("td",{parentName:"tr",align:null},"MetalLB \ubc30\ud3ec\uc5d0 \uc0ac\uc6a9")),(0,l.kt)("tr",{parentName:"tbody"},(0,l.kt)("td",{parentName:"tr",align:null},"BGP \ubaa8\ub4dc\ub97c \uc0ac\uc6a9\ud560 \uacbd\uc6b0"),(0,l.kt)("td",{parentName:"tr",align:null},"BGP \uae30\ub2a5\uc744 \uc9c0\uc6d0\ud558\ub294 \ud558\ub098 \uc774\uc0c1\uc758 \ub77c\uc6b0\ud130")),(0,l.kt)("tr",{parentName:"tbody"},(0,l.kt)("td",{parentName:"tr",align:null},"\ub178\ub4dc \uac04 \ud3ec\ud2b8 TCP/UDP 7946 \uc624\ud508"),(0,l.kt)("td",{parentName:"tr",align:null},"memberlist \uc694\uad6c \uc0ac\ud56d")))),(0,l.kt)("h2",{id:"metallb-\uc124\uce58"},"MetalLB \uc124\uce58"),(0,l.kt)("h3",{id:"preparation"},"Preparation"),(0,l.kt)("p",null,"IPVS \ubaa8\ub4dc\uc5d0\uc11c kube-proxy\ub97c \uc0ac\uc6a9\ud558\ub294 \uacbd\uc6b0 Kubernetes v1.14.2 \uc774\ud6c4\ubd80\ud130\ub294 \uc5c4\uaca9\ud55c ARP(strictARP) \ubaa8\ub4dc\ub97c \uc0ac\uc6a9\ud558\ub3c4\ub85d \uc124\uc815\ud574\uc57c \ud569\ub2c8\ub2e4.",(0,l.kt)("br",{parentName:"p"}),"\n","Kube-router\ub294 \uae30\ubcf8\uc801\uc73c\ub85c \uc5c4\uaca9\ud55c ARP\ub97c \ud65c\uc131\ud654\ud558\ubbc0\ub85c \uc11c\ube44\uc2a4 \ud504\ub85d\uc2dc\ub85c \uc0ac\uc6a9\ud560 \uacbd\uc6b0\uc5d0\ub294 \uc774 \uae30\ub2a5\uc774 \ud544\uc694\ud558\uc9c0 \uc54a\uc2b5\ub2c8\ub2e4.",(0,l.kt)("br",{parentName:"p"}),"\n","\uc5c4\uaca9\ud55c ARP \ubaa8\ub4dc\ub97c \uc801\uc6a9\ud558\uae30\uc5d0 \uc55e\uc11c, \ud604\uc7ac \ubaa8\ub4dc\ub97c \ud655\uc778\ud569\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"# see what changes would be made, returns nonzero returncode if different\nkubectl get configmap kube-proxy -n kube-system -o yaml | \\\ngrep strictARP\n")),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"strictARP: false\n")),(0,l.kt)("p",null,"strictARP: false \uac00 \ucd9c\ub825\ub418\ub294 \uacbd\uc6b0 \ub2e4\uc74c\uc744 \uc2e4\ud589\ud558\uc5ec strictARP: true\ub85c \ubcc0\uacbd\ud569\ub2c8\ub2e4.\n(strictARP: true\uac00 \uc774\ubbf8 \ucd9c\ub825\ub41c\ub2e4\uba74 \ub2e4\uc74c \ucee4\ub9e8\ub4dc\ub97c \uc218\ud589\ud558\uc9c0 \uc54a\uc73c\uc154\ub3c4 \ub429\ub2c8\ub2e4.)"),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},'# actually apply the changes, returns nonzero returncode on errors only\nkubectl get configmap kube-proxy -n kube-system -o yaml | \\\nsed -e "s/strictARP: false/strictARP: true/" | \\\nkubectl apply -f - -n kube-system\n')),(0,l.kt)("p",null,"\uc815\uc0c1\uc801\uc73c\ub85c \uc218\ud589\ub418\uba74 \ub2e4\uc74c\uacfc \uac19\uc774 \ucd9c\ub825\ub429\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"Warning: resource configmaps/kube-proxy is missing the kubectl.kubernetes.io/last-applied-configuration annotation which is required by kubectl apply. kubectl apply should only be used on resources created declaratively by either kubectl create --save-config or kubectl apply. The missing annotation will be patched automatically.\nconfigmap/kube-proxy configured\n")),(0,l.kt)("h3",{id:"\uc124\uce58---manifest"},"\uc124\uce58 - Manifest"),(0,l.kt)("h4",{id:"1-metallb-\ub97c-\uc124\uce58\ud569\ub2c8\ub2e4"},"1. MetalLB \ub97c \uc124\uce58\ud569\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"kubectl apply -f https://raw.githubusercontent.com/metallb/metallb/v0.11.0/manifests/namespace.yaml\nkubectl apply -f https://raw.githubusercontent.com/metallb/metallb/v0.11.0/manifests/metallb.yaml\n")),(0,l.kt)("h4",{id:"2-\uc815\uc0c1-\uc124\uce58-\ud655\uc778"},"2. \uc815\uc0c1 \uc124\uce58 \ud655\uc778"),(0,l.kt)("p",null,"metallb-system namespace \uc758 2 \uac1c\uc758 pod \uc774 \ubaa8\ub450 Running \uc774 \ub420 \ub54c\uae4c\uc9c0 \uae30\ub2e4\ub9bd\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"kubectl get pod -n metallb-system\n")),(0,l.kt)("p",null,"\ubaa8\ub450 Running \uc774 \ub418\uba74 \ub2e4\uc74c\uacfc \ube44\uc2b7\ud55c \uacb0\uacfc\uac00 \ucd9c\ub825\ub429\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"NAME READY STATUS RESTARTS AGE\ncontroller-7dcc8764f4-8n92q 1/1 Running 1 1m\nspeaker-fnf8l 1/1 Running 1 1m\n")),(0,l.kt)("p",null,"\ub9e4\ub2c8\ud398\uc2a4\ud2b8\uc758 \uad6c\uc131 \uc694\uc18c\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4."),(0,l.kt)("ul",null,(0,l.kt)("li",{parentName:"ul"},"metallb-system/controller",(0,l.kt)("ul",{parentName:"li"},(0,l.kt)("li",{parentName:"ul"},"deployment \ub85c \ubc30\ud3ec\ub418\uba70, \ub85c\ub4dc \ubca8\ub7f0\uc2f1\uc744 \uc218\ud589\ud560 external IP \uc8fc\uc18c\uc758 \ud560\ub2f9\uc744 \ucc98\ub9ac\ud558\ub294 \uc5ed\ud560\uc744 \ub2f4\ub2f9\ud569\ub2c8\ub2e4."))),(0,l.kt)("li",{parentName:"ul"},"metallb-system/speaker",(0,l.kt)("ul",{parentName:"li"},(0,l.kt)("li",{parentName:"ul"},"daemonset \ud615\ud0dc\ub85c \ubc30\ud3ec\ub418\uba70, \uc678\ubd80 \ud2b8\ub798\ud53d\uacfc \uc11c\ube44\uc2a4\ub97c \uc5f0\uacb0\ud574 \ub124\ud2b8\uc6cc\ud06c \ud1b5\uc2e0\uc774 \uac00\ub2a5\ud558\ub3c4\ub85d \uad6c\uc131\ud558\ub294 \uc5ed\ud560\uc744 \ub2f4\ub2f9\ud569\ub2c8\ub2e4.")))),(0,l.kt)("p",null,"\uc11c\ube44\uc2a4\uc5d0\ub294 \ucee8\ud2b8\ub864\ub7ec \ubc0f \uc2a4\ud53c\ucee4\uc640 \uad6c\uc131 \uc694\uc18c\uac00 \uc791\ub3d9\ud558\ub294 \ub370 \ud544\uc694\ud55c RBAC \uc0ac\uc6a9 \uad8c\ud55c\uc774 \ud3ec\ud568\ub429\ub2c8\ub2e4."),(0,l.kt)("h2",{id:"configuration"},"Configuration"),(0,l.kt)("p",null,"MetalLB \uc758 \ub85c\ub4dc \ubca8\ub7f0\uc2f1 \uc815\ucc45 \uc124\uc815\uc740 \uad00\ub828 \uc124\uc815 \uc815\ubcf4\ub97c \ub2f4\uc740 configmap \uc744 \ubc30\ud3ec\ud558\uc5ec \uc124\uc815\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,l.kt)("p",null,"MetalLB \uc5d0\uc11c \uad6c\uc131\ud560 \uc218 \uc788\ub294 \ubaa8\ub4dc\ub85c\ub294 \ub2e4\uc74c\uacfc \uac19\uc774 2\uac00\uc9c0\uac00 \uc788\uc2b5\ub2c8\ub2e4."),(0,l.kt)("ol",null,(0,l.kt)("li",{parentName:"ol"},(0,l.kt)("a",{parentName:"li",href:"https://metallb.universe.tf/concepts/layer2/"},"Layer 2 \ubaa8\ub4dc")),(0,l.kt)("li",{parentName:"ol"},(0,l.kt)("a",{parentName:"li",href:"https://metallb.universe.tf/concepts/bgp/"},"BGP \ubaa8\ub4dc"))),(0,l.kt)("p",null,"\uc5ec\uae30\uc5d0\uc11c\ub294 Layer 2 \ubaa8\ub4dc\ub85c \uc9c4\ud589\ud558\uaca0\uc2b5\ub2c8\ub2e4."),(0,l.kt)("h3",{id:"layer-2-configuration"},"Layer 2 Configuration"),(0,l.kt)("p",null,"Layer 2 \ubaa8\ub4dc\ub294 \uac04\ub2e8\ud558\uac8c \uc0ac\uc6a9\ud560 IP \uc8fc\uc18c\uc758 \ub300\uc5ed\ub9cc \uc124\uc815\ud558\uba74 \ub429\ub2c8\ub2e4.",(0,l.kt)("br",{parentName:"p"}),"\n","Layer 2 \ubaa8\ub4dc\ub97c \uc0ac\uc6a9\ud560 \uacbd\uc6b0 \uc6cc\ucee4 \ub178\ub4dc\uc758 \ub124\ud2b8\uc6cc\ud06c \uc778\ud130\ud398\uc774\uc2a4\uc5d0 IP\ub97c \ubc14\uc778\ub529 \ud558\uc9c0 \uc54a\uc544\ub3c4 \ub418\ub294\ub370 \ub85c\uceec \ub124\ud2b8\uc6cc\ud06c\uc758 ARP \uc694\uccad\uc5d0 \uc9c1\uc811 \uc751\ub2f5\ud558\uc5ec \ucef4\ud4e8\ud130\uc758 MAC\uc8fc\uc18c\ub97c \ud074\ub77c\uc774\uc5b8\ud2b8\uc5d0 \uc81c\uacf5\ud558\ub294 \ubc29\uc2dd\uc73c\ub85c \uc791\ub3d9\ud558\uae30 \ub54c\ubb38\uc785\ub2c8\ub2e4."),(0,l.kt)("p",null,"\ub2e4\uc74c ",(0,l.kt)("inlineCode",{parentName:"p"},"metallb_config.yaml")," \ud30c\uc77c\uc740 MetalLB \uac00 192.168.35.100 ~ 192.168.35.110\uc758 IP\uc5d0 \ub300\ud55c \uc81c\uc5b4 \uad8c\ud55c\uc744 \uc81c\uacf5\ud558\uace0 Layer 2 \ubaa8\ub4dc\ub97c \uad6c\uc131\ud558\ub294 \uc124\uc815\uc785\ub2c8\ub2e4."),(0,l.kt)("p",null,"\ud074\ub7ec\uc2a4\ud130 \ub178\ub4dc\uc640 \ud074\ub77c\uc774\uc5b8\ud2b8 \ub178\ub4dc\uac00 \ubd84\ub9ac\ub41c \uacbd\uc6b0, 192.168.35.100 ~ 192.168.35.110 \ub300\uc5ed\uc774 \ud074\ub77c\uc774\uc5b8\ud2b8 \ub178\ub4dc\uc640 \ud074\ub7ec\uc2a4\ud130 \ub178\ub4dc \ubaa8\ub450 \uc811\uadfc \uac00\ub2a5\ud55c \ub300\uc5ed\uc774\uc5b4\uc57c \ud569\ub2c8\ub2e4."),(0,l.kt)("h4",{id:"metallb_configyaml"},"metallb_config.yaml"),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"apiVersion: v1\nkind: ConfigMap\nmetadata:\n namespace: metallb-system\n name: config\ndata:\n config: |\n address-pools:\n - name: default\n protocol: layer2\n addresses:\n - 192.168.35.100-192.168.35.110 # IP \ub300\uc5ed\ud3ed\n")),(0,l.kt)("p",null,"\uc704\uc758 \uc124\uc815\uc744 \uc801\uc6a9\ud569\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-test"},"kubectl apply -f metallb_config.yaml \n")),(0,l.kt)("p",null,"\uc815\uc0c1\uc801\uc73c\ub85c \ubc30\ud3ec\ud558\uba74 \ub2e4\uc74c\uacfc \uac19\uc774 \ucd9c\ub825\ub429\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-test"},"configmap/config created\n")),(0,l.kt)("h2",{id:"metallb-\uc0ac\uc6a9"},"MetalLB \uc0ac\uc6a9"),(0,l.kt)("h3",{id:"kubeflow-dashboard"},"Kubeflow Dashboard"),(0,l.kt)("p",null,"\uba3c\uc800 kubeflow\uc758 Dashboard \ub97c \uc81c\uacf5\ud558\ub294 istio-system \ub124\uc784\uc2a4\ud398\uc774\uc2a4\uc758 istio-ingressgateway \uc11c\ube44\uc2a4\uc758 \ud0c0\uc785\uc744 ",(0,l.kt)("inlineCode",{parentName:"p"},"LoadBalancer"),"\ub85c \ubcc0\uacbd\ud558\uc5ec MetalLB\ub85c\ubd80\ud130 \ub85c\ub4dc \ubca8\ub7f0\uc2f1 \uae30\ub2a5\uc744 \uc81c\uacf5\ubc1b\uae30 \uc804\uc5d0, \ud604\uc7ac \uc0c1\ud0dc\ub97c \ud655\uc778\ud569\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"kubectl get svc/istio-ingressgateway -n istio-system\n")),(0,l.kt)("p",null,"\ud574\ub2f9 \uc11c\ube44\uc2a4\uc758 \ud0c0\uc785\uc740 ClusterIP\uc774\uba70, External-IP \uac12\uc740 ",(0,l.kt)("inlineCode",{parentName:"p"},"none")," \uc778 \uac83\uc744 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE\nistio-ingressgateway ClusterIP 10.103.72.5 15021/TCP,80/TCP,443/TCP,31400/TCP,15443/TCP 4h21m\n")),(0,l.kt)("p",null,"type \uc744 LoadBalancer \ub85c \ubcc0\uacbd\ud558\uace0 \uc6d0\ud558\ub294 IP \uc8fc\uc18c\ub97c \uc785\ub825\ud558\uace0 \uc2f6\uc740 \uacbd\uc6b0 loadBalancerIP \ud56d\ubaa9\uc744 \ucd94\uac00\ud569\ub2c8\ub2e4.",(0,l.kt)("br",{parentName:"p"}),"\n","\ucd94\uac00 \ud558\uc9c0 \uc54a\uc744 \uacbd\uc6b0\uc5d0\ub294 \uc704\uc5d0\uc11c \uc124\uc815\ud55c IP \uc8fc\uc18c\ud480\uc5d0\uc11c \uc21c\ucc28\uc801\uc73c\ub85c IP \uc8fc\uc18c\uac00 \ubc30\uc815\ub429\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"kubectl edit svc/istio-ingressgateway -n istio-system\n")),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"spec:\n clusterIP: 10.103.72.5\n clusterIPs:\n - 10.103.72.5\n ipFamilies:\n - IPv4\n ipFamilyPolicy: SingleStack\n ports:\n - name: status-port\n port: 15021\n protocol: TCP\n targetPort: 15021\n - name: http2\n port: 80\n protocol: TCP\n targetPort: 8080\n - name: https\n port: 443\n protocol: TCP\n targetPort: 8443\n - name: tcp\n port: 31400\n protocol: TCP\n targetPort: 31400\n - name: tls\n port: 15443\n protocol: TCP\n targetPort: 15443\n selector:\n app: istio-ingressgateway\n istio: ingressgateway\n sessionAffinity: None\n type: LoadBalancer # Change ClusterIP to LoadBalancer\n loadBalancerIP: 192.168.35.100 # Add IP\nstatus:\n loadBalancer: {}\n")),(0,l.kt)("p",null,"\ub2e4\uc2dc \ud655\uc778\uc744 \ud574\ubcf4\uba74 External-IP \uac12\uc774 ",(0,l.kt)("inlineCode",{parentName:"p"},"192.168.35.100")," \uc778 \uac83\uc744 \ud655\uc778\ud569\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"kubectl get svc/istio-ingressgateway -n istio-system\n")),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE\nistio-ingressgateway LoadBalancer 10.103.72.5 192.168.35.100 15021:31054/TCP,80:30853/TCP,443:30443/TCP,31400:30012/TCP,15443:31650/TCP 5h1m\n")),(0,l.kt)("p",null,"Web Browser \ub97c \uc5f4\uc5b4 ",(0,l.kt)("a",{parentName:"p",href:"http://192.168.35.100"},"http://192.168.35.100")," \uc73c\ub85c \uc811\uc18d\ud558\uc5ec, \ub2e4\uc74c\uacfc \uac19\uc740 \ud654\uba74\uc774 \ucd9c\ub825\ub418\ub294 \uac83\uc744 \ud655\uc778\ud569\ub2c8\ub2e4."),(0,l.kt)("p",null,(0,l.kt)("img",{alt:"login-after-istio-ingressgateway-setting.png",src:n(5568).Z,width:"1811",height:"1046"})),(0,l.kt)("h3",{id:"minio-dashboard"},"minio Dashboard"),(0,l.kt)("p",null,"\uba3c\uc800 minio \uc758 Dashboard \ub97c \uc81c\uacf5\ud558\ub294 kubeflow \ub124\uc784\uc2a4\ud398\uc774\uc2a4\uc758 minio-service \uc11c\ube44\uc2a4\uc758 \ud0c0\uc785\uc744 LoadBalancer\ub85c \ubcc0\uacbd\ud558\uc5ec MetalLB\ub85c\ubd80\ud130 \ub85c\ub4dc \ubca8\ub7f0\uc2f1 \uae30\ub2a5\uc744 \uc81c\uacf5\ubc1b\uae30 \uc804\uc5d0, \ud604\uc7ac \uc0c1\ud0dc\ub97c \ud655\uc778\ud569\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"kubectl get svc/minio-service -n kubeflow\n")),(0,l.kt)("p",null,"\ud574\ub2f9 \uc11c\ube44\uc2a4\uc758 \ud0c0\uc785\uc740 ClusterIP\uc774\uba70, External-IP \uac12\uc740 ",(0,l.kt)("inlineCode",{parentName:"p"},"none")," \uc778 \uac83\uc744 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE\nminio-service ClusterIP 10.109.209.87 9000/TCP 5h14m\n")),(0,l.kt)("p",null,"type \uc744 LoadBalancer \ub85c \ubcc0\uacbd\ud558\uace0 \uc6d0\ud558\ub294 IP \uc8fc\uc18c\ub97c \uc785\ub825\ud558\uace0 \uc2f6\uc740 \uacbd\uc6b0 loadBalancerIP \ud56d\ubaa9\uc744 \ucd94\uac00\ud569\ub2c8\ub2e4.",(0,l.kt)("br",{parentName:"p"}),"\n","\ucd94\uac00 \ud558\uc9c0 \uc54a\uc744 \uacbd\uc6b0\uc5d0\ub294 \uc704\uc5d0\uc11c \uc124\uc815\ud55c IP \uc8fc\uc18c\ud480\uc5d0\uc11c \uc21c\ucc28\uc801\uc73c\ub85c IP \uc8fc\uc18c\uac00 \ubc30\uc815\ub429\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"kubectl edit svc/minio-service -n kubeflow\n")),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},'apiVersion: v1\nkind: Service\nmetadata:\n annotations:\n kubectl.kubernetes.io/last-applied-configuration: |\n {"apiVersion":"v1","kind":"Service","metadata":{"annotations":{},"labels":{"application-crd-id":"kubeflow-pipelines"},"name":"minio-ser>\n creationTimestamp: "2022-01-05T08:44:23Z"\n labels:\n application-crd-id: kubeflow-pipelines\n name: minio-service\n namespace: kubeflow\n resourceVersion: "21120"\n uid: 0053ee28-4f87-47bb-ad6b-7ad68aa29a48\nspec:\n clusterIP: 10.109.209.87\n clusterIPs:\n - 10.109.209.87\n ipFamilies:\n - IPv4\n ipFamilyPolicy: SingleStack\n ports:\n - name: http\n port: 9000\n protocol: TCP\n targetPort: 9000\n selector:\n app: minio\n application-crd-id: kubeflow-pipelines\n sessionAffinity: None\n type: LoadBalancer # Change ClusterIP to LoadBalancer\n loadBalancerIP: 192.168.35.101 # Add IP\nstatus:\n loadBalancer: {}\n')),(0,l.kt)("p",null,"\ub2e4\uc2dc \ud655\uc778\uc744 \ud574\ubcf4\uba74 External-IP \uac12\uc774 ",(0,l.kt)("inlineCode",{parentName:"p"},"192.168.35.101")," \uc778 \uac83\uc744 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"kubectl get svc/minio-service -n kubeflow\n")),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE\nminio-service LoadBalancer 10.109.209.87 192.168.35.101 9000:31371/TCP 5h21m\n")),(0,l.kt)("p",null,"Web Browser \ub97c \uc5f4\uc5b4 ",(0,l.kt)("a",{parentName:"p",href:"http://192.168.35.101:9000"},"http://192.168.35.101:9000")," \uc73c\ub85c \uc811\uc18d\ud558\uc5ec, \ub2e4\uc74c\uacfc \uac19\uc740 \ud654\uba74\uc774 \ucd9c\ub825\ub418\ub294 \uac83\uc744 \ud655\uc778\ud569\ub2c8\ub2e4."),(0,l.kt)("p",null,(0,l.kt)("img",{alt:"login-after-minio-setting.png",src:n(369).Z,width:"1811",height:"1046"})),(0,l.kt)("h3",{id:"mlflow-dashboard"},"mlflow Dashboard"),(0,l.kt)("p",null,"\uba3c\uc800 mlflow \uc758 Dashboard \ub97c \uc81c\uacf5\ud558\ub294 mlflow-system \ub124\uc784\uc2a4\ud398\uc774\uc2a4\uc758 mlflow-server-service \uc11c\ube44\uc2a4\uc758 \ud0c0\uc785\uc744 LoadBalancer\ub85c \ubcc0\uacbd\ud558\uc5ec MetalLB\ub85c\ubd80\ud130 \ub85c\ub4dc \ubca8\ub7f0\uc2f1 \uae30\ub2a5\uc744 \uc81c\uacf5\ubc1b\uae30 \uc804\uc5d0, \ud604\uc7ac \uc0c1\ud0dc\ub97c \ud655\uc778\ud569\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"kubectl get svc/mlflow-server-service -n mlflow-system\n")),(0,l.kt)("p",null,"\ud574\ub2f9 \uc11c\ube44\uc2a4\uc758 \ud0c0\uc785\uc740 ClusterIP\uc774\uba70, External-IP \uac12\uc740 ",(0,l.kt)("inlineCode",{parentName:"p"},"none")," \uc778 \uac83\uc744 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE\nmlflow-server-service ClusterIP 10.111.173.209 5000/TCP 4m50s\n")),(0,l.kt)("p",null,"type \uc744 LoadBalancer \ub85c \ubcc0\uacbd\ud558\uace0 \uc6d0\ud558\ub294 IP \uc8fc\uc18c\ub97c \uc785\ub825\ud558\uace0 \uc2f6\uc740 \uacbd\uc6b0 loadBalancerIP \ud56d\ubaa9\uc744 \ucd94\uac00\ud569\ub2c8\ub2e4.",(0,l.kt)("br",{parentName:"p"}),"\n","\ucd94\uac00 \ud558\uc9c0 \uc54a\uc744 \uacbd\uc6b0\uc5d0\ub294 \uc704\uc5d0\uc11c \uc124\uc815\ud55c IP \uc8fc\uc18c\ud480\uc5d0\uc11c \uc21c\ucc28\uc801\uc73c\ub85c IP \uc8fc\uc18c\uac00 \ubc30\uc815\ub429\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"kubectl edit svc/mlflow-server-service -n mlflow-system\n")),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},'apiVersion: v1\nkind: Service\nmetadata:\n annotations:\n meta.helm.sh/release-name: mlflow-server\n meta.helm.sh/release-namespace: mlflow-system\n creationTimestamp: "2022-01-07T04:00:19Z"\n labels:\n app.kubernetes.io/managed-by: Helm\n name: mlflow-server-service\n namespace: mlflow-system\n resourceVersion: "276246"\n uid: e5d39fb7-ad98-47e7-b512-f9c673055356\nspec:\n clusterIP: 10.111.173.209\n clusterIPs:\n - 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NVIDIA-Docker \uc124\uce58"),(0,r.kt)("p",null,"NVIDIA-Docker\ub97c \uc124\uce58\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | \\\n sudo apt-key add -\ndistribution=$(. /etc/os-release;echo $ID$VERSION_ID)\ncurl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list\nsudo apt-get update\nsudo apt-get install -y nvidia-docker2 &&\nsudo systemctl restart docker\n")),(0,r.kt)("p",null,"\uc815\uc0c1\uc801\uc73c\ub85c \uc124\uce58\ub418\uc5c8\ub294\uc9c0 \ud655\uc778\ud558\uae30 \uc704\ud574, GPU\ub97c \uc0ac\uc6a9\ud558\ub294 \ub3c4\ucee4 \ucee8\ud14c\uc774\ub108\ub97c \uc2e4\ud589\ud574\ubd05\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"sudo docker run --rm --gpus all nvidia/cuda:11.0-base nvidia-smi\n")),(0,r.kt)("p",null,"\ub2e4\uc74c\uacfc \uac19\uc740 \uba54\uc2dc\uc9c0\uac00 \ubcf4\uc774\uba74 \uc815\uc0c1\uc801\uc73c\ub85c \uc124\uce58\ub41c \uac83\uc744 \uc758\ubbf8\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"mlops@ubuntu:~$ sudo docker run --rm --gpus all nvidia/cuda:11.0-base nvidia-smi\n+-----------------------------------------------------------------------------+\n| NVIDIA-SMI 470.86 Driver Version: 470.86 CUDA Version: 11.4 |\n|-------------------------------+----------------------+----------------------+\n| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n| | | MIG M. |\n|===============================+======================+======================|\n| 0 NVIDIA GeForce ... Off | 00000000:01:00.0 Off | N/A |\n| 25% 32C P8 4W / 120W | 211MiB / 6078MiB | 0% Default |\n| | | N/A |\n+-------------------------------+----------------------+----------------------+\n| 1 NVIDIA GeForce ... Off | 00000000:02:00.0 Off | N/A |\n| 0% 34C P8 6W / 175W | 5MiB / 7982MiB | 0% Default |\n| | | N/A |\n+-------------------------------+----------------------+----------------------+\n \n+-----------------------------------------------------------------------------+\n| Processes: |\n| GPU GI CI PID Type Process name GPU Memory |\n| ID ID Usage |\n|=============================================================================|\n+-----------------------------------------------------------------------------+\n")),(0,r.kt)("h2",{id:"3-nvidia-docker\ub97c-default-container-runtime\uc73c\ub85c-\uc124\uc815"},"3. NVIDIA-Docker\ub97c Default Container Runtime\uc73c\ub85c \uc124\uc815"),(0,r.kt)("p",null,"\ucfe0\ubc84\ub124\ud2f0\uc2a4\ub294 \uae30\ubcf8\uc801\uc73c\ub85c Docker-CE\ub97c Default Container Runtime\uc73c\ub85c \uc0ac\uc6a9\ud569\ub2c8\ub2e4.\n\ub530\ub77c\uc11c, Docker Container \ub0b4\uc5d0\uc11c NVIDIA GPU\ub97c \uc0ac\uc6a9\ud558\uae30 \uc704\ud574\uc11c\ub294 NVIDIA-Docker \ub97c Container Runtime \uc73c\ub85c \uc0ac\uc6a9\ud558\uc5ec pod\ub97c \uc0dd\uc131\ud560 \uc218 \uc788\ub3c4\ub85d Default Runtime\uc744 \uc218\uc815\ud574 \uc8fc\uc5b4\uc57c \ud569\ub2c8\ub2e4."),(0,r.kt)("ol",null,(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("p",{parentName:"li"},(0,r.kt)("inlineCode",{parentName:"p"},"/etc/docker/daemon.json")," \ud30c\uc77c\uc744 \uc5f4\uc5b4 \ub2e4\uc74c\uacfc \uac19\uc774 \uc218\uc815\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},'sudo vi /etc/docker/daemon.json\n\n{\n "default-runtime": "nvidia",\n "runtimes": {\n "nvidia": {\n "path": "nvidia-container-runtime",\n "runtimeArgs": []\n }\n }\n}\n'))),(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("p",{parentName:"li"},"\ud30c\uc77c\uc774 \ubcc0\uacbd\ub41c \uac83\uc744 \ud655\uc778\ud55c \ud6c4, Docker\ub97c \uc7ac\uc2dc\uc791\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"sudo systemctl daemon-reload\nsudo service docker restart\n"))),(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("p",{parentName:"li"},"\ubcc0\uacbd \uc0ac\ud56d\uc774 \ubc18\uc601\ub418\uc5c8\ub294\uc9c0 \ud655\uc778\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"sudo docker info | grep nvidia\n")),(0,r.kt)("p",{parentName:"li"},"\ub2e4\uc74c\uacfc \uac19\uc740 \uba54\uc2dc\uc9c0\uac00 \ubcf4\uc774\uba74 \uc815\uc0c1\uc801\uc73c\ub85c \uc124\uce58\ub41c \uac83\uc744 \uc758\ubbf8\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"mlops@ubuntu:~$ docker info | grep nvidia\nRuntimes: io.containerd.runc.v2 io.containerd.runtime.v1.linux nvidia runc\nDefault Runtime: nvidia\n")))),(0,r.kt)("h2",{id:"4-nvidia-device-plugin"},"4. Nvidia-Device-Plugin"),(0,r.kt)("ol",null,(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("p",{parentName:"li"},"nvidia-device-plugin daemonset\uc744 \uc0dd\uc131\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"kubectl create -f https://raw.githubusercontent.com/NVIDIA/k8s-device-plugin/v0.10.0/nvidia-device-plugin.yml\n"))),(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("p",{parentName:"li"},"nvidia-device-plugin pod\uc774 RUNNING \uc0c1\ud0dc\ub85c \uc0dd\uc131\ub418\uc5c8\ub294\uc9c0 \ud655\uc778\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"kubectl get pod -n kube-system | grep nvidia\n")),(0,r.kt)("p",{parentName:"li"},"\ub2e4\uc74c\uacfc \uac19\uc740 \uacb0\uacfc\uac00 \ucd9c\ub825\ub418\uc5b4\uc57c \ud569\ub2c8\ub2e4."),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"kube-system nvidia-device-plugin-daemonset-nlqh2 1/1 Running 0 1h\n"))),(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("p",{parentName:"li"},"node \uc815\ubcf4\uc5d0 gpu\uac00 \uc0ac\uc6a9\uac00\ub2a5\ud558\ub3c4\ub85d \uc124\uc815\ub418\uc5c8\ub294\uc9c0 \ud655\uc778\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},'kubectl get nodes "-o=custom-columns=NAME:.metadata.name,GPU:.status.allocatable.nvidia\\.com/gpu"\n')),(0,r.kt)("p",{parentName:"li"},"\ub2e4\uc74c\uacfc \uac19\uc740 \uba54\uc2dc\uc9c0\uac00 \ubcf4\uc774\uba74 \uc815\uc0c1\uc801\uc73c\ub85c \uc124\uc815\ub41c \uac83\uc744 \uc758\ubbf8\ud569\ub2c8\ub2e4.",(0,r.kt)("br",{parentName:"p"}),"\n","(",(0,r.kt)("em",{parentName:"p"},"\ubaa8\ub450\uc758 MLOps")," \uc5d0\uc11c \uc2e4\uc2b5\uc744 \uc9c4\ud589\ud55c \ud074\ub7ec\uc2a4\ud130\ub294 2\uac1c\uc758 GPU\uac00 \uc788\uc5b4\uc11c 2\uac00 \ucd9c\ub825\ub429\ub2c8\ub2e4.\n\ubcf8\uc778\uc758 \ud074\ub7ec\uc2a4\ud130\uc758 GPU \uac1c\uc218\uc640 \ub9de\ub294 \uc22b\uc790\uac00 \ucd9c\ub825\ub41c\ub2e4\uba74 \ub429\ub2c8\ub2e4.)"),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"NAME GPU\nubuntu 2\n")))),(0,r.kt)("p",null,"\uc124\uc815\ub418\uc9c0 \uc54a\uc740 \uacbd\uc6b0, GPU\uc758 value\uac00 ",(0,r.kt)("inlineCode",{parentName:"p"},"")," \uc73c\ub85c \ud45c\uc2dc\ub429\ub2c8\ub2e4."))}c.isMDXComponent=!0}}]); \ No newline at end of file diff --git a/assets/js/d5c893db.5769dd9d.js b/assets/js/d5c893db.1b68af2f.js similarity index 59% rename from assets/js/d5c893db.5769dd9d.js rename to assets/js/d5c893db.1b68af2f.js index 8d6281cd..9add6a97 100644 --- a/assets/js/d5c893db.5769dd9d.js +++ b/assets/js/d5c893db.1b68af2f.js @@ -1 +1 @@ -"use strict";(self.webpackChunkv_2=self.webpackChunkv_2||[]).push([[3225],{3905:(e,t,r)=>{r.d(t,{Zo:()=>u,kt:()=>d});var n=r(7294);function o(e,t,r){return t in e?Object.defineProperty(e,t,{value:r,enumerable:!0,configurable:!0,writable:!0}):e[t]=r,e}function i(e,t){var r=Object.keys(e);if(Object.getOwnPropertySymbols){var n=Object.getOwnPropertySymbols(e);t&&(n=n.filter((function(t){return Object.getOwnPropertyDescriptor(e,t).enumerable}))),r.push.apply(r,n)}return r}function l(e){for(var t=1;t=0||(o[r]=e[r]);return o}(e,t);if(Object.getOwnPropertySymbols){var i=Object.getOwnPropertySymbols(e);for(n=0;n=0||Object.prototype.propertyIsEnumerable.call(e,r)&&(o[r]=e[r])}return o}var c=n.createContext({}),p=function(e){var t=n.useContext(c),r=t;return e&&(r="function"==typeof e?e(t):l(l({},t),e)),r},u=function(e){var t=p(e.components);return n.createElement(c.Provider,{value:t},e.children)},s="mdxType",f={inlineCode:"code",wrapper:function(e){var t=e.children;return n.createElement(n.Fragment,{},t)}},b=n.forwardRef((function(e,t){var r=e.components,o=e.mdxType,i=e.originalType,c=e.parentName,u=a(e,["components","mdxType","originalType","parentName"]),s=p(r),b=o,d=s["".concat(c,".").concat(b)]||s[b]||f[b]||i;return r?n.createElement(d,l(l({ref:t},u),{},{components:r})):n.createElement(d,l({ref:t},u))}));function d(e,t){var r=arguments,o=t&&t.mdxType;if("string"==typeof e||o){var i=r.length,l=new Array(i);l[0]=b;var a={};for(var c in t)hasOwnProperty.call(t,c)&&(a[c]=t[c]);a.originalType=e,a[s]="string"==typeof e?e:o,l[1]=a;for(var p=2;p{r.r(t),r.d(t,{assets:()=>c,contentTitle:()=>l,default:()=>f,frontMatter:()=>i,metadata:()=>a,toc:()=>p});var n=r(7462),o=(r(7294),r(3905));const i={title:"1. 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"sepal length (cm)",\n "sepal width (cm)",\n "petal length (cm)",\n "petal width (cm)"\n ],\n "data": \n [\n [6.7, 3.1, 4.4, 1.4]\n ]\n}\n'))),(0,r.kt)("li",{parentName:"ul"},(0,r.kt)("p",{parentName:"li"},"requirements.txt"),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"mlflow\ndill\npandas\nscikit-learn\n"))),(0,r.kt)("li",{parentName:"ul"},(0,r.kt)("p",{parentName:"li"},"model.pkl"))),(0,r.kt)("h2",{id:"mlflow-on-server"},"MLFlow on Server"),(0,r.kt)("p",null,"\uc774\uc81c \uc800\uc7a5\ub41c \ubaa8\ub378\uc744 mlflow \uc11c\ubc84\uc5d0 \uc62c\ub9ac\ub294 \uc791\uc5c5\uc744 \ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-python"},'import mlflow\n\nwith mlflow.start_run():\n mlflow.log_artifact("svc/")\n')),(0,r.kt)("p",null,"\uc800\uc7a5\ud558\uace0 ",(0,r.kt)("inlineCode",{parentName:"p"},"mlruns")," \uac00 \uc0dd\uc131\ub41c \uacbd\ub85c\uc5d0\uc11c ",(0,r.kt)("inlineCode",{parentName:"p"},"mlflow ui")," \uba85\ub839\uc5b4\ub97c \uc774\uc6a9\ud574 mlflow \uc11c\ubc84\uc640 \ub300\uc2dc\ubcf4\ub4dc\ub97c \ub744\uc6c1\ub2c8\ub2e4.\nmlflow \ub300\uc2dc\ubcf4\ub4dc\uc5d0 \uc811\uc18d\ud558\uc5ec \uc0dd\uc131\ub41c run\uc744 \ud074\ub9ad\ud558\uba74 \ub2e4\uc74c\uacfc \uac19\uc774 \ubcf4\uc785\ub2c8\ub2e4."),(0,r.kt)("p",null,(0,r.kt)("img",{alt:"mlflow-0.png",src:a(4701).Z,width:"2782",height:"2496"}),"\n(\ud574\ub2f9 \ud654\uba74\uc740 mlflow \ubc84\uc804\uc5d0 \ub530\ub77c \ub2e4\ub97c \uc218 \uc788\uc2b5\ub2c8\ub2e4.)"),(0,r.kt)("h2",{id:"mlflow-component-1"},"MLFlow Component"),(0,r.kt)("p",null,"\uc774\uc81c Kubeflow\uc5d0\uc11c \uc7ac\uc0ac\uc6a9\ud560 \uc218 \uc788\ub294 \ucef4\ud3ec\ub10c\ud2b8\ub97c \uc791\uc131\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4."),(0,r.kt)("p",null,"\uc7ac\uc0ac\uc6a9\ud560 \uc218 \uc788\ub294 \ucef4\ud3ec\ub10c\ud2b8\ub97c \uc791\uc131\ud558\ub294 \ubc29\ubc95\uc740 \ud06c\uac8c 3\uac00\uc9c0\uac00 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\uc5c5\ub85c\ub4dc\ub97c \ub2f4\ub2f9"),(0,r.kt)("p",{parentName:"li"},(0,r.kt)("img",{alt:"mlflow-3.png",src:a(4119).Z,width:"578",height:"406"})))),(0,r.kt)("p",null,"\uc800\ud76c\ub294 \uc774 \uc911 1\ubc88\uc758 \uc811\uadfc \ubc29\ubc95\uc744 \ud1b5\ud574 \ubaa8\ub378\uc744 \uad00\ub9ac\ud558\ub824\uace0 \ud569\ub2c8\ub2e4.\n\uc774\uc720\ub294 MLFlow \ubaa8\ub378\uc744 \uc5c5\ub85c\ub4dc\ud558\ub294 \ucf54\ub4dc\ub294 \ubc14\ub00c\uc9c0 \uc54a\uae30 \ub54c\ubb38\uc5d0 \ub9e4\ubc88 3\ubc88\ucc98\ub7fc \ucef4\ud3ec\ub10c\ud2b8 \uc791\uc131\ub9c8\ub2e4 \uc791\uc131\ud560 \ud544\uc694\ub294 \uc5c6\uae30 \ub54c\ubb38\uc785\ub2c8\ub2e4."),(0,r.kt)("p",null,"\ucef4\ud3ec\ub10c\ud2b8\ub97c \uc7ac\ud65c\uc6a9\ud558\ub294 \ubc29\ubc95\uc740 1\ubc88\uacfc 2\ubc88\uc758 \ubc29\ubc95\uc73c\ub85c \uac00\ub2a5\ud569\ub2c8\ub2e4.\n\ub2e4\ub9cc 2\ubc88\uc758 \uacbd\uc6b0 \ubaa8\ub378\uc774 \ud559\uc2b5\ub41c \uc774\ubbf8\uc9c0\uc640 \ud328\ud0a4\uc9c0\ub4e4\uc744 \uc804\ub2ec\ud574\uc57c \ud558\ubbc0\ub85c \uacb0\uad6d \ucef4\ud3ec\ub10c\ud2b8\uc5d0 \ub300\ud55c \ucd94\uac00 \uc815\ubcf4\ub97c \uc804\ub2ec\ud574\uc57c \ud569\ub2c8\ub2e4."),(0,r.kt)("p",null,"1\ubc88\uc758 \ubc29\ubc95\uc73c\ub85c \uc9c4\ud589\ud558\uae30 \uc704\ud574\uc11c\ub294 \ud559\uc2b5\ud558\ub294 \ucef4\ud3ec\ub10c\ud2b8 \ub610\ud55c \ubcc0\uacbd\ub418\uc5b4\uc57c \ud569\ub2c8\ub2e4.\n\ubaa8\ub378\uc744 \uc800\uc7a5\ud558\ub294\ub370 \ud544\uc694\ud55c \ud658\uacbd\ub4e4\uc744 \uc800\uc7a5\ud574\uc8fc\ub294 \ucf54\ub4dc\uac00 \ucd94\uac00\ub418\uc5b4\uc57c \ud569\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-python"},'from functools import partial\nfrom kfp.components import InputPath, OutputPath, create_component_from_func\n\n@partial(\n create_component_from_func,\n packages_to_install=["dill", "pandas", "scikit-learn", "mlflow"],\n)\ndef train_from_csv(\n train_data_path: InputPath("csv"),\n train_target_path: InputPath("csv"),\n model_path: OutputPath("dill"),\n input_example_path: OutputPath("dill"),\n signature_path: OutputPath("dill"),\n conda_env_path: OutputPath("dill"),\n kernel: str,\n):\n import dill\n import pandas as pd\n from sklearn.svm import SVC\n\n from mlflow.models.signature import infer_signature\n from mlflow.utils.environment import _mlflow_conda_env\n\n train_data = pd.read_csv(train_data_path)\n train_target = pd.read_csv(train_target_path)\n\n clf = SVC(kernel=kernel)\n clf.fit(train_data, train_target)\n\n with open(model_path, mode="wb") as file_writer:\n dill.dump(clf, file_writer)\n\n input_example = train_data.sample(1)\n with open(input_example_path, "wb") as file_writer:\n dill.dump(input_example, file_writer)\n\n signature = infer_signature(train_data, clf.predict(train_data))\n with open(signature_path, "wb") as file_writer:\n dill.dump(signature, file_writer)\n\n conda_env = _mlflow_conda_env(\n additional_pip_deps=["dill", "pandas", "scikit-learn"]\n )\n with open(conda_env_path, "wb") as file_writer:\n dill.dump(conda_env, file_writer)\n\n')),(0,r.kt)("p",null,"\uadf8\ub9ac\uace0 MLFlow\uc5d0 \uc5c5\ub85c\ub4dc\ud558\ub294 \ucef4\ud3ec\ub10c\ud2b8\ub97c \uc791\uc131\ud569\ub2c8\ub2e4.\n\uc774 \ub54c \uc5c5\ub85c\ub4dc\ub418\ub294 MLflow\uc758 endpoint\ub97c \uc6b0\ub9ac\uac00 \uc124\uce58\ud55c ",(0,r.kt)("a",{parentName:"p",href:"/docs/setup-components/install-components-mlflow"},"mlflow service")," \ub85c \uc774\uc5b4\uc9c0\uac8c \uc124\uc815\ud574\uc8fc\uc5b4\uc57c \ud569\ub2c8\ub2e4.",(0,r.kt)("br",{parentName:"p"}),"\n","\uc774 \ub54c S3 Endpoint\uc758 \uc8fc\uc18c\ub294 MLflow Server \uc124\uce58 \ub2f9\uc2dc \uc124\uce58\ud55c minio\uc758 ",(0,r.kt)("a",{parentName:"p",href:"https://kubernetes.io/ko/docs/concepts/services-networking/dns-pod-service/"},"\ucfe0\ubc84\ub124\ud2f0\uc2a4 \uc11c\ube44\uc2a4 DNS \ub124\uc784\uc744 \ud65c\uc6a9"),"\ud569\ub2c8\ub2e4. \ud574\ub2f9 service \ub294 kubeflow namespace\uc5d0\uc11c minio-service\ub77c\ub294 \uc774\ub984\uc73c\ub85c \uc0dd\uc131\ub418\uc5c8\uc73c\ubbc0\ub85c, ",(0,r.kt)("inlineCode",{parentName:"p"},"http://minio-service.kubeflow.svc:9000")," \ub85c \uc124\uc815\ud569\ub2c8\ub2e4.",(0,r.kt)("br",{parentName:"p"}),"\n","\uc774\uc640 \ube44\uc2b7\ud558\uac8c tracking_uri\uc758 \uc8fc\uc18c\ub294 mlflow server\uc758 \ucfe0\ubc84\ub124\ud2f0\uc2a4 \uc11c\ube44\uc2a4 DNS \ub124\uc784\uc744 \ud65c\uc6a9\ud558\uc5ec, ",(0,r.kt)("inlineCode",{parentName:"p"},"http://mlflow-server-service.mlflow-system.svc:5000")," \ub85c \uc124\uc815\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-python"},'from functools import partial\nfrom kfp.components import InputPath, create_component_from_func\n\n@partial(\n create_component_from_func,\n packages_to_install=["dill", "pandas", "scikit-learn", "mlflow", "boto3"],\n)\ndef upload_sklearn_model_to_mlflow(\n model_name: str,\n model_path: InputPath("dill"),\n input_example_path: InputPath("dill"),\n signature_path: InputPath("dill"),\n conda_env_path: InputPath("dill"),\n):\n import os\n import dill\n from mlflow.sklearn import save_model\n \n from mlflow.tracking.client import MlflowClient\n\n os.environ["MLFLOW_S3_ENDPOINT_URL"] = "http://minio-service.kubeflow.svc:9000"\n os.environ["AWS_ACCESS_KEY_ID"] = "minio"\n os.environ["AWS_SECRET_ACCESS_KEY"] = "minio123"\n\n client = MlflowClient("http://mlflow-server-service.mlflow-system.svc:5000")\n\n with open(model_path, mode="rb") as file_reader:\n clf = dill.load(file_reader)\n\n with open(input_example_path, "rb") as file_reader:\n input_example = dill.load(file_reader)\n\n with open(signature_path, "rb") as file_reader:\n signature = dill.load(file_reader)\n\n with open(conda_env_path, "rb") as file_reader:\n conda_env = dill.load(file_reader)\n\n save_model(\n sk_model=clf,\n path=model_name,\n serialization_format="cloudpickle",\n conda_env=conda_env,\n signature=signature,\n input_example=input_example,\n )\n run = client.create_run(experiment_id="0")\n client.log_artifact(run.info.run_id, model_name)\n')),(0,r.kt)("h2",{id:"mlflow-pipeline"},"MLFlow Pipeline"),(0,r.kt)("p",null,"\uc774\uc81c \uc791\uc131\ud55c \ucef4\ud3ec\ub10c\ud2b8\ub4e4\uc744 \uc5f0\uacb0\ud574\uc11c \ud30c\uc774\ud504\ub77c\uc778\uc73c\ub85c \ub9cc\ub4e4\uc5b4 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4."),(0,r.kt)("h3",{id:"data-component"},"Data Component"),(0,r.kt)("p",null,"\ubaa8\ub378\uc744 \ud559\uc2b5\ud560 \ub54c \uc4f8 \ub370\uc774\ud130\ub294 sklearn\uc758 iris \uc785\ub2c8\ub2e4.\n\ub370\uc774\ud130\ub97c \uc0dd\uc131\ud558\ub294 \ucef4\ud3ec\ub10c\ud2b8\ub97c \uc791\uc131\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-python"},'from functools import partial\n\nfrom kfp.components import InputPath, OutputPath, create_component_from_func\n\n\n@partial(\n create_component_from_func,\n packages_to_install=["pandas", "scikit-learn"],\n)\ndef load_iris_data(\n data_path: OutputPath("csv"),\n target_path: OutputPath("csv"),\n):\n import pandas as pd\n from sklearn.datasets import load_iris\n\n iris = load_iris()\n\n data = pd.DataFrame(iris["data"], columns=iris["feature_names"])\n target = pd.DataFrame(iris["target"], columns=["target"])\n\n data.to_csv(data_path, index=False)\n target.to_csv(target_path, index=False)\n\n')),(0,r.kt)("h3",{id:"pipeline"},"Pipeline"),(0,r.kt)("p",null,"\ud30c\uc774\ud504\ub77c\uc778 \ucf54\ub4dc\ub294 \ub2e4\uc74c\uacfc \uac19\uc774 \uc791\uc131\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-python"},'from kfp.dsl import pipeline\n\n\n@pipeline(name="mlflow_pipeline")\ndef mlflow_pipeline(kernel: str, model_name: str):\n iris_data = load_iris_data()\n model = train_from_csv(\n train_data=iris_data.outputs["data"],\n train_target=iris_data.outputs["target"],\n kernel=kernel,\n )\n _ = upload_sklearn_model_to_mlflow(\n model_name=model_name,\n model=model.outputs["model"],\n input_example=model.outputs["input_example"],\n signature=model.outputs["signature"],\n conda_env=model.outputs["conda_env"],\n )\n')),(0,r.kt)("h3",{id:"run"},"Run"),(0,r.kt)("p",null,"\uc704\uc5d0\uc11c \uc791\uc131\ub41c \ucef4\ud3ec\ub10c\ud2b8\uc640 \ud30c\uc774\ud504\ub77c\uc778\uc744 \ud558\ub098\uc758 \ud30c\uc774\uc36c \ud30c\uc77c\uc5d0 \uc815\ub9ac\ud558\uba74 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-python"},'from functools import partial\n\nimport kfp\nfrom kfp.components import InputPath, OutputPath, create_component_from_func\nfrom kfp.dsl import pipeline\n\n\n@partial(\n create_component_from_func,\n packages_to_install=["pandas", "scikit-learn"],\n)\ndef load_iris_data(\n data_path: OutputPath("csv"),\n target_path: OutputPath("csv"),\n):\n import pandas as pd\n from sklearn.datasets import load_iris\n\n iris = load_iris()\n\n data = pd.DataFrame(iris["data"], columns=iris["feature_names"])\n target = pd.DataFrame(iris["target"], columns=["target"])\n\n data.to_csv(data_path, index=False)\n target.to_csv(target_path, index=False)\n\n\n@partial(\n create_component_from_func,\n packages_to_install=["dill", "pandas", "scikit-learn", "mlflow"],\n)\ndef train_from_csv(\n train_data_path: InputPath("csv"),\n train_target_path: InputPath("csv"),\n model_path: OutputPath("dill"),\n input_example_path: OutputPath("dill"),\n signature_path: OutputPath("dill"),\n conda_env_path: OutputPath("dill"),\n kernel: str,\n):\n import dill\n import pandas as pd\n from sklearn.svm import SVC\n\n from mlflow.models.signature import infer_signature\n from mlflow.utils.environment import _mlflow_conda_env\n\n train_data = pd.read_csv(train_data_path)\n train_target = pd.read_csv(train_target_path)\n\n clf = SVC(kernel=kernel)\n clf.fit(train_data, train_target)\n\n with open(model_path, mode="wb") as file_writer:\n dill.dump(clf, file_writer)\n\n input_example = train_data.sample(1)\n with open(input_example_path, "wb") as file_writer:\n dill.dump(input_example, file_writer)\n\n signature = infer_signature(train_data, clf.predict(train_data))\n with open(signature_path, "wb") as file_writer:\n dill.dump(signature, file_writer)\n\n conda_env = _mlflow_conda_env(\n additional_pip_deps=["dill", "pandas", "scikit-learn"]\n )\n with open(conda_env_path, "wb") as file_writer:\n dill.dump(conda_env, file_writer)\n\n\n@partial(\n create_component_from_func,\n packages_to_install=["dill", "pandas", "scikit-learn", "mlflow", "boto3"],\n)\ndef upload_sklearn_model_to_mlflow(\n model_name: str,\n model_path: InputPath("dill"),\n input_example_path: InputPath("dill"),\n signature_path: InputPath("dill"),\n conda_env_path: InputPath("dill"),\n):\n import os\n import dill\n from mlflow.sklearn import save_model\n \n from mlflow.tracking.client import MlflowClient\n\n os.environ["MLFLOW_S3_ENDPOINT_URL"] = "http://minio-service.kubeflow.svc:9000"\n os.environ["AWS_ACCESS_KEY_ID"] = "minio"\n os.environ["AWS_SECRET_ACCESS_KEY"] = "minio123"\n\n client = MlflowClient("http://mlflow-server-service.mlflow-system.svc:5000")\n\n with open(model_path, mode="rb") as file_reader:\n clf = dill.load(file_reader)\n\n with open(input_example_path, "rb") as file_reader:\n input_example = dill.load(file_reader)\n\n with open(signature_path, "rb") as file_reader:\n signature = dill.load(file_reader)\n\n with open(conda_env_path, "rb") as file_reader:\n conda_env = dill.load(file_reader)\n\n save_model(\n sk_model=clf,\n path=model_name,\n serialization_format="cloudpickle",\n conda_env=conda_env,\n signature=signature,\n input_example=input_example,\n )\n run = client.create_run(experiment_id="0")\n client.log_artifact(run.info.run_id, model_name)\n\n\n@pipeline(name="mlflow_pipeline")\ndef mlflow_pipeline(kernel: str, model_name: str):\n iris_data = load_iris_data()\n model = train_from_csv(\n train_data=iris_data.outputs["data"],\n train_target=iris_data.outputs["target"],\n kernel=kernel,\n )\n _ = upload_sklearn_model_to_mlflow(\n model_name=model_name,\n model=model.outputs["model"],\n input_example=model.outputs["input_example"],\n signature=model.outputs["signature"],\n conda_env=model.outputs["conda_env"],\n )\n\n\nif __name__ == "__main__":\n kfp.compiler.Compiler().compile(mlflow_pipeline, "mlflow_pipeline.yaml")\n')),(0,r.kt)("p",null,(0,r.kt)("details",null,(0,r.kt)("summary",null,"mlflow_pipeline.yaml"),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},'apiVersion: argoproj.io/v1alpha1\nkind: Workflow\nmetadata:\n generateName: mlflow-pipeline-\n annotations: {pipelines.kubeflow.org/kfp_sdk_version: 1.8.10, pipelines.kubeflow.org/pipeline_compilation_time: \'2022-01-19T14:14:11.999807\',\n pipelines.kubeflow.org/pipeline_spec: \'{"inputs": [{"name": "kernel", "type":\n "String"}, {"name": "model_name", "type": "String"}], "name": "mlflow_pipeline"}\'}\n labels: {pipelines.kubeflow.org/kfp_sdk_version: 1.8.10}\nspec:\n entrypoint: mlflow-pipeline\n templates:\n - name: load-iris-data\n container:\n args: [--data, /tmp/outputs/data/data, --target, /tmp/outputs/target/data]\n command:\n - sh\n - -c\n - (PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location\n \'pandas\' \'scikit-learn\' || PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip\n install --quiet --no-warn-script-location \'pandas\' \'scikit-learn\' --user)\n && "$0" "$@"\n - sh\n - -ec\n - |\n program_path=$(mktemp)\n printf "%s" "$0" > "$program_path"\n python3 -u "$program_path" "$@"\n - |\n def _make_parent_dirs_and_return_path(file_path: str):\n import os\n os.makedirs(os.path.dirname(file_path), exist_ok=True)\n return file_path\n\n def load_iris_data(\n data_path,\n target_path,\n ):\n import pandas as pd\n from sklearn.datasets import load_iris\n\n iris = load_iris()\n\n data = pd.DataFrame(iris["data"], columns=iris["feature_names"])\n target = pd.DataFrame(iris["target"], columns=["target"])\n\n data.to_csv(data_path, index=False)\n target.to_csv(target_path, index=False)\n\n import argparse\n _parser = argparse.ArgumentParser(prog=\'Load iris data\', description=\'\')\n _parser.add_argument("--data", dest="data_path", type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--target", dest="target_path", type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)\n _parsed_args = vars(_parser.parse_args())\n\n _outputs = load_iris_data(**_parsed_args)\n image: python:3.7\n outputs:\n artifacts:\n - {name: load-iris-data-data, path: /tmp/outputs/data/data}\n - {name: load-iris-data-target, path: /tmp/outputs/target/data}\n metadata:\n labels:\n pipelines.kubeflow.org/kfp_sdk_version: 1.8.10\n pipelines.kubeflow.org/pipeline-sdk-type: kfp\n pipelines.kubeflow.org/enable_caching: "true"\n annotations: {pipelines.kubeflow.org/component_spec: \'{"implementation": {"container":\n {"args": ["--data", {"outputPath": "data"}, "--target", {"outputPath": "target"}],\n "command": ["sh", "-c", "(PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip\n install --quiet --no-warn-script-location \'\'pandas\'\' \'\'scikit-learn\'\' ||\n PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location\n \'\'pandas\'\' \'\'scikit-learn\'\' --user) && \\"$0\\" \\"$@\\"", "sh", "-ec", "program_path=$(mktemp)\\nprintf\n \\"%s\\" \\"$0\\" > \\"$program_path\\"\\npython3 -u \\"$program_path\\" \\"$@\\"\\n",\n "def _make_parent_dirs_and_return_path(file_path: str):\\n import os\\n os.makedirs(os.path.dirname(file_path),\n exist_ok=True)\\n return file_path\\n\\ndef load_iris_data(\\n data_path,\\n target_path,\\n):\\n import\n pandas as pd\\n from sklearn.datasets import load_iris\\n\\n iris = load_iris()\\n\\n data\n = pd.DataFrame(iris[\\"data\\"], columns=iris[\\"feature_names\\"])\\n target\n = pd.DataFrame(iris[\\"target\\"], columns=[\\"target\\"])\\n\\n data.to_csv(data_path,\n index=False)\\n target.to_csv(target_path, index=False)\\n\\nimport argparse\\n_parser\n = argparse.ArgumentParser(prog=\'\'Load iris data\'\', description=\'\'\'\')\\n_parser.add_argument(\\"--data\\",\n dest=\\"data_path\\", type=_make_parent_dirs_and_return_path, required=True,\n default=argparse.SUPPRESS)\\n_parser.add_argument(\\"--target\\", dest=\\"target_path\\",\n type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)\\n_parsed_args\n = vars(_parser.parse_args())\\n\\n_outputs = load_iris_data(**_parsed_args)\\n"],\n "image": "python:3.7"}}, "name": "Load iris data", "outputs": [{"name":\n "data", "type": "csv"}, {"name": "target", "type": "csv"}]}\', pipelines.kubeflow.org/component_ref: \'{}\'}\n - name: mlflow-pipeline\n inputs:\n parameters:\n - {name: kernel}\n - {name: model_name}\n dag:\n tasks:\n - {name: load-iris-data, template: load-iris-data}\n - name: train-from-csv\n template: train-from-csv\n dependencies: [load-iris-data]\n arguments:\n parameters:\n - {name: kernel, value: \'{{inputs.parameters.kernel}}\'}\n artifacts:\n - {name: load-iris-data-data, from: \'{{tasks.load-iris-data.outputs.artifacts.load-iris-data-data}}\'}\n - {name: load-iris-data-target, from: \'{{tasks.load-iris-data.outputs.artifacts.load-iris-data-target}}\'}\n - name: upload-sklearn-model-to-mlflow\n template: upload-sklearn-model-to-mlflow\n dependencies: [train-from-csv]\n arguments:\n parameters:\n - {name: model_name, value: \'{{inputs.parameters.model_name}}\'}\n artifacts:\n - {name: train-from-csv-conda_env, from: \'{{tasks.train-from-csv.outputs.artifacts.train-from-csv-conda_env}}\'}\n - {name: train-from-csv-input_example, from: \'{{tasks.train-from-csv.outputs.artifacts.train-from-csv-input_example}}\'}\n - {name: train-from-csv-model, from: \'{{tasks.train-from-csv.outputs.artifacts.train-from-csv-model}}\'}\n - {name: train-from-csv-signature, from: \'{{tasks.train-from-csv.outputs.artifacts.train-from-csv-signature}}\'}\n - name: train-from-csv\n container:\n args: [--train-data, /tmp/inputs/train_data/data, --train-target, /tmp/inputs/train_target/data,\n --kernel, \'{{inputs.parameters.kernel}}\', --model, /tmp/outputs/model/data,\n --input-example, /tmp/outputs/input_example/data, --signature, /tmp/outputs/signature/data,\n --conda-env, /tmp/outputs/conda_env/data]\n command:\n - sh\n - -c\n - (PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location\n \'dill\' \'pandas\' \'scikit-learn\' \'mlflow\' || PIP_DISABLE_PIP_VERSION_CHECK=1\n python3 -m pip install --quiet --no-warn-script-location \'dill\' \'pandas\' \'scikit-learn\'\n \'mlflow\' --user) && "$0" "$@"\n - sh\n - -ec\n - |\n program_path=$(mktemp)\n printf "%s" "$0" > "$program_path"\n python3 -u "$program_path" "$@"\n - |\n def _make_parent_dirs_and_return_path(file_path: str):\n import os\n os.makedirs(os.path.dirname(file_path), exist_ok=True)\n return file_path\n\n def train_from_csv(\n train_data_path,\n train_target_path,\n model_path,\n input_example_path,\n signature_path,\n conda_env_path,\n kernel,\n ):\n import dill\n import pandas as pd\n from sklearn.svm import SVC\n\n from mlflow.models.signature import infer_signature\n from mlflow.utils.environment import _mlflow_conda_env\n\n train_data = pd.read_csv(train_data_path)\n train_target = pd.read_csv(train_target_path)\n\n clf = SVC(kernel=kernel)\n clf.fit(train_data, train_target)\n\n with open(model_path, mode="wb") as file_writer:\n dill.dump(clf, file_writer)\n\n input_example = train_data.sample(1)\n with open(input_example_path, "wb") as file_writer:\n dill.dump(input_example, file_writer)\n\n signature = infer_signature(train_data, clf.predict(train_data))\n with open(signature_path, "wb") as file_writer:\n dill.dump(signature, file_writer)\n\n conda_env = _mlflow_conda_env(\n additional_pip_deps=["dill", "pandas", "scikit-learn"]\n )\n with open(conda_env_path, "wb") as file_writer:\n dill.dump(conda_env, file_writer)\n\n import argparse\n _parser = argparse.ArgumentParser(prog=\'Train from csv\', description=\'\')\n _parser.add_argument("--train-data", dest="train_data_path", type=str, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--train-target", dest="train_target_path", type=str, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--kernel", dest="kernel", type=str, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--model", dest="model_path", type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--input-example", dest="input_example_path", type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--signature", dest="signature_path", type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--conda-env", dest="conda_env_path", type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)\n _parsed_args = vars(_parser.parse_args())\n\n _outputs = train_from_csv(**_parsed_args)\n image: python:3.7\n inputs:\n parameters:\n - {name: kernel}\n artifacts:\n - {name: load-iris-data-data, path: /tmp/inputs/train_data/data}\n - {name: load-iris-data-target, path: /tmp/inputs/train_target/data}\n outputs:\n artifacts:\n - {name: train-from-csv-conda_env, path: /tmp/outputs/conda_env/data}\n - {name: train-from-csv-input_example, path: /tmp/outputs/input_example/data}\n - {name: train-from-csv-model, path: /tmp/outputs/model/data}\n - {name: train-from-csv-signature, path: /tmp/outputs/signature/data}\n metadata:\n labels:\n pipelines.kubeflow.org/kfp_sdk_version: 1.8.10\n pipelines.kubeflow.org/pipeline-sdk-type: kfp\n pipelines.kubeflow.org/enable_caching: "true"\n annotations: {pipelines.kubeflow.org/component_spec: \'{"implementation": {"container":\n {"args": ["--train-data", {"inputPath": "train_data"}, "--train-target",\n {"inputPath": "train_target"}, "--kernel", {"inputValue": "kernel"}, "--model",\n {"outputPath": "model"}, "--input-example", {"outputPath": "input_example"},\n "--signature", {"outputPath": "signature"}, "--conda-env", {"outputPath":\n "conda_env"}], "command": ["sh", "-c", "(PIP_DISABLE_PIP_VERSION_CHECK=1\n python3 -m pip install --quiet --no-warn-script-location \'\'dill\'\' \'\'pandas\'\'\n \'\'scikit-learn\'\' \'\'mlflow\'\' || PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m\n pip install --quiet --no-warn-script-location \'\'dill\'\' \'\'pandas\'\' \'\'scikit-learn\'\'\n \'\'mlflow\'\' --user) && \\"$0\\" \\"$@\\"", "sh", "-ec", "program_path=$(mktemp)\\nprintf\n \\"%s\\" \\"$0\\" > \\"$program_path\\"\\npython3 -u \\"$program_path\\" \\"$@\\"\\n",\n "def _make_parent_dirs_and_return_path(file_path: str):\\n import os\\n os.makedirs(os.path.dirname(file_path),\n exist_ok=True)\\n return file_path\\n\\ndef train_from_csv(\\n train_data_path,\\n train_target_path,\\n model_path,\\n input_example_path,\\n signature_path,\\n conda_env_path,\\n kernel,\\n):\\n import\n dill\\n import pandas as pd\\n from sklearn.svm import SVC\\n\\n from\n mlflow.models.signature import infer_signature\\n from mlflow.utils.environment\n import _mlflow_conda_env\\n\\n train_data = pd.read_csv(train_data_path)\\n train_target\n = pd.read_csv(train_target_path)\\n\\n clf = SVC(kernel=kernel)\\n clf.fit(train_data,\n train_target)\\n\\n with open(model_path, mode=\\"wb\\") as file_writer:\\n dill.dump(clf,\n file_writer)\\n\\n input_example = train_data.sample(1)\\n with open(input_example_path,\n \\"wb\\") as file_writer:\\n dill.dump(input_example, file_writer)\\n\\n signature\n = infer_signature(train_data, clf.predict(train_data))\\n with open(signature_path,\n \\"wb\\") as file_writer:\\n dill.dump(signature, file_writer)\\n\\n conda_env\n = _mlflow_conda_env(\\n additional_pip_deps=[\\"dill\\", \\"pandas\\",\n \\"scikit-learn\\"]\\n )\\n with open(conda_env_path, \\"wb\\") as file_writer:\\n dill.dump(conda_env,\n file_writer)\\n\\nimport argparse\\n_parser = argparse.ArgumentParser(prog=\'\'Train\n from csv\'\', description=\'\'\'\')\\n_parser.add_argument(\\"--train-data\\", dest=\\"train_data_path\\",\n type=str, required=True, default=argparse.SUPPRESS)\\n_parser.add_argument(\\"--train-target\\",\n dest=\\"train_target_path\\", type=str, required=True, default=argparse.SUPPRESS)\\n_parser.add_argument(\\"--kernel\\",\n dest=\\"kernel\\", type=str, required=True, default=argparse.SUPPRESS)\\n_parser.add_argument(\\"--model\\",\n dest=\\"model_path\\", type=_make_parent_dirs_and_return_path, required=True,\n default=argparse.SUPPRESS)\\n_parser.add_argument(\\"--input-example\\", dest=\\"input_example_path\\",\n type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)\\n_parser.add_argument(\\"--signature\\",\n dest=\\"signature_path\\", type=_make_parent_dirs_and_return_path, required=True,\n default=argparse.SUPPRESS)\\n_parser.add_argument(\\"--conda-env\\", dest=\\"conda_env_path\\",\n type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)\\n_parsed_args\n = vars(_parser.parse_args())\\n\\n_outputs = train_from_csv(**_parsed_args)\\n"],\n "image": "python:3.7"}}, "inputs": [{"name": "train_data", "type": "csv"},\n {"name": "train_target", "type": "csv"}, {"name": "kernel", "type": "String"}],\n "name": "Train from csv", "outputs": [{"name": "model", "type": "dill"},\n {"name": "input_example", "type": "dill"}, {"name": "signature", "type":\n "dill"}, {"name": "conda_env", "type": "dill"}]}\', pipelines.kubeflow.org/component_ref: \'{}\',\n pipelines.kubeflow.org/arguments.parameters: \'{"kernel": "{{inputs.parameters.kernel}}"}\'}\n - name: upload-sklearn-model-to-mlflow\n container:\n args: [--model-name, \'{{inputs.parameters.model_name}}\', --model, /tmp/inputs/model/data,\n --input-example, /tmp/inputs/input_example/data, --signature, /tmp/inputs/signature/data,\n --conda-env, /tmp/inputs/conda_env/data]\n command:\n - sh\n - -c\n - (PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location\n \'dill\' \'pandas\' \'scikit-learn\' \'mlflow\' \'boto3\' || PIP_DISABLE_PIP_VERSION_CHECK=1\n python3 -m pip install --quiet --no-warn-script-location \'dill\' \'pandas\' \'scikit-learn\'\n \'mlflow\' \'boto3\' --user) && "$0" "$@"\n - sh\n - -ec\n - |\n program_path=$(mktemp)\n printf "%s" "$0" > "$program_path"\n python3 -u "$program_path" "$@"\n - |\n def upload_sklearn_model_to_mlflow(\n model_name,\n model_path,\n input_example_path,\n signature_path,\n conda_env_path,\n ):\n import os\n import dill\n from mlflow.sklearn import save_model\n\n from mlflow.tracking.client import MlflowClient\n\n os.environ["MLFLOW_S3_ENDPOINT_URL"] = "http://minio-service.kubeflow.svc:9000"\n os.environ["AWS_ACCESS_KEY_ID"] = "minio"\n os.environ["AWS_SECRET_ACCESS_KEY"] = "minio123"\n\n client = MlflowClient("http://mlflow-server-service.mlflow-system.svc:5000")\n\n with open(model_path, mode="rb") as file_reader:\n clf = dill.load(file_reader)\n\n with open(input_example_path, "rb") as file_reader:\n input_example = dill.load(file_reader)\n\n with open(signature_path, "rb") as file_reader:\n signature = dill.load(file_reader)\n\n with open(conda_env_path, "rb") as file_reader:\n conda_env = dill.load(file_reader)\n\n save_model(\n sk_model=clf,\n path=model_name,\n serialization_format="cloudpickle",\n conda_env=conda_env,\n signature=signature,\n input_example=input_example,\n )\n run = client.create_run(experiment_id="0")\n client.log_artifact(run.info.run_id, model_name)\n\n import argparse\n _parser = argparse.ArgumentParser(prog=\'Upload sklearn model to mlflow\', description=\'\')\n _parser.add_argument("--model-name", dest="model_name", type=str, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--model", dest="model_path", type=str, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--input-example", dest="input_example_path", type=str, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--signature", dest="signature_path", type=str, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--conda-env", dest="conda_env_path", type=str, required=True, default=argparse.SUPPRESS)\n _parsed_args = vars(_parser.parse_args())\n\n _outputs = upload_sklearn_model_to_mlflow(**_parsed_args)\n image: python:3.7\n inputs:\n parameters:\n - {name: model_name}\n artifacts:\n - {name: train-from-csv-conda_env, path: /tmp/inputs/conda_env/data}\n - {name: train-from-csv-input_example, path: /tmp/inputs/input_example/data}\n - {name: train-from-csv-model, path: /tmp/inputs/model/data}\n - {name: train-from-csv-signature, path: /tmp/inputs/signature/data}\n metadata:\n labels:\n pipelines.kubeflow.org/kfp_sdk_version: 1.8.10\n pipelines.kubeflow.org/pipeline-sdk-type: kfp\n pipelines.kubeflow.org/enable_caching: "true"\n annotations: {pipelines.kubeflow.org/component_spec: \'{"implementation": {"container":\n {"args": ["--model-name", {"inputValue": "model_name"}, "--model", {"inputPath":\n "model"}, "--input-example", {"inputPath": "input_example"}, "--signature",\n {"inputPath": "signature"}, "--conda-env", {"inputPath": "conda_env"}],\n "command": ["sh", "-c", "(PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip\n install --quiet --no-warn-script-location \'\'dill\'\' \'\'pandas\'\' \'\'scikit-learn\'\'\n \'\'mlflow\'\' \'\'boto3\'\' || PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install\n --quiet --no-warn-script-location \'\'dill\'\' \'\'pandas\'\' \'\'scikit-learn\'\' \'\'mlflow\'\'\n \'\'boto3\'\' --user) && \\"$0\\" \\"$@\\"", "sh", "-ec", "program_path=$(mktemp)\\nprintf\n \\"%s\\" \\"$0\\" > \\"$program_path\\"\\npython3 -u \\"$program_path\\" \\"$@\\"\\n",\n "def upload_sklearn_model_to_mlflow(\\n model_name,\\n model_path,\\n input_example_path,\\n signature_path,\\n conda_env_path,\\n):\\n import\n os\\n import dill\\n from mlflow.sklearn import save_model\\n\\n from\n mlflow.tracking.client import MlflowClient\\n\\n os.environ[\\"MLFLOW_S3_ENDPOINT_URL\\"]\n = \\"http://minio-service.kubeflow.svc:9000\\"\\n os.environ[\\"AWS_ACCESS_KEY_ID\\"]\n = \\"minio\\"\\n os.environ[\\"AWS_SECRET_ACCESS_KEY\\"] = \\"minio123\\"\\n\\n client\n = MlflowClient(\\"http://mlflow-server-service.mlflow-system.svc:5000\\")\\n\\n with\n open(model_path, mode=\\"rb\\") as file_reader:\\n clf = dill.load(file_reader)\\n\\n with\n open(input_example_path, \\"rb\\") as file_reader:\\n input_example\n = dill.load(file_reader)\\n\\n with open(signature_path, \\"rb\\") as file_reader:\\n signature\n = dill.load(file_reader)\\n\\n with open(conda_env_path, \\"rb\\") as file_reader:\\n conda_env\n = dill.load(file_reader)\\n\\n save_model(\\n sk_model=clf,\\n path=model_name,\\n serialization_format=\\"cloudpickle\\",\\n conda_env=conda_env,\\n signature=signature,\\n input_example=input_example,\\n )\\n run\n = client.create_run(experiment_id=\\"0\\")\\n client.log_artifact(run.info.run_id,\n model_name)\\n\\nimport argparse\\n_parser = argparse.ArgumentParser(prog=\'\'Upload\n sklearn model to mlflow\'\', description=\'\'\'\')\\n_parser.add_argument(\\"--model-name\\",\n dest=\\"model_name\\", type=str, required=True, default=argparse.SUPPRESS)\\n_parser.add_argument(\\"--model\\",\n dest=\\"model_path\\", type=str, required=True, default=argparse.SUPPRESS)\\n_parser.add_argument(\\"--input-example\\",\n dest=\\"input_example_path\\", type=str, required=True, default=argparse.SUPPRESS)\\n_parser.add_argument(\\"--signature\\",\n dest=\\"signature_path\\", type=str, required=True, default=argparse.SUPPRESS)\\n_parser.add_argument(\\"--conda-env\\",\n dest=\\"conda_env_path\\", type=str, required=True, default=argparse.SUPPRESS)\\n_parsed_args\n = vars(_parser.parse_args())\\n\\n_outputs = upload_sklearn_model_to_mlflow(**_parsed_args)\\n"],\n "image": "python:3.7"}}, "inputs": [{"name": "model_name", "type": "String"},\n {"name": "model", "type": "dill"}, {"name": "input_example", "type": "dill"},\n {"name": "signature", "type": "dill"}, {"name": "conda_env", "type": "dill"}],\n "name": "Upload sklearn model to mlflow"}\', pipelines.kubeflow.org/component_ref: \'{}\',\n pipelines.kubeflow.org/arguments.parameters: \'{"model_name": "{{inputs.parameters.model_name}}"}\'}\n arguments:\n parameters:\n - {name: kernel}\n - {name: model_name}\n serviceAccountName: pipeline-runner\n')))),(0,r.kt)("p",null,"\uc2e4\ud589\ud6c4 \uc0dd\uc131\ub41c mlflow_pipeline.yaml \ud30c\uc77c\uc744 \ud30c\uc774\ud504\ub77c\uc778 \uc5c5\ub85c\ub4dc\ud55c \ud6c4, \uc2e4\ud589\ud558\uc5ec run \uc758 \uacb0\uacfc\ub97c 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\uc5c5\ub85c\ub4dc\ub97c \ub2f4\ub2f9"),(0,r.kt)("p",{parentName:"li"},(0,r.kt)("img",{alt:"mlflow-3.png",src:a(4119).Z,width:"578",height:"406"})))),(0,r.kt)("p",null,"\uc800\ud76c\ub294 \uc774 \uc911 1\ubc88\uc758 \uc811\uadfc \ubc29\ubc95\uc744 \ud1b5\ud574 \ubaa8\ub378\uc744 \uad00\ub9ac\ud558\ub824\uace0 \ud569\ub2c8\ub2e4.\n\uc774\uc720\ub294 MLFlow \ubaa8\ub378\uc744 \uc5c5\ub85c\ub4dc\ud558\ub294 \ucf54\ub4dc\ub294 \ubc14\ub00c\uc9c0 \uc54a\uae30 \ub54c\ubb38\uc5d0 \ub9e4\ubc88 3\ubc88\ucc98\ub7fc \ucef4\ud3ec\ub10c\ud2b8 \uc791\uc131\ub9c8\ub2e4 \uc791\uc131\ud560 \ud544\uc694\ub294 \uc5c6\uae30 \ub54c\ubb38\uc785\ub2c8\ub2e4."),(0,r.kt)("p",null,"\ucef4\ud3ec\ub10c\ud2b8\ub97c \uc7ac\ud65c\uc6a9\ud558\ub294 \ubc29\ubc95\uc740 1\ubc88\uacfc 2\ubc88\uc758 \ubc29\ubc95\uc73c\ub85c \uac00\ub2a5\ud569\ub2c8\ub2e4.\n\ub2e4\ub9cc 2\ubc88\uc758 \uacbd\uc6b0 \ubaa8\ub378\uc774 \ud559\uc2b5\ub41c \uc774\ubbf8\uc9c0\uc640 \ud328\ud0a4\uc9c0\ub4e4\uc744 \uc804\ub2ec\ud574\uc57c \ud558\ubbc0\ub85c \uacb0\uad6d \ucef4\ud3ec\ub10c\ud2b8\uc5d0 \ub300\ud55c \ucd94\uac00 \uc815\ubcf4\ub97c \uc804\ub2ec\ud574\uc57c \ud569\ub2c8\ub2e4."),(0,r.kt)("p",null,"1\ubc88\uc758 \ubc29\ubc95\uc73c\ub85c \uc9c4\ud589\ud558\uae30 \uc704\ud574\uc11c\ub294 \ud559\uc2b5\ud558\ub294 \ucef4\ud3ec\ub10c\ud2b8 \ub610\ud55c \ubcc0\uacbd\ub418\uc5b4\uc57c \ud569\ub2c8\ub2e4.\n\ubaa8\ub378\uc744 \uc800\uc7a5\ud558\ub294\ub370 \ud544\uc694\ud55c \ud658\uacbd\ub4e4\uc744 \uc800\uc7a5\ud574\uc8fc\ub294 \ucf54\ub4dc\uac00 \ucd94\uac00\ub418\uc5b4\uc57c \ud569\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-python"},'from functools import partial\nfrom kfp.components import InputPath, OutputPath, create_component_from_func\n\n@partial(\n create_component_from_func,\n packages_to_install=["dill", "pandas", "scikit-learn", "mlflow"],\n)\ndef train_from_csv(\n train_data_path: InputPath("csv"),\n train_target_path: InputPath("csv"),\n model_path: OutputPath("dill"),\n input_example_path: OutputPath("dill"),\n signature_path: OutputPath("dill"),\n conda_env_path: OutputPath("dill"),\n kernel: str,\n):\n import dill\n import pandas as pd\n from sklearn.svm import SVC\n\n from mlflow.models.signature import infer_signature\n from mlflow.utils.environment import _mlflow_conda_env\n\n train_data = pd.read_csv(train_data_path)\n train_target = pd.read_csv(train_target_path)\n\n clf = SVC(kernel=kernel)\n clf.fit(train_data, train_target)\n\n with open(model_path, mode="wb") as file_writer:\n dill.dump(clf, file_writer)\n\n input_example = train_data.sample(1)\n with open(input_example_path, "wb") as file_writer:\n dill.dump(input_example, file_writer)\n\n signature = infer_signature(train_data, clf.predict(train_data))\n with open(signature_path, "wb") as file_writer:\n dill.dump(signature, file_writer)\n\n conda_env = _mlflow_conda_env(\n additional_pip_deps=["dill", "pandas", "scikit-learn"]\n )\n with open(conda_env_path, "wb") as file_writer:\n dill.dump(conda_env, file_writer)\n\n')),(0,r.kt)("p",null,"\uadf8\ub9ac\uace0 MLFlow\uc5d0 \uc5c5\ub85c\ub4dc\ud558\ub294 \ucef4\ud3ec\ub10c\ud2b8\ub97c \uc791\uc131\ud569\ub2c8\ub2e4.\n\uc774 \ub54c \uc5c5\ub85c\ub4dc\ub418\ub294 MLflow\uc758 endpoint\ub97c \uc6b0\ub9ac\uac00 \uc124\uce58\ud55c ",(0,r.kt)("a",{parentName:"p",href:"/docs/setup-components/install-components-mlflow"},"mlflow service")," \ub85c \uc774\uc5b4\uc9c0\uac8c \uc124\uc815\ud574\uc8fc\uc5b4\uc57c \ud569\ub2c8\ub2e4.",(0,r.kt)("br",{parentName:"p"}),"\n","\uc774 \ub54c S3 Endpoint\uc758 \uc8fc\uc18c\ub294 MLflow Server \uc124\uce58 \ub2f9\uc2dc \uc124\uce58\ud55c minio\uc758 ",(0,r.kt)("a",{parentName:"p",href:"https://kubernetes.io/ko/docs/concepts/services-networking/dns-pod-service/"},"\ucfe0\ubc84\ub124\ud2f0\uc2a4 \uc11c\ube44\uc2a4 DNS \ub124\uc784\uc744 \ud65c\uc6a9"),"\ud569\ub2c8\ub2e4. \ud574\ub2f9 service \ub294 kubeflow namespace\uc5d0\uc11c minio-service\ub77c\ub294 \uc774\ub984\uc73c\ub85c \uc0dd\uc131\ub418\uc5c8\uc73c\ubbc0\ub85c, ",(0,r.kt)("inlineCode",{parentName:"p"},"http://minio-service.kubeflow.svc:9000")," \ub85c \uc124\uc815\ud569\ub2c8\ub2e4.",(0,r.kt)("br",{parentName:"p"}),"\n","\uc774\uc640 \ube44\uc2b7\ud558\uac8c tracking_uri\uc758 \uc8fc\uc18c\ub294 mlflow server\uc758 \ucfe0\ubc84\ub124\ud2f0\uc2a4 \uc11c\ube44\uc2a4 DNS \ub124\uc784\uc744 \ud65c\uc6a9\ud558\uc5ec, ",(0,r.kt)("inlineCode",{parentName:"p"},"http://mlflow-server-service.mlflow-system.svc:5000")," \ub85c \uc124\uc815\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-python"},'from functools import partial\nfrom kfp.components import InputPath, create_component_from_func\n\n@partial(\n create_component_from_func,\n packages_to_install=["dill", "pandas", "scikit-learn", "mlflow", "boto3"],\n)\ndef upload_sklearn_model_to_mlflow(\n model_name: str,\n model_path: InputPath("dill"),\n input_example_path: InputPath("dill"),\n signature_path: InputPath("dill"),\n conda_env_path: InputPath("dill"),\n):\n import os\n import dill\n from mlflow.sklearn import save_model\n \n from mlflow.tracking.client import MlflowClient\n\n os.environ["MLFLOW_S3_ENDPOINT_URL"] = "http://minio-service.kubeflow.svc:9000"\n os.environ["AWS_ACCESS_KEY_ID"] = "minio"\n os.environ["AWS_SECRET_ACCESS_KEY"] = "minio123"\n\n client = MlflowClient("http://mlflow-server-service.mlflow-system.svc:5000")\n\n with open(model_path, mode="rb") as file_reader:\n clf = dill.load(file_reader)\n\n with open(input_example_path, "rb") as file_reader:\n input_example = dill.load(file_reader)\n\n with open(signature_path, "rb") as file_reader:\n signature = dill.load(file_reader)\n\n with open(conda_env_path, "rb") as file_reader:\n conda_env = dill.load(file_reader)\n\n save_model(\n sk_model=clf,\n path=model_name,\n serialization_format="cloudpickle",\n conda_env=conda_env,\n signature=signature,\n input_example=input_example,\n )\n run = client.create_run(experiment_id="0")\n client.log_artifact(run.info.run_id, model_name)\n')),(0,r.kt)("h2",{id:"mlflow-pipeline"},"MLFlow Pipeline"),(0,r.kt)("p",null,"\uc774\uc81c \uc791\uc131\ud55c \ucef4\ud3ec\ub10c\ud2b8\ub4e4\uc744 \uc5f0\uacb0\ud574\uc11c \ud30c\uc774\ud504\ub77c\uc778\uc73c\ub85c \ub9cc\ub4e4\uc5b4 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4."),(0,r.kt)("h3",{id:"data-component"},"Data Component"),(0,r.kt)("p",null,"\ubaa8\ub378\uc744 \ud559\uc2b5\ud560 \ub54c \uc4f8 \ub370\uc774\ud130\ub294 sklearn\uc758 iris \uc785\ub2c8\ub2e4.\n\ub370\uc774\ud130\ub97c \uc0dd\uc131\ud558\ub294 \ucef4\ud3ec\ub10c\ud2b8\ub97c \uc791\uc131\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-python"},'from functools import partial\n\nfrom kfp.components import InputPath, OutputPath, create_component_from_func\n\n\n@partial(\n create_component_from_func,\n packages_to_install=["pandas", "scikit-learn"],\n)\ndef load_iris_data(\n data_path: OutputPath("csv"),\n target_path: OutputPath("csv"),\n):\n import pandas as pd\n from sklearn.datasets import load_iris\n\n iris = load_iris()\n\n data = pd.DataFrame(iris["data"], columns=iris["feature_names"])\n target = pd.DataFrame(iris["target"], columns=["target"])\n\n data.to_csv(data_path, index=False)\n target.to_csv(target_path, index=False)\n\n')),(0,r.kt)("h3",{id:"pipeline"},"Pipeline"),(0,r.kt)("p",null,"\ud30c\uc774\ud504\ub77c\uc778 \ucf54\ub4dc\ub294 \ub2e4\uc74c\uacfc \uac19\uc774 \uc791\uc131\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-python"},'from kfp.dsl import pipeline\n\n\n@pipeline(name="mlflow_pipeline")\ndef mlflow_pipeline(kernel: str, model_name: str):\n iris_data = load_iris_data()\n model = train_from_csv(\n train_data=iris_data.outputs["data"],\n train_target=iris_data.outputs["target"],\n kernel=kernel,\n )\n _ = upload_sklearn_model_to_mlflow(\n model_name=model_name,\n model=model.outputs["model"],\n input_example=model.outputs["input_example"],\n signature=model.outputs["signature"],\n conda_env=model.outputs["conda_env"],\n )\n')),(0,r.kt)("h3",{id:"run"},"Run"),(0,r.kt)("p",null,"\uc704\uc5d0\uc11c \uc791\uc131\ub41c \ucef4\ud3ec\ub10c\ud2b8\uc640 \ud30c\uc774\ud504\ub77c\uc778\uc744 \ud558\ub098\uc758 \ud30c\uc774\uc36c \ud30c\uc77c\uc5d0 \uc815\ub9ac\ud558\uba74 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-python"},'from functools import partial\n\nimport kfp\nfrom kfp.components import InputPath, OutputPath, create_component_from_func\nfrom kfp.dsl import pipeline\n\n\n@partial(\n create_component_from_func,\n packages_to_install=["pandas", "scikit-learn"],\n)\ndef load_iris_data(\n data_path: OutputPath("csv"),\n target_path: OutputPath("csv"),\n):\n import pandas as pd\n from sklearn.datasets import load_iris\n\n iris = load_iris()\n\n data = pd.DataFrame(iris["data"], columns=iris["feature_names"])\n target = pd.DataFrame(iris["target"], columns=["target"])\n\n data.to_csv(data_path, index=False)\n target.to_csv(target_path, index=False)\n\n\n@partial(\n create_component_from_func,\n packages_to_install=["dill", "pandas", "scikit-learn", "mlflow"],\n)\ndef train_from_csv(\n train_data_path: InputPath("csv"),\n train_target_path: InputPath("csv"),\n model_path: OutputPath("dill"),\n input_example_path: OutputPath("dill"),\n signature_path: OutputPath("dill"),\n conda_env_path: OutputPath("dill"),\n kernel: str,\n):\n import dill\n import pandas as pd\n from sklearn.svm import SVC\n\n from mlflow.models.signature import infer_signature\n from mlflow.utils.environment import _mlflow_conda_env\n\n train_data = pd.read_csv(train_data_path)\n train_target = pd.read_csv(train_target_path)\n\n clf = SVC(kernel=kernel)\n clf.fit(train_data, train_target)\n\n with open(model_path, mode="wb") as file_writer:\n dill.dump(clf, file_writer)\n\n input_example = train_data.sample(1)\n with open(input_example_path, "wb") as file_writer:\n dill.dump(input_example, file_writer)\n\n signature = infer_signature(train_data, clf.predict(train_data))\n with open(signature_path, "wb") as file_writer:\n dill.dump(signature, file_writer)\n\n conda_env = _mlflow_conda_env(\n additional_pip_deps=["dill", "pandas", "scikit-learn"]\n )\n with open(conda_env_path, "wb") as file_writer:\n dill.dump(conda_env, file_writer)\n\n\n@partial(\n create_component_from_func,\n packages_to_install=["dill", "pandas", "scikit-learn", "mlflow", "boto3"],\n)\ndef upload_sklearn_model_to_mlflow(\n model_name: str,\n model_path: InputPath("dill"),\n input_example_path: InputPath("dill"),\n signature_path: InputPath("dill"),\n conda_env_path: InputPath("dill"),\n):\n import os\n import dill\n from mlflow.sklearn import save_model\n \n from mlflow.tracking.client import MlflowClient\n\n os.environ["MLFLOW_S3_ENDPOINT_URL"] = "http://minio-service.kubeflow.svc:9000"\n os.environ["AWS_ACCESS_KEY_ID"] = "minio"\n os.environ["AWS_SECRET_ACCESS_KEY"] = "minio123"\n\n client = MlflowClient("http://mlflow-server-service.mlflow-system.svc:5000")\n\n with open(model_path, mode="rb") as file_reader:\n clf = dill.load(file_reader)\n\n with open(input_example_path, "rb") as file_reader:\n input_example = dill.load(file_reader)\n\n with open(signature_path, "rb") as file_reader:\n signature = dill.load(file_reader)\n\n with open(conda_env_path, "rb") as file_reader:\n conda_env = dill.load(file_reader)\n\n save_model(\n sk_model=clf,\n path=model_name,\n serialization_format="cloudpickle",\n conda_env=conda_env,\n signature=signature,\n input_example=input_example,\n )\n run = client.create_run(experiment_id="0")\n client.log_artifact(run.info.run_id, model_name)\n\n\n@pipeline(name="mlflow_pipeline")\ndef mlflow_pipeline(kernel: str, model_name: str):\n iris_data = load_iris_data()\n model = train_from_csv(\n train_data=iris_data.outputs["data"],\n train_target=iris_data.outputs["target"],\n kernel=kernel,\n )\n _ = upload_sklearn_model_to_mlflow(\n model_name=model_name,\n model=model.outputs["model"],\n input_example=model.outputs["input_example"],\n signature=model.outputs["signature"],\n conda_env=model.outputs["conda_env"],\n )\n\n\nif __name__ == "__main__":\n kfp.compiler.Compiler().compile(mlflow_pipeline, "mlflow_pipeline.yaml")\n')),(0,r.kt)("p",null,(0,r.kt)("details",null,(0,r.kt)("summary",null,"mlflow_pipeline.yaml"),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},'apiVersion: argoproj.io/v1alpha1\nkind: Workflow\nmetadata:\n generateName: mlflow-pipeline-\n annotations: {pipelines.kubeflow.org/kfp_sdk_version: 1.8.10, pipelines.kubeflow.org/pipeline_compilation_time: \'2022-01-19T14:14:11.999807\',\n pipelines.kubeflow.org/pipeline_spec: \'{"inputs": [{"name": "kernel", "type":\n "String"}, {"name": "model_name", "type": "String"}], "name": "mlflow_pipeline"}\'}\n labels: {pipelines.kubeflow.org/kfp_sdk_version: 1.8.10}\nspec:\n entrypoint: mlflow-pipeline\n templates:\n - name: load-iris-data\n container:\n args: [--data, /tmp/outputs/data/data, --target, /tmp/outputs/target/data]\n command:\n - sh\n - -c\n - (PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location\n \'pandas\' \'scikit-learn\' || PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip\n install --quiet --no-warn-script-location \'pandas\' \'scikit-learn\' --user)\n && "$0" "$@"\n - sh\n - -ec\n - |\n program_path=$(mktemp)\n printf "%s" "$0" > "$program_path"\n python3 -u "$program_path" "$@"\n - |\n def _make_parent_dirs_and_return_path(file_path: str):\n import os\n os.makedirs(os.path.dirname(file_path), exist_ok=True)\n return file_path\n\n def load_iris_data(\n data_path,\n target_path,\n ):\n import pandas as pd\n from sklearn.datasets import load_iris\n\n iris = load_iris()\n\n data = pd.DataFrame(iris["data"], columns=iris["feature_names"])\n target = pd.DataFrame(iris["target"], columns=["target"])\n\n data.to_csv(data_path, index=False)\n target.to_csv(target_path, index=False)\n\n import argparse\n _parser = argparse.ArgumentParser(prog=\'Load iris data\', description=\'\')\n _parser.add_argument("--data", dest="data_path", type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--target", dest="target_path", type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)\n _parsed_args = vars(_parser.parse_args())\n\n _outputs = load_iris_data(**_parsed_args)\n image: python:3.7\n outputs:\n artifacts:\n - {name: load-iris-data-data, path: /tmp/outputs/data/data}\n - {name: load-iris-data-target, path: /tmp/outputs/target/data}\n metadata:\n labels:\n pipelines.kubeflow.org/kfp_sdk_version: 1.8.10\n pipelines.kubeflow.org/pipeline-sdk-type: kfp\n pipelines.kubeflow.org/enable_caching: "true"\n annotations: {pipelines.kubeflow.org/component_spec: \'{"implementation": {"container":\n {"args": ["--data", {"outputPath": "data"}, "--target", {"outputPath": "target"}],\n "command": ["sh", "-c", "(PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip\n install --quiet --no-warn-script-location \'\'pandas\'\' \'\'scikit-learn\'\' ||\n PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location\n \'\'pandas\'\' \'\'scikit-learn\'\' --user) && \\"$0\\" \\"$@\\"", "sh", "-ec", "program_path=$(mktemp)\\nprintf\n \\"%s\\" \\"$0\\" > \\"$program_path\\"\\npython3 -u \\"$program_path\\" \\"$@\\"\\n",\n "def _make_parent_dirs_and_return_path(file_path: str):\\n import os\\n os.makedirs(os.path.dirname(file_path),\n exist_ok=True)\\n return file_path\\n\\ndef load_iris_data(\\n data_path,\\n target_path,\\n):\\n import\n pandas as pd\\n from sklearn.datasets import load_iris\\n\\n iris = load_iris()\\n\\n data\n = pd.DataFrame(iris[\\"data\\"], columns=iris[\\"feature_names\\"])\\n target\n = pd.DataFrame(iris[\\"target\\"], columns=[\\"target\\"])\\n\\n data.to_csv(data_path,\n index=False)\\n target.to_csv(target_path, index=False)\\n\\nimport argparse\\n_parser\n = argparse.ArgumentParser(prog=\'\'Load iris data\'\', description=\'\'\'\')\\n_parser.add_argument(\\"--data\\",\n dest=\\"data_path\\", type=_make_parent_dirs_and_return_path, required=True,\n default=argparse.SUPPRESS)\\n_parser.add_argument(\\"--target\\", dest=\\"target_path\\",\n type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)\\n_parsed_args\n = vars(_parser.parse_args())\\n\\n_outputs = load_iris_data(**_parsed_args)\\n"],\n "image": "python:3.7"}}, "name": "Load iris data", "outputs": [{"name":\n "data", "type": "csv"}, {"name": "target", "type": "csv"}]}\', pipelines.kubeflow.org/component_ref: \'{}\'}\n - name: mlflow-pipeline\n inputs:\n parameters:\n - {name: kernel}\n - {name: model_name}\n dag:\n tasks:\n - {name: load-iris-data, template: load-iris-data}\n - name: train-from-csv\n template: train-from-csv\n dependencies: [load-iris-data]\n arguments:\n parameters:\n - {name: kernel, value: \'{{inputs.parameters.kernel}}\'}\n artifacts:\n - {name: load-iris-data-data, from: \'{{tasks.load-iris-data.outputs.artifacts.load-iris-data-data}}\'}\n - {name: load-iris-data-target, from: \'{{tasks.load-iris-data.outputs.artifacts.load-iris-data-target}}\'}\n - name: upload-sklearn-model-to-mlflow\n template: upload-sklearn-model-to-mlflow\n dependencies: [train-from-csv]\n arguments:\n parameters:\n - {name: model_name, value: \'{{inputs.parameters.model_name}}\'}\n artifacts:\n - {name: train-from-csv-conda_env, from: \'{{tasks.train-from-csv.outputs.artifacts.train-from-csv-conda_env}}\'}\n - {name: train-from-csv-input_example, from: \'{{tasks.train-from-csv.outputs.artifacts.train-from-csv-input_example}}\'}\n - {name: train-from-csv-model, from: \'{{tasks.train-from-csv.outputs.artifacts.train-from-csv-model}}\'}\n - {name: train-from-csv-signature, from: \'{{tasks.train-from-csv.outputs.artifacts.train-from-csv-signature}}\'}\n - name: train-from-csv\n container:\n args: [--train-data, /tmp/inputs/train_data/data, --train-target, /tmp/inputs/train_target/data,\n --kernel, \'{{inputs.parameters.kernel}}\', --model, /tmp/outputs/model/data,\n --input-example, /tmp/outputs/input_example/data, --signature, /tmp/outputs/signature/data,\n --conda-env, /tmp/outputs/conda_env/data]\n command:\n - sh\n - -c\n - (PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location\n \'dill\' \'pandas\' \'scikit-learn\' \'mlflow\' || PIP_DISABLE_PIP_VERSION_CHECK=1\n python3 -m pip install --quiet --no-warn-script-location \'dill\' \'pandas\' \'scikit-learn\'\n \'mlflow\' --user) && "$0" "$@"\n - sh\n - -ec\n - |\n program_path=$(mktemp)\n printf "%s" "$0" > "$program_path"\n python3 -u "$program_path" "$@"\n - |\n def _make_parent_dirs_and_return_path(file_path: str):\n import os\n os.makedirs(os.path.dirname(file_path), exist_ok=True)\n return file_path\n\n def train_from_csv(\n train_data_path,\n train_target_path,\n model_path,\n input_example_path,\n signature_path,\n conda_env_path,\n kernel,\n ):\n import dill\n import pandas as pd\n from sklearn.svm import SVC\n\n from mlflow.models.signature import infer_signature\n from mlflow.utils.environment import _mlflow_conda_env\n\n train_data = pd.read_csv(train_data_path)\n train_target = pd.read_csv(train_target_path)\n\n clf = SVC(kernel=kernel)\n clf.fit(train_data, train_target)\n\n with open(model_path, mode="wb") as file_writer:\n dill.dump(clf, file_writer)\n\n input_example = train_data.sample(1)\n with open(input_example_path, "wb") as file_writer:\n dill.dump(input_example, file_writer)\n\n signature = infer_signature(train_data, clf.predict(train_data))\n with open(signature_path, "wb") as file_writer:\n dill.dump(signature, file_writer)\n\n conda_env = _mlflow_conda_env(\n additional_pip_deps=["dill", "pandas", "scikit-learn"]\n )\n with open(conda_env_path, "wb") as file_writer:\n dill.dump(conda_env, file_writer)\n\n import argparse\n _parser = argparse.ArgumentParser(prog=\'Train from csv\', description=\'\')\n _parser.add_argument("--train-data", dest="train_data_path", type=str, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--train-target", dest="train_target_path", type=str, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--kernel", dest="kernel", type=str, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--model", dest="model_path", type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--input-example", dest="input_example_path", type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--signature", dest="signature_path", type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--conda-env", dest="conda_env_path", type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)\n _parsed_args = vars(_parser.parse_args())\n\n _outputs = train_from_csv(**_parsed_args)\n image: python:3.7\n inputs:\n parameters:\n - {name: kernel}\n artifacts:\n - {name: load-iris-data-data, path: /tmp/inputs/train_data/data}\n - {name: load-iris-data-target, path: /tmp/inputs/train_target/data}\n outputs:\n artifacts:\n - {name: train-from-csv-conda_env, path: /tmp/outputs/conda_env/data}\n - {name: train-from-csv-input_example, path: /tmp/outputs/input_example/data}\n - {name: train-from-csv-model, path: /tmp/outputs/model/data}\n - {name: train-from-csv-signature, path: /tmp/outputs/signature/data}\n metadata:\n labels:\n pipelines.kubeflow.org/kfp_sdk_version: 1.8.10\n pipelines.kubeflow.org/pipeline-sdk-type: kfp\n pipelines.kubeflow.org/enable_caching: "true"\n annotations: {pipelines.kubeflow.org/component_spec: \'{"implementation": {"container":\n {"args": ["--train-data", {"inputPath": "train_data"}, "--train-target",\n {"inputPath": "train_target"}, "--kernel", {"inputValue": "kernel"}, "--model",\n {"outputPath": "model"}, "--input-example", {"outputPath": "input_example"},\n "--signature", {"outputPath": "signature"}, "--conda-env", {"outputPath":\n "conda_env"}], "command": ["sh", "-c", "(PIP_DISABLE_PIP_VERSION_CHECK=1\n python3 -m pip install --quiet --no-warn-script-location \'\'dill\'\' \'\'pandas\'\'\n \'\'scikit-learn\'\' \'\'mlflow\'\' || PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m\n pip install --quiet --no-warn-script-location \'\'dill\'\' \'\'pandas\'\' \'\'scikit-learn\'\'\n \'\'mlflow\'\' --user) && \\"$0\\" \\"$@\\"", "sh", "-ec", "program_path=$(mktemp)\\nprintf\n \\"%s\\" \\"$0\\" > \\"$program_path\\"\\npython3 -u \\"$program_path\\" \\"$@\\"\\n",\n "def _make_parent_dirs_and_return_path(file_path: str):\\n import os\\n os.makedirs(os.path.dirname(file_path),\n exist_ok=True)\\n return file_path\\n\\ndef train_from_csv(\\n train_data_path,\\n train_target_path,\\n model_path,\\n input_example_path,\\n signature_path,\\n conda_env_path,\\n kernel,\\n):\\n import\n dill\\n import pandas as pd\\n from sklearn.svm import SVC\\n\\n from\n mlflow.models.signature import infer_signature\\n from mlflow.utils.environment\n import _mlflow_conda_env\\n\\n train_data = pd.read_csv(train_data_path)\\n train_target\n = pd.read_csv(train_target_path)\\n\\n clf = SVC(kernel=kernel)\\n clf.fit(train_data,\n train_target)\\n\\n with open(model_path, mode=\\"wb\\") as file_writer:\\n dill.dump(clf,\n file_writer)\\n\\n input_example = train_data.sample(1)\\n with open(input_example_path,\n \\"wb\\") as file_writer:\\n dill.dump(input_example, file_writer)\\n\\n signature\n = infer_signature(train_data, clf.predict(train_data))\\n with open(signature_path,\n \\"wb\\") as file_writer:\\n dill.dump(signature, file_writer)\\n\\n conda_env\n = _mlflow_conda_env(\\n additional_pip_deps=[\\"dill\\", \\"pandas\\",\n \\"scikit-learn\\"]\\n )\\n with open(conda_env_path, \\"wb\\") as file_writer:\\n dill.dump(conda_env,\n file_writer)\\n\\nimport argparse\\n_parser = argparse.ArgumentParser(prog=\'\'Train\n from csv\'\', description=\'\'\'\')\\n_parser.add_argument(\\"--train-data\\", dest=\\"train_data_path\\",\n type=str, required=True, default=argparse.SUPPRESS)\\n_parser.add_argument(\\"--train-target\\",\n dest=\\"train_target_path\\", type=str, required=True, default=argparse.SUPPRESS)\\n_parser.add_argument(\\"--kernel\\",\n dest=\\"kernel\\", type=str, required=True, default=argparse.SUPPRESS)\\n_parser.add_argument(\\"--model\\",\n dest=\\"model_path\\", type=_make_parent_dirs_and_return_path, required=True,\n default=argparse.SUPPRESS)\\n_parser.add_argument(\\"--input-example\\", dest=\\"input_example_path\\",\n type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)\\n_parser.add_argument(\\"--signature\\",\n dest=\\"signature_path\\", type=_make_parent_dirs_and_return_path, required=True,\n default=argparse.SUPPRESS)\\n_parser.add_argument(\\"--conda-env\\", dest=\\"conda_env_path\\",\n type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)\\n_parsed_args\n = vars(_parser.parse_args())\\n\\n_outputs = train_from_csv(**_parsed_args)\\n"],\n "image": "python:3.7"}}, "inputs": [{"name": "train_data", "type": "csv"},\n {"name": "train_target", "type": "csv"}, {"name": "kernel", "type": "String"}],\n "name": "Train from csv", "outputs": [{"name": "model", "type": "dill"},\n {"name": "input_example", "type": "dill"}, {"name": "signature", "type":\n "dill"}, {"name": "conda_env", "type": "dill"}]}\', pipelines.kubeflow.org/component_ref: \'{}\',\n pipelines.kubeflow.org/arguments.parameters: \'{"kernel": "{{inputs.parameters.kernel}}"}\'}\n - name: upload-sklearn-model-to-mlflow\n container:\n args: [--model-name, \'{{inputs.parameters.model_name}}\', --model, /tmp/inputs/model/data,\n --input-example, /tmp/inputs/input_example/data, --signature, /tmp/inputs/signature/data,\n --conda-env, /tmp/inputs/conda_env/data]\n command:\n - sh\n - -c\n - (PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location\n \'dill\' \'pandas\' \'scikit-learn\' \'mlflow\' \'boto3\' || PIP_DISABLE_PIP_VERSION_CHECK=1\n python3 -m pip install --quiet --no-warn-script-location \'dill\' \'pandas\' \'scikit-learn\'\n \'mlflow\' \'boto3\' --user) && "$0" "$@"\n - sh\n - -ec\n - |\n program_path=$(mktemp)\n printf "%s" "$0" > "$program_path"\n python3 -u "$program_path" "$@"\n - |\n def upload_sklearn_model_to_mlflow(\n model_name,\n model_path,\n input_example_path,\n signature_path,\n conda_env_path,\n ):\n import os\n import dill\n from mlflow.sklearn import save_model\n\n from mlflow.tracking.client import MlflowClient\n\n os.environ["MLFLOW_S3_ENDPOINT_URL"] = "http://minio-service.kubeflow.svc:9000"\n os.environ["AWS_ACCESS_KEY_ID"] = "minio"\n os.environ["AWS_SECRET_ACCESS_KEY"] = "minio123"\n\n client = MlflowClient("http://mlflow-server-service.mlflow-system.svc:5000")\n\n with open(model_path, mode="rb") as file_reader:\n clf = dill.load(file_reader)\n\n with open(input_example_path, "rb") as file_reader:\n input_example = dill.load(file_reader)\n\n with open(signature_path, "rb") as file_reader:\n signature = dill.load(file_reader)\n\n with open(conda_env_path, "rb") as file_reader:\n conda_env = dill.load(file_reader)\n\n save_model(\n sk_model=clf,\n path=model_name,\n serialization_format="cloudpickle",\n conda_env=conda_env,\n signature=signature,\n input_example=input_example,\n )\n run = client.create_run(experiment_id="0")\n client.log_artifact(run.info.run_id, model_name)\n\n import argparse\n _parser = argparse.ArgumentParser(prog=\'Upload sklearn model to mlflow\', description=\'\')\n _parser.add_argument("--model-name", dest="model_name", type=str, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--model", dest="model_path", type=str, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--input-example", dest="input_example_path", type=str, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--signature", dest="signature_path", type=str, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--conda-env", dest="conda_env_path", type=str, required=True, default=argparse.SUPPRESS)\n _parsed_args = vars(_parser.parse_args())\n\n _outputs = upload_sklearn_model_to_mlflow(**_parsed_args)\n image: python:3.7\n inputs:\n parameters:\n - {name: model_name}\n artifacts:\n - {name: train-from-csv-conda_env, path: /tmp/inputs/conda_env/data}\n - {name: train-from-csv-input_example, path: /tmp/inputs/input_example/data}\n - {name: train-from-csv-model, path: /tmp/inputs/model/data}\n - {name: train-from-csv-signature, path: /tmp/inputs/signature/data}\n metadata:\n labels:\n pipelines.kubeflow.org/kfp_sdk_version: 1.8.10\n pipelines.kubeflow.org/pipeline-sdk-type: kfp\n pipelines.kubeflow.org/enable_caching: "true"\n annotations: {pipelines.kubeflow.org/component_spec: \'{"implementation": {"container":\n {"args": ["--model-name", {"inputValue": "model_name"}, "--model", {"inputPath":\n "model"}, "--input-example", {"inputPath": "input_example"}, "--signature",\n {"inputPath": "signature"}, "--conda-env", {"inputPath": "conda_env"}],\n "command": ["sh", "-c", "(PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip\n install --quiet --no-warn-script-location \'\'dill\'\' \'\'pandas\'\' \'\'scikit-learn\'\'\n \'\'mlflow\'\' \'\'boto3\'\' || PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install\n --quiet --no-warn-script-location \'\'dill\'\' \'\'pandas\'\' \'\'scikit-learn\'\' \'\'mlflow\'\'\n \'\'boto3\'\' --user) && \\"$0\\" \\"$@\\"", "sh", "-ec", "program_path=$(mktemp)\\nprintf\n \\"%s\\" \\"$0\\" > \\"$program_path\\"\\npython3 -u \\"$program_path\\" \\"$@\\"\\n",\n "def upload_sklearn_model_to_mlflow(\\n model_name,\\n model_path,\\n input_example_path,\\n signature_path,\\n conda_env_path,\\n):\\n import\n os\\n import dill\\n from mlflow.sklearn import save_model\\n\\n from\n mlflow.tracking.client import MlflowClient\\n\\n os.environ[\\"MLFLOW_S3_ENDPOINT_URL\\"]\n = \\"http://minio-service.kubeflow.svc:9000\\"\\n os.environ[\\"AWS_ACCESS_KEY_ID\\"]\n = \\"minio\\"\\n os.environ[\\"AWS_SECRET_ACCESS_KEY\\"] = \\"minio123\\"\\n\\n client\n = MlflowClient(\\"http://mlflow-server-service.mlflow-system.svc:5000\\")\\n\\n with\n open(model_path, mode=\\"rb\\") as file_reader:\\n clf = dill.load(file_reader)\\n\\n with\n open(input_example_path, \\"rb\\") as file_reader:\\n input_example\n = dill.load(file_reader)\\n\\n with open(signature_path, \\"rb\\") as file_reader:\\n signature\n = dill.load(file_reader)\\n\\n with open(conda_env_path, \\"rb\\") as file_reader:\\n conda_env\n = dill.load(file_reader)\\n\\n save_model(\\n sk_model=clf,\\n path=model_name,\\n serialization_format=\\"cloudpickle\\",\\n conda_env=conda_env,\\n signature=signature,\\n input_example=input_example,\\n )\\n run\n = client.create_run(experiment_id=\\"0\\")\\n client.log_artifact(run.info.run_id,\n model_name)\\n\\nimport argparse\\n_parser = argparse.ArgumentParser(prog=\'\'Upload\n sklearn model to mlflow\'\', description=\'\'\'\')\\n_parser.add_argument(\\"--model-name\\",\n dest=\\"model_name\\", type=str, required=True, default=argparse.SUPPRESS)\\n_parser.add_argument(\\"--model\\",\n dest=\\"model_path\\", type=str, required=True, default=argparse.SUPPRESS)\\n_parser.add_argument(\\"--input-example\\",\n dest=\\"input_example_path\\", type=str, required=True, default=argparse.SUPPRESS)\\n_parser.add_argument(\\"--signature\\",\n dest=\\"signature_path\\", type=str, required=True, default=argparse.SUPPRESS)\\n_parser.add_argument(\\"--conda-env\\",\n dest=\\"conda_env_path\\", type=str, required=True, 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\ucee8\ud14c\uc774\ub108\ub77c\uc774\uc81c\uc774\uc158(Containerization) \uae30\uc220\uc785\ub2c8\ub2e4.\n\uc774\ub807\uac8c \ud328\ud0a4\uc9d5\ub41c \ud615\ud0dc\ub97c \ucee8\ud14c\uc774\ub108 \uc774\ubbf8\uc9c0\ub77c\uace0 \ubd80\ub974\uba70, \ucee8\ud14c\uc774\ub108 \uc774\ubbf8\uc9c0\ub97c \uacf5\uc720\ud568\uc73c\ub85c\uc368 \uc0ac\uc6a9\uc790\ub4e4\uc740 \uc5b4\ub5a4 \uc2dc\uc2a4\ud15c\uc5d0\uc11c\ub4e0 \uac19\uc740 \uc2e4\ud589 \uacb0\uacfc\ub97c \ubcf4\uc7a5\ud560 \uc218 \uc788\uac8c \ub429\ub2c8\ub2e4.",(0,o.kt)("br",{parentName:"p"}),"\n","\uc989, \ub2e8\uc21c\ud788 Jupyter Notebook \ud30c\uc77c\uc774\ub098, \ubaa8\ub378\uc758 \uc18c\uc2a4 \ucf54\ub4dc\uc640 requirements.txt \ud30c\uc77c\uc744 \uacf5\uc720\ud558\ub294 \uac83\uc774 \uc544\ub2cc, \ubaa8\ub4e0 \uc2e4\ud589 \ud658\uacbd\uc774 \ub2f4\uae34 \ucee8\ud14c\uc774\ub108 \uc774\ubbf8\uc9c0\ub97c \uacf5\uc720\ud55c\ub2e4\uba74 ",(0,o.kt)("em",{parentName:"p"},'"\uc81c \ub178\ud2b8\ubd81\uc5d0\uc11c\ub294 \uc798 \ub418\ub294\ub370\uc694?"')," \uc640 \uac19\uc740 \uc0c1\ud669\uc744 \ud53c\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,o.kt)("p",null,'\ucee8\ud14c\uc774\ub108\ub97c \ucc98\uc74c \uc811\ud558\uc2dc\ub294 \ubd84\ub4e4\uc774 \ud754\ud788 \ud558\uc2dc\ub294 \uc624\ud574 \uc911 \ud558\ub098\ub294 "',(0,o.kt)("strong",{parentName:"p"},"\ucee8\ud14c\uc774\ub108 == \ub3c4\ucee4"),'"\ub77c\uace0 \ubc1b\uc544\ub4e4\uc774\ub294 \uac83\uc785\ub2c8\ub2e4.',(0,o.kt)("br",{parentName:"p"}),"\n","\ub3c4\ucee4\ub294 \ucee8\ud14c\uc774\ub108\uc640 \uac19\uc740 \uc758\ubbf8\ub97c \uc9c0\ub2c8\ub294 \uac1c\ub150\uc774 \uc544\ub2c8\ub77c, \ucee8\ud14c\uc774\ub108\ub97c \ub744\uc6b0\uac70\ub098, \ucee8\ud14c\uc774\ub108 \uc774\ubbf8\uc9c0\ub97c \ub9cc\ub4e4\uace0 \uacf5\uc720\ud558\ub294 \uac83\uacfc \uac19\uc774 \ucee8\ud14c\uc774\ub108\ub97c \ub354\uc6b1\ub354 \uc27d\uace0 \uc720\uc5f0\ud558\uac8c \uc0ac\uc6a9\ud560 \uc218 \uc788\ub294 \uae30\ub2a5\uc744 \uc81c\uacf5\ud574\uc8fc\ub294 \ub3c4\uad6c\uc785\ub2c8\ub2e4. \uc815\ub9ac\ud558\uc790\uba74 \ucee8\ud14c\uc774\ub108\ub294 \uac00\uc0c1\ud654 \uae30\uc220\uc774\uace0, \ub3c4\ucee4\ub294 \uac00\uc0c1\ud654 \uae30\uc220\uc758 \uad6c\ud604\uccb4\ub77c\uace0 \ub9d0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,o.kt)("p",null,"\ub2e4\ub9cc, \ub3c4\ucee4\ub294 \uc5ec\ub7ec \ucee8\ud14c\uc774\ub108 \uac00\uc0c1\ud654 \ub3c4\uad6c \uc911\uc5d0\uc11c \uc26c\uc6b4 \uc0ac\uc6a9\uc131\uacfc \ub192\uc740 \ud6a8\uc728\uc131\uc744 \ubc14\ud0d5\uc73c\ub85c \uac00\uc7a5 \ube60\ub974\uac8c \uc131\uc7a5\ud558\uc5ec \ub300\uc138\uac00 \ub418\uc5c8\uae30\uc5d0 \ucee8\ud14c\uc774\ub108\ud558\uba74 \ub3c4\ucee4\ub77c\ub294 \uc774\ubbf8\uc9c0\uac00 \uc790\ub3d9\uc73c\ub85c \ub5a0\uc624\ub974\uac8c \ub418\uc5c8\uc2b5\ub2c8\ub2e4. \uc774\ub807\uac8c \ucee8\ud14c\uc774\ub108\uc640 \ub3c4\ucee4 \uc0dd\ud0dc\uacc4\uac00 \ub300\uc138\uac00 \ub418\uae30\uae4c\uc9c0\ub294 \ub2e4\uc591\ud55c \uc774\uc720\uac00 \uc788\uc9c0\ub9cc, \uae30\uc220\uc801\uc73c\ub85c \uc790\uc138\ud55c \uc774\uc57c\uae30\ub294 ",(0,o.kt)("em",{parentName:"p"},"\ubaa8\ub450\uc758 MLOps"),"\uc758 \ubc94\uc704\ub97c \ub118\uc5b4\uc11c\uae30 \ub54c\ubb38\uc5d0 \ub2e4\ub8e8\uc9c0\ub294 \uc54a\uaca0\uc2b5\ub2c8\ub2e4."),(0,o.kt)("p",null,"\ucee8\ud14c\uc774\ub108 \ud639\uc740 \ub3c4\ucee4\ub97c \ucc98\uc74c \ub4e4\uc5b4\ubcf4\uc2dc\ub294 \ubd84\ub4e4\uc5d0\uac8c\ub294 ",(0,o.kt)("em",{parentName:"p"},"\ubaa8\ub450\uc758 MLOps"),"\uc758 \ub0b4\uc6a9\uc774 \ub2e4\uc18c \uc5b4\ub835\uac8c \ub290\uaef4\uc9c8 \uc218 \uc788\uc73c\ubbc0\ub85c, ",(0,o.kt)("a",{parentName:"p",href:"https://opentutorials.org/course/4781"},"\uc0dd\ud65c\ucf54\ub529"),", ",(0,o.kt)("a",{parentName:"p",href:"https://subicura.com/2017/01/19/docker-guide-for-beginners-1.html"},"subicura \ub2d8\uc758 \uac1c\uc778 \ube14\ub85c\uadf8 \uae00")," \ub4f1\uc758 \uc790\ub8cc\ub97c \uba3c\uc800 \uc0b4\ud3b4\ubcf4\ub294 \uac83\uc744 \uad8c\uc7a5\ud569\ub2c8\ub2e4."),(0,o.kt)("h2",{id:"container-orchestration-system"},"Container Orchestration System"),(0,o.kt)("p",null,"\uadf8\ub807\ub2e4\uba74 \ucee8\ud14c\uc774\ub108 \uc624\ucf00\uc2a4\ud2b8\ub808\uc774\uc158 \uc2dc\uc2a4\ud15c\uc740 \ubb34\uc5c7\uc77c\uae4c\uc694? 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"),(0,o.kt)("p",null,"\uba38\uc2e0\ub7ec\ub2dd\uc5d0\uc11c\ub294 \uc5b4\ub5bb\uac8c \uc4f0\uc77c \uc218 \uc788\uc744\uae4c\uc694?",(0,o.kt)("br",{parentName:"p"}),"\n","\uc608\ub97c \ub4e4\uc5b4\uc11c GPU\uac00 \uc788\uc5b4\uc57c \ud558\ub294 \ub525\ub7ec\ub2dd \ud559\uc2b5 \ucf54\ub4dc\uac00 \ud328\ud0a4\uc9d5\ub41c \ucee8\ud14c\uc774\ub108\ub294 \uc0ac\uc6a9 \uac00\ub2a5\ud55c GPU\uac00 \uc788\ub294 \ud074\ub7ec\uc2a4\ud130\uc5d0\uc11c \uc218\ud589\ud558\uace0, \ub9ce\uc740 \uba54\ubaa8\ub9ac\ub97c \ud544\uc694\ub85c \ud558\ub294 \ub370\uc774\ud130 \uc804\ucc98\ub9ac \ucf54\ub4dc\uac00 \ud328\ud0a4\uc9d5\ub41c \ucee8\ud14c\uc774\ub108\ub294 \uba54\ubaa8\ub9ac\uc758 \uc5ec\uc720\uac00 \ub9ce\uc740 \ud074\ub7ec\uc2a4\ud130\uc5d0\uc11c \uc218\ud589\ud558\uace0, \ud559\uc2b5 \uc911\uc5d0 \ud074\ub7ec\uc2a4\ud130\uc5d0 \ubb38\uc81c\uac00 \uc0dd\uae30\uba74 \uc790\ub3d9\uc73c\ub85c \uac19\uc740 \ucee8\ud14c\uc774\ub108\ub97c \ub2e4\ub978 \ud074\ub7ec\uc2a4\ud130\ub85c \uc774\ub3d9\uc2dc\ud0a4\uace0 \ub2e4\uc2dc \ud559\uc2b5\uc744 \uc9c4\ud589\ud558\ub294 \ub4f1\uc758 \uc791\uc5c5\uc744 \uc0ac\ub78c\uc774 \uc77c\uc77c\uc774 \uc218\ud589\ud558\uc9c0 \uc54a\uace0, \uc790\ub3d9\uc73c\ub85c \uad00\ub9ac\ud558\ub294 \uc2dc\uc2a4\ud15c\uc744 \uac1c\ubc1c\ud55c \ub4a4 \ub9e1\uae30\ub294 \uac83\uc785\ub2c8\ub2e4."),(0,o.kt)("p",null,"\uc9d1\ud544\uc744 \ud558\ub294 2022\ub144\uc744 \uae30\uc900\uc73c\ub85c \ucfe0\ubc84\ub124\ud2f0\uc2a4\ub294 \ucee8\ud14c\uc774\ub108 \uc624\ucf00\uc2a4\ud2b8\ub808\uc774\uc158 \uc2dc\uc2a4\ud15c\uc758 \uc0ac\uc2e4\uc0c1\uc758 \ud45c\uc900(De facto standard)\uc785\ub2c8\ub2e4."),(0,o.kt)("p",null,"CNCF\uc5d0\uc11c 2018\ub144 \ubc1c\ud45c\ud55c ",(0,o.kt)("a",{parentName:"p",href:"https://www.cncf.io/blog/2018/08/29/cncf-survey-use-of-cloud-native-technologies-in-production-has-grown-over-200-percent/"},"Survey")," \uc5d0 \ub530\ub974\uba74 \ub2e4\uc74c \uadf8\ub9bc\uacfc \uac19\uc774 \uc774\ubbf8 \ub450\uac01\uc744 \ub098\ud0c0\ub0b4\uace0 \uc788\uc5c8\uc73c\uba70, 2019\ub144 \ubc1c\ud45c\ud55c ",(0,o.kt)("a",{parentName:"p",href:"https://www.cncf.io/wp-content/uploads/2020/08/CNCF_Survey_Report.pdf"},"Survey"),"\uc5d0 \ub530\ub974\uba74 \uadf8\uc911 78%\uac00 \uc0c1\uc6a9 \uc218\uc900(Production Level)\uc5d0\uc11c \uc0ac\uc6a9\ud558\uace0 \uc788\ub2e4\ub294 \uac83\uc744 \uc54c \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,o.kt)("p",null,(0,o.kt)("img",{alt:"k8s-graph",src:n(8870).Z,width:"2048",height:"1317"})),(0,o.kt)("p",null,"\ucfe0\ubc84\ub124\ud2f0\uc2a4 \uc0dd\ud0dc\uacc4\uac00 \uc774\ucc98\ub7fc \ucee4\uc9c0\uac8c \ub41c \uc774\uc720\uc5d0\ub294 \uc5ec\ub7ec \uac00\uc9c0 \uc774\uc720\uac00 \uc788\uc2b5\ub2c8\ub2e4. \ud558\uc9c0\ub9cc \ub3c4\ucee4\uc640 \ub9c8\ucc2c\uac00\uc9c0\ub85c \ucfe0\ubc84\ub124\ud2f0\uc2a4 \uc5ed\uc2dc \uba38\uc2e0\ub7ec\ub2dd \uae30\ubc18\uc758 \uc11c\ube44\uc2a4\uc5d0\uc11c\ub9cc \uc0ac\uc6a9\ud558\ub294 \uae30\uc220\uc774 \uc544\ub2c8\uae30\uc5d0, \uc790\uc138\ud788 \ub2e4\ub8e8\uae30\uc5d0\ub294 \uc0c1\ub2f9\ud788 \ub9ce\uc740 \uc591\uc758 \uae30\uc220\uc801\uc778 \ub0b4\uc6a9\uc744 \ub2e4\ub8e8\uc5b4\uc57c \ud558\ubbc0\ub85c \uc774\ubc88 ",(0,o.kt)("em",{parentName:"p"},"\ubaa8\ub450\uc758 MLOps"),"\uc5d0\uc11c\ub294 \uc790\uc138\ud55c \ub0b4\uc6a9\uc740 \uc0dd\ub7b5\ud560 \uc608\uc815\uc785\ub2c8\ub2e4."),(0,o.kt)("p",null,"\ub2e4\ub9cc, ",(0,o.kt)("em",{parentName:"p"},"\ubaa8\ub450\uc758 MLOps"),"\uc5d0\uc11c \uc55e\uc73c\ub85c \ub2e4\ub8f0 \ub0b4\uc6a9\uc740 \ub3c4\ucee4\uc640 \ucfe0\ubc84\ub124\ud2f0\uc2a4\uc5d0 \ub300\ud55c \ub0b4\uc6a9\uc744 \uc5b4\ub290 \uc815\ub3c4 \uc54c\uace0 \uacc4\uc2e0 \ubd84\ub4e4\uc744 \ub300\uc0c1\uc73c\ub85c \uc791\uc131\ud558\uc600\uc2b5\ub2c8\ub2e4. \ub530\ub77c\uc11c \ucfe0\ubc84\ub124\ud2f0\uc2a4\uc5d0 \ub300\ud574 \uc775\uc219\ud558\uc9c0 \uc54a\uc73c\uc2e0 \ubd84\ub4e4\uc740 \ub2e4\uc74c ",(0,o.kt)("a",{parentName:"p",href:"https://kubernetes.io/ko/docs/concepts/overview/what-is-kubernetes/"},"\ucfe0\ubc84\ub124\ud2f0\uc2a4 \uacf5\uc2dd \ubb38\uc11c"),", ",(0,o.kt)("a",{parentName:"p",href:"https://subicura.com/k8s/"},"subicura \ub2d8\uc758 \uac1c\uc778 \ube14\ub85c\uadf8 \uae00")," \ub4f1\uc758 \uc27d\uace0 \uc790\uc138\ud55c \uc790\ub8cc\ub4e4\uc744 \uba3c\uc800 \ucc38\uace0\ud574\uc8fc\uc2dc\ub294 \uac83\uc744 \uad8c\uc7a5\ud569\ub2c8\ub2e4."))}m.isMDXComponent=!0},8870:(e,t,n)=>{n.d(t,{Z:()=>r});const r=n.p+"assets/images/k8s-graph-4320bbc5bf9fc0dccdeb1edc0157e8ec.png"}}]); 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\uc560\ud50c\ub9ac\ucf00\uc774\uc158\uc758 \ud45c\uc900\ud654\ub41c \uc774\uc2dd \uac00\ub2a5\ud55c \ud328\ud0a4\uc9d5")),(0,o.kt)("p",null,"\uadf8\ub7f0\ub370 \uc65c \uba38\uc2e0\ub7ec\ub2dd\uc5d0\uc11c \ucee8\ud14c\uc774\ub108\uac00 \ud544\uc694\ud560\uae4c\uc694? \uba38\uc2e0\ub7ec\ub2dd \ubaa8\ub378\ub4e4\uc740 \uc6b4\uc601\uccb4\uc81c\ub098 Python \uc2e4\ud589 \ud658\uacbd, \ud328\ud0a4\uc9c0 \ubc84\uc804 \ub4f1\uc5d0 \ub530\ub77c \ub2e4\ub974\uac8c \ub3d9\uc791\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.",(0,o.kt)("br",{parentName:"p"}),"\n","\uc774\ub97c \ubc29\uc9c0\ud558\uae30 \uc704\ud574\uc11c \uba38\uc2e0\ub7ec\ub2dd\uc5d0 \uc0ac\uc6a9\ub41c \uc18c\uc2a4 \ucf54\ub4dc\uc640 \ud568\uaed8 \uc885\uc18d\uc801\uc778 \uc2e4\ud589 \ud658\uacbd \uc804\uccb4\ub97c ",(0,o.kt)("strong",{parentName:"p"},"\ud558\ub098\ub85c \ubb36\uc5b4\uc11c(\ud328\ud0a4\uc9d5\ud574\uc11c)")," \uacf5\uc720\ud558\uace0 \uc2e4\ud589\ud558\ub294 \ub370 \ud65c\uc6a9\ud560 \uc218 \uc788\ub294 \uae30\uc220\uc774 \ucee8\ud14c\uc774\ub108\ub77c\uc774\uc81c\uc774\uc158(Containerization) \uae30\uc220\uc785\ub2c8\ub2e4.\n\uc774\ub807\uac8c \ud328\ud0a4\uc9d5\ub41c \ud615\ud0dc\ub97c \ucee8\ud14c\uc774\ub108 \uc774\ubbf8\uc9c0\ub77c\uace0 \ubd80\ub974\uba70, \ucee8\ud14c\uc774\ub108 \uc774\ubbf8\uc9c0\ub97c \uacf5\uc720\ud568\uc73c\ub85c\uc368 \uc0ac\uc6a9\uc790\ub4e4\uc740 \uc5b4\ub5a4 \uc2dc\uc2a4\ud15c\uc5d0\uc11c\ub4e0 \uac19\uc740 \uc2e4\ud589 \uacb0\uacfc\ub97c \ubcf4\uc7a5\ud560 \uc218 \uc788\uac8c \ub429\ub2c8\ub2e4.",(0,o.kt)("br",{parentName:"p"}),"\n","\uc989, \ub2e8\uc21c\ud788 Jupyter Notebook \ud30c\uc77c\uc774\ub098, \ubaa8\ub378\uc758 \uc18c\uc2a4 \ucf54\ub4dc\uc640 requirements.txt \ud30c\uc77c\uc744 \uacf5\uc720\ud558\ub294 \uac83\uc774 \uc544\ub2cc, \ubaa8\ub4e0 \uc2e4\ud589 \ud658\uacbd\uc774 \ub2f4\uae34 \ucee8\ud14c\uc774\ub108 \uc774\ubbf8\uc9c0\ub97c \uacf5\uc720\ud55c\ub2e4\uba74 ",(0,o.kt)("em",{parentName:"p"},'"\uc81c \ub178\ud2b8\ubd81\uc5d0\uc11c\ub294 \uc798 \ub418\ub294\ub370\uc694?"')," \uc640 \uac19\uc740 \uc0c1\ud669\uc744 \ud53c\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,o.kt)("p",null,'\ucee8\ud14c\uc774\ub108\ub97c \ucc98\uc74c \uc811\ud558\uc2dc\ub294 \ubd84\ub4e4\uc774 \ud754\ud788 \ud558\uc2dc\ub294 \uc624\ud574 \uc911 \ud558\ub098\ub294 "',(0,o.kt)("strong",{parentName:"p"},"\ucee8\ud14c\uc774\ub108 == \ub3c4\ucee4"),'"\ub77c\uace0 \ubc1b\uc544\ub4e4\uc774\ub294 \uac83\uc785\ub2c8\ub2e4.',(0,o.kt)("br",{parentName:"p"}),"\n","\ub3c4\ucee4\ub294 \ucee8\ud14c\uc774\ub108\uc640 \uac19\uc740 \uc758\ubbf8\ub97c \uc9c0\ub2c8\ub294 \uac1c\ub150\uc774 \uc544\ub2c8\ub77c, \ucee8\ud14c\uc774\ub108\ub97c \ub744\uc6b0\uac70\ub098, \ucee8\ud14c\uc774\ub108 \uc774\ubbf8\uc9c0\ub97c \ub9cc\ub4e4\uace0 \uacf5\uc720\ud558\ub294 \uac83\uacfc \uac19\uc774 \ucee8\ud14c\uc774\ub108\ub97c \ub354\uc6b1\ub354 \uc27d\uace0 \uc720\uc5f0\ud558\uac8c \uc0ac\uc6a9\ud560 \uc218 \uc788\ub294 \uae30\ub2a5\uc744 \uc81c\uacf5\ud574\uc8fc\ub294 \ub3c4\uad6c\uc785\ub2c8\ub2e4. \uc815\ub9ac\ud558\uc790\uba74 \ucee8\ud14c\uc774\ub108\ub294 \uac00\uc0c1\ud654 \uae30\uc220\uc774\uace0, \ub3c4\ucee4\ub294 \uac00\uc0c1\ud654 \uae30\uc220\uc758 \uad6c\ud604\uccb4\ub77c\uace0 \ub9d0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,o.kt)("p",null,"\ub2e4\ub9cc, \ub3c4\ucee4\ub294 \uc5ec\ub7ec \ucee8\ud14c\uc774\ub108 \uac00\uc0c1\ud654 \ub3c4\uad6c \uc911\uc5d0\uc11c \uc26c\uc6b4 \uc0ac\uc6a9\uc131\uacfc \ub192\uc740 \ud6a8\uc728\uc131\uc744 \ubc14\ud0d5\uc73c\ub85c \uac00\uc7a5 \ube60\ub974\uac8c \uc131\uc7a5\ud558\uc5ec \ub300\uc138\uac00 \ub418\uc5c8\uae30\uc5d0 \ucee8\ud14c\uc774\ub108\ud558\uba74 \ub3c4\ucee4\ub77c\ub294 \uc774\ubbf8\uc9c0\uac00 \uc790\ub3d9\uc73c\ub85c \ub5a0\uc624\ub974\uac8c \ub418\uc5c8\uc2b5\ub2c8\ub2e4. \uc774\ub807\uac8c \ucee8\ud14c\uc774\ub108\uc640 \ub3c4\ucee4 \uc0dd\ud0dc\uacc4\uac00 \ub300\uc138\uac00 \ub418\uae30\uae4c\uc9c0\ub294 \ub2e4\uc591\ud55c \uc774\uc720\uac00 \uc788\uc9c0\ub9cc, \uae30\uc220\uc801\uc73c\ub85c \uc790\uc138\ud55c \uc774\uc57c\uae30\ub294 ",(0,o.kt)("em",{parentName:"p"},"\ubaa8\ub450\uc758 MLOps"),"\uc758 \ubc94\uc704\ub97c \ub118\uc5b4\uc11c\uae30 \ub54c\ubb38\uc5d0 \ub2e4\ub8e8\uc9c0\ub294 \uc54a\uaca0\uc2b5\ub2c8\ub2e4."),(0,o.kt)("p",null,"\ucee8\ud14c\uc774\ub108 \ud639\uc740 \ub3c4\ucee4\ub97c \ucc98\uc74c \ub4e4\uc5b4\ubcf4\uc2dc\ub294 \ubd84\ub4e4\uc5d0\uac8c\ub294 ",(0,o.kt)("em",{parentName:"p"},"\ubaa8\ub450\uc758 MLOps"),"\uc758 \ub0b4\uc6a9\uc774 \ub2e4\uc18c \uc5b4\ub835\uac8c \ub290\uaef4\uc9c8 \uc218 \uc788\uc73c\ubbc0\ub85c, ",(0,o.kt)("a",{parentName:"p",href:"https://opentutorials.org/course/4781"},"\uc0dd\ud65c\ucf54\ub529"),", ",(0,o.kt)("a",{parentName:"p",href:"https://subicura.com/2017/01/19/docker-guide-for-beginners-1.html"},"subicura \ub2d8\uc758 \uac1c\uc778 \ube14\ub85c\uadf8 \uae00")," \ub4f1\uc758 \uc790\ub8cc\ub97c \uba3c\uc800 \uc0b4\ud3b4\ubcf4\ub294 \uac83\uc744 \uad8c\uc7a5\ud569\ub2c8\ub2e4."),(0,o.kt)("h2",{id:"container-orchestration-system"},"Container Orchestration System"),(0,o.kt)("p",null,"\uadf8\ub807\ub2e4\uba74 \ucee8\ud14c\uc774\ub108 \uc624\ucf00\uc2a4\ud2b8\ub808\uc774\uc158 \uc2dc\uc2a4\ud15c\uc740 \ubb34\uc5c7\uc77c\uae4c\uc694? ",(0,o.kt)("strong",{parentName:"p"},"\uc624\ucf00\uc2a4\ud2b8\ub808\uc774\uc158"),"\uc774\ub77c\ub294 \ub2e8\uc5b4\uc5d0\uc11c \ucd94\uce21\ud574 \ubcfc \uc218 \uc788\ub4ef\uc774, \uc218\ub9ce\uc740 \ucee8\ud14c\uc774\ub108\uac00 \uc788\uc744 \ub54c \ucee8\ud14c\uc774\ub108\ub4e4\uc774 \uc11c\ub85c \uc870\ud654\ub86d\uac8c \uad6c\ub3d9\ub420 \uc218 \uc788\ub3c4\ub85d \uc9c0\ud718\ud558\ub294 \uc2dc\uc2a4\ud15c\uc5d0 \ube44\uc720\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,o.kt)("p",null,"\ucee8\ud14c\uc774\ub108 \uae30\ubc18\uc758 \uc2dc\uc2a4\ud15c\uc5d0\uc11c \uc11c\ube44\uc2a4\ub294 \ucee8\ud14c\uc774\ub108\uc758 \ud615\ud0dc\ub85c \uc0ac\uc6a9\uc790\ub4e4\uc5d0\uac8c \uc81c\uacf5\ub429\ub2c8\ub2e4. \uc774\ub54c \uad00\ub9ac\ud574\uc57c \ud560 \ucee8\ud14c\uc774\ub108\uc758 \uc218\uac00 \uc801\ub2e4\uba74 \uc6b4\uc601 \ub2f4\ub2f9\uc790 \ud55c \uba85\uc774\uc11c\ub3c4 \ucda9\ubd84\ud788 \ubaa8\ub4e0 \uc0c1\ud669\uc5d0 \ub300\uc751\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.",(0,o.kt)("br",{parentName:"p"}),"\n","\ud558\uc9c0\ub9cc, \uc218\ubc31 \uac1c \uc774\uc0c1\uc758 \ucee8\ud14c\uc774\ub108\uac00 \uc218 \uc2ed \ub300 \uc774\uc0c1\uc758 \ud074\ub7ec\uc2a4\ud130\uc5d0\uc11c \uad6c\ub3d9\ub418\uace0 \uc788\uace0 \uc7a5\uc560\ub97c \uc77c\uc73c\ud0a4\uc9c0 \uc54a\uace0 \ud56d\uc0c1 \uc815\uc0c1 \ub3d9\uc791\ud574\uc57c \ud55c\ub2e4\uba74, \ubaa8\ub4e0 \uc11c\ube44\uc2a4\uc758 \uc815\uc0c1 \ub3d9\uc791 \uc5ec\ubd80\ub97c \ub2f4\ub2f9\uc790 \ud55c \uba85\uc774 \ud30c\uc545\ud558\uace0 \uc774\uc288\uc5d0 \ub300\uc751\ud558\ub294 \uac83\uc740 \ubd88\uac00\ub2a5\uc5d0 \uac00\uae5d\uc2b5\ub2c8\ub2e4."),(0,o.kt)("p",null,"\uc608\ub97c \ub4e4\uba74, \ubaa8\ub4e0 \uc11c\ube44\uc2a4\uac00 \uc815\uc0c1\uc801\uc73c\ub85c \ub3d9\uc791\ud558\uace0 \uc788\ub294\uc9c0\ub97c \uacc4\uc18d\ud574\uc11c \ubaa8\ub2c8\ud130\ub9c1(Monitoring)\ud574\uc57c \ud569\ub2c8\ub2e4.",(0,o.kt)("br",{parentName:"p"}),"\n","\ub9cc\uc57d, \ud2b9\uc815 \uc11c\ube44\uc2a4\uac00 \uc7a5\uc560\ub97c \uc77c\uc73c\ucf30\ub2e4\uba74 \uc5ec\ub7ec \ucee8\ud14c\uc774\ub108\uc758 \ub85c\uadf8\ub97c \ud655\uc778\ud574\uac00\uba70 \ubb38\uc81c\ub97c \ud30c\uc545\ud574\uc57c \ud569\ub2c8\ub2e4.",(0,o.kt)("br",{parentName:"p"}),"\n","\ub610\ud55c, \ud2b9\uc815 \ud074\ub7ec\uc2a4\ud130\ub098 \ud2b9\uc815 \ucee8\ud14c\uc774\ub108\uc5d0 \uc791\uc5c5\uc774 \ubab0\ub9ac\uc9c0 \uc54a\ub3c4\ub85d \uc2a4\ucf00\uc904\ub9c1(Scheduling)\ud558\uace0 \ub85c\ub4dc \ubc38\ub7f0\uc2f1(Load Balancing)\ud558\uba70, \uc2a4\ucf00\uc77c\ub9c1(Scaling)\ud558\ub294 \ub4f1\uc758 \uc218\ub9ce\uc740 \uc791\uc5c5\uc744 \ub2f4\ub2f9\ud574\uc57c \ud569\ub2c8\ub2e4.\n\uc774\ub807\uac8c \uc218\ub9ce\uc740 \ucee8\ud14c\uc774\ub108\uc758 \uc0c1\ud0dc\ub97c \uc9c0\uc18d\ud574\uc11c \uad00\ub9ac\ud558\uace0 \uc6b4\uc601\ud558\ub294 \uacfc\uc815\uc744 \uc870\uae08\uc774\ub098\ub9c8 \uc27d\uac8c, \uc790\ub3d9\uc73c\ub85c \ud560 \uc218 \uc788\ub294 \uae30\ub2a5\uc744 \uc81c\uacf5\ud574\uc8fc\ub294 \uc18c\ud504\ud2b8\uc6e8\uc5b4\uac00 \ubc14\ub85c \ucee8\ud14c\uc774\ub108 \uc624\ucf00\uc2a4\ud2b8\ub808\uc774\uc158 \uc2dc\uc2a4\ud15c\uc785\ub2c8\ub2e4. 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number_2)\n\n@pipeline(name="example_pipeline")\ndef example_pipeline(number_1: int, number_2: int):\n number_1_result = print_and_return_number(number_1)\n number_2_result = print_and_return_number(number_2)\n sum_result = sum_and_print_numbers(\n number_1=number_1_result.output, number_2=number_2_result.output\n )\n\nif __name__ == "__main__":\n kfp.compiler.Compiler().compile(example_pipeline, "example_pipeline.yaml")\n')),(0,p.kt)("p",null,"\ucef4\ud30c\uc77c\ub41c \uacb0\uacfc\ub97c \ubcf4\uba74 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4."),(0,p.kt)("details",null,(0,p.kt)("summary",null,"example_pipeline.yaml"),(0,p.kt)("pre",null,(0,p.kt)("code",{parentName:"pre",className:"language-bash"},'apiVersion: argoproj.io/v1alpha1\nkind: Workflow\nmetadata:\n generateName: example-pipeline-\n annotations: {pipelines.kubeflow.org/kfp_sdk_version: 1.6.3, pipelines.kubeflow.org/pipeline_compilation_time: \'2021-12-05T13:38:51.566777\',\n pipelines.kubeflow.org/pipeline_spec: \'{"inputs": [{"name": "number_1", "type":\n "Integer"}, {"name": "number_2", "type": "Integer"}], "name": "example_pipeline"}\'}\n labels: {pipelines.kubeflow.org/kfp_sdk_version: 1.6.3}\nspec:\n entrypoint: example-pipeline\n templates:\n - name: example-pipeline\n inputs:\n parameters:\n - {name: number_1}\n - {name: number_2}\n dag:\n tasks:\n - name: print-and-return-number\n template: print-and-return-number\n arguments:\n parameters:\n - {name: number_1, value: \'{{inputs.parameters.number_1}}\'}\n - name: print-and-return-number-2\n template: print-and-return-number-2\n arguments:\n parameters:\n - {name: number_2, value: \'{{inputs.parameters.number_2}}\'}\n - name: sum-and-print-numbers\n template: sum-and-print-numbers\n dependencies: [print-and-return-number, print-and-return-number-2]\n arguments:\n parameters:\n - {name: print-and-return-number-2-Output, value: \'{{tasks.print-and-return-number-2.outputs.parameters.print-and-return-number-2-Output}}\'}\n - {name: print-and-return-number-Output, value: \'{{tasks.print-and-return-number.outputs.parameters.print-and-return-number-Output}}\'}\n - name: print-and-return-number\n container:\n args: [--number, \'{{inputs.parameters.number_1}}\', \'----output-paths\', /tmp/outputs/Output/data]\n command:\n - sh\n - -ec\n - |\n program_path=$(mktemp)\n printf "%s" "$0" > "$program_path"\n python3 -u "$program_path" "$@"\n - |\n def print_and_return_number(number):\n print(number)\n return number\n\n def _serialize_int(int_value: int) -> str:\n if isinstance(int_value, str):\n return int_value\n if not isinstance(int_value, int):\n raise TypeError(\'Value "{}" has type "{}" instead of int.\'.format(str(int_value), str(type(int_value))))\n return str(int_value)\n\n import argparse\n _parser = argparse.ArgumentParser(prog=\'Print and return number\', description=\'\')\n _parser.add_argument("--number", dest="number", type=int, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("----output-paths", dest="_output_paths", type=str, nargs=1)\n _parsed_args = vars(_parser.parse_args())\n _output_files = _parsed_args.pop("_output_paths", [])\n\n _outputs = print_and_return_number(**_parsed_args)\n\n _outputs = [_outputs]\n\n _output_serializers = [\n _serialize_int,\n\n ]\n\n import os\n for idx, output_file in enumerate(_output_files):\n try:\n os.makedirs(os.path.dirname(output_file))\n except OSError:\n pass\n with open(output_file, \'w\') as f:\n f.write(_output_serializers[idx](_outputs[idx]))\n image: python:3.7\n inputs:\n parameters:\n - {name: number_1}\n outputs:\n parameters:\n - name: print-and-return-number-Output\n valueFrom: {path: /tmp/outputs/Output/data}\n artifacts:\n - {name: print-and-return-number-Output, path: /tmp/outputs/Output/data}\n metadata:\n labels: {pipelines.kubeflow.org/kfp_sdk_version: 1.6.3, pipelines.kubeflow.org/pipeline-sdk-type: kfp}\n annotations: {pipelines.kubeflow.org/component_spec: \'{"implementation": {"container":\n {"args": ["--number", {"inputValue": "number"}, "----output-paths", {"outputPath":\n "Output"}], "command": ["sh", "-ec", "program_path=$(mktemp)\\nprintf \\"%s\\"\n \\"$0\\" > \\"$program_path\\"\\npython3 -u \\"$program_path\\" \\"$@\\"\\n", "def\n print_and_return_number(number):\\n print(number)\\n return number\\n\\ndef\n _serialize_int(int_value: int) -> str:\\n if isinstance(int_value, str):\\n return\n int_value\\n if not isinstance(int_value, int):\\n raise TypeError(\'\'Value\n \\"{}\\" has type \\"{}\\" instead of int.\'\'.format(str(int_value), str(type(int_value))))\\n return\n str(int_value)\\n\\nimport argparse\\n_parser = argparse.ArgumentParser(prog=\'\'Print\n and return number\'\', description=\'\'\'\')\\n_parser.add_argument(\\"--number\\",\n dest=\\"number\\", type=int, required=True, default=argparse.SUPPRESS)\\n_parser.add_argument(\\"----output-paths\\",\n dest=\\"_output_paths\\", type=str, nargs=1)\\n_parsed_args = vars(_parser.parse_args())\\n_output_files\n = _parsed_args.pop(\\"_output_paths\\", [])\\n\\n_outputs = print_and_return_number(**_parsed_args)\\n\\n_outputs\n = [_outputs]\\n\\n_output_serializers = [\\n _serialize_int,\\n\\n]\\n\\nimport\n os\\nfor idx, output_file in enumerate(_output_files):\\n try:\\n os.makedirs(os.path.dirname(output_file))\\n except\n OSError:\\n pass\\n with open(output_file, \'\'w\'\') as f:\\n f.write(_output_serializers[idx](_outputs[idx]))\\n"],\n "image": "python:3.7"}}, "inputs": [{"name": "number", "type": "Integer"}],\n "name": "Print and return number", "outputs": [{"name": "Output", "type":\n "Integer"}]}\', pipelines.kubeflow.org/component_ref: \'{}\', pipelines.kubeflow.org/arguments.parameters: \'{"number":\n "{{inputs.parameters.number_1}}"}\'}\n - name: print-and-return-number-2\n container:\n args: [--number, \'{{inputs.parameters.number_2}}\', \'----output-paths\', /tmp/outputs/Output/data]\n command:\n - sh\n - -ec\n - |\n program_path=$(mktemp)\n printf "%s" "$0" > "$program_path"\n python3 -u "$program_path" "$@"\n - |\n def print_and_return_number(number):\n print(number)\n return number\n\n def _serialize_int(int_value: int) -> str:\n if isinstance(int_value, str):\n return int_value\n if not isinstance(int_value, int):\n raise TypeError(\'Value "{}" has type "{}" instead of int.\'.format(str(int_value), str(type(int_value))))\n return str(int_value)\n\n import argparse\n _parser = argparse.ArgumentParser(prog=\'Print and return number\', description=\'\')\n _parser.add_argument("--number", dest="number", type=int, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("----output-paths", dest="_output_paths", type=str, nargs=1)\n _parsed_args = vars(_parser.parse_args())\n _output_files = _parsed_args.pop("_output_paths", [])\n\n _outputs = print_and_return_number(**_parsed_args)\n\n _outputs = [_outputs]\n\n _output_serializers = [\n _serialize_int,\n\n ]\n\n import os\n for idx, output_file in enumerate(_output_files):\n try:\n os.makedirs(os.path.dirname(output_file))\n except OSError:\n pass\n with open(output_file, \'w\') as f:\n f.write(_output_serializers[idx](_outputs[idx]))\n image: python:3.7\n inputs:\n parameters:\n - {name: number_2}\n outputs:\n parameters:\n - name: print-and-return-number-2-Output\n valueFrom: {path: /tmp/outputs/Output/data}\n artifacts:\n - {name: print-and-return-number-2-Output, path: /tmp/outputs/Output/data}\n metadata:\n labels: {pipelines.kubeflow.org/kfp_sdk_version: 1.6.3, pipelines.kubeflow.org/pipeline-sdk-type: kfp}\n annotations: {pipelines.kubeflow.org/component_spec: \'{"implementation": {"container":\n {"args": ["--number", {"inputValue": "number"}, "----output-paths", {"outputPath":\n "Output"}], "command": ["sh", "-ec", "program_path=$(mktemp)\\nprintf \\"%s\\"\n \\"$0\\" > \\"$program_path\\"\\npython3 -u \\"$program_path\\" \\"$@\\"\\n", "def\n print_and_return_number(number):\\n print(number)\\n return number\\n\\ndef\n _serialize_int(int_value: int) -> str:\\n if isinstance(int_value, str):\\n return\n int_value\\n if not isinstance(int_value, int):\\n raise TypeError(\'\'Value\n \\"{}\\" has type \\"{}\\" instead of int.\'\'.format(str(int_value), str(type(int_value))))\\n return\n str(int_value)\\n\\nimport argparse\\n_parser = argparse.ArgumentParser(prog=\'\'Print\n and return number\'\', description=\'\'\'\')\\n_parser.add_argument(\\"--number\\",\n dest=\\"number\\", type=int, required=True, default=argparse.SUPPRESS)\\n_parser.add_argument(\\"----output-paths\\",\n dest=\\"_output_paths\\", type=str, nargs=1)\\n_parsed_args = vars(_parser.parse_args())\\n_output_files\n = _parsed_args.pop(\\"_output_paths\\", [])\\n\\n_outputs = print_and_return_number(**_parsed_args)\\n\\n_outputs\n = [_outputs]\\n\\n_output_serializers = [\\n _serialize_int,\\n\\n]\\n\\nimport\n os\\nfor idx, output_file in enumerate(_output_files):\\n try:\\n os.makedirs(os.path.dirname(output_file))\\n except\n OSError:\\n pass\\n with open(output_file, \'\'w\'\') as f:\\n f.write(_output_serializers[idx](_outputs[idx]))\\n"],\n "image": "python:3.7"}}, "inputs": [{"name": "number", "type": "Integer"}],\n "name": "Print and return number", "outputs": [{"name": "Output", "type":\n "Integer"}]}\', pipelines.kubeflow.org/component_ref: \'{}\', pipelines.kubeflow.org/arguments.parameters: \'{"number":\n "{{inputs.parameters.number_2}}"}\'}\n - name: sum-and-print-numbers\n container:\n args: [--number-1, \'{{inputs.parameters.print-and-return-number-Output}}\', --number-2,\n \'{{inputs.parameters.print-and-return-number-2-Output}}\']\n command:\n - sh\n - -ec\n - |\n program_path=$(mktemp)\n printf "%s" "$0" > "$program_path"\n python3 -u "$program_path" "$@"\n - |\n def sum_and_print_numbers(number_1, number_2):\n print(number_1 + number_2)\n\n import argparse\n _parser = argparse.ArgumentParser(prog=\'Sum and print numbers\', description=\'\')\n _parser.add_argument("--number-1", dest="number_1", type=int, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--number-2", dest="number_2", type=int, required=True, default=argparse.SUPPRESS)\n _parsed_args = vars(_parser.parse_args())\n\n _outputs = sum_and_print_numbers(**_parsed_args)\n image: python:3.7\n inputs:\n parameters:\n - {name: print-and-return-number-2-Output}\n - {name: print-and-return-number-Output}\n metadata:\n labels: {pipelines.kubeflow.org/kfp_sdk_version: 1.6.3, pipelines.kubeflow.org/pipeline-sdk-type: kfp}\n annotations: {pipelines.kubeflow.org/component_spec: \'{"implementation": {"container":\n {"args": ["--number-1", {"inputValue": "number_1"}, "--number-2", {"inputValue":\n "number_2"}], "command": ["sh", "-ec", "program_path=$(mktemp)\\nprintf \\"%s\\"\n \\"$0\\" > \\"$program_path\\"\\npython3 -u \\"$program_path\\" \\"$@\\"\\n", "def\n sum_and_print_numbers(number_1, number_2):\\n print(number_1 + number_2)\\n\\nimport\n argparse\\n_parser = argparse.ArgumentParser(prog=\'\'Sum and print numbers\'\',\n description=\'\'\'\')\\n_parser.add_argument(\\"--number-1\\", dest=\\"number_1\\",\n type=int, required=True, default=argparse.SUPPRESS)\\n_parser.add_argument(\\"--number-2\\",\n dest=\\"number_2\\", type=int, required=True, default=argparse.SUPPRESS)\\n_parsed_args\n = vars(_parser.parse_args())\\n\\n_outputs = sum_and_print_numbers(**_parsed_args)\\n"],\n "image": "python:3.7"}}, "inputs": [{"name": "number_1", "type": "Integer"},\n {"name": "number_2", "type": "Integer"}], "name": "Sum and print numbers"}\',\n pipelines.kubeflow.org/component_ref: \'{}\', pipelines.kubeflow.org/arguments.parameters: \'{"number_1":\n "{{inputs.parameters.print-and-return-number-Output}}", "number_2": "{{inputs.parameters.print-and-return-number-2-Output}}"}\'}\n arguments:\n parameters:\n - {name: number_1}\n - {name: number_2}\n serviceAccountName: pipeline-runner\n'))))}l.isMDXComponent=!0},5740:(n,e,r)=>{r.d(e,{Z:()=>t});const 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[{"name": "number_1", "type":\n "Integer"}, {"name": "number_2", "type": "Integer"}], "name": "example_pipeline"}\'}\n labels: {pipelines.kubeflow.org/kfp_sdk_version: 1.6.3}\nspec:\n entrypoint: example-pipeline\n templates:\n - name: example-pipeline\n inputs:\n parameters:\n - {name: number_1}\n - {name: number_2}\n dag:\n tasks:\n - name: print-and-return-number\n template: print-and-return-number\n arguments:\n parameters:\n - {name: number_1, value: \'{{inputs.parameters.number_1}}\'}\n - name: print-and-return-number-2\n template: print-and-return-number-2\n arguments:\n parameters:\n - {name: number_2, value: \'{{inputs.parameters.number_2}}\'}\n - name: sum-and-print-numbers\n template: sum-and-print-numbers\n dependencies: [print-and-return-number, print-and-return-number-2]\n arguments:\n parameters:\n - {name: print-and-return-number-2-Output, value: \'{{tasks.print-and-return-number-2.outputs.parameters.print-and-return-number-2-Output}}\'}\n - {name: print-and-return-number-Output, value: \'{{tasks.print-and-return-number.outputs.parameters.print-and-return-number-Output}}\'}\n - name: print-and-return-number\n container:\n args: [--number, \'{{inputs.parameters.number_1}}\', \'----output-paths\', /tmp/outputs/Output/data]\n command:\n - sh\n - -ec\n - |\n program_path=$(mktemp)\n printf "%s" "$0" > "$program_path"\n python3 -u "$program_path" "$@"\n - |\n def print_and_return_number(number):\n print(number)\n return number\n\n def _serialize_int(int_value: int) -> str:\n if isinstance(int_value, str):\n return int_value\n if not isinstance(int_value, int):\n raise TypeError(\'Value "{}" has type "{}" instead of int.\'.format(str(int_value), str(type(int_value))))\n return str(int_value)\n\n import argparse\n _parser = argparse.ArgumentParser(prog=\'Print and return number\', description=\'\')\n _parser.add_argument("--number", dest="number", type=int, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("----output-paths", dest="_output_paths", type=str, nargs=1)\n _parsed_args = vars(_parser.parse_args())\n _output_files = _parsed_args.pop("_output_paths", [])\n\n _outputs = print_and_return_number(**_parsed_args)\n\n _outputs = [_outputs]\n\n _output_serializers = [\n _serialize_int,\n\n ]\n\n import os\n for idx, output_file in enumerate(_output_files):\n try:\n os.makedirs(os.path.dirname(output_file))\n except OSError:\n pass\n with open(output_file, \'w\') as f:\n f.write(_output_serializers[idx](_outputs[idx]))\n image: python:3.7\n inputs:\n parameters:\n - {name: number_1}\n outputs:\n parameters:\n - name: print-and-return-number-Output\n valueFrom: {path: /tmp/outputs/Output/data}\n artifacts:\n - {name: print-and-return-number-Output, path: /tmp/outputs/Output/data}\n metadata:\n labels: {pipelines.kubeflow.org/kfp_sdk_version: 1.6.3, pipelines.kubeflow.org/pipeline-sdk-type: kfp}\n annotations: {pipelines.kubeflow.org/component_spec: \'{"implementation": {"container":\n {"args": ["--number", {"inputValue": "number"}, "----output-paths", {"outputPath":\n "Output"}], "command": ["sh", "-ec", "program_path=$(mktemp)\\nprintf \\"%s\\"\n \\"$0\\" > \\"$program_path\\"\\npython3 -u \\"$program_path\\" \\"$@\\"\\n", "def\n print_and_return_number(number):\\n print(number)\\n return number\\n\\ndef\n _serialize_int(int_value: int) -> str:\\n if isinstance(int_value, str):\\n return\n int_value\\n if not isinstance(int_value, int):\\n raise TypeError(\'\'Value\n \\"{}\\" has type \\"{}\\" instead of int.\'\'.format(str(int_value), str(type(int_value))))\\n return\n str(int_value)\\n\\nimport argparse\\n_parser = argparse.ArgumentParser(prog=\'\'Print\n and return number\'\', description=\'\'\'\')\\n_parser.add_argument(\\"--number\\",\n dest=\\"number\\", type=int, required=True, default=argparse.SUPPRESS)\\n_parser.add_argument(\\"----output-paths\\",\n dest=\\"_output_paths\\", type=str, nargs=1)\\n_parsed_args = vars(_parser.parse_args())\\n_output_files\n = _parsed_args.pop(\\"_output_paths\\", [])\\n\\n_outputs = print_and_return_number(**_parsed_args)\\n\\n_outputs\n = [_outputs]\\n\\n_output_serializers = [\\n _serialize_int,\\n\\n]\\n\\nimport\n os\\nfor idx, output_file in enumerate(_output_files):\\n try:\\n os.makedirs(os.path.dirname(output_file))\\n except\n OSError:\\n pass\\n with open(output_file, \'\'w\'\') as f:\\n f.write(_output_serializers[idx](_outputs[idx]))\\n"],\n "image": "python:3.7"}}, "inputs": [{"name": "number", "type": "Integer"}],\n "name": "Print and return number", "outputs": [{"name": "Output", "type":\n "Integer"}]}\', pipelines.kubeflow.org/component_ref: \'{}\', pipelines.kubeflow.org/arguments.parameters: \'{"number":\n "{{inputs.parameters.number_1}}"}\'}\n - name: print-and-return-number-2\n container:\n args: [--number, \'{{inputs.parameters.number_2}}\', \'----output-paths\', /tmp/outputs/Output/data]\n command:\n - sh\n - -ec\n - |\n program_path=$(mktemp)\n printf "%s" "$0" > "$program_path"\n python3 -u "$program_path" "$@"\n - |\n def print_and_return_number(number):\n print(number)\n return number\n\n def _serialize_int(int_value: int) -> str:\n if isinstance(int_value, str):\n return int_value\n if not isinstance(int_value, int):\n raise TypeError(\'Value "{}" has type "{}" instead of int.\'.format(str(int_value), str(type(int_value))))\n return str(int_value)\n\n import argparse\n _parser = argparse.ArgumentParser(prog=\'Print and return number\', description=\'\')\n _parser.add_argument("--number", dest="number", type=int, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("----output-paths", dest="_output_paths", type=str, nargs=1)\n _parsed_args = vars(_parser.parse_args())\n _output_files = _parsed_args.pop("_output_paths", [])\n\n _outputs = print_and_return_number(**_parsed_args)\n\n _outputs = [_outputs]\n\n _output_serializers = [\n _serialize_int,\n\n ]\n\n import os\n for idx, output_file in enumerate(_output_files):\n try:\n os.makedirs(os.path.dirname(output_file))\n except OSError:\n pass\n with open(output_file, \'w\') as f:\n f.write(_output_serializers[idx](_outputs[idx]))\n image: python:3.7\n inputs:\n parameters:\n - {name: number_2}\n outputs:\n parameters:\n - name: print-and-return-number-2-Output\n valueFrom: {path: /tmp/outputs/Output/data}\n artifacts:\n - {name: print-and-return-number-2-Output, path: /tmp/outputs/Output/data}\n metadata:\n labels: {pipelines.kubeflow.org/kfp_sdk_version: 1.6.3, pipelines.kubeflow.org/pipeline-sdk-type: kfp}\n annotations: {pipelines.kubeflow.org/component_spec: \'{"implementation": {"container":\n {"args": ["--number", {"inputValue": "number"}, "----output-paths", {"outputPath":\n "Output"}], "command": ["sh", "-ec", "program_path=$(mktemp)\\nprintf \\"%s\\"\n \\"$0\\" > \\"$program_path\\"\\npython3 -u \\"$program_path\\" \\"$@\\"\\n", "def\n print_and_return_number(number):\\n print(number)\\n return number\\n\\ndef\n _serialize_int(int_value: int) -> str:\\n if isinstance(int_value, str):\\n return\n int_value\\n if not isinstance(int_value, int):\\n raise TypeError(\'\'Value\n \\"{}\\" has type \\"{}\\" instead of int.\'\'.format(str(int_value), str(type(int_value))))\\n return\n str(int_value)\\n\\nimport argparse\\n_parser = argparse.ArgumentParser(prog=\'\'Print\n and return number\'\', description=\'\'\'\')\\n_parser.add_argument(\\"--number\\",\n dest=\\"number\\", type=int, required=True, default=argparse.SUPPRESS)\\n_parser.add_argument(\\"----output-paths\\",\n dest=\\"_output_paths\\", type=str, nargs=1)\\n_parsed_args = vars(_parser.parse_args())\\n_output_files\n = _parsed_args.pop(\\"_output_paths\\", [])\\n\\n_outputs = print_and_return_number(**_parsed_args)\\n\\n_outputs\n = [_outputs]\\n\\n_output_serializers = [\\n _serialize_int,\\n\\n]\\n\\nimport\n os\\nfor idx, output_file in enumerate(_output_files):\\n try:\\n os.makedirs(os.path.dirname(output_file))\\n except\n OSError:\\n pass\\n with open(output_file, \'\'w\'\') as f:\\n f.write(_output_serializers[idx](_outputs[idx]))\\n"],\n "image": "python:3.7"}}, "inputs": [{"name": "number", "type": "Integer"}],\n "name": "Print and return number", "outputs": [{"name": "Output", "type":\n "Integer"}]}\', pipelines.kubeflow.org/component_ref: \'{}\', pipelines.kubeflow.org/arguments.parameters: \'{"number":\n "{{inputs.parameters.number_2}}"}\'}\n - name: sum-and-print-numbers\n container:\n args: [--number-1, \'{{inputs.parameters.print-and-return-number-Output}}\', --number-2,\n \'{{inputs.parameters.print-and-return-number-2-Output}}\']\n command:\n - sh\n - -ec\n - |\n program_path=$(mktemp)\n printf "%s" "$0" > "$program_path"\n python3 -u "$program_path" "$@"\n - |\n def sum_and_print_numbers(number_1, number_2):\n print(number_1 + number_2)\n\n import argparse\n _parser = argparse.ArgumentParser(prog=\'Sum and print numbers\', description=\'\')\n _parser.add_argument("--number-1", dest="number_1", type=int, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--number-2", dest="number_2", type=int, required=True, default=argparse.SUPPRESS)\n _parsed_args = vars(_parser.parse_args())\n\n _outputs = sum_and_print_numbers(**_parsed_args)\n image: python:3.7\n inputs:\n parameters:\n - {name: print-and-return-number-2-Output}\n - {name: print-and-return-number-Output}\n metadata:\n labels: {pipelines.kubeflow.org/kfp_sdk_version: 1.6.3, pipelines.kubeflow.org/pipeline-sdk-type: kfp}\n annotations: {pipelines.kubeflow.org/component_spec: \'{"implementation": {"container":\n {"args": ["--number-1", {"inputValue": "number_1"}, "--number-2", {"inputValue":\n "number_2"}], "command": ["sh", "-ec", "program_path=$(mktemp)\\nprintf \\"%s\\"\n \\"$0\\" > \\"$program_path\\"\\npython3 -u \\"$program_path\\" \\"$@\\"\\n", "def\n sum_and_print_numbers(number_1, number_2):\\n print(number_1 + number_2)\\n\\nimport\n argparse\\n_parser = argparse.ArgumentParser(prog=\'\'Sum and print numbers\'\',\n description=\'\'\'\')\\n_parser.add_argument(\\"--number-1\\", dest=\\"number_1\\",\n type=int, required=True, default=argparse.SUPPRESS)\\n_parser.add_argument(\\"--number-2\\",\n dest=\\"number_2\\", type=int, required=True, default=argparse.SUPPRESS)\\n_parsed_args\n = vars(_parser.parse_args())\\n\\n_outputs = sum_and_print_numbers(**_parsed_args)\\n"],\n "image": "python:3.7"}}, "inputs": [{"name": "number_1", "type": "Integer"},\n {"name": "number_2", "type": "Integer"}], "name": "Sum and print numbers"}\',\n pipelines.kubeflow.org/component_ref: \'{}\', pipelines.kubeflow.org/arguments.parameters: \'{"number_1":\n "{{inputs.parameters.print-and-return-number-Output}}", "number_2": "{{inputs.parameters.print-and-return-number-2-Output}}"}\'}\n arguments:\n parameters:\n - {name: number_1}\n - {name: number_2}\n serviceAccountName: pipeline-runner\n'))))}l.isMDXComponent=!0},5740:(n,e,r)=>{r.d(e,{Z:()=>t});const 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MLFLow Infos"),(0,r.kt)("p",null,"mlflow\uc5d0 \ud544\uc694\ud55c \uc815\ubcf4\ub4e4\uc744 \ub9cc\ub4dc\ub294 \uacfc\uc815\uc785\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-python"},'from mlflow.models.signature import infer_signature\nfrom mlflow.utils.environment import _mlflow_conda_env\n\ninput_example = data.sample(1)\nsignature = infer_signature(data, clf.predict(data))\nconda_env = _mlflow_conda_env(additional_pip_deps=["dill", "pandas", "scikit-learn"])\n')),(0,r.kt)("p",null,"\uac01 \ubcc0\uc218\uc758 \ub0b4\uc6a9\uc744 \ud655\uc778\ud558\uba74 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4."),(0,r.kt)("ul",null,(0,r.kt)("li",{parentName:"ul"},(0,r.kt)("p",{parentName:"li"},(0,r.kt)("inlineCode",{parentName:"p"},"input_example")),(0,r.kt)("table",{parentName:"li"},(0,r.kt)("thead",{parentName:"table"},(0,r.kt)("tr",{parentName:"thead"},(0,r.kt)("th",{parentName:"tr",align:null},"sepal length (cm)"),(0,r.kt)("th",{parentName:"tr",align:null},"sepal width (cm)"),(0,r.kt)("th",{parentName:"tr",align:null},"petal length (cm)"),(0,r.kt)("th",{parentName:"tr",align:null},"petal width (cm)"))),(0,r.kt)("tbody",{parentName:"table"},(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"6.5"),(0,r.kt)("td",{parentName:"tr",align:null},"6.7"),(0,r.kt)("td",{parentName:"tr",align:null},"3.1"),(0,r.kt)("td",{parentName:"tr",align:null},"4.4"))))),(0,r.kt)("li",{parentName:"ul"},(0,r.kt)("p",{parentName:"li"},(0,r.kt)("inlineCode",{parentName:"p"},"signature")),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-python"},"inputs:\n ['sepal length (cm)': double, 'sepal width (cm)': double, 'petal length (cm)': double, 'petal width (cm)': double]\noutputs:\n [Tensor('int64', (-1,))]\n"))),(0,r.kt)("li",{parentName:"ul"},(0,r.kt)("p",{parentName:"li"},(0,r.kt)("inlineCode",{parentName:"p"},"conda_env")),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-python"},"{'name': 'mlflow-env',\n 'channels': ['conda-forge'],\n 'dependencies': ['python=3.8.10',\n 'pip',\n {'pip': ['mlflow', 'dill', 'pandas', 'scikit-learn']}]}\n")))),(0,r.kt)("h3",{id:"3-save-mlflow-infos"},"3. Save MLFLow Infos"),(0,r.kt)("p",null,"\ub2e4\uc74c\uc73c\ub85c \ud559\uc2b5\ud55c \uc815\ubcf4\ub4e4\uacfc \ubaa8\ub378\uc744 \uc800\uc7a5\ud569\ub2c8\ub2e4.\n\ud559\uc2b5\ud55c \ubaa8\ub378\uc774 sklearn \ud328\ud0a4\uc9c0\ub97c \uc774\uc6a9\ud558\uae30 \ub54c\ubb38\uc5d0 ",(0,r.kt)("inlineCode",{parentName:"p"},"mlflow.sklearn")," \uc744 \uc774\uc6a9\ud558\uba74 \uc27d\uac8c \ubaa8\ub378\uc744 \uc800\uc7a5\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-python"},'from mlflow.sklearn import save_model\n\nsave_model(\n sk_model=clf,\n path="svc",\n serialization_format="cloudpickle",\n conda_env=conda_env,\n signature=signature,\n input_example=input_example,\n)\n')),(0,r.kt)("p",null,"\ub85c\uceec\uc5d0\uc11c \uc791\uc5c5\ud558\uba74 \ub2e4\uc74c\uacfc \uac19\uc740 svc \ud3f4\ub354\uac00 \uc0dd\uae30\uba70 \uc544\ub798\uc640 \uac19\uc740 \ud30c\uc77c\ub4e4\uc774 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1.0.1\nsaved_input_example_info:\n artifact_path: input_example.json\n pandas_orient: split\n type: dataframe\nsignature:\n inputs: \'[{"name": "sepal length (cm)", "type": "double"}, {"name": "sepal width\n (cm)", "type": "double"}, {"name": "petal length (cm)", "type": "double"}, {"name":\n "petal width (cm)", "type": "double"}]\'\n outputs: \'[{"type": "tensor", "tensor-spec": {"dtype": "int64", "shape": [-1]}}]\'\nutc_time_created: \'2021-12-06 06:52:30.612810\'\n'))),(0,r.kt)("li",{parentName:"ul"},(0,r.kt)("p",{parentName:"li"},"conda.yaml"),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"channels:\n- conda-forge\ndependencies:\n- python=3.8.10\n- pip\n- pip:\n - mlflow\n - dill\n - pandas\n - scikit-learn\nname: mlflow-env\n"))),(0,r.kt)("li",{parentName:"ul"},(0,r.kt)("p",{parentName:"li"},"input_example.json"),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},'{\n "columns": \n [\n "sepal length (cm)",\n "sepal width (cm)",\n "petal length (cm)",\n "petal width (cm)"\n ],\n "data": \n [\n [6.7, 3.1, 4.4, 1.4]\n ]\n}\n'))),(0,r.kt)("li",{parentName:"ul"},(0,r.kt)("p",{parentName:"li"},"requirements.txt"),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"mlflow\ndill\npandas\nscikit-learn\n"))),(0,r.kt)("li",{parentName:"ul"},(0,r.kt)("p",{parentName:"li"},"model.pkl"))),(0,r.kt)("h2",{id:"mlflow-on-server"},"MLFlow on Server"),(0,r.kt)("p",null,"\uc774\uc81c \uc800\uc7a5\ub41c \ubaa8\ub378\uc744 mlflow \uc11c\ubc84\uc5d0 \uc62c\ub9ac\ub294 \uc791\uc5c5\uc744 \ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-python"},'import mlflow\n\nwith mlflow.start_run():\n mlflow.log_artifact("svc/")\n')),(0,r.kt)("p",null,"\uc800\uc7a5\ud558\uace0 ",(0,r.kt)("inlineCode",{parentName:"p"},"mlruns")," \uac00 \uc0dd\uc131\ub41c \uacbd\ub85c\uc5d0\uc11c 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\ud558\ubbc0\ub85c \uacb0\uad6d \ucef4\ud3ec\ub10c\ud2b8\uc5d0 \ub300\ud55c \ucd94\uac00 \uc815\ubcf4\ub97c \uc804\ub2ec\ud574\uc57c \ud569\ub2c8\ub2e4."),(0,r.kt)("p",null,"1\ubc88\uc758 \ubc29\ubc95\uc73c\ub85c \uc9c4\ud589\ud558\uae30 \uc704\ud574\uc11c\ub294 \ud559\uc2b5\ud558\ub294 \ucef4\ud3ec\ub10c\ud2b8 \ub610\ud55c \ubcc0\uacbd\ub418\uc5b4\uc57c \ud569\ub2c8\ub2e4.\n\ubaa8\ub378\uc744 \uc800\uc7a5\ud558\ub294\ub370 \ud544\uc694\ud55c \ud658\uacbd\ub4e4\uc744 \uc800\uc7a5\ud574\uc8fc\ub294 \ucf54\ub4dc\uac00 \ucd94\uac00\ub418\uc5b4\uc57c \ud569\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-python"},'from functools import partial\nfrom kfp.components import InputPath, OutputPath, create_component_from_func\n\n@partial(\n create_component_from_func,\n packages_to_install=["dill", "pandas", "scikit-learn", "mlflow"],\n)\ndef train_from_csv(\n train_data_path: InputPath("csv"),\n train_target_path: InputPath("csv"),\n model_path: OutputPath("dill"),\n input_example_path: OutputPath("dill"),\n signature_path: OutputPath("dill"),\n conda_env_path: OutputPath("dill"),\n kernel: str,\n):\n import dill\n import pandas as pd\n from sklearn.svm import SVC\n\n from mlflow.models.signature import infer_signature\n from mlflow.utils.environment import _mlflow_conda_env\n\n train_data = pd.read_csv(train_data_path)\n train_target = pd.read_csv(train_target_path)\n\n clf = SVC(kernel=kernel)\n clf.fit(train_data, train_target)\n\n with open(model_path, mode="wb") as file_writer:\n dill.dump(clf, file_writer)\n\n input_example = train_data.sample(1)\n with open(input_example_path, "wb") as file_writer:\n dill.dump(input_example, file_writer)\n\n signature = infer_signature(train_data, clf.predict(train_data))\n with open(signature_path, "wb") as file_writer:\n dill.dump(signature, file_writer)\n\n conda_env = _mlflow_conda_env(\n additional_pip_deps=["dill", "pandas", "scikit-learn"]\n )\n with open(conda_env_path, "wb") as file_writer:\n dill.dump(conda_env, file_writer)\n\n')),(0,r.kt)("p",null,"\uadf8\ub9ac\uace0 MLFlow\uc5d0 \uc5c5\ub85c\ub4dc\ud558\ub294 \ucef4\ud3ec\ub10c\ud2b8\ub97c \uc791\uc131\ud569\ub2c8\ub2e4.\n\uc774 \ub54c \uc5c5\ub85c\ub4dc\ub418\ub294 MLflow\uc758 endpoint\ub97c \uc6b0\ub9ac\uac00 \uc124\uce58\ud55c ",(0,r.kt)("a",{parentName:"p",href:"/docs/1.0/setup-components/install-components-mlflow"},"mlflow service")," \ub85c \uc774\uc5b4\uc9c0\uac8c \uc124\uc815\ud574\uc8fc\uc5b4\uc57c \ud569\ub2c8\ub2e4.",(0,r.kt)("br",{parentName:"p"}),"\n","\uc774 \ub54c S3 Endpoint\uc758 \uc8fc\uc18c\ub294 MLflow Server \uc124\uce58 \ub2f9\uc2dc \uc124\uce58\ud55c minio\uc758 ",(0,r.kt)("a",{parentName:"p",href:"https://kubernetes.io/ko/docs/concepts/services-networking/dns-pod-service/"},"\ucfe0\ubc84\ub124\ud2f0\uc2a4 \uc11c\ube44\uc2a4 DNS \ub124\uc784\uc744 \ud65c\uc6a9"),"\ud569\ub2c8\ub2e4. \ud574\ub2f9 service \ub294 kubeflow namespace\uc5d0\uc11c minio-service\ub77c\ub294 \uc774\ub984\uc73c\ub85c \uc0dd\uc131\ub418\uc5c8\uc73c\ubbc0\ub85c, ",(0,r.kt)("inlineCode",{parentName:"p"},"http://minio-service.kubeflow.svc:9000")," \ub85c \uc124\uc815\ud569\ub2c8\ub2e4.",(0,r.kt)("br",{parentName:"p"}),"\n","\uc774\uc640 \ube44\uc2b7\ud558\uac8c tracking_uri\uc758 \uc8fc\uc18c\ub294 mlflow server\uc758 \ucfe0\ubc84\ub124\ud2f0\uc2a4 \uc11c\ube44\uc2a4 DNS \ub124\uc784\uc744 \ud65c\uc6a9\ud558\uc5ec, ",(0,r.kt)("inlineCode",{parentName:"p"},"http://mlflow-server-service.mlflow-system.svc:5000")," \ub85c \uc124\uc815\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-python"},'from functools import partial\nfrom kfp.components import InputPath, create_component_from_func\n\n@partial(\n create_component_from_func,\n packages_to_install=["dill", "pandas", "scikit-learn", "mlflow", "boto3"],\n)\ndef upload_sklearn_model_to_mlflow(\n model_name: str,\n model_path: InputPath("dill"),\n input_example_path: InputPath("dill"),\n signature_path: InputPath("dill"),\n conda_env_path: InputPath("dill"),\n):\n import os\n import dill\n from mlflow.sklearn import save_model\n \n from mlflow.tracking.client import MlflowClient\n\n os.environ["MLFLOW_S3_ENDPOINT_URL"] = "http://minio-service.kubeflow.svc:9000"\n os.environ["AWS_ACCESS_KEY_ID"] = "minio"\n os.environ["AWS_SECRET_ACCESS_KEY"] = "minio123"\n\n client = MlflowClient("http://mlflow-server-service.mlflow-system.svc:5000")\n\n with open(model_path, mode="rb") as file_reader:\n clf = dill.load(file_reader)\n\n with open(input_example_path, "rb") as file_reader:\n input_example = dill.load(file_reader)\n\n with open(signature_path, "rb") as file_reader:\n signature = dill.load(file_reader)\n\n with open(conda_env_path, "rb") as file_reader:\n conda_env = dill.load(file_reader)\n\n save_model(\n sk_model=clf,\n path=model_name,\n serialization_format="cloudpickle",\n conda_env=conda_env,\n signature=signature,\n input_example=input_example,\n )\n run = client.create_run(experiment_id="0")\n client.log_artifact(run.info.run_id, model_name)\n')),(0,r.kt)("h2",{id:"mlflow-pipeline"},"MLFlow Pipeline"),(0,r.kt)("p",null,"\uc774\uc81c \uc791\uc131\ud55c \ucef4\ud3ec\ub10c\ud2b8\ub4e4\uc744 \uc5f0\uacb0\ud574\uc11c \ud30c\uc774\ud504\ub77c\uc778\uc73c\ub85c \ub9cc\ub4e4\uc5b4 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4."),(0,r.kt)("h3",{id:"data-component"},"Data Component"),(0,r.kt)("p",null,"\ubaa8\ub378\uc744 \ud559\uc2b5\ud560 \ub54c \uc4f8 \ub370\uc774\ud130\ub294 sklearn\uc758 iris \uc785\ub2c8\ub2e4.\n\ub370\uc774\ud130\ub97c \uc0dd\uc131\ud558\ub294 \ucef4\ud3ec\ub10c\ud2b8\ub97c \uc791\uc131\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-python"},'from functools import partial\n\nfrom kfp.components import InputPath, OutputPath, create_component_from_func\n\n\n@partial(\n create_component_from_func,\n packages_to_install=["pandas", "scikit-learn"],\n)\ndef load_iris_data(\n data_path: OutputPath("csv"),\n target_path: OutputPath("csv"),\n):\n import pandas as pd\n from sklearn.datasets import load_iris\n\n iris = load_iris()\n\n data = pd.DataFrame(iris["data"], columns=iris["feature_names"])\n target = pd.DataFrame(iris["target"], columns=["target"])\n\n data.to_csv(data_path, index=False)\n target.to_csv(target_path, index=False)\n\n')),(0,r.kt)("h3",{id:"pipeline"},"Pipeline"),(0,r.kt)("p",null,"\ud30c\uc774\ud504\ub77c\uc778 \ucf54\ub4dc\ub294 \ub2e4\uc74c\uacfc \uac19\uc774 \uc791\uc131\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-python"},'from kfp.dsl import pipeline\n\n\n@pipeline(name="mlflow_pipeline")\ndef mlflow_pipeline(kernel: str, model_name: str):\n iris_data = load_iris_data()\n model = train_from_csv(\n train_data=iris_data.outputs["data"],\n train_target=iris_data.outputs["target"],\n kernel=kernel,\n )\n _ = upload_sklearn_model_to_mlflow(\n model_name=model_name,\n model=model.outputs["model"],\n input_example=model.outputs["input_example"],\n signature=model.outputs["signature"],\n conda_env=model.outputs["conda_env"],\n )\n')),(0,r.kt)("h3",{id:"run"},"Run"),(0,r.kt)("p",null,"\uc704\uc5d0\uc11c \uc791\uc131\ub41c \ucef4\ud3ec\ub10c\ud2b8\uc640 \ud30c\uc774\ud504\ub77c\uc778\uc744 \ud558\ub098\uc758 \ud30c\uc774\uc36c \ud30c\uc77c\uc5d0 \uc815\ub9ac\ud558\uba74 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-python"},'from functools import partial\n\nimport kfp\nfrom kfp.components import InputPath, OutputPath, create_component_from_func\nfrom kfp.dsl import pipeline\n\n\n@partial(\n create_component_from_func,\n packages_to_install=["pandas", "scikit-learn"],\n)\ndef load_iris_data(\n data_path: OutputPath("csv"),\n target_path: OutputPath("csv"),\n):\n import pandas as pd\n from sklearn.datasets import load_iris\n\n iris = load_iris()\n\n data = pd.DataFrame(iris["data"], columns=iris["feature_names"])\n target = pd.DataFrame(iris["target"], columns=["target"])\n\n data.to_csv(data_path, index=False)\n target.to_csv(target_path, index=False)\n\n\n@partial(\n create_component_from_func,\n packages_to_install=["dill", "pandas", "scikit-learn", "mlflow"],\n)\ndef train_from_csv(\n train_data_path: InputPath("csv"),\n train_target_path: InputPath("csv"),\n model_path: OutputPath("dill"),\n input_example_path: OutputPath("dill"),\n signature_path: OutputPath("dill"),\n conda_env_path: OutputPath("dill"),\n kernel: str,\n):\n import dill\n import pandas as pd\n from sklearn.svm import SVC\n\n from mlflow.models.signature import infer_signature\n from mlflow.utils.environment import _mlflow_conda_env\n\n train_data = pd.read_csv(train_data_path)\n train_target = pd.read_csv(train_target_path)\n\n clf = SVC(kernel=kernel)\n clf.fit(train_data, train_target)\n\n with open(model_path, mode="wb") as file_writer:\n dill.dump(clf, file_writer)\n\n input_example = train_data.sample(1)\n with open(input_example_path, "wb") as file_writer:\n dill.dump(input_example, file_writer)\n\n signature = infer_signature(train_data, clf.predict(train_data))\n with open(signature_path, "wb") as file_writer:\n dill.dump(signature, file_writer)\n\n conda_env = _mlflow_conda_env(\n additional_pip_deps=["dill", "pandas", "scikit-learn"]\n )\n with open(conda_env_path, "wb") as file_writer:\n dill.dump(conda_env, file_writer)\n\n\n@partial(\n create_component_from_func,\n packages_to_install=["dill", "pandas", "scikit-learn", "mlflow", "boto3"],\n)\ndef upload_sklearn_model_to_mlflow(\n model_name: str,\n model_path: InputPath("dill"),\n input_example_path: InputPath("dill"),\n signature_path: InputPath("dill"),\n conda_env_path: InputPath("dill"),\n):\n import os\n import dill\n from mlflow.sklearn import save_model\n \n from mlflow.tracking.client import MlflowClient\n\n os.environ["MLFLOW_S3_ENDPOINT_URL"] = "http://minio-service.kubeflow.svc:9000"\n os.environ["AWS_ACCESS_KEY_ID"] = "minio"\n os.environ["AWS_SECRET_ACCESS_KEY"] = "minio123"\n\n client = MlflowClient("http://mlflow-server-service.mlflow-system.svc:5000")\n\n with open(model_path, mode="rb") as file_reader:\n clf = dill.load(file_reader)\n\n with open(input_example_path, "rb") as file_reader:\n input_example = dill.load(file_reader)\n\n with open(signature_path, "rb") as file_reader:\n signature = dill.load(file_reader)\n\n with open(conda_env_path, "rb") as file_reader:\n conda_env = dill.load(file_reader)\n\n save_model(\n sk_model=clf,\n path=model_name,\n serialization_format="cloudpickle",\n conda_env=conda_env,\n signature=signature,\n input_example=input_example,\n )\n run = client.create_run(experiment_id="0")\n client.log_artifact(run.info.run_id, model_name)\n\n\n@pipeline(name="mlflow_pipeline")\ndef mlflow_pipeline(kernel: str, model_name: str):\n iris_data = load_iris_data()\n model = train_from_csv(\n train_data=iris_data.outputs["data"],\n train_target=iris_data.outputs["target"],\n kernel=kernel,\n )\n _ = upload_sklearn_model_to_mlflow(\n model_name=model_name,\n model=model.outputs["model"],\n input_example=model.outputs["input_example"],\n signature=model.outputs["signature"],\n conda_env=model.outputs["conda_env"],\n )\n\n\nif __name__ == "__main__":\n kfp.compiler.Compiler().compile(mlflow_pipeline, "mlflow_pipeline.yaml")\n')),(0,r.kt)("p",null,(0,r.kt)("details",null,(0,r.kt)("summary",null,"mlflow_pipeline.yaml"),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},'apiVersion: argoproj.io/v1alpha1\nkind: Workflow\nmetadata:\n generateName: mlflow-pipeline-\n annotations: {pipelines.kubeflow.org/kfp_sdk_version: 1.8.10, pipelines.kubeflow.org/pipeline_compilation_time: \'2022-01-19T14:14:11.999807\',\n pipelines.kubeflow.org/pipeline_spec: \'{"inputs": [{"name": "kernel", "type":\n "String"}, {"name": "model_name", "type": "String"}], "name": "mlflow_pipeline"}\'}\n labels: {pipelines.kubeflow.org/kfp_sdk_version: 1.8.10}\nspec:\n entrypoint: mlflow-pipeline\n templates:\n - name: load-iris-data\n container:\n args: [--data, /tmp/outputs/data/data, --target, /tmp/outputs/target/data]\n command:\n - sh\n - -c\n - (PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location\n \'pandas\' \'scikit-learn\' || PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip\n install --quiet --no-warn-script-location \'pandas\' \'scikit-learn\' --user)\n && "$0" "$@"\n - sh\n - -ec\n - |\n program_path=$(mktemp)\n printf "%s" "$0" > "$program_path"\n python3 -u "$program_path" "$@"\n - |\n def _make_parent_dirs_and_return_path(file_path: str):\n import os\n os.makedirs(os.path.dirname(file_path), exist_ok=True)\n return file_path\n\n def load_iris_data(\n data_path,\n target_path,\n ):\n import pandas as pd\n from sklearn.datasets import load_iris\n\n iris = load_iris()\n\n data = pd.DataFrame(iris["data"], columns=iris["feature_names"])\n target = pd.DataFrame(iris["target"], columns=["target"])\n\n data.to_csv(data_path, index=False)\n target.to_csv(target_path, index=False)\n\n import argparse\n _parser = argparse.ArgumentParser(prog=\'Load iris data\', description=\'\')\n _parser.add_argument("--data", dest="data_path", type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--target", dest="target_path", type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)\n _parsed_args = vars(_parser.parse_args())\n\n _outputs = load_iris_data(**_parsed_args)\n image: python:3.7\n outputs:\n artifacts:\n - {name: load-iris-data-data, path: /tmp/outputs/data/data}\n - {name: load-iris-data-target, path: /tmp/outputs/target/data}\n metadata:\n labels:\n pipelines.kubeflow.org/kfp_sdk_version: 1.8.10\n pipelines.kubeflow.org/pipeline-sdk-type: kfp\n pipelines.kubeflow.org/enable_caching: "true"\n annotations: {pipelines.kubeflow.org/component_spec: \'{"implementation": {"container":\n {"args": ["--data", {"outputPath": "data"}, "--target", {"outputPath": "target"}],\n "command": ["sh", "-c", "(PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip\n install --quiet --no-warn-script-location \'\'pandas\'\' \'\'scikit-learn\'\' ||\n PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location\n \'\'pandas\'\' \'\'scikit-learn\'\' --user) && \\"$0\\" \\"$@\\"", "sh", "-ec", "program_path=$(mktemp)\\nprintf\n \\"%s\\" \\"$0\\" > \\"$program_path\\"\\npython3 -u \\"$program_path\\" \\"$@\\"\\n",\n "def _make_parent_dirs_and_return_path(file_path: str):\\n import os\\n os.makedirs(os.path.dirname(file_path),\n exist_ok=True)\\n return file_path\\n\\ndef load_iris_data(\\n data_path,\\n target_path,\\n):\\n import\n pandas as pd\\n from sklearn.datasets import load_iris\\n\\n iris = load_iris()\\n\\n data\n = pd.DataFrame(iris[\\"data\\"], columns=iris[\\"feature_names\\"])\\n target\n = pd.DataFrame(iris[\\"target\\"], columns=[\\"target\\"])\\n\\n data.to_csv(data_path,\n index=False)\\n target.to_csv(target_path, index=False)\\n\\nimport argparse\\n_parser\n = argparse.ArgumentParser(prog=\'\'Load iris data\'\', description=\'\'\'\')\\n_parser.add_argument(\\"--data\\",\n dest=\\"data_path\\", type=_make_parent_dirs_and_return_path, required=True,\n default=argparse.SUPPRESS)\\n_parser.add_argument(\\"--target\\", dest=\\"target_path\\",\n type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)\\n_parsed_args\n = vars(_parser.parse_args())\\n\\n_outputs = load_iris_data(**_parsed_args)\\n"],\n "image": "python:3.7"}}, "name": "Load iris data", "outputs": [{"name":\n "data", "type": "csv"}, {"name": "target", "type": "csv"}]}\', pipelines.kubeflow.org/component_ref: \'{}\'}\n - name: mlflow-pipeline\n inputs:\n parameters:\n - {name: kernel}\n - {name: model_name}\n dag:\n tasks:\n - {name: load-iris-data, template: load-iris-data}\n - name: train-from-csv\n template: train-from-csv\n dependencies: [load-iris-data]\n arguments:\n parameters:\n - {name: kernel, value: \'{{inputs.parameters.kernel}}\'}\n artifacts:\n - {name: load-iris-data-data, from: \'{{tasks.load-iris-data.outputs.artifacts.load-iris-data-data}}\'}\n - {name: load-iris-data-target, from: \'{{tasks.load-iris-data.outputs.artifacts.load-iris-data-target}}\'}\n - name: upload-sklearn-model-to-mlflow\n template: upload-sklearn-model-to-mlflow\n dependencies: [train-from-csv]\n arguments:\n parameters:\n - {name: model_name, value: \'{{inputs.parameters.model_name}}\'}\n artifacts:\n - {name: train-from-csv-conda_env, from: \'{{tasks.train-from-csv.outputs.artifacts.train-from-csv-conda_env}}\'}\n - {name: train-from-csv-input_example, from: \'{{tasks.train-from-csv.outputs.artifacts.train-from-csv-input_example}}\'}\n - {name: train-from-csv-model, from: \'{{tasks.train-from-csv.outputs.artifacts.train-from-csv-model}}\'}\n - {name: train-from-csv-signature, from: \'{{tasks.train-from-csv.outputs.artifacts.train-from-csv-signature}}\'}\n - name: train-from-csv\n container:\n args: [--train-data, /tmp/inputs/train_data/data, --train-target, /tmp/inputs/train_target/data,\n --kernel, \'{{inputs.parameters.kernel}}\', --model, /tmp/outputs/model/data,\n --input-example, /tmp/outputs/input_example/data, --signature, /tmp/outputs/signature/data,\n --conda-env, /tmp/outputs/conda_env/data]\n command:\n - sh\n - -c\n - (PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location\n \'dill\' \'pandas\' \'scikit-learn\' \'mlflow\' || PIP_DISABLE_PIP_VERSION_CHECK=1\n python3 -m pip install --quiet --no-warn-script-location \'dill\' \'pandas\' \'scikit-learn\'\n \'mlflow\' --user) && "$0" "$@"\n - sh\n - -ec\n - |\n program_path=$(mktemp)\n printf "%s" "$0" > "$program_path"\n python3 -u "$program_path" "$@"\n - |\n def _make_parent_dirs_and_return_path(file_path: str):\n import os\n os.makedirs(os.path.dirname(file_path), exist_ok=True)\n return file_path\n\n def train_from_csv(\n train_data_path,\n train_target_path,\n model_path,\n input_example_path,\n signature_path,\n conda_env_path,\n kernel,\n ):\n import dill\n import pandas as pd\n from sklearn.svm import SVC\n\n from mlflow.models.signature import infer_signature\n from mlflow.utils.environment import _mlflow_conda_env\n\n train_data = pd.read_csv(train_data_path)\n train_target = pd.read_csv(train_target_path)\n\n clf = SVC(kernel=kernel)\n clf.fit(train_data, train_target)\n\n with open(model_path, mode="wb") as file_writer:\n dill.dump(clf, file_writer)\n\n input_example = train_data.sample(1)\n with open(input_example_path, "wb") as file_writer:\n dill.dump(input_example, file_writer)\n\n signature = infer_signature(train_data, clf.predict(train_data))\n with open(signature_path, "wb") as file_writer:\n dill.dump(signature, file_writer)\n\n conda_env = _mlflow_conda_env(\n additional_pip_deps=["dill", "pandas", "scikit-learn"]\n )\n with open(conda_env_path, "wb") as file_writer:\n dill.dump(conda_env, file_writer)\n\n import argparse\n _parser = argparse.ArgumentParser(prog=\'Train from csv\', description=\'\')\n _parser.add_argument("--train-data", dest="train_data_path", type=str, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--train-target", dest="train_target_path", type=str, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--kernel", dest="kernel", type=str, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--model", dest="model_path", type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--input-example", dest="input_example_path", type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--signature", dest="signature_path", type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--conda-env", dest="conda_env_path", type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)\n _parsed_args = vars(_parser.parse_args())\n\n _outputs = train_from_csv(**_parsed_args)\n image: python:3.7\n inputs:\n parameters:\n - {name: kernel}\n artifacts:\n - {name: load-iris-data-data, path: /tmp/inputs/train_data/data}\n - {name: load-iris-data-target, path: /tmp/inputs/train_target/data}\n outputs:\n artifacts:\n - {name: train-from-csv-conda_env, path: /tmp/outputs/conda_env/data}\n - {name: train-from-csv-input_example, path: /tmp/outputs/input_example/data}\n - {name: train-from-csv-model, path: /tmp/outputs/model/data}\n - {name: train-from-csv-signature, path: /tmp/outputs/signature/data}\n metadata:\n labels:\n pipelines.kubeflow.org/kfp_sdk_version: 1.8.10\n pipelines.kubeflow.org/pipeline-sdk-type: kfp\n pipelines.kubeflow.org/enable_caching: "true"\n annotations: {pipelines.kubeflow.org/component_spec: \'{"implementation": {"container":\n {"args": ["--train-data", {"inputPath": "train_data"}, "--train-target",\n {"inputPath": "train_target"}, "--kernel", {"inputValue": "kernel"}, "--model",\n {"outputPath": "model"}, "--input-example", {"outputPath": "input_example"},\n "--signature", {"outputPath": "signature"}, "--conda-env", {"outputPath":\n "conda_env"}], "command": ["sh", "-c", "(PIP_DISABLE_PIP_VERSION_CHECK=1\n python3 -m pip install --quiet --no-warn-script-location \'\'dill\'\' \'\'pandas\'\'\n \'\'scikit-learn\'\' \'\'mlflow\'\' || PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m\n pip install --quiet --no-warn-script-location \'\'dill\'\' \'\'pandas\'\' \'\'scikit-learn\'\'\n \'\'mlflow\'\' --user) && \\"$0\\" \\"$@\\"", "sh", "-ec", "program_path=$(mktemp)\\nprintf\n \\"%s\\" \\"$0\\" > \\"$program_path\\"\\npython3 -u \\"$program_path\\" \\"$@\\"\\n",\n "def _make_parent_dirs_and_return_path(file_path: str):\\n import os\\n os.makedirs(os.path.dirname(file_path),\n exist_ok=True)\\n return file_path\\n\\ndef train_from_csv(\\n train_data_path,\\n train_target_path,\\n model_path,\\n input_example_path,\\n signature_path,\\n conda_env_path,\\n kernel,\\n):\\n import\n dill\\n import pandas as pd\\n from sklearn.svm import SVC\\n\\n from\n mlflow.models.signature import infer_signature\\n from mlflow.utils.environment\n import _mlflow_conda_env\\n\\n train_data = pd.read_csv(train_data_path)\\n train_target\n = pd.read_csv(train_target_path)\\n\\n clf = SVC(kernel=kernel)\\n clf.fit(train_data,\n train_target)\\n\\n with open(model_path, mode=\\"wb\\") as file_writer:\\n dill.dump(clf,\n file_writer)\\n\\n input_example = train_data.sample(1)\\n with open(input_example_path,\n \\"wb\\") as file_writer:\\n dill.dump(input_example, file_writer)\\n\\n signature\n = infer_signature(train_data, clf.predict(train_data))\\n with open(signature_path,\n \\"wb\\") as file_writer:\\n dill.dump(signature, file_writer)\\n\\n conda_env\n = _mlflow_conda_env(\\n additional_pip_deps=[\\"dill\\", \\"pandas\\",\n \\"scikit-learn\\"]\\n )\\n with open(conda_env_path, \\"wb\\") as file_writer:\\n dill.dump(conda_env,\n file_writer)\\n\\nimport argparse\\n_parser = argparse.ArgumentParser(prog=\'\'Train\n from csv\'\', description=\'\'\'\')\\n_parser.add_argument(\\"--train-data\\", dest=\\"train_data_path\\",\n type=str, required=True, default=argparse.SUPPRESS)\\n_parser.add_argument(\\"--train-target\\",\n dest=\\"train_target_path\\", type=str, required=True, default=argparse.SUPPRESS)\\n_parser.add_argument(\\"--kernel\\",\n dest=\\"kernel\\", type=str, required=True, default=argparse.SUPPRESS)\\n_parser.add_argument(\\"--model\\",\n dest=\\"model_path\\", type=_make_parent_dirs_and_return_path, required=True,\n default=argparse.SUPPRESS)\\n_parser.add_argument(\\"--input-example\\", dest=\\"input_example_path\\",\n type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)\\n_parser.add_argument(\\"--signature\\",\n dest=\\"signature_path\\", type=_make_parent_dirs_and_return_path, required=True,\n default=argparse.SUPPRESS)\\n_parser.add_argument(\\"--conda-env\\", dest=\\"conda_env_path\\",\n type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)\\n_parsed_args\n = vars(_parser.parse_args())\\n\\n_outputs = train_from_csv(**_parsed_args)\\n"],\n "image": "python:3.7"}}, "inputs": [{"name": "train_data", "type": "csv"},\n {"name": "train_target", "type": "csv"}, {"name": "kernel", "type": "String"}],\n "name": "Train from csv", "outputs": [{"name": "model", "type": "dill"},\n {"name": "input_example", "type": "dill"}, {"name": "signature", "type":\n "dill"}, {"name": "conda_env", "type": "dill"}]}\', pipelines.kubeflow.org/component_ref: \'{}\',\n pipelines.kubeflow.org/arguments.parameters: \'{"kernel": "{{inputs.parameters.kernel}}"}\'}\n - name: upload-sklearn-model-to-mlflow\n container:\n args: [--model-name, \'{{inputs.parameters.model_name}}\', --model, /tmp/inputs/model/data,\n --input-example, /tmp/inputs/input_example/data, --signature, /tmp/inputs/signature/data,\n --conda-env, /tmp/inputs/conda_env/data]\n command:\n - sh\n - -c\n - (PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location\n \'dill\' \'pandas\' \'scikit-learn\' \'mlflow\' \'boto3\' || PIP_DISABLE_PIP_VERSION_CHECK=1\n python3 -m pip install --quiet --no-warn-script-location \'dill\' \'pandas\' \'scikit-learn\'\n \'mlflow\' \'boto3\' --user) && "$0" "$@"\n - sh\n - -ec\n - |\n program_path=$(mktemp)\n printf "%s" "$0" > "$program_path"\n python3 -u "$program_path" "$@"\n - |\n def upload_sklearn_model_to_mlflow(\n model_name,\n model_path,\n input_example_path,\n signature_path,\n conda_env_path,\n ):\n import os\n import dill\n from mlflow.sklearn import save_model\n\n from mlflow.tracking.client import MlflowClient\n\n os.environ["MLFLOW_S3_ENDPOINT_URL"] = "http://minio-service.kubeflow.svc:9000"\n os.environ["AWS_ACCESS_KEY_ID"] = "minio"\n os.environ["AWS_SECRET_ACCESS_KEY"] = "minio123"\n\n client = MlflowClient("http://mlflow-server-service.mlflow-system.svc:5000")\n\n with open(model_path, mode="rb") as file_reader:\n clf = dill.load(file_reader)\n\n with open(input_example_path, "rb") as file_reader:\n input_example = dill.load(file_reader)\n\n with open(signature_path, "rb") as file_reader:\n signature = dill.load(file_reader)\n\n with open(conda_env_path, "rb") as file_reader:\n conda_env = dill.load(file_reader)\n\n save_model(\n sk_model=clf,\n path=model_name,\n serialization_format="cloudpickle",\n conda_env=conda_env,\n signature=signature,\n input_example=input_example,\n )\n run = client.create_run(experiment_id="0")\n client.log_artifact(run.info.run_id, model_name)\n\n import argparse\n _parser = argparse.ArgumentParser(prog=\'Upload sklearn model to mlflow\', description=\'\')\n _parser.add_argument("--model-name", dest="model_name", type=str, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--model", dest="model_path", type=str, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--input-example", dest="input_example_path", type=str, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--signature", dest="signature_path", type=str, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--conda-env", dest="conda_env_path", type=str, required=True, default=argparse.SUPPRESS)\n _parsed_args = vars(_parser.parse_args())\n\n _outputs = upload_sklearn_model_to_mlflow(**_parsed_args)\n image: python:3.7\n inputs:\n parameters:\n - {name: model_name}\n artifacts:\n - {name: train-from-csv-conda_env, path: /tmp/inputs/conda_env/data}\n - {name: train-from-csv-input_example, path: /tmp/inputs/input_example/data}\n - {name: train-from-csv-model, path: /tmp/inputs/model/data}\n - {name: train-from-csv-signature, path: /tmp/inputs/signature/data}\n metadata:\n labels:\n pipelines.kubeflow.org/kfp_sdk_version: 1.8.10\n pipelines.kubeflow.org/pipeline-sdk-type: kfp\n pipelines.kubeflow.org/enable_caching: "true"\n annotations: {pipelines.kubeflow.org/component_spec: \'{"implementation": {"container":\n {"args": ["--model-name", {"inputValue": "model_name"}, "--model", {"inputPath":\n "model"}, "--input-example", {"inputPath": "input_example"}, "--signature",\n {"inputPath": "signature"}, "--conda-env", {"inputPath": "conda_env"}],\n "command": ["sh", "-c", "(PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip\n install --quiet --no-warn-script-location \'\'dill\'\' \'\'pandas\'\' \'\'scikit-learn\'\'\n \'\'mlflow\'\' \'\'boto3\'\' || PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install\n --quiet --no-warn-script-location \'\'dill\'\' \'\'pandas\'\' \'\'scikit-learn\'\' \'\'mlflow\'\'\n \'\'boto3\'\' --user) && \\"$0\\" \\"$@\\"", "sh", "-ec", "program_path=$(mktemp)\\nprintf\n \\"%s\\" \\"$0\\" > \\"$program_path\\"\\npython3 -u \\"$program_path\\" \\"$@\\"\\n",\n "def upload_sklearn_model_to_mlflow(\\n model_name,\\n model_path,\\n input_example_path,\\n signature_path,\\n conda_env_path,\\n):\\n import\n os\\n import dill\\n from mlflow.sklearn import save_model\\n\\n from\n mlflow.tracking.client import MlflowClient\\n\\n os.environ[\\"MLFLOW_S3_ENDPOINT_URL\\"]\n = \\"http://minio-service.kubeflow.svc:9000\\"\\n os.environ[\\"AWS_ACCESS_KEY_ID\\"]\n = \\"minio\\"\\n os.environ[\\"AWS_SECRET_ACCESS_KEY\\"] = \\"minio123\\"\\n\\n client\n = MlflowClient(\\"http://mlflow-server-service.mlflow-system.svc:5000\\")\\n\\n with\n open(model_path, mode=\\"rb\\") as file_reader:\\n clf = dill.load(file_reader)\\n\\n with\n open(input_example_path, \\"rb\\") as file_reader:\\n input_example\n = dill.load(file_reader)\\n\\n with open(signature_path, \\"rb\\") as file_reader:\\n signature\n = dill.load(file_reader)\\n\\n with open(conda_env_path, \\"rb\\") as file_reader:\\n conda_env\n = dill.load(file_reader)\\n\\n save_model(\\n sk_model=clf,\\n path=model_name,\\n serialization_format=\\"cloudpickle\\",\\n conda_env=conda_env,\\n signature=signature,\\n input_example=input_example,\\n )\\n run\n = client.create_run(experiment_id=\\"0\\")\\n client.log_artifact(run.info.run_id,\n model_name)\\n\\nimport argparse\\n_parser = argparse.ArgumentParser(prog=\'\'Upload\n sklearn model to mlflow\'\', description=\'\'\'\')\\n_parser.add_argument(\\"--model-name\\",\n dest=\\"model_name\\", type=str, required=True, default=argparse.SUPPRESS)\\n_parser.add_argument(\\"--model\\",\n dest=\\"model_path\\", type=str, required=True, default=argparse.SUPPRESS)\\n_parser.add_argument(\\"--input-example\\",\n dest=\\"input_example_path\\", type=str, required=True, default=argparse.SUPPRESS)\\n_parser.add_argument(\\"--signature\\",\n dest=\\"signature_path\\", type=str, required=True, default=argparse.SUPPRESS)\\n_parser.add_argument(\\"--conda-env\\",\n dest=\\"conda_env_path\\", type=str, required=True, 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Component - MLFlow",description:"",sidebar_position:12,date:"2021-12-13T00:00:00.000Z",lastmod:"2021-12-20T00:00:00.000Z",contributors:["Jongseob Jeon","SeungTae Kim"]},sidebar:"tutorialSidebar",previous:{title:"11. Pipeline - Run Result",permalink:"/docs/1.0/kubeflow/advanced-run"},next:{title:"13. Component - Debugging",permalink:"/docs/1.0/kubeflow/how-to-debug"}},o={},s=[{value:"MLFlow Component",id:"mlflow-component",level:2},{value:"MLFlow in Local",id:"mlflow-in-local",level:2},{value:"1. \ubaa8\ub378 \ud559\uc2b5",id:"1-\ubaa8\ub378-\ud559\uc2b5",level:3},{value:"2. MLFLow Infos",id:"2-mlflow-infos",level:3},{value:"3. 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MLFLow Infos"),(0,r.kt)("p",null,"mlflow\uc5d0 \ud544\uc694\ud55c \uc815\ubcf4\ub4e4\uc744 \ub9cc\ub4dc\ub294 \uacfc\uc815\uc785\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-python"},'from mlflow.models.signature import infer_signature\nfrom mlflow.utils.environment import _mlflow_conda_env\n\ninput_example = data.sample(1)\nsignature = infer_signature(data, clf.predict(data))\nconda_env = _mlflow_conda_env(additional_pip_deps=["dill", "pandas", "scikit-learn"])\n')),(0,r.kt)("p",null,"\uac01 \ubcc0\uc218\uc758 \ub0b4\uc6a9\uc744 \ud655\uc778\ud558\uba74 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4."),(0,r.kt)("ul",null,(0,r.kt)("li",{parentName:"ul"},(0,r.kt)("p",{parentName:"li"},(0,r.kt)("inlineCode",{parentName:"p"},"input_example")),(0,r.kt)("table",{parentName:"li"},(0,r.kt)("thead",{parentName:"table"},(0,r.kt)("tr",{parentName:"thead"},(0,r.kt)("th",{parentName:"tr",align:null},"sepal length (cm)"),(0,r.kt)("th",{parentName:"tr",align:null},"sepal width (cm)"),(0,r.kt)("th",{parentName:"tr",align:null},"petal length (cm)"),(0,r.kt)("th",{parentName:"tr",align:null},"petal width (cm)"))),(0,r.kt)("tbody",{parentName:"table"},(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"6.5"),(0,r.kt)("td",{parentName:"tr",align:null},"6.7"),(0,r.kt)("td",{parentName:"tr",align:null},"3.1"),(0,r.kt)("td",{parentName:"tr",align:null},"4.4"))))),(0,r.kt)("li",{parentName:"ul"},(0,r.kt)("p",{parentName:"li"},(0,r.kt)("inlineCode",{parentName:"p"},"signature")),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-python"},"inputs:\n ['sepal length (cm)': double, 'sepal width (cm)': double, 'petal length (cm)': double, 'petal width (cm)': double]\noutputs:\n [Tensor('int64', (-1,))]\n"))),(0,r.kt)("li",{parentName:"ul"},(0,r.kt)("p",{parentName:"li"},(0,r.kt)("inlineCode",{parentName:"p"},"conda_env")),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-python"},"{'name': 'mlflow-env',\n 'channels': ['conda-forge'],\n 'dependencies': ['python=3.8.10',\n 'pip',\n {'pip': ['mlflow', 'dill', 'pandas', 'scikit-learn']}]}\n")))),(0,r.kt)("h3",{id:"3-save-mlflow-infos"},"3. Save MLFLow Infos"),(0,r.kt)("p",null,"\ub2e4\uc74c\uc73c\ub85c \ud559\uc2b5\ud55c \uc815\ubcf4\ub4e4\uacfc \ubaa8\ub378\uc744 \uc800\uc7a5\ud569\ub2c8\ub2e4.\n\ud559\uc2b5\ud55c \ubaa8\ub378\uc774 sklearn \ud328\ud0a4\uc9c0\ub97c \uc774\uc6a9\ud558\uae30 \ub54c\ubb38\uc5d0 ",(0,r.kt)("inlineCode",{parentName:"p"},"mlflow.sklearn")," \uc744 \uc774\uc6a9\ud558\uba74 \uc27d\uac8c \ubaa8\ub378\uc744 \uc800\uc7a5\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-python"},'from mlflow.sklearn import save_model\n\nsave_model(\n sk_model=clf,\n path="svc",\n serialization_format="cloudpickle",\n conda_env=conda_env,\n signature=signature,\n input_example=input_example,\n)\n')),(0,r.kt)("p",null,"\ub85c\uceec\uc5d0\uc11c \uc791\uc5c5\ud558\uba74 \ub2e4\uc74c\uacfc \uac19\uc740 svc \ud3f4\ub354\uac00 \uc0dd\uae30\uba70 \uc544\ub798\uc640 \uac19\uc740 \ud30c\uc77c\ub4e4\uc774 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1.0.1\nsaved_input_example_info:\n artifact_path: input_example.json\n pandas_orient: split\n type: dataframe\nsignature:\n inputs: \'[{"name": "sepal length (cm)", "type": "double"}, {"name": "sepal width\n (cm)", "type": "double"}, {"name": "petal length (cm)", "type": "double"}, {"name":\n "petal width (cm)", "type": "double"}]\'\n outputs: \'[{"type": "tensor", "tensor-spec": {"dtype": "int64", "shape": [-1]}}]\'\nutc_time_created: \'2021-12-06 06:52:30.612810\'\n'))),(0,r.kt)("li",{parentName:"ul"},(0,r.kt)("p",{parentName:"li"},"conda.yaml"),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"channels:\n- conda-forge\ndependencies:\n- python=3.8.10\n- pip\n- pip:\n - mlflow\n - dill\n - pandas\n - scikit-learn\nname: mlflow-env\n"))),(0,r.kt)("li",{parentName:"ul"},(0,r.kt)("p",{parentName:"li"},"input_example.json"),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},'{\n "columns": \n [\n "sepal length (cm)",\n "sepal width (cm)",\n "petal length (cm)",\n "petal width (cm)"\n ],\n "data": \n [\n [6.7, 3.1, 4.4, 1.4]\n ]\n}\n'))),(0,r.kt)("li",{parentName:"ul"},(0,r.kt)("p",{parentName:"li"},"requirements.txt"),(0,r.kt)("pre",{parentName:"li"},(0,r.kt)("code",{parentName:"pre",className:"language-bash"},"mlflow\ndill\npandas\nscikit-learn\n"))),(0,r.kt)("li",{parentName:"ul"},(0,r.kt)("p",{parentName:"li"},"model.pkl"))),(0,r.kt)("h2",{id:"mlflow-on-server"},"MLFlow on Server"),(0,r.kt)("p",null,"\uc774\uc81c \uc800\uc7a5\ub41c \ubaa8\ub378\uc744 mlflow \uc11c\ubc84\uc5d0 \uc62c\ub9ac\ub294 \uc791\uc5c5\uc744 \ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-python"},'import mlflow\n\nwith mlflow.start_run():\n mlflow.log_artifact("svc/")\n')),(0,r.kt)("p",null,"\uc800\uc7a5\ud558\uace0 ",(0,r.kt)("inlineCode",{parentName:"p"},"mlruns")," \uac00 \uc0dd\uc131\ub41c \uacbd\ub85c\uc5d0\uc11c 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\ud558\ubbc0\ub85c \uacb0\uad6d \ucef4\ud3ec\ub10c\ud2b8\uc5d0 \ub300\ud55c \ucd94\uac00 \uc815\ubcf4\ub97c \uc804\ub2ec\ud574\uc57c \ud569\ub2c8\ub2e4."),(0,r.kt)("p",null,"1\ubc88\uc758 \ubc29\ubc95\uc73c\ub85c \uc9c4\ud589\ud558\uae30 \uc704\ud574\uc11c\ub294 \ud559\uc2b5\ud558\ub294 \ucef4\ud3ec\ub10c\ud2b8 \ub610\ud55c \ubcc0\uacbd\ub418\uc5b4\uc57c \ud569\ub2c8\ub2e4.\n\ubaa8\ub378\uc744 \uc800\uc7a5\ud558\ub294\ub370 \ud544\uc694\ud55c \ud658\uacbd\ub4e4\uc744 \uc800\uc7a5\ud574\uc8fc\ub294 \ucf54\ub4dc\uac00 \ucd94\uac00\ub418\uc5b4\uc57c \ud569\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-python"},'from functools import partial\nfrom kfp.components import InputPath, OutputPath, create_component_from_func\n\n@partial(\n create_component_from_func,\n packages_to_install=["dill", "pandas", "scikit-learn", "mlflow"],\n)\ndef train_from_csv(\n train_data_path: InputPath("csv"),\n train_target_path: InputPath("csv"),\n model_path: OutputPath("dill"),\n input_example_path: OutputPath("dill"),\n signature_path: OutputPath("dill"),\n conda_env_path: OutputPath("dill"),\n kernel: str,\n):\n import dill\n import pandas as pd\n from sklearn.svm import SVC\n\n from mlflow.models.signature import infer_signature\n from mlflow.utils.environment import _mlflow_conda_env\n\n train_data = pd.read_csv(train_data_path)\n train_target = pd.read_csv(train_target_path)\n\n clf = SVC(kernel=kernel)\n clf.fit(train_data, train_target)\n\n with open(model_path, mode="wb") as file_writer:\n dill.dump(clf, file_writer)\n\n input_example = train_data.sample(1)\n with open(input_example_path, "wb") as file_writer:\n dill.dump(input_example, file_writer)\n\n signature = infer_signature(train_data, clf.predict(train_data))\n with open(signature_path, "wb") as file_writer:\n dill.dump(signature, file_writer)\n\n conda_env = _mlflow_conda_env(\n additional_pip_deps=["dill", "pandas", "scikit-learn"]\n )\n with open(conda_env_path, "wb") as file_writer:\n dill.dump(conda_env, file_writer)\n\n')),(0,r.kt)("p",null,"\uadf8\ub9ac\uace0 MLFlow\uc5d0 \uc5c5\ub85c\ub4dc\ud558\ub294 \ucef4\ud3ec\ub10c\ud2b8\ub97c \uc791\uc131\ud569\ub2c8\ub2e4.\n\uc774 \ub54c \uc5c5\ub85c\ub4dc\ub418\ub294 MLflow\uc758 endpoint\ub97c \uc6b0\ub9ac\uac00 \uc124\uce58\ud55c ",(0,r.kt)("a",{parentName:"p",href:"/docs/1.0/setup-components/install-components-mlflow"},"mlflow service")," \ub85c \uc774\uc5b4\uc9c0\uac8c \uc124\uc815\ud574\uc8fc\uc5b4\uc57c \ud569\ub2c8\ub2e4.",(0,r.kt)("br",{parentName:"p"}),"\n","\uc774 \ub54c S3 Endpoint\uc758 \uc8fc\uc18c\ub294 MLflow Server \uc124\uce58 \ub2f9\uc2dc \uc124\uce58\ud55c minio\uc758 ",(0,r.kt)("a",{parentName:"p",href:"https://kubernetes.io/ko/docs/concepts/services-networking/dns-pod-service/"},"\ucfe0\ubc84\ub124\ud2f0\uc2a4 \uc11c\ube44\uc2a4 DNS \ub124\uc784\uc744 \ud65c\uc6a9"),"\ud569\ub2c8\ub2e4. \ud574\ub2f9 service \ub294 kubeflow namespace\uc5d0\uc11c minio-service\ub77c\ub294 \uc774\ub984\uc73c\ub85c \uc0dd\uc131\ub418\uc5c8\uc73c\ubbc0\ub85c, ",(0,r.kt)("inlineCode",{parentName:"p"},"http://minio-service.kubeflow.svc:9000")," \ub85c \uc124\uc815\ud569\ub2c8\ub2e4.",(0,r.kt)("br",{parentName:"p"}),"\n","\uc774\uc640 \ube44\uc2b7\ud558\uac8c tracking_uri\uc758 \uc8fc\uc18c\ub294 mlflow server\uc758 \ucfe0\ubc84\ub124\ud2f0\uc2a4 \uc11c\ube44\uc2a4 DNS \ub124\uc784\uc744 \ud65c\uc6a9\ud558\uc5ec, ",(0,r.kt)("inlineCode",{parentName:"p"},"http://mlflow-server-service.mlflow-system.svc:5000")," \ub85c \uc124\uc815\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-python"},'from functools import partial\nfrom kfp.components import InputPath, create_component_from_func\n\n@partial(\n create_component_from_func,\n packages_to_install=["dill", "pandas", "scikit-learn", "mlflow", "boto3"],\n)\ndef upload_sklearn_model_to_mlflow(\n model_name: str,\n model_path: InputPath("dill"),\n input_example_path: InputPath("dill"),\n signature_path: InputPath("dill"),\n conda_env_path: InputPath("dill"),\n):\n import os\n import dill\n from mlflow.sklearn import save_model\n \n from mlflow.tracking.client import MlflowClient\n\n os.environ["MLFLOW_S3_ENDPOINT_URL"] = "http://minio-service.kubeflow.svc:9000"\n os.environ["AWS_ACCESS_KEY_ID"] = "minio"\n os.environ["AWS_SECRET_ACCESS_KEY"] = "minio123"\n\n client = MlflowClient("http://mlflow-server-service.mlflow-system.svc:5000")\n\n with open(model_path, mode="rb") as file_reader:\n clf = dill.load(file_reader)\n\n with open(input_example_path, "rb") as file_reader:\n input_example = dill.load(file_reader)\n\n with open(signature_path, "rb") as file_reader:\n signature = dill.load(file_reader)\n\n with open(conda_env_path, "rb") as file_reader:\n conda_env = dill.load(file_reader)\n\n save_model(\n sk_model=clf,\n path=model_name,\n serialization_format="cloudpickle",\n conda_env=conda_env,\n signature=signature,\n input_example=input_example,\n )\n run = client.create_run(experiment_id="0")\n client.log_artifact(run.info.run_id, model_name)\n')),(0,r.kt)("h2",{id:"mlflow-pipeline"},"MLFlow Pipeline"),(0,r.kt)("p",null,"\uc774\uc81c \uc791\uc131\ud55c \ucef4\ud3ec\ub10c\ud2b8\ub4e4\uc744 \uc5f0\uacb0\ud574\uc11c \ud30c\uc774\ud504\ub77c\uc778\uc73c\ub85c \ub9cc\ub4e4\uc5b4 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4."),(0,r.kt)("h3",{id:"data-component"},"Data Component"),(0,r.kt)("p",null,"\ubaa8\ub378\uc744 \ud559\uc2b5\ud560 \ub54c \uc4f8 \ub370\uc774\ud130\ub294 sklearn\uc758 iris \uc785\ub2c8\ub2e4.\n\ub370\uc774\ud130\ub97c \uc0dd\uc131\ud558\ub294 \ucef4\ud3ec\ub10c\ud2b8\ub97c \uc791\uc131\ud569\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-python"},'from functools import partial\n\nfrom kfp.components import InputPath, OutputPath, create_component_from_func\n\n\n@partial(\n create_component_from_func,\n packages_to_install=["pandas", "scikit-learn"],\n)\ndef load_iris_data(\n data_path: OutputPath("csv"),\n target_path: OutputPath("csv"),\n):\n import pandas as pd\n from sklearn.datasets import load_iris\n\n iris = load_iris()\n\n data = pd.DataFrame(iris["data"], columns=iris["feature_names"])\n target = pd.DataFrame(iris["target"], columns=["target"])\n\n data.to_csv(data_path, index=False)\n target.to_csv(target_path, index=False)\n\n')),(0,r.kt)("h3",{id:"pipeline"},"Pipeline"),(0,r.kt)("p",null,"\ud30c\uc774\ud504\ub77c\uc778 \ucf54\ub4dc\ub294 \ub2e4\uc74c\uacfc \uac19\uc774 \uc791\uc131\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-python"},'from kfp.dsl import pipeline\n\n\n@pipeline(name="mlflow_pipeline")\ndef mlflow_pipeline(kernel: str, model_name: str):\n iris_data = load_iris_data()\n model = train_from_csv(\n train_data=iris_data.outputs["data"],\n train_target=iris_data.outputs["target"],\n kernel=kernel,\n )\n _ = upload_sklearn_model_to_mlflow(\n model_name=model_name,\n model=model.outputs["model"],\n input_example=model.outputs["input_example"],\n signature=model.outputs["signature"],\n conda_env=model.outputs["conda_env"],\n )\n')),(0,r.kt)("h3",{id:"run"},"Run"),(0,r.kt)("p",null,"\uc704\uc5d0\uc11c \uc791\uc131\ub41c \ucef4\ud3ec\ub10c\ud2b8\uc640 \ud30c\uc774\ud504\ub77c\uc778\uc744 \ud558\ub098\uc758 \ud30c\uc774\uc36c \ud30c\uc77c\uc5d0 \uc815\ub9ac\ud558\uba74 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-python"},'from functools import partial\n\nimport kfp\nfrom kfp.components import InputPath, OutputPath, create_component_from_func\nfrom kfp.dsl import pipeline\n\n\n@partial(\n create_component_from_func,\n packages_to_install=["pandas", "scikit-learn"],\n)\ndef load_iris_data(\n data_path: OutputPath("csv"),\n target_path: OutputPath("csv"),\n):\n import pandas as pd\n from sklearn.datasets import load_iris\n\n iris = load_iris()\n\n data = pd.DataFrame(iris["data"], columns=iris["feature_names"])\n target = pd.DataFrame(iris["target"], columns=["target"])\n\n data.to_csv(data_path, index=False)\n target.to_csv(target_path, index=False)\n\n\n@partial(\n create_component_from_func,\n packages_to_install=["dill", "pandas", "scikit-learn", "mlflow"],\n)\ndef train_from_csv(\n train_data_path: InputPath("csv"),\n train_target_path: InputPath("csv"),\n model_path: OutputPath("dill"),\n input_example_path: OutputPath("dill"),\n signature_path: OutputPath("dill"),\n conda_env_path: OutputPath("dill"),\n kernel: str,\n):\n import dill\n import pandas as pd\n from sklearn.svm import SVC\n\n from mlflow.models.signature import infer_signature\n from mlflow.utils.environment import _mlflow_conda_env\n\n train_data = pd.read_csv(train_data_path)\n train_target = pd.read_csv(train_target_path)\n\n clf = SVC(kernel=kernel)\n clf.fit(train_data, train_target)\n\n with open(model_path, mode="wb") as file_writer:\n dill.dump(clf, file_writer)\n\n input_example = train_data.sample(1)\n with open(input_example_path, "wb") as file_writer:\n dill.dump(input_example, file_writer)\n\n signature = infer_signature(train_data, clf.predict(train_data))\n with open(signature_path, "wb") as file_writer:\n dill.dump(signature, file_writer)\n\n conda_env = _mlflow_conda_env(\n additional_pip_deps=["dill", "pandas", "scikit-learn"]\n )\n with open(conda_env_path, "wb") as file_writer:\n dill.dump(conda_env, file_writer)\n\n\n@partial(\n create_component_from_func,\n packages_to_install=["dill", "pandas", "scikit-learn", "mlflow", "boto3"],\n)\ndef upload_sklearn_model_to_mlflow(\n model_name: str,\n model_path: InputPath("dill"),\n input_example_path: InputPath("dill"),\n signature_path: InputPath("dill"),\n conda_env_path: InputPath("dill"),\n):\n import os\n import dill\n from mlflow.sklearn import save_model\n \n from mlflow.tracking.client import MlflowClient\n\n os.environ["MLFLOW_S3_ENDPOINT_URL"] = "http://minio-service.kubeflow.svc:9000"\n os.environ["AWS_ACCESS_KEY_ID"] = "minio"\n os.environ["AWS_SECRET_ACCESS_KEY"] = "minio123"\n\n client = MlflowClient("http://mlflow-server-service.mlflow-system.svc:5000")\n\n with open(model_path, mode="rb") as file_reader:\n clf = dill.load(file_reader)\n\n with open(input_example_path, "rb") as file_reader:\n input_example = dill.load(file_reader)\n\n with open(signature_path, "rb") as file_reader:\n signature = dill.load(file_reader)\n\n with open(conda_env_path, "rb") as file_reader:\n conda_env = dill.load(file_reader)\n\n save_model(\n sk_model=clf,\n path=model_name,\n serialization_format="cloudpickle",\n conda_env=conda_env,\n signature=signature,\n input_example=input_example,\n )\n run = client.create_run(experiment_id="0")\n client.log_artifact(run.info.run_id, model_name)\n\n\n@pipeline(name="mlflow_pipeline")\ndef mlflow_pipeline(kernel: str, model_name: str):\n iris_data = load_iris_data()\n model = train_from_csv(\n train_data=iris_data.outputs["data"],\n train_target=iris_data.outputs["target"],\n kernel=kernel,\n )\n _ = upload_sklearn_model_to_mlflow(\n model_name=model_name,\n model=model.outputs["model"],\n input_example=model.outputs["input_example"],\n signature=model.outputs["signature"],\n conda_env=model.outputs["conda_env"],\n )\n\n\nif __name__ == "__main__":\n kfp.compiler.Compiler().compile(mlflow_pipeline, "mlflow_pipeline.yaml")\n')),(0,r.kt)("p",null,(0,r.kt)("details",null,(0,r.kt)("summary",null,"mlflow_pipeline.yaml"),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-bash"},'apiVersion: argoproj.io/v1alpha1\nkind: Workflow\nmetadata:\n generateName: mlflow-pipeline-\n annotations: {pipelines.kubeflow.org/kfp_sdk_version: 1.8.10, pipelines.kubeflow.org/pipeline_compilation_time: \'2022-01-19T14:14:11.999807\',\n pipelines.kubeflow.org/pipeline_spec: \'{"inputs": [{"name": "kernel", "type":\n "String"}, {"name": "model_name", "type": "String"}], "name": "mlflow_pipeline"}\'}\n labels: {pipelines.kubeflow.org/kfp_sdk_version: 1.8.10}\nspec:\n entrypoint: mlflow-pipeline\n templates:\n - name: load-iris-data\n container:\n args: [--data, /tmp/outputs/data/data, --target, /tmp/outputs/target/data]\n command:\n - sh\n - -c\n - (PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location\n \'pandas\' \'scikit-learn\' || PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip\n install --quiet --no-warn-script-location \'pandas\' \'scikit-learn\' --user)\n && "$0" "$@"\n - sh\n - -ec\n - |\n program_path=$(mktemp)\n printf "%s" "$0" > "$program_path"\n python3 -u "$program_path" "$@"\n - |\n def _make_parent_dirs_and_return_path(file_path: str):\n import os\n os.makedirs(os.path.dirname(file_path), exist_ok=True)\n return file_path\n\n def load_iris_data(\n data_path,\n target_path,\n ):\n import pandas as pd\n from sklearn.datasets import load_iris\n\n iris = load_iris()\n\n data = pd.DataFrame(iris["data"], columns=iris["feature_names"])\n target = pd.DataFrame(iris["target"], columns=["target"])\n\n data.to_csv(data_path, index=False)\n target.to_csv(target_path, index=False)\n\n import argparse\n _parser = argparse.ArgumentParser(prog=\'Load iris data\', description=\'\')\n _parser.add_argument("--data", dest="data_path", type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--target", dest="target_path", type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)\n _parsed_args = vars(_parser.parse_args())\n\n _outputs = load_iris_data(**_parsed_args)\n image: python:3.7\n outputs:\n artifacts:\n - {name: load-iris-data-data, path: /tmp/outputs/data/data}\n - {name: load-iris-data-target, path: /tmp/outputs/target/data}\n metadata:\n labels:\n pipelines.kubeflow.org/kfp_sdk_version: 1.8.10\n pipelines.kubeflow.org/pipeline-sdk-type: kfp\n pipelines.kubeflow.org/enable_caching: "true"\n annotations: {pipelines.kubeflow.org/component_spec: \'{"implementation": {"container":\n {"args": ["--data", {"outputPath": "data"}, "--target", {"outputPath": "target"}],\n "command": ["sh", "-c", "(PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip\n install --quiet --no-warn-script-location \'\'pandas\'\' \'\'scikit-learn\'\' ||\n PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location\n \'\'pandas\'\' \'\'scikit-learn\'\' --user) && \\"$0\\" \\"$@\\"", "sh", "-ec", "program_path=$(mktemp)\\nprintf\n \\"%s\\" \\"$0\\" > \\"$program_path\\"\\npython3 -u \\"$program_path\\" \\"$@\\"\\n",\n "def _make_parent_dirs_and_return_path(file_path: str):\\n import os\\n os.makedirs(os.path.dirname(file_path),\n exist_ok=True)\\n return file_path\\n\\ndef load_iris_data(\\n data_path,\\n target_path,\\n):\\n import\n pandas as pd\\n from sklearn.datasets import load_iris\\n\\n iris = load_iris()\\n\\n data\n = pd.DataFrame(iris[\\"data\\"], columns=iris[\\"feature_names\\"])\\n target\n = pd.DataFrame(iris[\\"target\\"], columns=[\\"target\\"])\\n\\n data.to_csv(data_path,\n index=False)\\n target.to_csv(target_path, index=False)\\n\\nimport argparse\\n_parser\n = argparse.ArgumentParser(prog=\'\'Load iris data\'\', description=\'\'\'\')\\n_parser.add_argument(\\"--data\\",\n dest=\\"data_path\\", type=_make_parent_dirs_and_return_path, required=True,\n default=argparse.SUPPRESS)\\n_parser.add_argument(\\"--target\\", dest=\\"target_path\\",\n type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)\\n_parsed_args\n = vars(_parser.parse_args())\\n\\n_outputs = load_iris_data(**_parsed_args)\\n"],\n "image": "python:3.7"}}, "name": "Load iris data", "outputs": [{"name":\n "data", "type": "csv"}, {"name": "target", "type": "csv"}]}\', pipelines.kubeflow.org/component_ref: \'{}\'}\n - name: mlflow-pipeline\n inputs:\n parameters:\n - {name: kernel}\n - {name: model_name}\n dag:\n tasks:\n - {name: load-iris-data, template: load-iris-data}\n - name: train-from-csv\n template: train-from-csv\n dependencies: [load-iris-data]\n arguments:\n parameters:\n - {name: kernel, value: \'{{inputs.parameters.kernel}}\'}\n artifacts:\n - {name: load-iris-data-data, from: \'{{tasks.load-iris-data.outputs.artifacts.load-iris-data-data}}\'}\n - {name: load-iris-data-target, from: \'{{tasks.load-iris-data.outputs.artifacts.load-iris-data-target}}\'}\n - name: upload-sklearn-model-to-mlflow\n template: upload-sklearn-model-to-mlflow\n dependencies: [train-from-csv]\n arguments:\n parameters:\n - {name: model_name, value: \'{{inputs.parameters.model_name}}\'}\n artifacts:\n - {name: train-from-csv-conda_env, from: \'{{tasks.train-from-csv.outputs.artifacts.train-from-csv-conda_env}}\'}\n - {name: train-from-csv-input_example, from: \'{{tasks.train-from-csv.outputs.artifacts.train-from-csv-input_example}}\'}\n - {name: train-from-csv-model, from: \'{{tasks.train-from-csv.outputs.artifacts.train-from-csv-model}}\'}\n - {name: train-from-csv-signature, from: \'{{tasks.train-from-csv.outputs.artifacts.train-from-csv-signature}}\'}\n - name: train-from-csv\n container:\n args: [--train-data, /tmp/inputs/train_data/data, --train-target, /tmp/inputs/train_target/data,\n --kernel, \'{{inputs.parameters.kernel}}\', --model, /tmp/outputs/model/data,\n --input-example, /tmp/outputs/input_example/data, --signature, /tmp/outputs/signature/data,\n --conda-env, /tmp/outputs/conda_env/data]\n command:\n - sh\n - -c\n - (PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location\n \'dill\' \'pandas\' \'scikit-learn\' \'mlflow\' || PIP_DISABLE_PIP_VERSION_CHECK=1\n python3 -m pip install --quiet --no-warn-script-location \'dill\' \'pandas\' \'scikit-learn\'\n \'mlflow\' --user) && "$0" "$@"\n - sh\n - -ec\n - |\n program_path=$(mktemp)\n printf "%s" "$0" > "$program_path"\n python3 -u "$program_path" "$@"\n - |\n def _make_parent_dirs_and_return_path(file_path: str):\n import os\n os.makedirs(os.path.dirname(file_path), exist_ok=True)\n return file_path\n\n def train_from_csv(\n train_data_path,\n train_target_path,\n model_path,\n input_example_path,\n signature_path,\n conda_env_path,\n kernel,\n ):\n import dill\n import pandas as pd\n from sklearn.svm import SVC\n\n from mlflow.models.signature import infer_signature\n from mlflow.utils.environment import _mlflow_conda_env\n\n train_data = pd.read_csv(train_data_path)\n train_target = pd.read_csv(train_target_path)\n\n clf = SVC(kernel=kernel)\n clf.fit(train_data, train_target)\n\n with open(model_path, mode="wb") as file_writer:\n dill.dump(clf, file_writer)\n\n input_example = train_data.sample(1)\n with open(input_example_path, "wb") as file_writer:\n dill.dump(input_example, file_writer)\n\n signature = infer_signature(train_data, clf.predict(train_data))\n with open(signature_path, "wb") as file_writer:\n dill.dump(signature, file_writer)\n\n conda_env = _mlflow_conda_env(\n additional_pip_deps=["dill", "pandas", "scikit-learn"]\n )\n with open(conda_env_path, "wb") as file_writer:\n dill.dump(conda_env, file_writer)\n\n import argparse\n _parser = argparse.ArgumentParser(prog=\'Train from csv\', description=\'\')\n _parser.add_argument("--train-data", dest="train_data_path", type=str, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--train-target", dest="train_target_path", type=str, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--kernel", dest="kernel", type=str, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--model", dest="model_path", type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--input-example", dest="input_example_path", type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--signature", dest="signature_path", type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--conda-env", dest="conda_env_path", type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)\n _parsed_args = vars(_parser.parse_args())\n\n _outputs = train_from_csv(**_parsed_args)\n image: python:3.7\n inputs:\n parameters:\n - {name: kernel}\n artifacts:\n - {name: load-iris-data-data, path: /tmp/inputs/train_data/data}\n - {name: load-iris-data-target, path: /tmp/inputs/train_target/data}\n outputs:\n artifacts:\n - {name: train-from-csv-conda_env, path: /tmp/outputs/conda_env/data}\n - {name: train-from-csv-input_example, path: /tmp/outputs/input_example/data}\n - {name: train-from-csv-model, path: /tmp/outputs/model/data}\n - {name: train-from-csv-signature, path: /tmp/outputs/signature/data}\n metadata:\n labels:\n pipelines.kubeflow.org/kfp_sdk_version: 1.8.10\n pipelines.kubeflow.org/pipeline-sdk-type: kfp\n pipelines.kubeflow.org/enable_caching: "true"\n annotations: {pipelines.kubeflow.org/component_spec: \'{"implementation": {"container":\n {"args": ["--train-data", {"inputPath": "train_data"}, "--train-target",\n {"inputPath": "train_target"}, "--kernel", {"inputValue": "kernel"}, "--model",\n {"outputPath": "model"}, "--input-example", {"outputPath": "input_example"},\n "--signature", {"outputPath": "signature"}, "--conda-env", {"outputPath":\n "conda_env"}], "command": ["sh", "-c", "(PIP_DISABLE_PIP_VERSION_CHECK=1\n python3 -m pip install --quiet --no-warn-script-location \'\'dill\'\' \'\'pandas\'\'\n \'\'scikit-learn\'\' \'\'mlflow\'\' || PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m\n pip install --quiet --no-warn-script-location \'\'dill\'\' \'\'pandas\'\' \'\'scikit-learn\'\'\n \'\'mlflow\'\' --user) && \\"$0\\" \\"$@\\"", "sh", "-ec", "program_path=$(mktemp)\\nprintf\n \\"%s\\" \\"$0\\" > \\"$program_path\\"\\npython3 -u \\"$program_path\\" \\"$@\\"\\n",\n "def _make_parent_dirs_and_return_path(file_path: str):\\n import os\\n os.makedirs(os.path.dirname(file_path),\n exist_ok=True)\\n return file_path\\n\\ndef train_from_csv(\\n train_data_path,\\n train_target_path,\\n model_path,\\n input_example_path,\\n signature_path,\\n conda_env_path,\\n kernel,\\n):\\n import\n dill\\n import pandas as pd\\n from sklearn.svm import SVC\\n\\n from\n mlflow.models.signature import infer_signature\\n from mlflow.utils.environment\n import _mlflow_conda_env\\n\\n train_data = pd.read_csv(train_data_path)\\n train_target\n = pd.read_csv(train_target_path)\\n\\n clf = SVC(kernel=kernel)\\n clf.fit(train_data,\n train_target)\\n\\n with open(model_path, mode=\\"wb\\") as file_writer:\\n dill.dump(clf,\n file_writer)\\n\\n input_example = train_data.sample(1)\\n with open(input_example_path,\n \\"wb\\") as file_writer:\\n dill.dump(input_example, file_writer)\\n\\n signature\n = infer_signature(train_data, clf.predict(train_data))\\n with open(signature_path,\n \\"wb\\") as file_writer:\\n dill.dump(signature, file_writer)\\n\\n conda_env\n = _mlflow_conda_env(\\n additional_pip_deps=[\\"dill\\", \\"pandas\\",\n \\"scikit-learn\\"]\\n )\\n with open(conda_env_path, \\"wb\\") as file_writer:\\n dill.dump(conda_env,\n file_writer)\\n\\nimport argparse\\n_parser = argparse.ArgumentParser(prog=\'\'Train\n from csv\'\', description=\'\'\'\')\\n_parser.add_argument(\\"--train-data\\", dest=\\"train_data_path\\",\n type=str, required=True, default=argparse.SUPPRESS)\\n_parser.add_argument(\\"--train-target\\",\n dest=\\"train_target_path\\", type=str, required=True, default=argparse.SUPPRESS)\\n_parser.add_argument(\\"--kernel\\",\n dest=\\"kernel\\", type=str, required=True, default=argparse.SUPPRESS)\\n_parser.add_argument(\\"--model\\",\n dest=\\"model_path\\", type=_make_parent_dirs_and_return_path, required=True,\n default=argparse.SUPPRESS)\\n_parser.add_argument(\\"--input-example\\", dest=\\"input_example_path\\",\n type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)\\n_parser.add_argument(\\"--signature\\",\n dest=\\"signature_path\\", type=_make_parent_dirs_and_return_path, required=True,\n default=argparse.SUPPRESS)\\n_parser.add_argument(\\"--conda-env\\", dest=\\"conda_env_path\\",\n type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)\\n_parsed_args\n = vars(_parser.parse_args())\\n\\n_outputs = train_from_csv(**_parsed_args)\\n"],\n "image": "python:3.7"}}, "inputs": [{"name": "train_data", "type": "csv"},\n {"name": "train_target", "type": "csv"}, {"name": "kernel", "type": "String"}],\n "name": "Train from csv", "outputs": [{"name": "model", "type": "dill"},\n {"name": "input_example", "type": "dill"}, {"name": "signature", "type":\n "dill"}, {"name": "conda_env", "type": "dill"}]}\', pipelines.kubeflow.org/component_ref: \'{}\',\n pipelines.kubeflow.org/arguments.parameters: \'{"kernel": "{{inputs.parameters.kernel}}"}\'}\n - name: upload-sklearn-model-to-mlflow\n container:\n args: [--model-name, \'{{inputs.parameters.model_name}}\', --model, /tmp/inputs/model/data,\n --input-example, /tmp/inputs/input_example/data, --signature, /tmp/inputs/signature/data,\n --conda-env, /tmp/inputs/conda_env/data]\n command:\n - sh\n - -c\n - (PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location\n \'dill\' \'pandas\' \'scikit-learn\' \'mlflow\' \'boto3\' || PIP_DISABLE_PIP_VERSION_CHECK=1\n python3 -m pip install --quiet --no-warn-script-location \'dill\' \'pandas\' \'scikit-learn\'\n \'mlflow\' \'boto3\' --user) && "$0" "$@"\n - sh\n - -ec\n - |\n program_path=$(mktemp)\n printf "%s" "$0" > "$program_path"\n python3 -u "$program_path" "$@"\n - |\n def upload_sklearn_model_to_mlflow(\n model_name,\n model_path,\n input_example_path,\n signature_path,\n conda_env_path,\n ):\n import os\n import dill\n from mlflow.sklearn import save_model\n\n from mlflow.tracking.client import MlflowClient\n\n os.environ["MLFLOW_S3_ENDPOINT_URL"] = "http://minio-service.kubeflow.svc:9000"\n os.environ["AWS_ACCESS_KEY_ID"] = "minio"\n os.environ["AWS_SECRET_ACCESS_KEY"] = "minio123"\n\n client = MlflowClient("http://mlflow-server-service.mlflow-system.svc:5000")\n\n with open(model_path, mode="rb") as file_reader:\n clf = dill.load(file_reader)\n\n with open(input_example_path, "rb") as file_reader:\n input_example = dill.load(file_reader)\n\n with open(signature_path, "rb") as file_reader:\n signature = dill.load(file_reader)\n\n with open(conda_env_path, "rb") as file_reader:\n conda_env = dill.load(file_reader)\n\n save_model(\n sk_model=clf,\n path=model_name,\n serialization_format="cloudpickle",\n conda_env=conda_env,\n signature=signature,\n input_example=input_example,\n )\n run = client.create_run(experiment_id="0")\n client.log_artifact(run.info.run_id, model_name)\n\n import argparse\n _parser = argparse.ArgumentParser(prog=\'Upload sklearn model to mlflow\', description=\'\')\n _parser.add_argument("--model-name", dest="model_name", type=str, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--model", dest="model_path", type=str, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--input-example", dest="input_example_path", type=str, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--signature", dest="signature_path", type=str, required=True, default=argparse.SUPPRESS)\n _parser.add_argument("--conda-env", dest="conda_env_path", type=str, required=True, default=argparse.SUPPRESS)\n _parsed_args = vars(_parser.parse_args())\n\n _outputs = upload_sklearn_model_to_mlflow(**_parsed_args)\n image: python:3.7\n inputs:\n parameters:\n - {name: model_name}\n artifacts:\n - {name: train-from-csv-conda_env, path: /tmp/inputs/conda_env/data}\n - {name: train-from-csv-input_example, path: /tmp/inputs/input_example/data}\n - {name: train-from-csv-model, path: /tmp/inputs/model/data}\n - {name: train-from-csv-signature, path: /tmp/inputs/signature/data}\n 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\\"$program_path\\" \\"$@\\"\\n",\n "def upload_sklearn_model_to_mlflow(\\n model_name,\\n model_path,\\n input_example_path,\\n signature_path,\\n conda_env_path,\\n):\\n import\n os\\n import dill\\n from mlflow.sklearn import save_model\\n\\n from\n mlflow.tracking.client import MlflowClient\\n\\n os.environ[\\"MLFLOW_S3_ENDPOINT_URL\\"]\n = \\"http://minio-service.kubeflow.svc:9000\\"\\n os.environ[\\"AWS_ACCESS_KEY_ID\\"]\n = \\"minio\\"\\n os.environ[\\"AWS_SECRET_ACCESS_KEY\\"] = \\"minio123\\"\\n\\n client\n = MlflowClient(\\"http://mlflow-server-service.mlflow-system.svc:5000\\")\\n\\n with\n open(model_path, mode=\\"rb\\") as file_reader:\\n clf = dill.load(file_reader)\\n\\n with\n open(input_example_path, \\"rb\\") as file_reader:\\n input_example\n = dill.load(file_reader)\\n\\n with open(signature_path, \\"rb\\") as file_reader:\\n signature\n = dill.load(file_reader)\\n\\n with open(conda_env_path, \\"rb\\") as file_reader:\\n conda_env\n = dill.load(file_reader)\\n\\n save_model(\\n sk_model=clf,\\n path=model_name,\\n serialization_format=\\"cloudpickle\\",\\n conda_env=conda_env,\\n signature=signature,\\n input_example=input_example,\\n )\\n run\n = client.create_run(experiment_id=\\"0\\")\\n client.log_artifact(run.info.run_id,\n model_name)\\n\\nimport argparse\\n_parser = argparse.ArgumentParser(prog=\'\'Upload\n sklearn model to mlflow\'\', description=\'\'\'\')\\n_parser.add_argument(\\"--model-name\\",\n dest=\\"model_name\\", type=str, required=True, default=argparse.SUPPRESS)\\n_parser.add_argument(\\"--model\\",\n dest=\\"model_path\\", type=str, required=True, default=argparse.SUPPRESS)\\n_parser.add_argument(\\"--input-example\\",\n dest=\\"input_example_path\\", type=str, required=True, default=argparse.SUPPRESS)\\n_parser.add_argument(\\"--signature\\",\n dest=\\"signature_path\\", type=str, required=True, default=argparse.SUPPRESS)\\n_parser.add_argument(\\"--conda-env\\",\n dest=\\"conda_env_path\\", type=str, required=True, 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Model training"),(0,i.kt)("p",null," \ubaa8\ub378 \ud559\uc2b5(Model training)\uc740 \ubaa8\ub378 \ud559\uc2b5\uc744 \uc704\ud55c \uc54c\uace0\ub9ac\uc998\uc744 \ud6a8\uc728\uc801\uc73c\ub85c \uc2e4\ud589\uc2dc\ucf1c\uc8fc\ub294 \ub2e4\uc74c\uacfc \uac19\uc740 \uae30\ub2a5\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4."),(0,i.kt)("ul",null,(0,i.kt)("li",{parentName:"ul"},"ML \ud504\ub808\uc784\uc6cc\ud06c\uc758 \uc2e4\ud589\uc744 \uc704\ud55c \ud658\uacbd \uc81c\uacf5"),(0,i.kt)("li",{parentName:"ul"},"\ub2e4\uc218\uc758 GPU / \ubd84\uc0b0 \ud559\uc2b5 \uc0ac\uc6a9\uc744 \uc704\ud55c \ubd84\uc0b0 \ud559\uc2b5 \ud658\uacbd \uc81c\uacf5"),(0,i.kt)("li",{parentName:"ul"},"\ud558\uc774\ud37c \ud30c\ub77c\ubbf8\ud130 \ud29c\ub2dd\uacfc \ucd5c\uc801\ud654 \uae30\ub2a5 \uc81c\uacf5")),(0,i.kt)("h3",{id:"4-model-evaluation"},"4. Model evaluation"),(0,i.kt)("p",null," \ubaa8\ub378 \ud3c9\uac00(Model evaluation)\ub294 \uc2e4\ud5d8 \ud658\uacbd\uacfc \uc0c1\uc6a9 \ud658\uacbd\uc5d0\uc11c \ub3d9\uc791\ud558\ub294 \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \uad00\ucc30\ud560 \uc218 \uc788\ub294 \ub2e4\uc74c\uacfc \uac19\uc740 \uae30\ub2a5\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4."),(0,i.kt)("ul",null,(0,i.kt)("li",{parentName:"ul"},"\ud3c9\uac00 \ub370\uc774\ud130\uc5d0 \ub300\ud55c \ubaa8\ub378 \uc131\ub2a5 \ud3c9\uac00 \uae30\ub2a5"),(0,i.kt)("li",{parentName:"ul"},"\uc11c\ub85c \ub2e4\ub978 \uc9c0\uc18d \ud559\uc2b5 \uc2e4\ud589 \uacb0\uacfc\uc5d0 \ub300\ud55c \uc608\uce21 \uc131\ub2a5 \ucd94\uc801"),(0,i.kt)("li",{parentName:"ul"},"\uc11c\ub85c \ub2e4\ub978 \ubaa8\ub378\uc758 \uc131\ub2a5 \ube44\uad50\uc640 \uc2dc\uac01\ud654"),(0,i.kt)("li",{parentName:"ul"},"\ud574\uc11d\ud560 \uc218 \uc788\ub294 AI \uae30\uc220\uc744 \uc774\uc6a9\ud55c \ubaa8\ub378 \ucd9c\ub825 \ud574\uc11d \uae30\ub2a5 \uc81c\uacf5")),(0,i.kt)("h3",{id:"5-model-serving"},"5. Model serving"),(0,i.kt)("p",null," \ubaa8\ub378 \uc11c\ube59(Model serving)\uc740 \uc0c1\uc6a9 \ud658\uacbd\uc5d0 \ubaa8\ub378\uc744 \ubc30\ud3ec\ud558\uace0 \uc11c\ube59\ud558\uae30 \uc704\ud55c \ub2e4\uc74c\uacfc \uac19\uc740 \uae30\ub2a5\ub4e4\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4."),(0,i.kt)("ul",null,(0,i.kt)("li",{parentName:"ul"},"\uc800 \uc9c0\uc5f0 \ucd94\ub860\uacfc \uace0\uac00\uc6a9\uc131 \ucd94\ub860 \uae30\ub2a5 \uc81c\uacf5"),(0,i.kt)("li",{parentName:"ul"},"\ub2e4\uc591\ud55c ML \ubaa8\ub378 \uc11c\ube59 \ud504\ub808\uc784\uc6cc\ud06c \uc9c0\uc6d0(Tensorflow Serving, TorchServe, NVIDIA Triton, Scikit-learn, XGGoost. etc)"),(0,i.kt)("li",{parentName:"ul"},"\ubcf5\uc7a1\ud55c \ud615\ud0dc\uc758 \ucd94\ub860 \ub8e8\ud2f4 \uae30\ub2a5 \uc81c\uacf5, \uc608\ub97c \ub4e4\uc5b4 \uc804\ucc98\ub9ac(preprocess) \ub610\ub294 \ud6c4\ucc98\ub9ac(postprocess) \uae30\ub2a5\uacfc \ucd5c\uc885 \uacb0\uacfc\ub97c \uc704\ud574 \ub2e4\uc218\uc758 \ubaa8\ub378\uc774 \uc0ac\uc6a9\ub418\ub294 \uacbd\uc6b0\ub97c \ub9d0\ud569\ub2c8\ub2e4."),(0,i.kt)("li",{parentName:"ul"},"\uc21c\uac04\uc801\uc73c\ub85c \uce58\uc19f\ub294 \ucd94\ub860 \uc694\uccad\uc744 \ucc98\ub9ac\ud558\uae30 \uc704\ud55c \uc624\ud1a0 \uc2a4\ucf00\uc77c\ub9c1(autoscaling) \uae30\ub2a5 \uc81c\uacf5"),(0,i.kt)("li",{parentName:"ul"},"\ucd94\ub860 \uc694\uccad\uacfc \ucd94\ub860 \uacb0\uacfc\uc5d0 \ub300\ud55c \ub85c\uae45 \uae30\ub2a5 \uc81c\uacf5")),(0,i.kt)("h3",{id:"6-online-experimentation"},"6. 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Model Monitoring"),(0,i.kt)("p",null,"\ubaa8\ub378 \ubaa8\ub2c8\ud130\ub9c1(Model Monitoring)\uc740 \uc0c1\uc6a9 \ud658\uacbd\uc5d0 \ubc30\ud3ec\ub41c \ubaa8\ub378\uc774 \uc815\uc0c1\uc801\uc73c\ub85c \ub3d9\uc791\ud558\uace0 \uc788\ub294\uc9c0\ub97c \ubaa8\ub2c8\ud130\ub9c1\ud558\ub294 \uae30\ub2a5\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4 \ubaa8\ub378\uc758 \uc131\ub2a5\uc774 \ub5a8\uc5b4\uc838 \uc5c5\ub370\uc774\ud2b8\uac00 \ud544\uc694\ud55c\uc9c0\uc5d0 \ub300\ud55c \uc815\ubcf4 \ub4f1\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4."),(0,i.kt)("h3",{id:"8-ml-pipeline"},"8. 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open(mlpipeline_ui_metadata, "w") as html_writer:\n json.dump(metadata, html_writer)\n\n import argparse\n _parser = argparse.ArgumentParser(prog=\'Plot linear\', description=\'\')\n _parser.add_argument("--mlpipeline-ui-metadata", dest="mlpipeline_ui_metadata", type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)\n _parsed_args = vars(_parser.parse_args())\n _outputs = plot_linear(**_parsed_args)\n image: python:3.7\n outputs:\n artifacts:\n - {name: mlpipeline-ui-metadata, path: /tmp/outputs/mlpipeline_ui_metadata/data}\n metadata:\n labels:\n pipelines.kubeflow.org/kfp_sdk_version: 1.8.9\n pipelines.kubeflow.org/pipeline-sdk-type: kfp\n pipelines.kubeflow.org/enable_caching: "true"\n annotations: {pipelines.kubeflow.org/component_spec: \'{"implementation": {"container":\n {"args": ["--mlpipeline-ui-metadata", {"outputPath": "mlpipeline_ui_metadata"}],\n "command": ["sh", "-c", "(PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip\n install --quiet --no-warn-script-location \'\'matplotlib\'\' || PIP_DISABLE_PIP_VERSION_CHECK=1\n python3 -m pip install --quiet --no-warn-script-location \'\'matplotlib\'\'\n --user) && \\"$0\\" \\"$@\\"", "sh", "-ec", "program_path=$(mktemp)\\nprintf\n \\"%s\\" \\"$0\\" > \\"$program_path\\"\\npython3 -u \\"$program_path\\" \\"$@\\"\\n",\n "def _make_parent_dirs_and_return_path(file_path: str):\\n import os\\n os.makedirs(os.path.dirname(file_path),\n exist_ok=True)\\n return file_path\\n\\ndef plot_linear(mlpipeline_ui_metadata):\\n import\n base64\\n import json\\n from io import BytesIO\\n\\n import matplotlib.pyplot\n as plt\\n\\n plt.plot([1, 2, 3], [1, 2, 3])\\n\\n tmpfile = BytesIO()\\n plt.savefig(tmpfile,\n format=\\"png\\")\\n encoded = base64.b64encode(tmpfile.getvalue()).decode(\\"utf-8\\")\\n\\n html\n = f\\"\\"\\n metadata = {\\n \\"outputs\\":\n [\\n {\\n \\"type\\": \\"web-app\\",\\n \\"storage\\":\n \\"inline\\",\\n \\"source\\": html,\\n },\\n ],\\n }\\n with\n open(mlpipeline_ui_metadata, \\"w\\") as html_writer:\\n json.dump(metadata,\n html_writer)\\n\\nimport argparse\\n_parser = argparse.ArgumentParser(prog=\'\'Plot\n linear\'\', description=\'\'\'\')\\n_parser.add_argument(\\"--mlpipeline-ui-metadata\\",\n dest=\\"mlpipeline_ui_metadata\\", type=_make_parent_dirs_and_return_path,\n required=True, default=argparse.SUPPRESS)\\n_parsed_args = vars(_parser.parse_args())\\n\\n_outputs\n = plot_linear(**_parsed_args)\\n"], "image": "python:3.7"}}, "name": "Plot\n linear", "outputs": [{"name": "mlpipeline_ui_metadata", "type": "UI_Metadata"}]}\',\n pipelines.kubeflow.org/component_ref: \'{}\'}\n - name: plot-pipeline\n dag:\n tasks:\n - {name: plot-linear, template: plot-linear}\n arguments:\n parameters: []\n serviceAccountName: pipeline-runner\n')))),(0,p.kt)("p",null,"\uc2e4\ud589 \ud6c4 Visualization\uc744 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create_component_from_func, OutputPath\nfrom kfp.dsl import pipeline\n\n\n@create_component_from_func\ndef print_and_return_number(number: int) -> int:\n print(number)\n return number\n\n@create_component_from_func\ndef sum_and_print_numbers(number_1: int, number_2: int) -> int:\n sum_number = number_1 + number_2\n print(sum_number)\n return sum_number\n\n@create_component_from_func\ndef show_metric_of_sum(\n number: int,\n mlpipeline_metrics_path: OutputPath("Metrics"),\n ):\n import json\n metrics = {\n "metrics": [\n {\n "name": "sum_value",\n "numberValue": number,\n },\n ],\n }\n with open(mlpipeline_metrics_path, "w") as f:\n json.dump(metrics, f)\n\n@pipeline(name="example_pipeline")\ndef example_pipeline(number_1: int, number_2: int):\n number_1_result = print_and_return_number(number_1)\n number_2_result = print_and_return_number(number_2)\n sum_result = sum_and_print_numbers(\n number_1=number_1_result.output, number_2=number_2_result.output\n )\n 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\ub429\ub2c8\ub2e4."),(0,p.kt)("pre",null,(0,p.kt)("code",{parentName:"pre",className:"language-python"},'from functools import partial\n\nimport kfp\nfrom kfp.components import create_component_from_func, OutputPath\nfrom kfp.dsl import pipeline\n\n\n@partial(\n create_component_from_func,\n packages_to_install=["matplotlib"],\n)\ndef plot_linear(mlpipeline_ui_metadata: OutputPath("UI_Metadata")):\n import base64\n import json\n from io import BytesIO\n\n import matplotlib.pyplot as plt\n\n plt.plot([1, 2, 3], [1, 2, 3])\n\n tmpfile = BytesIO()\n plt.savefig(tmpfile, format="png")\n encoded = base64.b64encode(tmpfile.getvalue()).decode("utf-8")\n\n html = f""\n metadata = {\n "outputs": [\n {\n "type": "web-app",\n "storage": "inline",\n "source": html,\n },\n ],\n }\n with open(mlpipeline_ui_metadata, "w") as html_writer:\n json.dump(metadata, html_writer)\n\n\n@pipeline(name="plot_pipeline")\ndef plot_pipeline():\n plot_linear()\n\n\nif __name__ == "__main__":\n 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/tmp/outputs/mlpipeline_ui_metadata/data]\n command:\n - sh\n - -c\n - (PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location\n \'matplotlib\' || PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet\n --no-warn-script-location \'matplotlib\' --user) && "$0" "$@"\n - sh\n - -ec\n - |\n program_path=$(mktemp)\n printf "%s" "$0" > "$program_path"\n python3 -u "$program_path" "$@"\n - |\n def _make_parent_dirs_and_return_path(file_path: str):\n import os\n os.makedirs(os.path.dirname(file_path), exist_ok=True)\n return file_path\n def plot_linear(mlpipeline_ui_metadata):\n import base64\n import json\n from io import BytesIO\n import matplotlib.pyplot as plt\n plt.plot([1, 2, 3], [1, 2, 3])\n tmpfile = BytesIO()\n plt.savefig(tmpfile, format="png")\n encoded = base64.b64encode(tmpfile.getvalue()).decode("utf-8")\n html = f""\n metadata = {\n "outputs": [\n {\n "type": "web-app",\n "storage": "inline",\n "source": html,\n },\n ],\n }\n with open(mlpipeline_ui_metadata, "w") as html_writer:\n json.dump(metadata, html_writer)\n\n import argparse\n _parser = argparse.ArgumentParser(prog=\'Plot linear\', description=\'\')\n _parser.add_argument("--mlpipeline-ui-metadata", dest="mlpipeline_ui_metadata", type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)\n _parsed_args = vars(_parser.parse_args())\n _outputs = plot_linear(**_parsed_args)\n image: python:3.7\n outputs:\n artifacts:\n - {name: mlpipeline-ui-metadata, path: /tmp/outputs/mlpipeline_ui_metadata/data}\n metadata:\n labels:\n pipelines.kubeflow.org/kfp_sdk_version: 1.8.9\n pipelines.kubeflow.org/pipeline-sdk-type: kfp\n pipelines.kubeflow.org/enable_caching: "true"\n annotations: {pipelines.kubeflow.org/component_spec: \'{"implementation": {"container":\n {"args": ["--mlpipeline-ui-metadata", {"outputPath": "mlpipeline_ui_metadata"}],\n "command": ["sh", "-c", "(PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip\n install --quiet --no-warn-script-location \'\'matplotlib\'\' || PIP_DISABLE_PIP_VERSION_CHECK=1\n python3 -m pip install --quiet --no-warn-script-location \'\'matplotlib\'\'\n --user) && \\"$0\\" \\"$@\\"", "sh", "-ec", "program_path=$(mktemp)\\nprintf\n \\"%s\\" \\"$0\\" > \\"$program_path\\"\\npython3 -u \\"$program_path\\" \\"$@\\"\\n",\n "def _make_parent_dirs_and_return_path(file_path: str):\\n import os\\n os.makedirs(os.path.dirname(file_path),\n exist_ok=True)\\n return file_path\\n\\ndef plot_linear(mlpipeline_ui_metadata):\\n import\n base64\\n import json\\n from io import BytesIO\\n\\n import matplotlib.pyplot\n as plt\\n\\n plt.plot([1, 2, 3], [1, 2, 3])\\n\\n tmpfile = BytesIO()\\n plt.savefig(tmpfile,\n format=\\"png\\")\\n encoded = base64.b64encode(tmpfile.getvalue()).decode(\\"utf-8\\")\\n\\n html\n = f\\"\\"\\n metadata = {\\n \\"outputs\\":\n [\\n {\\n \\"type\\": \\"web-app\\",\\n \\"storage\\":\n \\"inline\\",\\n \\"source\\": html,\\n },\\n ],\\n }\\n with\n open(mlpipeline_ui_metadata, \\"w\\") as html_writer:\\n json.dump(metadata,\n html_writer)\\n\\nimport argparse\\n_parser = argparse.ArgumentParser(prog=\'\'Plot\n linear\'\', description=\'\'\'\')\\n_parser.add_argument(\\"--mlpipeline-ui-metadata\\",\n dest=\\"mlpipeline_ui_metadata\\", type=_make_parent_dirs_and_return_path,\n required=True, default=argparse.SUPPRESS)\\n_parsed_args = vars(_parser.parse_args())\\n\\n_outputs\n = plot_linear(**_parsed_args)\\n"], "image": "python:3.7"}}, "name": "Plot\n linear", "outputs": [{"name": "mlpipeline_ui_metadata", "type": "UI_Metadata"}]}\',\n pipelines.kubeflow.org/component_ref: \'{}\'}\n - name: plot-pipeline\n dag:\n tasks:\n - {name: plot-linear, template: plot-linear}\n arguments:\n parameters: []\n serviceAccountName: pipeline-runner\n')))),(0,p.kt)("p",null,"\uc2e4\ud589 \ud6c4 Visualization\uc744 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nvidia-driver-installer | minikube | disabled | google |\n| nvidia-gpu-device-plugin | minikube | disabled | unknown (third-party) |\n| olm | minikube | disabled | unknown (third-party) |\n| pod-security-policy | minikube | disabled | unknown (third-party) |\n| portainer | minikube | disabled | portainer.io |\n| registry | minikube | disabled | google |\n| registry-aliases | minikube | disabled | unknown (third-party) |\n| registry-creds | minikube | disabled | unknown (third-party) |\n| storage-provisioner | minikube | disabled | kubernetes |\n| storage-provisioner-gluster | minikube | disabled | unknown (third-party) |\n| volumesnapshots | minikube | disabled | kubernetes |\n|-----------------------------|----------|--------------|-----------------------|\n")),(0,r.kt)("h2",{id:"3-\ucfe0\ubc84\ub124\ud2f0\uc2a4-\ud074\ub77c\uc774\uc5b8\ud2b8-\uc14b\uc5c5"},"3. \ucfe0\ubc84\ub124\ud2f0\uc2a4 \ud074\ub77c\uc774\uc5b8\ud2b8 \uc14b\uc5c5"),(0,r.kt)("p",null,"\uc774\ubc88\uc5d0\ub294 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The missing annotation will be patched automatically.\nconfigmap/kube-proxy configured\n")),(0,l.kt)("h3",{id:"\uc124\uce58---manifest"},"\uc124\uce58 - Manifest"),(0,l.kt)("h4",{id:"1-metallb-\ub97c-\uc124\uce58\ud569\ub2c8\ub2e4"},"1. 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name: default\n protocol: layer2\n addresses:\n - 192.168.35.100-192.168.35.110 # IP \ub300\uc5ed\ud3ed\n")),(0,l.kt)("p",null,"\uc704\uc758 \uc124\uc815\uc744 \uc801\uc6a9\ud569\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-test"},"kubectl apply -f metallb_config.yaml \n")),(0,l.kt)("p",null,"\uc815\uc0c1\uc801\uc73c\ub85c \ubc30\ud3ec\ud558\uba74 \ub2e4\uc74c\uacfc \uac19\uc774 \ucd9c\ub825\ub429\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-test"},"configmap/config created\n")),(0,l.kt)("h2",{id:"metallb-\uc0ac\uc6a9"},"MetalLB \uc0ac\uc6a9"),(0,l.kt)("h3",{id:"kubeflow-dashboard"},"Kubeflow Dashboard"),(0,l.kt)("p",null,"\uba3c\uc800 kubeflow\uc758 Dashboard \ub97c \uc81c\uacf5\ud558\ub294 istio-system \ub124\uc784\uc2a4\ud398\uc774\uc2a4\uc758 istio-ingressgateway \uc11c\ube44\uc2a4\uc758 \ud0c0\uc785\uc744 ",(0,l.kt)("inlineCode",{parentName:"p"},"LoadBalancer"),"\ub85c \ubcc0\uacbd\ud558\uc5ec MetalLB\ub85c\ubd80\ud130 \ub85c\ub4dc \ubca8\ub7f0\uc2f1 \uae30\ub2a5\uc744 \uc81c\uacf5\ubc1b\uae30 \uc804\uc5d0, \ud604\uc7ac \uc0c1\ud0dc\ub97c \ud655\uc778\ud569\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"kubectl get svc/istio-ingressgateway -n istio-system\n")),(0,l.kt)("p",null,"\ud574\ub2f9 \uc11c\ube44\uc2a4\uc758 \ud0c0\uc785\uc740 ClusterIP\uc774\uba70, External-IP \uac12\uc740 ",(0,l.kt)("inlineCode",{parentName:"p"},"none")," \uc778 \uac83\uc744 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE\nistio-ingressgateway ClusterIP 10.103.72.5 15021/TCP,80/TCP,443/TCP,31400/TCP,15443/TCP 4h21m\n")),(0,l.kt)("p",null,"type \uc744 LoadBalancer \ub85c \ubcc0\uacbd\ud558\uace0 \uc6d0\ud558\ub294 IP \uc8fc\uc18c\ub97c \uc785\ub825\ud558\uace0 \uc2f6\uc740 \uacbd\uc6b0 loadBalancerIP \ud56d\ubaa9\uc744 \ucd94\uac00\ud569\ub2c8\ub2e4.",(0,l.kt)("br",{parentName:"p"}),"\n","\ucd94\uac00 \ud558\uc9c0 \uc54a\uc744 \uacbd\uc6b0\uc5d0\ub294 \uc704\uc5d0\uc11c \uc124\uc815\ud55c IP \uc8fc\uc18c\ud480\uc5d0\uc11c \uc21c\ucc28\uc801\uc73c\ub85c IP \uc8fc\uc18c\uac00 \ubc30\uc815\ub429\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"kubectl edit svc/istio-ingressgateway -n istio-system\n")),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"spec:\n clusterIP: 10.103.72.5\n clusterIPs:\n - 10.103.72.5\n ipFamilies:\n - IPv4\n ipFamilyPolicy: SingleStack\n ports:\n - name: status-port\n port: 15021\n protocol: TCP\n targetPort: 15021\n - name: http2\n port: 80\n protocol: TCP\n targetPort: 8080\n - name: https\n port: 443\n protocol: TCP\n targetPort: 8443\n - name: tcp\n port: 31400\n protocol: TCP\n targetPort: 31400\n - name: tls\n port: 15443\n protocol: TCP\n targetPort: 15443\n selector:\n app: istio-ingressgateway\n istio: ingressgateway\n sessionAffinity: None\n type: LoadBalancer # Change ClusterIP to LoadBalancer\n loadBalancerIP: 192.168.35.100 # Add IP\nstatus:\n loadBalancer: {}\n")),(0,l.kt)("p",null,"\ub2e4\uc2dc \ud655\uc778\uc744 \ud574\ubcf4\uba74 External-IP \uac12\uc774 ",(0,l.kt)("inlineCode",{parentName:"p"},"192.168.35.100")," \uc778 \uac83\uc744 \ud655\uc778\ud569\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"kubectl get svc/istio-ingressgateway -n istio-system\n")),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE\nistio-ingressgateway LoadBalancer 10.103.72.5 192.168.35.100 15021:31054/TCP,80:30853/TCP,443:30443/TCP,31400:30012/TCP,15443:31650/TCP 5h1m\n")),(0,l.kt)("p",null,"Web Browser \ub97c \uc5f4\uc5b4 ",(0,l.kt)("a",{parentName:"p",href:"http://192.168.35.100"},"http://192.168.35.100")," \uc73c\ub85c \uc811\uc18d\ud558\uc5ec, \ub2e4\uc74c\uacfc \uac19\uc740 \ud654\uba74\uc774 \ucd9c\ub825\ub418\ub294 \uac83\uc744 \ud655\uc778\ud569\ub2c8\ub2e4."),(0,l.kt)("p",null,(0,l.kt)("img",{alt:"login-after-istio-ingressgateway-setting.png",src:n(5556).Z,width:"1811",height:"1046"})),(0,l.kt)("h3",{id:"minio-dashboard"},"minio Dashboard"),(0,l.kt)("p",null,"\uba3c\uc800 minio \uc758 Dashboard \ub97c \uc81c\uacf5\ud558\ub294 kubeflow \ub124\uc784\uc2a4\ud398\uc774\uc2a4\uc758 minio-service \uc11c\ube44\uc2a4\uc758 \ud0c0\uc785\uc744 LoadBalancer\ub85c \ubcc0\uacbd\ud558\uc5ec MetalLB\ub85c\ubd80\ud130 \ub85c\ub4dc \ubca8\ub7f0\uc2f1 \uae30\ub2a5\uc744 \uc81c\uacf5\ubc1b\uae30 \uc804\uc5d0, \ud604\uc7ac \uc0c1\ud0dc\ub97c \ud655\uc778\ud569\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"kubectl get svc/minio-service -n kubeflow\n")),(0,l.kt)("p",null,"\ud574\ub2f9 \uc11c\ube44\uc2a4\uc758 \ud0c0\uc785\uc740 ClusterIP\uc774\uba70, External-IP \uac12\uc740 ",(0,l.kt)("inlineCode",{parentName:"p"},"none")," \uc778 \uac83\uc744 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE\nminio-service ClusterIP 10.109.209.87 9000/TCP 5h14m\n")),(0,l.kt)("p",null,"type \uc744 LoadBalancer \ub85c \ubcc0\uacbd\ud558\uace0 \uc6d0\ud558\ub294 IP \uc8fc\uc18c\ub97c \uc785\ub825\ud558\uace0 \uc2f6\uc740 \uacbd\uc6b0 loadBalancerIP \ud56d\ubaa9\uc744 \ucd94\uac00\ud569\ub2c8\ub2e4.",(0,l.kt)("br",{parentName:"p"}),"\n","\ucd94\uac00 \ud558\uc9c0 \uc54a\uc744 \uacbd\uc6b0\uc5d0\ub294 \uc704\uc5d0\uc11c \uc124\uc815\ud55c IP \uc8fc\uc18c\ud480\uc5d0\uc11c \uc21c\ucc28\uc801\uc73c\ub85c IP \uc8fc\uc18c\uac00 \ubc30\uc815\ub429\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"kubectl edit svc/minio-service -n kubeflow\n")),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},'apiVersion: v1\nkind: Service\nmetadata:\n annotations:\n kubectl.kubernetes.io/last-applied-configuration: |\n {"apiVersion":"v1","kind":"Service","metadata":{"annotations":{},"labels":{"application-crd-id":"kubeflow-pipelines"},"name":"minio-ser>\n creationTimestamp: "2022-01-05T08:44:23Z"\n labels:\n application-crd-id: kubeflow-pipelines\n name: minio-service\n namespace: kubeflow\n resourceVersion: "21120"\n uid: 0053ee28-4f87-47bb-ad6b-7ad68aa29a48\nspec:\n clusterIP: 10.109.209.87\n clusterIPs:\n - 10.109.209.87\n ipFamilies:\n - IPv4\n ipFamilyPolicy: SingleStack\n ports:\n - name: http\n port: 9000\n protocol: TCP\n targetPort: 9000\n selector:\n app: minio\n application-crd-id: kubeflow-pipelines\n sessionAffinity: None\n type: LoadBalancer # Change ClusterIP to LoadBalancer\n loadBalancerIP: 192.168.35.101 # Add IP\nstatus:\n loadBalancer: {}\n')),(0,l.kt)("p",null,"\ub2e4\uc2dc \ud655\uc778\uc744 \ud574\ubcf4\uba74 External-IP \uac12\uc774 ",(0,l.kt)("inlineCode",{parentName:"p"},"192.168.35.101")," \uc778 \uac83\uc744 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"kubectl get svc/minio-service -n kubeflow\n")),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE\nminio-service LoadBalancer 10.109.209.87 192.168.35.101 9000:31371/TCP 5h21m\n")),(0,l.kt)("p",null,"Web Browser \ub97c \uc5f4\uc5b4 ",(0,l.kt)("a",{parentName:"p",href:"http://192.168.35.101:9000"},"http://192.168.35.101:9000")," \uc73c\ub85c \uc811\uc18d\ud558\uc5ec, \ub2e4\uc74c\uacfc \uac19\uc740 \ud654\uba74\uc774 \ucd9c\ub825\ub418\ub294 \uac83\uc744 \ud655\uc778\ud569\ub2c8\ub2e4."),(0,l.kt)("p",null,(0,l.kt)("img",{alt:"login-after-minio-setting.png",src:n(6589).Z,width:"1811",height:"1046"})),(0,l.kt)("h3",{id:"mlflow-dashboard"},"mlflow Dashboard"),(0,l.kt)("p",null,"\uba3c\uc800 mlflow \uc758 Dashboard \ub97c \uc81c\uacf5\ud558\ub294 mlflow-system \ub124\uc784\uc2a4\ud398\uc774\uc2a4\uc758 mlflow-server-service \uc11c\ube44\uc2a4\uc758 \ud0c0\uc785\uc744 LoadBalancer\ub85c \ubcc0\uacbd\ud558\uc5ec MetalLB\ub85c\ubd80\ud130 \ub85c\ub4dc \ubca8\ub7f0\uc2f1 \uae30\ub2a5\uc744 \uc81c\uacf5\ubc1b\uae30 \uc804\uc5d0, \ud604\uc7ac \uc0c1\ud0dc\ub97c \ud655\uc778\ud569\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"kubectl get svc/mlflow-server-service -n mlflow-system\n")),(0,l.kt)("p",null,"\ud574\ub2f9 \uc11c\ube44\uc2a4\uc758 \ud0c0\uc785\uc740 ClusterIP\uc774\uba70, External-IP \uac12\uc740 ",(0,l.kt)("inlineCode",{parentName:"p"},"none")," \uc778 \uac83\uc744 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE\nmlflow-server-service ClusterIP 10.111.173.209 5000/TCP 4m50s\n")),(0,l.kt)("p",null,"type \uc744 LoadBalancer \ub85c \ubcc0\uacbd\ud558\uace0 \uc6d0\ud558\ub294 IP \uc8fc\uc18c\ub97c \uc785\ub825\ud558\uace0 \uc2f6\uc740 \uacbd\uc6b0 loadBalancerIP \ud56d\ubaa9\uc744 \ucd94\uac00\ud569\ub2c8\ub2e4.",(0,l.kt)("br",{parentName:"p"}),"\n","\ucd94\uac00 \ud558\uc9c0 \uc54a\uc744 \uacbd\uc6b0\uc5d0\ub294 \uc704\uc5d0\uc11c \uc124\uc815\ud55c IP \uc8fc\uc18c\ud480\uc5d0\uc11c \uc21c\ucc28\uc801\uc73c\ub85c IP \uc8fc\uc18c\uac00 \ubc30\uc815\ub429\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"kubectl edit svc/mlflow-server-service -n mlflow-system\n")),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},'apiVersion: v1\nkind: Service\nmetadata:\n annotations:\n meta.helm.sh/release-name: mlflow-server\n meta.helm.sh/release-namespace: mlflow-system\n creationTimestamp: "2022-01-07T04:00:19Z"\n labels:\n app.kubernetes.io/managed-by: Helm\n name: mlflow-server-service\n namespace: mlflow-system\n resourceVersion: "276246"\n uid: e5d39fb7-ad98-47e7-b512-f9c673055356\nspec:\n clusterIP: 10.111.173.209\n clusterIPs:\n - 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\ud50c\ub7ab\ud3fc\uc5d0\uc11c\ub294 \uc790\uccb4\uc801\uc73c\ub85c \ub85c\ub4dc \ubca8\ub7f0\uc11c(Load Balancer)\ub97c \uc81c\uacf5\ud574 \uc8fc\uc9c0\ub9cc, \uc628\ud504\ub808\ubbf8\uc2a4 \ud074\ub7ec\uc2a4\ud130\uc5d0\uc11c\ub294 \ub85c\ub4dc \ubca8\ub7f0\uc2f1 \uae30\ub2a5\uc744 \uc81c\uacf5\ud558\ub294 \ubaa8\ub4c8\uc744 \ucd94\uac00\uc801\uc73c\ub85c \uc124\uce58\ud574\uc57c \ud569\ub2c8\ub2e4.",(0,l.kt)("br",{parentName:"p"}),"\n",(0,l.kt)("a",{parentName:"p",href:"https://metallb.universe.tf/"},"MetalLB"),"\ub294 \ubca0\uc5b4\uba54\ud0c8 \ud658\uacbd\uc5d0\uc11c \uc0ac\uc6a9\ud560 \uc218 \uc788\ub294 \ub85c\ub4dc \ubca8\ub7f0\uc11c\ub97c \uc81c\uacf5\ud558\ub294 \uc624\ud508\uc18c\uc2a4 \ud504\ub85c\uc81d\ud2b8 \uc785\ub2c8\ub2e4."),(0,l.kt)("h2",{id:"\uc694\uad6c\uc0ac\ud56d"},"\uc694\uad6c\uc0ac\ud56d"),(0,l.kt)("table",null,(0,l.kt)("thead",{parentName:"table"},(0,l.kt)("tr",{parentName:"thead"},(0,l.kt)("th",{parentName:"tr",align:null},"\uc694\uad6c \uc0ac\ud56d"),(0,l.kt)("th",{parentName:"tr",align:null},"\ubc84\uc804 \ubc0f \ub0b4\uc6a9"))),(0,l.kt)("tbody",{parentName:"table"},(0,l.kt)("tr",{parentName:"tbody"},(0,l.kt)("td",{parentName:"tr",align:null},"Kubernetes"),(0,l.kt)("td",{parentName:"tr",align:null},"\ub85c\ub4dc \ubca8\ub7f0\uc2f1 \uae30\ub2a5\uc774 \uc5c6\ub294 >= v1.13.0")),(0,l.kt)("tr",{parentName:"tbody"},(0,l.kt)("td",{parentName:"tr",align:null},(0,l.kt)("a",{parentName:"td",href:"https://metallb.universe.tf/installation/network-addons/"},"\ud638\ud658\uac00\ub2a5\ud55c \ub124\ud2b8\uc6cc\ud06c CNI")),(0,l.kt)("td",{parentName:"tr",align:null},"Calico, Canal, Cilium, Flannel, Kube-ovn, Kube-router, Weave Net")),(0,l.kt)("tr",{parentName:"tbody"},(0,l.kt)("td",{parentName:"tr",align:null},"IPv4 \uc8fc\uc18c"),(0,l.kt)("td",{parentName:"tr",align:null},"MetalLB \ubc30\ud3ec\uc5d0 \uc0ac\uc6a9")),(0,l.kt)("tr",{parentName:"tbody"},(0,l.kt)("td",{parentName:"tr",align:null},"BGP \ubaa8\ub4dc\ub97c \uc0ac\uc6a9\ud560 \uacbd\uc6b0"),(0,l.kt)("td",{parentName:"tr",align:null},"BGP \uae30\ub2a5\uc744 \uc9c0\uc6d0\ud558\ub294 \ud558\ub098 \uc774\uc0c1\uc758 \ub77c\uc6b0\ud130")),(0,l.kt)("tr",{parentName:"tbody"},(0,l.kt)("td",{parentName:"tr",align:null},"\ub178\ub4dc \uac04 \ud3ec\ud2b8 TCP/UDP 7946 \uc624\ud508"),(0,l.kt)("td",{parentName:"tr",align:null},"memberlist \uc694\uad6c \uc0ac\ud56d")))),(0,l.kt)("h2",{id:"metallb-\uc124\uce58"},"MetalLB \uc124\uce58"),(0,l.kt)("h3",{id:"preparation"},"Preparation"),(0,l.kt)("p",null,"IPVS \ubaa8\ub4dc\uc5d0\uc11c kube-proxy\ub97c \uc0ac\uc6a9\ud558\ub294 \uacbd\uc6b0 Kubernetes v1.14.2 \uc774\ud6c4\ubd80\ud130\ub294 \uc5c4\uaca9\ud55c ARP(strictARP) \ubaa8\ub4dc\ub97c \uc0ac\uc6a9\ud558\ub3c4\ub85d \uc124\uc815\ud574\uc57c \ud569\ub2c8\ub2e4.",(0,l.kt)("br",{parentName:"p"}),"\n","Kube-router\ub294 \uae30\ubcf8\uc801\uc73c\ub85c \uc5c4\uaca9\ud55c ARP\ub97c \ud65c\uc131\ud654\ud558\ubbc0\ub85c \uc11c\ube44\uc2a4 \ud504\ub85d\uc2dc\ub85c \uc0ac\uc6a9\ud560 \uacbd\uc6b0\uc5d0\ub294 \uc774 \uae30\ub2a5\uc774 \ud544\uc694\ud558\uc9c0 \uc54a\uc2b5\ub2c8\ub2e4.",(0,l.kt)("br",{parentName:"p"}),"\n","\uc5c4\uaca9\ud55c ARP \ubaa8\ub4dc\ub97c \uc801\uc6a9\ud558\uae30\uc5d0 \uc55e\uc11c, \ud604\uc7ac \ubaa8\ub4dc\ub97c \ud655\uc778\ud569\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"# see what changes would be made, returns nonzero returncode if different\nkubectl get configmap kube-proxy -n kube-system -o yaml | \\\ngrep strictARP\n")),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"strictARP: false\n")),(0,l.kt)("p",null,"strictARP: false \uac00 \ucd9c\ub825\ub418\ub294 \uacbd\uc6b0 \ub2e4\uc74c\uc744 \uc2e4\ud589\ud558\uc5ec strictARP: true\ub85c \ubcc0\uacbd\ud569\ub2c8\ub2e4.\n(strictARP: true\uac00 \uc774\ubbf8 \ucd9c\ub825\ub41c\ub2e4\uba74 \ub2e4\uc74c \ucee4\ub9e8\ub4dc\ub97c \uc218\ud589\ud558\uc9c0 \uc54a\uc73c\uc154\ub3c4 \ub429\ub2c8\ub2e4.)"),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},'# actually apply the changes, returns nonzero returncode on errors only\nkubectl get configmap kube-proxy -n kube-system -o yaml | \\\nsed -e "s/strictARP: false/strictARP: true/" | \\\nkubectl apply -f - -n kube-system\n')),(0,l.kt)("p",null,"\uc815\uc0c1\uc801\uc73c\ub85c \uc218\ud589\ub418\uba74 \ub2e4\uc74c\uacfc \uac19\uc774 \ucd9c\ub825\ub429\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"Warning: resource configmaps/kube-proxy is missing the kubectl.kubernetes.io/last-applied-configuration annotation which is required by kubectl apply. kubectl apply should only be used on resources created declaratively by either kubectl create --save-config or kubectl apply. The missing annotation will be patched automatically.\nconfigmap/kube-proxy configured\n")),(0,l.kt)("h3",{id:"\uc124\uce58---manifest"},"\uc124\uce58 - Manifest"),(0,l.kt)("h4",{id:"1-metallb-\ub97c-\uc124\uce58\ud569\ub2c8\ub2e4"},"1. MetalLB \ub97c \uc124\uce58\ud569\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"kubectl apply -f https://raw.githubusercontent.com/metallb/metallb/v0.11.0/manifests/namespace.yaml\nkubectl apply -f https://raw.githubusercontent.com/metallb/metallb/v0.11.0/manifests/metallb.yaml\n")),(0,l.kt)("h4",{id:"2-\uc815\uc0c1-\uc124\uce58-\ud655\uc778"},"2. \uc815\uc0c1 \uc124\uce58 \ud655\uc778"),(0,l.kt)("p",null,"metallb-system namespace \uc758 2 \uac1c\uc758 pod \uc774 \ubaa8\ub450 Running \uc774 \ub420 \ub54c\uae4c\uc9c0 \uae30\ub2e4\ub9bd\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"kubectl get pod -n metallb-system\n")),(0,l.kt)("p",null,"\ubaa8\ub450 Running \uc774 \ub418\uba74 \ub2e4\uc74c\uacfc \ube44\uc2b7\ud55c \uacb0\uacfc\uac00 \ucd9c\ub825\ub429\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"NAME READY STATUS RESTARTS AGE\ncontroller-7dcc8764f4-8n92q 1/1 Running 1 1m\nspeaker-fnf8l 1/1 Running 1 1m\n")),(0,l.kt)("p",null,"\ub9e4\ub2c8\ud398\uc2a4\ud2b8\uc758 \uad6c\uc131 \uc694\uc18c\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4."),(0,l.kt)("ul",null,(0,l.kt)("li",{parentName:"ul"},"metallb-system/controller",(0,l.kt)("ul",{parentName:"li"},(0,l.kt)("li",{parentName:"ul"},"deployment \ub85c \ubc30\ud3ec\ub418\uba70, \ub85c\ub4dc \ubca8\ub7f0\uc2f1\uc744 \uc218\ud589\ud560 external IP \uc8fc\uc18c\uc758 \ud560\ub2f9\uc744 \ucc98\ub9ac\ud558\ub294 \uc5ed\ud560\uc744 \ub2f4\ub2f9\ud569\ub2c8\ub2e4."))),(0,l.kt)("li",{parentName:"ul"},"metallb-system/speaker",(0,l.kt)("ul",{parentName:"li"},(0,l.kt)("li",{parentName:"ul"},"daemonset \ud615\ud0dc\ub85c \ubc30\ud3ec\ub418\uba70, \uc678\ubd80 \ud2b8\ub798\ud53d\uacfc \uc11c\ube44\uc2a4\ub97c \uc5f0\uacb0\ud574 \ub124\ud2b8\uc6cc\ud06c \ud1b5\uc2e0\uc774 \uac00\ub2a5\ud558\ub3c4\ub85d \uad6c\uc131\ud558\ub294 \uc5ed\ud560\uc744 \ub2f4\ub2f9\ud569\ub2c8\ub2e4.")))),(0,l.kt)("p",null,"\uc11c\ube44\uc2a4\uc5d0\ub294 \ucee8\ud2b8\ub864\ub7ec \ubc0f \uc2a4\ud53c\ucee4\uc640 \uad6c\uc131 \uc694\uc18c\uac00 \uc791\ub3d9\ud558\ub294 \ub370 \ud544\uc694\ud55c RBAC \uc0ac\uc6a9 \uad8c\ud55c\uc774 \ud3ec\ud568\ub429\ub2c8\ub2e4."),(0,l.kt)("h2",{id:"configuration"},"Configuration"),(0,l.kt)("p",null,"MetalLB \uc758 \ub85c\ub4dc \ubca8\ub7f0\uc2f1 \uc815\ucc45 \uc124\uc815\uc740 \uad00\ub828 \uc124\uc815 \uc815\ubcf4\ub97c \ub2f4\uc740 configmap \uc744 \ubc30\ud3ec\ud558\uc5ec \uc124\uc815\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,l.kt)("p",null,"MetalLB \uc5d0\uc11c \uad6c\uc131\ud560 \uc218 \uc788\ub294 \ubaa8\ub4dc\ub85c\ub294 \ub2e4\uc74c\uacfc \uac19\uc774 2\uac00\uc9c0\uac00 \uc788\uc2b5\ub2c8\ub2e4."),(0,l.kt)("ol",null,(0,l.kt)("li",{parentName:"ol"},(0,l.kt)("a",{parentName:"li",href:"https://metallb.universe.tf/concepts/layer2/"},"Layer 2 \ubaa8\ub4dc")),(0,l.kt)("li",{parentName:"ol"},(0,l.kt)("a",{parentName:"li",href:"https://metallb.universe.tf/concepts/bgp/"},"BGP \ubaa8\ub4dc"))),(0,l.kt)("p",null,"\uc5ec\uae30\uc5d0\uc11c\ub294 Layer 2 \ubaa8\ub4dc\ub85c \uc9c4\ud589\ud558\uaca0\uc2b5\ub2c8\ub2e4."),(0,l.kt)("h3",{id:"layer-2-configuration"},"Layer 2 Configuration"),(0,l.kt)("p",null,"Layer 2 \ubaa8\ub4dc\ub294 \uac04\ub2e8\ud558\uac8c \uc0ac\uc6a9\ud560 IP \uc8fc\uc18c\uc758 \ub300\uc5ed\ub9cc \uc124\uc815\ud558\uba74 \ub429\ub2c8\ub2e4.",(0,l.kt)("br",{parentName:"p"}),"\n","Layer 2 \ubaa8\ub4dc\ub97c \uc0ac\uc6a9\ud560 \uacbd\uc6b0 \uc6cc\ucee4 \ub178\ub4dc\uc758 \ub124\ud2b8\uc6cc\ud06c \uc778\ud130\ud398\uc774\uc2a4\uc5d0 IP\ub97c \ubc14\uc778\ub529 \ud558\uc9c0 \uc54a\uc544\ub3c4 \ub418\ub294\ub370 \ub85c\uceec \ub124\ud2b8\uc6cc\ud06c\uc758 ARP \uc694\uccad\uc5d0 \uc9c1\uc811 \uc751\ub2f5\ud558\uc5ec \ucef4\ud4e8\ud130\uc758 MAC\uc8fc\uc18c\ub97c \ud074\ub77c\uc774\uc5b8\ud2b8\uc5d0 \uc81c\uacf5\ud558\ub294 \ubc29\uc2dd\uc73c\ub85c \uc791\ub3d9\ud558\uae30 \ub54c\ubb38\uc785\ub2c8\ub2e4."),(0,l.kt)("p",null,"\ub2e4\uc74c ",(0,l.kt)("inlineCode",{parentName:"p"},"metallb_config.yaml")," \ud30c\uc77c\uc740 MetalLB \uac00 192.168.35.100 ~ 192.168.35.110\uc758 IP\uc5d0 \ub300\ud55c \uc81c\uc5b4 \uad8c\ud55c\uc744 \uc81c\uacf5\ud558\uace0 Layer 2 \ubaa8\ub4dc\ub97c \uad6c\uc131\ud558\ub294 \uc124\uc815\uc785\ub2c8\ub2e4."),(0,l.kt)("p",null,"\ud074\ub7ec\uc2a4\ud130 \ub178\ub4dc\uc640 \ud074\ub77c\uc774\uc5b8\ud2b8 \ub178\ub4dc\uac00 \ubd84\ub9ac\ub41c \uacbd\uc6b0, 192.168.35.100 ~ 192.168.35.110 \ub300\uc5ed\uc774 \ud074\ub77c\uc774\uc5b8\ud2b8 \ub178\ub4dc\uc640 \ud074\ub7ec\uc2a4\ud130 \ub178\ub4dc \ubaa8\ub450 \uc811\uadfc \uac00\ub2a5\ud55c \ub300\uc5ed\uc774\uc5b4\uc57c \ud569\ub2c8\ub2e4."),(0,l.kt)("h4",{id:"metallb_configyaml"},"metallb_config.yaml"),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"apiVersion: v1\nkind: ConfigMap\nmetadata:\n namespace: metallb-system\n name: config\ndata:\n config: |\n address-pools:\n - name: default\n protocol: layer2\n addresses:\n - 192.168.35.100-192.168.35.110 # IP \ub300\uc5ed\ud3ed\n")),(0,l.kt)("p",null,"\uc704\uc758 \uc124\uc815\uc744 \uc801\uc6a9\ud569\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-test"},"kubectl apply -f metallb_config.yaml \n")),(0,l.kt)("p",null,"\uc815\uc0c1\uc801\uc73c\ub85c \ubc30\ud3ec\ud558\uba74 \ub2e4\uc74c\uacfc \uac19\uc774 \ucd9c\ub825\ub429\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-test"},"configmap/config created\n")),(0,l.kt)("h2",{id:"metallb-\uc0ac\uc6a9"},"MetalLB \uc0ac\uc6a9"),(0,l.kt)("h3",{id:"kubeflow-dashboard"},"Kubeflow Dashboard"),(0,l.kt)("p",null,"\uba3c\uc800 kubeflow\uc758 Dashboard \ub97c \uc81c\uacf5\ud558\ub294 istio-system \ub124\uc784\uc2a4\ud398\uc774\uc2a4\uc758 istio-ingressgateway \uc11c\ube44\uc2a4\uc758 \ud0c0\uc785\uc744 ",(0,l.kt)("inlineCode",{parentName:"p"},"LoadBalancer"),"\ub85c \ubcc0\uacbd\ud558\uc5ec MetalLB\ub85c\ubd80\ud130 \ub85c\ub4dc \ubca8\ub7f0\uc2f1 \uae30\ub2a5\uc744 \uc81c\uacf5\ubc1b\uae30 \uc804\uc5d0, \ud604\uc7ac \uc0c1\ud0dc\ub97c \ud655\uc778\ud569\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"kubectl get svc/istio-ingressgateway -n istio-system\n")),(0,l.kt)("p",null,"\ud574\ub2f9 \uc11c\ube44\uc2a4\uc758 \ud0c0\uc785\uc740 ClusterIP\uc774\uba70, External-IP \uac12\uc740 ",(0,l.kt)("inlineCode",{parentName:"p"},"none")," \uc778 \uac83\uc744 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE\nistio-ingressgateway ClusterIP 10.103.72.5 15021/TCP,80/TCP,443/TCP,31400/TCP,15443/TCP 4h21m\n")),(0,l.kt)("p",null,"type \uc744 LoadBalancer \ub85c \ubcc0\uacbd\ud558\uace0 \uc6d0\ud558\ub294 IP \uc8fc\uc18c\ub97c \uc785\ub825\ud558\uace0 \uc2f6\uc740 \uacbd\uc6b0 loadBalancerIP \ud56d\ubaa9\uc744 \ucd94\uac00\ud569\ub2c8\ub2e4.",(0,l.kt)("br",{parentName:"p"}),"\n","\ucd94\uac00 \ud558\uc9c0 \uc54a\uc744 \uacbd\uc6b0\uc5d0\ub294 \uc704\uc5d0\uc11c \uc124\uc815\ud55c IP \uc8fc\uc18c\ud480\uc5d0\uc11c \uc21c\ucc28\uc801\uc73c\ub85c IP \uc8fc\uc18c\uac00 \ubc30\uc815\ub429\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"kubectl edit svc/istio-ingressgateway -n istio-system\n")),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"spec:\n clusterIP: 10.103.72.5\n clusterIPs:\n - 10.103.72.5\n ipFamilies:\n - IPv4\n ipFamilyPolicy: SingleStack\n ports:\n - name: status-port\n port: 15021\n protocol: TCP\n targetPort: 15021\n - name: http2\n port: 80\n protocol: TCP\n targetPort: 8080\n - name: https\n port: 443\n protocol: TCP\n targetPort: 8443\n - name: tcp\n port: 31400\n protocol: TCP\n targetPort: 31400\n - name: tls\n port: 15443\n protocol: TCP\n targetPort: 15443\n selector:\n app: istio-ingressgateway\n istio: ingressgateway\n sessionAffinity: None\n type: LoadBalancer # Change ClusterIP to LoadBalancer\n loadBalancerIP: 192.168.35.100 # Add IP\nstatus:\n loadBalancer: {}\n")),(0,l.kt)("p",null,"\ub2e4\uc2dc \ud655\uc778\uc744 \ud574\ubcf4\uba74 External-IP \uac12\uc774 ",(0,l.kt)("inlineCode",{parentName:"p"},"192.168.35.100")," \uc778 \uac83\uc744 \ud655\uc778\ud569\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"kubectl get svc/istio-ingressgateway -n istio-system\n")),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE\nistio-ingressgateway LoadBalancer 10.103.72.5 192.168.35.100 15021:31054/TCP,80:30853/TCP,443:30443/TCP,31400:30012/TCP,15443:31650/TCP 5h1m\n")),(0,l.kt)("p",null,"Web Browser \ub97c \uc5f4\uc5b4 ",(0,l.kt)("a",{parentName:"p",href:"http://192.168.35.100"},"http://192.168.35.100")," \uc73c\ub85c \uc811\uc18d\ud558\uc5ec, \ub2e4\uc74c\uacfc \uac19\uc740 \ud654\uba74\uc774 \ucd9c\ub825\ub418\ub294 \uac83\uc744 \ud655\uc778\ud569\ub2c8\ub2e4."),(0,l.kt)("p",null,(0,l.kt)("img",{alt:"login-after-istio-ingressgateway-setting.png",src:n(5568).Z,width:"1811",height:"1046"})),(0,l.kt)("h3",{id:"minio-dashboard"},"minio Dashboard"),(0,l.kt)("p",null,"\uba3c\uc800 minio \uc758 Dashboard \ub97c \uc81c\uacf5\ud558\ub294 kubeflow \ub124\uc784\uc2a4\ud398\uc774\uc2a4\uc758 minio-service \uc11c\ube44\uc2a4\uc758 \ud0c0\uc785\uc744 LoadBalancer\ub85c \ubcc0\uacbd\ud558\uc5ec MetalLB\ub85c\ubd80\ud130 \ub85c\ub4dc \ubca8\ub7f0\uc2f1 \uae30\ub2a5\uc744 \uc81c\uacf5\ubc1b\uae30 \uc804\uc5d0, \ud604\uc7ac \uc0c1\ud0dc\ub97c \ud655\uc778\ud569\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"kubectl get svc/minio-service -n kubeflow\n")),(0,l.kt)("p",null,"\ud574\ub2f9 \uc11c\ube44\uc2a4\uc758 \ud0c0\uc785\uc740 ClusterIP\uc774\uba70, External-IP \uac12\uc740 ",(0,l.kt)("inlineCode",{parentName:"p"},"none")," \uc778 \uac83\uc744 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE\nminio-service ClusterIP 10.109.209.87 9000/TCP 5h14m\n")),(0,l.kt)("p",null,"type \uc744 LoadBalancer \ub85c \ubcc0\uacbd\ud558\uace0 \uc6d0\ud558\ub294 IP \uc8fc\uc18c\ub97c \uc785\ub825\ud558\uace0 \uc2f6\uc740 \uacbd\uc6b0 loadBalancerIP \ud56d\ubaa9\uc744 \ucd94\uac00\ud569\ub2c8\ub2e4.",(0,l.kt)("br",{parentName:"p"}),"\n","\ucd94\uac00 \ud558\uc9c0 \uc54a\uc744 \uacbd\uc6b0\uc5d0\ub294 \uc704\uc5d0\uc11c \uc124\uc815\ud55c IP \uc8fc\uc18c\ud480\uc5d0\uc11c \uc21c\ucc28\uc801\uc73c\ub85c IP \uc8fc\uc18c\uac00 \ubc30\uc815\ub429\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"kubectl edit svc/minio-service -n kubeflow\n")),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},'apiVersion: v1\nkind: Service\nmetadata:\n annotations:\n kubectl.kubernetes.io/last-applied-configuration: |\n {"apiVersion":"v1","kind":"Service","metadata":{"annotations":{},"labels":{"application-crd-id":"kubeflow-pipelines"},"name":"minio-ser>\n creationTimestamp: "2022-01-05T08:44:23Z"\n labels:\n application-crd-id: kubeflow-pipelines\n name: minio-service\n namespace: kubeflow\n resourceVersion: "21120"\n uid: 0053ee28-4f87-47bb-ad6b-7ad68aa29a48\nspec:\n clusterIP: 10.109.209.87\n clusterIPs:\n - 10.109.209.87\n ipFamilies:\n - IPv4\n ipFamilyPolicy: SingleStack\n ports:\n - name: http\n port: 9000\n protocol: TCP\n targetPort: 9000\n selector:\n app: minio\n application-crd-id: kubeflow-pipelines\n sessionAffinity: None\n type: LoadBalancer # Change ClusterIP to LoadBalancer\n loadBalancerIP: 192.168.35.101 # Add IP\nstatus:\n loadBalancer: {}\n')),(0,l.kt)("p",null,"\ub2e4\uc2dc \ud655\uc778\uc744 \ud574\ubcf4\uba74 External-IP \uac12\uc774 ",(0,l.kt)("inlineCode",{parentName:"p"},"192.168.35.101")," \uc778 \uac83\uc744 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"kubectl get svc/minio-service -n kubeflow\n")),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE\nminio-service LoadBalancer 10.109.209.87 192.168.35.101 9000:31371/TCP 5h21m\n")),(0,l.kt)("p",null,"Web Browser \ub97c \uc5f4\uc5b4 ",(0,l.kt)("a",{parentName:"p",href:"http://192.168.35.101:9000"},"http://192.168.35.101:9000")," \uc73c\ub85c \uc811\uc18d\ud558\uc5ec, \ub2e4\uc74c\uacfc \uac19\uc740 \ud654\uba74\uc774 \ucd9c\ub825\ub418\ub294 \uac83\uc744 \ud655\uc778\ud569\ub2c8\ub2e4."),(0,l.kt)("p",null,(0,l.kt)("img",{alt:"login-after-minio-setting.png",src:n(369).Z,width:"1811",height:"1046"})),(0,l.kt)("h3",{id:"mlflow-dashboard"},"mlflow Dashboard"),(0,l.kt)("p",null,"\uba3c\uc800 mlflow \uc758 Dashboard \ub97c \uc81c\uacf5\ud558\ub294 mlflow-system \ub124\uc784\uc2a4\ud398\uc774\uc2a4\uc758 mlflow-server-service \uc11c\ube44\uc2a4\uc758 \ud0c0\uc785\uc744 LoadBalancer\ub85c \ubcc0\uacbd\ud558\uc5ec MetalLB\ub85c\ubd80\ud130 \ub85c\ub4dc \ubca8\ub7f0\uc2f1 \uae30\ub2a5\uc744 \uc81c\uacf5\ubc1b\uae30 \uc804\uc5d0, \ud604\uc7ac \uc0c1\ud0dc\ub97c \ud655\uc778\ud569\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"kubectl get svc/mlflow-server-service -n mlflow-system\n")),(0,l.kt)("p",null,"\ud574\ub2f9 \uc11c\ube44\uc2a4\uc758 \ud0c0\uc785\uc740 ClusterIP\uc774\uba70, External-IP \uac12\uc740 ",(0,l.kt)("inlineCode",{parentName:"p"},"none")," \uc778 \uac83\uc744 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE\nmlflow-server-service ClusterIP 10.111.173.209 5000/TCP 4m50s\n")),(0,l.kt)("p",null,"type \uc744 LoadBalancer \ub85c \ubcc0\uacbd\ud558\uace0 \uc6d0\ud558\ub294 IP \uc8fc\uc18c\ub97c \uc785\ub825\ud558\uace0 \uc2f6\uc740 \uacbd\uc6b0 loadBalancerIP \ud56d\ubaa9\uc744 \ucd94\uac00\ud569\ub2c8\ub2e4.",(0,l.kt)("br",{parentName:"p"}),"\n","\ucd94\uac00 \ud558\uc9c0 \uc54a\uc744 \uacbd\uc6b0\uc5d0\ub294 \uc704\uc5d0\uc11c \uc124\uc815\ud55c IP \uc8fc\uc18c\ud480\uc5d0\uc11c \uc21c\ucc28\uc801\uc73c\ub85c IP \uc8fc\uc18c\uac00 \ubc30\uc815\ub429\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"kubectl edit svc/mlflow-server-service -n mlflow-system\n")),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},'apiVersion: v1\nkind: Service\nmetadata:\n annotations:\n meta.helm.sh/release-name: mlflow-server\n meta.helm.sh/release-namespace: mlflow-system\n creationTimestamp: "2022-01-07T04:00:19Z"\n labels:\n app.kubernetes.io/managed-by: Helm\n name: mlflow-server-service\n namespace: mlflow-system\n resourceVersion: "276246"\n uid: e5d39fb7-ad98-47e7-b512-f9c673055356\nspec:\n clusterIP: 10.111.173.209\n clusterIPs:\n - 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\ud50c\ub7ab\ud3fc\uc5d0\uc11c\ub294 \uc790\uccb4\uc801\uc73c\ub85c \ub85c\ub4dc \ubca8\ub7f0\uc11c(Load Balancer)\ub97c \uc81c\uacf5\ud574 \uc8fc\uc9c0\ub9cc, \uc628\ud504\ub808\ubbf8\uc2a4 \ud074\ub7ec\uc2a4\ud130\uc5d0\uc11c\ub294 \ub85c\ub4dc \ubca8\ub7f0\uc2f1 \uae30\ub2a5\uc744 \uc81c\uacf5\ud558\ub294 \ubaa8\ub4c8\uc744 \ucd94\uac00\uc801\uc73c\ub85c \uc124\uce58\ud574\uc57c \ud569\ub2c8\ub2e4.",(0,l.kt)("br",{parentName:"p"}),"\n",(0,l.kt)("a",{parentName:"p",href:"https://metallb.universe.tf/"},"MetalLB"),"\ub294 \ubca0\uc5b4\uba54\ud0c8 \ud658\uacbd\uc5d0\uc11c \uc0ac\uc6a9\ud560 \uc218 \uc788\ub294 \ub85c\ub4dc \ubca8\ub7f0\uc11c\ub97c \uc81c\uacf5\ud558\ub294 \uc624\ud508\uc18c\uc2a4 \ud504\ub85c\uc81d\ud2b8 \uc785\ub2c8\ub2e4."),(0,l.kt)("h2",{id:"\uc694\uad6c\uc0ac\ud56d"},"\uc694\uad6c\uc0ac\ud56d"),(0,l.kt)("table",null,(0,l.kt)("thead",{parentName:"table"},(0,l.kt)("tr",{parentName:"thead"},(0,l.kt)("th",{parentName:"tr",align:null},"\uc694\uad6c \uc0ac\ud56d"),(0,l.kt)("th",{parentName:"tr",align:null},"\ubc84\uc804 \ubc0f \ub0b4\uc6a9"))),(0,l.kt)("tbody",{parentName:"table"},(0,l.kt)("tr",{parentName:"tbody"},(0,l.kt)("td",{parentName:"tr",align:null},"Kubernetes"),(0,l.kt)("td",{parentName:"tr",align:null},"\ub85c\ub4dc \ubca8\ub7f0\uc2f1 \uae30\ub2a5\uc774 \uc5c6\ub294 >= v1.13.0")),(0,l.kt)("tr",{parentName:"tbody"},(0,l.kt)("td",{parentName:"tr",align:null},(0,l.kt)("a",{parentName:"td",href:"https://metallb.universe.tf/installation/network-addons/"},"\ud638\ud658\uac00\ub2a5\ud55c \ub124\ud2b8\uc6cc\ud06c CNI")),(0,l.kt)("td",{parentName:"tr",align:null},"Calico, Canal, Cilium, Flannel, Kube-ovn, Kube-router, Weave Net")),(0,l.kt)("tr",{parentName:"tbody"},(0,l.kt)("td",{parentName:"tr",align:null},"IPv4 \uc8fc\uc18c"),(0,l.kt)("td",{parentName:"tr",align:null},"MetalLB \ubc30\ud3ec\uc5d0 \uc0ac\uc6a9")),(0,l.kt)("tr",{parentName:"tbody"},(0,l.kt)("td",{parentName:"tr",align:null},"BGP \ubaa8\ub4dc\ub97c \uc0ac\uc6a9\ud560 \uacbd\uc6b0"),(0,l.kt)("td",{parentName:"tr",align:null},"BGP \uae30\ub2a5\uc744 \uc9c0\uc6d0\ud558\ub294 \ud558\ub098 \uc774\uc0c1\uc758 \ub77c\uc6b0\ud130")),(0,l.kt)("tr",{parentName:"tbody"},(0,l.kt)("td",{parentName:"tr",align:null},"\ub178\ub4dc \uac04 \ud3ec\ud2b8 TCP/UDP 7946 \uc624\ud508"),(0,l.kt)("td",{parentName:"tr",align:null},"memberlist \uc694\uad6c \uc0ac\ud56d")))),(0,l.kt)("h2",{id:"metallb-\uc124\uce58"},"MetalLB \uc124\uce58"),(0,l.kt)("h3",{id:"preparation"},"Preparation"),(0,l.kt)("p",null,"IPVS \ubaa8\ub4dc\uc5d0\uc11c kube-proxy\ub97c \uc0ac\uc6a9\ud558\ub294 \uacbd\uc6b0 Kubernetes v1.14.2 \uc774\ud6c4\ubd80\ud130\ub294 \uc5c4\uaca9\ud55c ARP(strictARP) \ubaa8\ub4dc\ub97c \uc0ac\uc6a9\ud558\ub3c4\ub85d \uc124\uc815\ud574\uc57c \ud569\ub2c8\ub2e4.",(0,l.kt)("br",{parentName:"p"}),"\n","Kube-router\ub294 \uae30\ubcf8\uc801\uc73c\ub85c \uc5c4\uaca9\ud55c ARP\ub97c \ud65c\uc131\ud654\ud558\ubbc0\ub85c \uc11c\ube44\uc2a4 \ud504\ub85d\uc2dc\ub85c \uc0ac\uc6a9\ud560 \uacbd\uc6b0\uc5d0\ub294 \uc774 \uae30\ub2a5\uc774 \ud544\uc694\ud558\uc9c0 \uc54a\uc2b5\ub2c8\ub2e4.",(0,l.kt)("br",{parentName:"p"}),"\n","\uc5c4\uaca9\ud55c ARP \ubaa8\ub4dc\ub97c \uc801\uc6a9\ud558\uae30\uc5d0 \uc55e\uc11c, \ud604\uc7ac \ubaa8\ub4dc\ub97c \ud655\uc778\ud569\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"# see what changes would be made, returns nonzero returncode if different\nkubectl get configmap kube-proxy -n kube-system -o yaml | \\\ngrep strictARP\n")),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"strictARP: false\n")),(0,l.kt)("p",null,"strictARP: false \uac00 \ucd9c\ub825\ub418\ub294 \uacbd\uc6b0 \ub2e4\uc74c\uc744 \uc2e4\ud589\ud558\uc5ec strictARP: true\ub85c \ubcc0\uacbd\ud569\ub2c8\ub2e4.\n(strictARP: true\uac00 \uc774\ubbf8 \ucd9c\ub825\ub41c\ub2e4\uba74 \ub2e4\uc74c \ucee4\ub9e8\ub4dc\ub97c \uc218\ud589\ud558\uc9c0 \uc54a\uc73c\uc154\ub3c4 \ub429\ub2c8\ub2e4.)"),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},'# actually apply the changes, returns nonzero returncode on errors only\nkubectl get configmap kube-proxy -n kube-system -o yaml | \\\nsed -e "s/strictARP: false/strictARP: true/" | \\\nkubectl apply -f - -n kube-system\n')),(0,l.kt)("p",null,"\uc815\uc0c1\uc801\uc73c\ub85c \uc218\ud589\ub418\uba74 \ub2e4\uc74c\uacfc \uac19\uc774 \ucd9c\ub825\ub429\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"Warning: resource configmaps/kube-proxy is missing the kubectl.kubernetes.io/last-applied-configuration annotation which is required by kubectl apply. kubectl apply should only be used on resources created declaratively by either kubectl create --save-config or kubectl apply. The missing annotation will be patched automatically.\nconfigmap/kube-proxy configured\n")),(0,l.kt)("h3",{id:"\uc124\uce58---manifest"},"\uc124\uce58 - Manifest"),(0,l.kt)("h4",{id:"1-metallb-\ub97c-\uc124\uce58\ud569\ub2c8\ub2e4"},"1. MetalLB \ub97c \uc124\uce58\ud569\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"kubectl apply -f https://raw.githubusercontent.com/metallb/metallb/v0.11.0/manifests/namespace.yaml\nkubectl apply -f https://raw.githubusercontent.com/metallb/metallb/v0.11.0/manifests/metallb.yaml\n")),(0,l.kt)("h4",{id:"2-\uc815\uc0c1-\uc124\uce58-\ud655\uc778"},"2. \uc815\uc0c1 \uc124\uce58 \ud655\uc778"),(0,l.kt)("p",null,"metallb-system namespace \uc758 2 \uac1c\uc758 pod \uc774 \ubaa8\ub450 Running \uc774 \ub420 \ub54c\uae4c\uc9c0 \uae30\ub2e4\ub9bd\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"kubectl get pod -n metallb-system\n")),(0,l.kt)("p",null,"\ubaa8\ub450 Running \uc774 \ub418\uba74 \ub2e4\uc74c\uacfc \ube44\uc2b7\ud55c \uacb0\uacfc\uac00 \ucd9c\ub825\ub429\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"NAME READY STATUS RESTARTS AGE\ncontroller-7dcc8764f4-8n92q 1/1 Running 1 1m\nspeaker-fnf8l 1/1 Running 1 1m\n")),(0,l.kt)("p",null,"\ub9e4\ub2c8\ud398\uc2a4\ud2b8\uc758 \uad6c\uc131 \uc694\uc18c\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4."),(0,l.kt)("ul",null,(0,l.kt)("li",{parentName:"ul"},"metallb-system/controller",(0,l.kt)("ul",{parentName:"li"},(0,l.kt)("li",{parentName:"ul"},"deployment \ub85c \ubc30\ud3ec\ub418\uba70, \ub85c\ub4dc \ubca8\ub7f0\uc2f1\uc744 \uc218\ud589\ud560 external IP \uc8fc\uc18c\uc758 \ud560\ub2f9\uc744 \ucc98\ub9ac\ud558\ub294 \uc5ed\ud560\uc744 \ub2f4\ub2f9\ud569\ub2c8\ub2e4."))),(0,l.kt)("li",{parentName:"ul"},"metallb-system/speaker",(0,l.kt)("ul",{parentName:"li"},(0,l.kt)("li",{parentName:"ul"},"daemonset \ud615\ud0dc\ub85c \ubc30\ud3ec\ub418\uba70, \uc678\ubd80 \ud2b8\ub798\ud53d\uacfc \uc11c\ube44\uc2a4\ub97c \uc5f0\uacb0\ud574 \ub124\ud2b8\uc6cc\ud06c \ud1b5\uc2e0\uc774 \uac00\ub2a5\ud558\ub3c4\ub85d \uad6c\uc131\ud558\ub294 \uc5ed\ud560\uc744 \ub2f4\ub2f9\ud569\ub2c8\ub2e4.")))),(0,l.kt)("p",null,"\uc11c\ube44\uc2a4\uc5d0\ub294 \ucee8\ud2b8\ub864\ub7ec \ubc0f \uc2a4\ud53c\ucee4\uc640 \uad6c\uc131 \uc694\uc18c\uac00 \uc791\ub3d9\ud558\ub294 \ub370 \ud544\uc694\ud55c RBAC \uc0ac\uc6a9 \uad8c\ud55c\uc774 \ud3ec\ud568\ub429\ub2c8\ub2e4."),(0,l.kt)("h2",{id:"configuration"},"Configuration"),(0,l.kt)("p",null,"MetalLB \uc758 \ub85c\ub4dc \ubca8\ub7f0\uc2f1 \uc815\ucc45 \uc124\uc815\uc740 \uad00\ub828 \uc124\uc815 \uc815\ubcf4\ub97c \ub2f4\uc740 configmap \uc744 \ubc30\ud3ec\ud558\uc5ec \uc124\uc815\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,l.kt)("p",null,"MetalLB \uc5d0\uc11c \uad6c\uc131\ud560 \uc218 \uc788\ub294 \ubaa8\ub4dc\ub85c\ub294 \ub2e4\uc74c\uacfc \uac19\uc774 2\uac00\uc9c0\uac00 \uc788\uc2b5\ub2c8\ub2e4."),(0,l.kt)("ol",null,(0,l.kt)("li",{parentName:"ol"},(0,l.kt)("a",{parentName:"li",href:"https://metallb.universe.tf/concepts/layer2/"},"Layer 2 \ubaa8\ub4dc")),(0,l.kt)("li",{parentName:"ol"},(0,l.kt)("a",{parentName:"li",href:"https://metallb.universe.tf/concepts/bgp/"},"BGP \ubaa8\ub4dc"))),(0,l.kt)("p",null,"\uc5ec\uae30\uc5d0\uc11c\ub294 Layer 2 \ubaa8\ub4dc\ub85c \uc9c4\ud589\ud558\uaca0\uc2b5\ub2c8\ub2e4."),(0,l.kt)("h3",{id:"layer-2-configuration"},"Layer 2 Configuration"),(0,l.kt)("p",null,"Layer 2 \ubaa8\ub4dc\ub294 \uac04\ub2e8\ud558\uac8c \uc0ac\uc6a9\ud560 IP \uc8fc\uc18c\uc758 \ub300\uc5ed\ub9cc \uc124\uc815\ud558\uba74 \ub429\ub2c8\ub2e4.",(0,l.kt)("br",{parentName:"p"}),"\n","Layer 2 \ubaa8\ub4dc\ub97c \uc0ac\uc6a9\ud560 \uacbd\uc6b0 \uc6cc\ucee4 \ub178\ub4dc\uc758 \ub124\ud2b8\uc6cc\ud06c \uc778\ud130\ud398\uc774\uc2a4\uc5d0 IP\ub97c \ubc14\uc778\ub529 \ud558\uc9c0 \uc54a\uc544\ub3c4 \ub418\ub294\ub370 \ub85c\uceec \ub124\ud2b8\uc6cc\ud06c\uc758 ARP \uc694\uccad\uc5d0 \uc9c1\uc811 \uc751\ub2f5\ud558\uc5ec \ucef4\ud4e8\ud130\uc758 MAC\uc8fc\uc18c\ub97c \ud074\ub77c\uc774\uc5b8\ud2b8\uc5d0 \uc81c\uacf5\ud558\ub294 \ubc29\uc2dd\uc73c\ub85c \uc791\ub3d9\ud558\uae30 \ub54c\ubb38\uc785\ub2c8\ub2e4."),(0,l.kt)("p",null,"\ub2e4\uc74c ",(0,l.kt)("inlineCode",{parentName:"p"},"metallb_config.yaml")," \ud30c\uc77c\uc740 MetalLB \uac00 192.168.35.100 ~ 192.168.35.110\uc758 IP\uc5d0 \ub300\ud55c \uc81c\uc5b4 \uad8c\ud55c\uc744 \uc81c\uacf5\ud558\uace0 Layer 2 \ubaa8\ub4dc\ub97c \uad6c\uc131\ud558\ub294 \uc124\uc815\uc785\ub2c8\ub2e4."),(0,l.kt)("p",null,"\ud074\ub7ec\uc2a4\ud130 \ub178\ub4dc\uc640 \ud074\ub77c\uc774\uc5b8\ud2b8 \ub178\ub4dc\uac00 \ubd84\ub9ac\ub41c \uacbd\uc6b0, 192.168.35.100 ~ 192.168.35.110 \ub300\uc5ed\uc774 \ud074\ub77c\uc774\uc5b8\ud2b8 \ub178\ub4dc\uc640 \ud074\ub7ec\uc2a4\ud130 \ub178\ub4dc \ubaa8\ub450 \uc811\uadfc \uac00\ub2a5\ud55c \ub300\uc5ed\uc774\uc5b4\uc57c \ud569\ub2c8\ub2e4."),(0,l.kt)("h4",{id:"metallb_configyaml"},"metallb_config.yaml"),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"apiVersion: v1\nkind: ConfigMap\nmetadata:\n namespace: metallb-system\n name: config\ndata:\n config: |\n address-pools:\n - name: default\n protocol: layer2\n addresses:\n - 192.168.35.100-192.168.35.110 # IP \ub300\uc5ed\ud3ed\n")),(0,l.kt)("p",null,"\uc704\uc758 \uc124\uc815\uc744 \uc801\uc6a9\ud569\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-test"},"kubectl apply -f metallb_config.yaml \n")),(0,l.kt)("p",null,"\uc815\uc0c1\uc801\uc73c\ub85c \ubc30\ud3ec\ud558\uba74 \ub2e4\uc74c\uacfc \uac19\uc774 \ucd9c\ub825\ub429\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-test"},"configmap/config created\n")),(0,l.kt)("h2",{id:"metallb-\uc0ac\uc6a9"},"MetalLB \uc0ac\uc6a9"),(0,l.kt)("h3",{id:"kubeflow-dashboard"},"Kubeflow Dashboard"),(0,l.kt)("p",null,"\uba3c\uc800 kubeflow\uc758 Dashboard \ub97c \uc81c\uacf5\ud558\ub294 istio-system \ub124\uc784\uc2a4\ud398\uc774\uc2a4\uc758 istio-ingressgateway \uc11c\ube44\uc2a4\uc758 \ud0c0\uc785\uc744 ",(0,l.kt)("inlineCode",{parentName:"p"},"LoadBalancer"),"\ub85c \ubcc0\uacbd\ud558\uc5ec MetalLB\ub85c\ubd80\ud130 \ub85c\ub4dc \ubca8\ub7f0\uc2f1 \uae30\ub2a5\uc744 \uc81c\uacf5\ubc1b\uae30 \uc804\uc5d0, \ud604\uc7ac \uc0c1\ud0dc\ub97c \ud655\uc778\ud569\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"kubectl get svc/istio-ingressgateway -n istio-system\n")),(0,l.kt)("p",null,"\ud574\ub2f9 \uc11c\ube44\uc2a4\uc758 \ud0c0\uc785\uc740 ClusterIP\uc774\uba70, External-IP \uac12\uc740 ",(0,l.kt)("inlineCode",{parentName:"p"},"none")," \uc778 \uac83\uc744 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE\nistio-ingressgateway ClusterIP 10.103.72.5 15021/TCP,80/TCP,443/TCP,31400/TCP,15443/TCP 4h21m\n")),(0,l.kt)("p",null,"type \uc744 LoadBalancer \ub85c \ubcc0\uacbd\ud558\uace0 \uc6d0\ud558\ub294 IP \uc8fc\uc18c\ub97c \uc785\ub825\ud558\uace0 \uc2f6\uc740 \uacbd\uc6b0 loadBalancerIP \ud56d\ubaa9\uc744 \ucd94\uac00\ud569\ub2c8\ub2e4.",(0,l.kt)("br",{parentName:"p"}),"\n","\ucd94\uac00 \ud558\uc9c0 \uc54a\uc744 \uacbd\uc6b0\uc5d0\ub294 \uc704\uc5d0\uc11c \uc124\uc815\ud55c IP \uc8fc\uc18c\ud480\uc5d0\uc11c \uc21c\ucc28\uc801\uc73c\ub85c IP \uc8fc\uc18c\uac00 \ubc30\uc815\ub429\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"kubectl edit svc/istio-ingressgateway -n istio-system\n")),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"spec:\n clusterIP: 10.103.72.5\n clusterIPs:\n - 10.103.72.5\n ipFamilies:\n - IPv4\n ipFamilyPolicy: SingleStack\n ports:\n - name: status-port\n port: 15021\n protocol: TCP\n targetPort: 15021\n - name: http2\n port: 80\n protocol: TCP\n targetPort: 8080\n - name: https\n port: 443\n protocol: TCP\n targetPort: 8443\n - name: tcp\n port: 31400\n protocol: TCP\n targetPort: 31400\n - name: tls\n port: 15443\n protocol: TCP\n targetPort: 15443\n selector:\n app: istio-ingressgateway\n istio: ingressgateway\n sessionAffinity: None\n type: LoadBalancer # Change ClusterIP to LoadBalancer\n loadBalancerIP: 192.168.35.100 # Add IP\nstatus:\n loadBalancer: {}\n")),(0,l.kt)("p",null,"\ub2e4\uc2dc \ud655\uc778\uc744 \ud574\ubcf4\uba74 External-IP \uac12\uc774 ",(0,l.kt)("inlineCode",{parentName:"p"},"192.168.35.100")," \uc778 \uac83\uc744 \ud655\uc778\ud569\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"kubectl get svc/istio-ingressgateway -n istio-system\n")),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE\nistio-ingressgateway LoadBalancer 10.103.72.5 192.168.35.100 15021:31054/TCP,80:30853/TCP,443:30443/TCP,31400:30012/TCP,15443:31650/TCP 5h1m\n")),(0,l.kt)("p",null,"Web Browser \ub97c \uc5f4\uc5b4 ",(0,l.kt)("a",{parentName:"p",href:"http://192.168.35.100"},"http://192.168.35.100")," \uc73c\ub85c \uc811\uc18d\ud558\uc5ec, \ub2e4\uc74c\uacfc \uac19\uc740 \ud654\uba74\uc774 \ucd9c\ub825\ub418\ub294 \uac83\uc744 \ud655\uc778\ud569\ub2c8\ub2e4."),(0,l.kt)("p",null,(0,l.kt)("img",{alt:"login-after-istio-ingressgateway-setting.png",src:n(5556).Z,width:"1811",height:"1046"})),(0,l.kt)("h3",{id:"minio-dashboard"},"minio Dashboard"),(0,l.kt)("p",null,"\uba3c\uc800 minio \uc758 Dashboard \ub97c \uc81c\uacf5\ud558\ub294 kubeflow \ub124\uc784\uc2a4\ud398\uc774\uc2a4\uc758 minio-service \uc11c\ube44\uc2a4\uc758 \ud0c0\uc785\uc744 LoadBalancer\ub85c \ubcc0\uacbd\ud558\uc5ec MetalLB\ub85c\ubd80\ud130 \ub85c\ub4dc \ubca8\ub7f0\uc2f1 \uae30\ub2a5\uc744 \uc81c\uacf5\ubc1b\uae30 \uc804\uc5d0, \ud604\uc7ac \uc0c1\ud0dc\ub97c \ud655\uc778\ud569\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"kubectl get svc/minio-service -n kubeflow\n")),(0,l.kt)("p",null,"\ud574\ub2f9 \uc11c\ube44\uc2a4\uc758 \ud0c0\uc785\uc740 ClusterIP\uc774\uba70, External-IP \uac12\uc740 ",(0,l.kt)("inlineCode",{parentName:"p"},"none")," \uc778 \uac83\uc744 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE\nminio-service ClusterIP 10.109.209.87 9000/TCP 5h14m\n")),(0,l.kt)("p",null,"type \uc744 LoadBalancer \ub85c \ubcc0\uacbd\ud558\uace0 \uc6d0\ud558\ub294 IP \uc8fc\uc18c\ub97c \uc785\ub825\ud558\uace0 \uc2f6\uc740 \uacbd\uc6b0 loadBalancerIP \ud56d\ubaa9\uc744 \ucd94\uac00\ud569\ub2c8\ub2e4.",(0,l.kt)("br",{parentName:"p"}),"\n","\ucd94\uac00 \ud558\uc9c0 \uc54a\uc744 \uacbd\uc6b0\uc5d0\ub294 \uc704\uc5d0\uc11c \uc124\uc815\ud55c IP \uc8fc\uc18c\ud480\uc5d0\uc11c \uc21c\ucc28\uc801\uc73c\ub85c IP \uc8fc\uc18c\uac00 \ubc30\uc815\ub429\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"kubectl edit svc/minio-service -n kubeflow\n")),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},'apiVersion: v1\nkind: Service\nmetadata:\n annotations:\n kubectl.kubernetes.io/last-applied-configuration: |\n {"apiVersion":"v1","kind":"Service","metadata":{"annotations":{},"labels":{"application-crd-id":"kubeflow-pipelines"},"name":"minio-ser>\n creationTimestamp: "2022-01-05T08:44:23Z"\n labels:\n application-crd-id: kubeflow-pipelines\n name: minio-service\n namespace: kubeflow\n resourceVersion: "21120"\n uid: 0053ee28-4f87-47bb-ad6b-7ad68aa29a48\nspec:\n clusterIP: 10.109.209.87\n clusterIPs:\n - 10.109.209.87\n ipFamilies:\n - IPv4\n ipFamilyPolicy: SingleStack\n ports:\n - name: http\n port: 9000\n protocol: TCP\n targetPort: 9000\n selector:\n app: minio\n application-crd-id: kubeflow-pipelines\n sessionAffinity: None\n type: LoadBalancer # Change ClusterIP to LoadBalancer\n loadBalancerIP: 192.168.35.101 # Add IP\nstatus:\n loadBalancer: {}\n')),(0,l.kt)("p",null,"\ub2e4\uc2dc \ud655\uc778\uc744 \ud574\ubcf4\uba74 External-IP \uac12\uc774 ",(0,l.kt)("inlineCode",{parentName:"p"},"192.168.35.101")," \uc778 \uac83\uc744 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"kubectl get svc/minio-service -n kubeflow\n")),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE\nminio-service LoadBalancer 10.109.209.87 192.168.35.101 9000:31371/TCP 5h21m\n")),(0,l.kt)("p",null,"Web Browser \ub97c \uc5f4\uc5b4 ",(0,l.kt)("a",{parentName:"p",href:"http://192.168.35.101:9000"},"http://192.168.35.101:9000")," \uc73c\ub85c \uc811\uc18d\ud558\uc5ec, \ub2e4\uc74c\uacfc \uac19\uc740 \ud654\uba74\uc774 \ucd9c\ub825\ub418\ub294 \uac83\uc744 \ud655\uc778\ud569\ub2c8\ub2e4."),(0,l.kt)("p",null,(0,l.kt)("img",{alt:"login-after-minio-setting.png",src:n(6589).Z,width:"1811",height:"1046"})),(0,l.kt)("h3",{id:"mlflow-dashboard"},"mlflow Dashboard"),(0,l.kt)("p",null,"\uba3c\uc800 mlflow \uc758 Dashboard \ub97c \uc81c\uacf5\ud558\ub294 mlflow-system \ub124\uc784\uc2a4\ud398\uc774\uc2a4\uc758 mlflow-server-service \uc11c\ube44\uc2a4\uc758 \ud0c0\uc785\uc744 LoadBalancer\ub85c \ubcc0\uacbd\ud558\uc5ec MetalLB\ub85c\ubd80\ud130 \ub85c\ub4dc \ubca8\ub7f0\uc2f1 \uae30\ub2a5\uc744 \uc81c\uacf5\ubc1b\uae30 \uc804\uc5d0, \ud604\uc7ac \uc0c1\ud0dc\ub97c \ud655\uc778\ud569\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"kubectl get svc/mlflow-server-service -n mlflow-system\n")),(0,l.kt)("p",null,"\ud574\ub2f9 \uc11c\ube44\uc2a4\uc758 \ud0c0\uc785\uc740 ClusterIP\uc774\uba70, External-IP \uac12\uc740 ",(0,l.kt)("inlineCode",{parentName:"p"},"none")," \uc778 \uac83\uc744 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE\nmlflow-server-service ClusterIP 10.111.173.209 5000/TCP 4m50s\n")),(0,l.kt)("p",null,"type \uc744 LoadBalancer \ub85c \ubcc0\uacbd\ud558\uace0 \uc6d0\ud558\ub294 IP \uc8fc\uc18c\ub97c \uc785\ub825\ud558\uace0 \uc2f6\uc740 \uacbd\uc6b0 loadBalancerIP \ud56d\ubaa9\uc744 \ucd94\uac00\ud569\ub2c8\ub2e4.",(0,l.kt)("br",{parentName:"p"}),"\n","\ucd94\uac00 \ud558\uc9c0 \uc54a\uc744 \uacbd\uc6b0\uc5d0\ub294 \uc704\uc5d0\uc11c \uc124\uc815\ud55c IP \uc8fc\uc18c\ud480\uc5d0\uc11c \uc21c\ucc28\uc801\uc73c\ub85c IP \uc8fc\uc18c\uac00 \ubc30\uc815\ub429\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"kubectl edit svc/mlflow-server-service -n mlflow-system\n")),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},'apiVersion: v1\nkind: Service\nmetadata:\n annotations:\n meta.helm.sh/release-name: mlflow-server\n meta.helm.sh/release-namespace: mlflow-system\n creationTimestamp: "2022-01-07T04:00:19Z"\n labels:\n app.kubernetes.io/managed-by: Helm\n name: mlflow-server-service\n namespace: mlflow-system\n resourceVersion: "276246"\n uid: e5d39fb7-ad98-47e7-b512-f9c673055356\nspec:\n clusterIP: 10.111.173.209\n clusterIPs:\n - 10.111.173.209\n ipFamilies:\n - IPv4\n ipFamilyPolicy: SingleStack\n ports:\n - port: 5000\n protocol: TCP\n targetPort: 5000\n selector:\n app.kubernetes.io/name: mlflow-server\n sessionAffinity: None\n type: LoadBalancer # Change ClusterIP to LoadBalancer\n loadBalancerIP: 192.168.35.102 # Add IP\nstatus:\n loadBalancer: {}\n')),(0,l.kt)("p",null,"\ub2e4\uc2dc \ud655\uc778\uc744 \ud574\ubcf4\uba74 External-IP \uac12\uc774 ",(0,l.kt)("inlineCode",{parentName:"p"},"192.168.35.102")," \uc778 \uac83\uc744 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"kubectl get svc/mlflow-server-service -n mlflow-system\n")),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE\nmlflow-server-service LoadBalancer 10.111.173.209 192.168.35.102 5000:32287/TCP 6m11s\n")),(0,l.kt)("p",null,"Web Browser \ub97c \uc5f4\uc5b4 ",(0,l.kt)("a",{parentName:"p",href:"http://192.168.35.102:5000"},"http://192.168.35.102:5000")," \uc73c\ub85c \uc811\uc18d\ud558\uc5ec, \ub2e4\uc74c\uacfc \uac19\uc740 \ud654\uba74\uc774 \ucd9c\ub825\ub418\ub294 \uac83\uc744 \ud655\uc778\ud569\ub2c8\ub2e4."),(0,l.kt)("p",null,(0,l.kt)("img",{alt:"login-after-mlflow-setting.png",src:n(5864).Z,width:"1922",height:"1082"})),(0,l.kt)("h3",{id:"grafana-dashboard"},"Grafana Dashboard"),(0,l.kt)("p",null,"\uba3c\uc800 Grafana \uc758 Dashboard \ub97c \uc81c\uacf5\ud558\ub294 seldon-system \ub124\uc784\uc2a4\ud398\uc774\uc2a4\uc758 seldon-core-analytics-grafana \uc11c\ube44\uc2a4\uc758 \ud0c0\uc785\uc744 LoadBalancer\ub85c \ubcc0\uacbd\ud558\uc5ec MetalLB\ub85c\ubd80\ud130 \ub85c\ub4dc \ubca8\ub7f0\uc2f1 \uae30\ub2a5\uc744 \uc81c\uacf5\ubc1b\uae30 \uc804\uc5d0, \ud604\uc7ac \uc0c1\ud0dc\ub97c \ud655\uc778\ud569\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"kubectl get svc/seldon-core-analytics-grafana -n seldon-system\n")),(0,l.kt)("p",null,"\ud574\ub2f9 \uc11c\ube44\uc2a4\uc758 \ud0c0\uc785\uc740 ClusterIP\uc774\uba70, External-IP \uac12\uc740 ",(0,l.kt)("inlineCode",{parentName:"p"},"none")," \uc778 \uac83\uc744 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE\nseldon-core-analytics-grafana ClusterIP 10.109.20.161 80/TCP 94s\n")),(0,l.kt)("p",null,"type \uc744 LoadBalancer \ub85c \ubcc0\uacbd\ud558\uace0 \uc6d0\ud558\ub294 IP \uc8fc\uc18c\ub97c \uc785\ub825\ud558\uace0 \uc2f6\uc740 \uacbd\uc6b0 loadBalancerIP \ud56d\ubaa9\uc744 \ucd94\uac00\ud569\ub2c8\ub2e4.",(0,l.kt)("br",{parentName:"p"}),"\n","\ucd94\uac00 \ud558\uc9c0 \uc54a\uc744 \uacbd\uc6b0\uc5d0\ub294 \uc704\uc5d0\uc11c \uc124\uc815\ud55c IP \uc8fc\uc18c\ud480\uc5d0\uc11c \uc21c\ucc28\uc801\uc73c\ub85c IP \uc8fc\uc18c\uac00 \ubc30\uc815\ub429\ub2c8\ub2e4."),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},"kubectl edit svc/seldon-core-analytics-grafana -n seldon-system\n")),(0,l.kt)("pre",null,(0,l.kt)("code",{parentName:"pre",className:"language-bash"},'apiVersion: v1\nkind: Service\nmetadata:\n annotations:\n meta.helm.sh/release-name: seldon-core-analytics\n meta.helm.sh/release-namespace: seldon-system\n creationTimestamp: "2022-01-07T04:16:47Z"\n labels:\n app.kubernetes.io/instance: seldon-core-analytics\n app.kubernetes.io/managed-by: Helm\n app.kubernetes.io/name: grafana\n app.kubernetes.io/version: 7.0.3\n helm.sh/chart: grafana-5.1.4\n name: seldon-core-analytics-grafana\n namespace: seldon-system\n resourceVersion: "280605"\n uid: 75073b78-92ec-472c-b0d5-240038ea8fa5\nspec:\n clusterIP: 10.109.20.161\n clusterIPs:\n - 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