notebooks/03-mlops-deployment-nlp/03-mlops-deployment-nlp.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Module 3: MLOps and Real World Scenarios: Get Hands-On"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 3 - MLOps Deployment with NLP\n",
    "## News Article Summarization and Keyword Extraction via NLP\n",
    "\n",
    "This notebook demonstrates how to create and server an NLP pipeline that summarizes and extract keywords from a news article URL. The state-of-the-art transformer models such as BERT will be used to perform the NLP tasks.\n",
    "\n",
    "**Tutorial Steps:**\n",
    "- [**Install MLRun and Dependencies**](#install-mlrun-dependencies)\n",
    "- [**Setup the Project**](#mlrun-project-setup)\n",
    "- [**Create the MLRun Function**](#code-to-function-nlp-transformations)\n",
    "- [**Define Real-Time Serving Graph**](#define-real-time-serving-graph)\n",
    "\n",
    "**NLP Transformations:**\n",
    "- Retrieve news article text and metadata from URL using newspaper3k.\n",
    "- Summarize article text via Huggingface pipeline using DistilBart model.\n",
    "- Extract article keywords via KeyBERT using BERT-embeddings and cosine similarity.\n",
    "- Remove the original article text from the response (optional)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"install-mlrun-dependencies\"></a>\n",
    "## Install MLRun and Dependencies\n",
    "\n",
    "This tutorial expects the MLRun backend service to be running on a Kubernetes Cluster.\n",
    "\n",
    "**Note:** Throughout this tutorial, the GKE Cluster which was set up in Part 0 will be used to run the MLRun backend service."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python 3.9.13\n",
      "pip 22.3.1 from /opt/conda/lib/python3.9/site-packages/pip (python 3.9)\n"
     ]
    }
   ],
   "source": [
    "# Ensure that Python 3 (preferred version: 3.9.x) and pip 22.x.y are installed on the machine.\n",
    "\n",
    "# Check the Python version.\n",
    "!python --version\n",
    "\n",
    "# Check the pip version.\n",
    "!python -m pip --version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Install MLRun, Transformers, Newspaper3k, KeyBERT, and other dependencies.\n",
    "\n",
    "# Note: This cell needs to be run only once. The notebook needs to be restarted after a successful installation.\n",
    "\n",
    "!pip install mlrun transformers==4.11.3 newspaper3k==0.2.8 keybert~=0.7.0"
   ]
  },
  {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Restart the notebook kernel after the pip installation.**\n",
    "\n",
    "![Screenshot 2023-08-04 at 14.00.38.png](attachment:28dfd9ba-4646-4f16-b6c4-7eaee3f9c2a6.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"mlrun-project-setup\"></a>\n",
    "## Setup the Project\n",
    "\n",
    "Create an MLRun project using `mlrun.get_or_create_project()` (make sure to load the desired project in case one already exists)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> 2023-08-04 20:07:21,786 [info] Project loaded successfully: {'project_name': 'nlp-demo'}\n"
     ]
    }
   ],
   "source": [
    "# Import the required Python packages.\n",
    "import mlrun\n",
    "import os\n",
    "\n",
    "# Set MLRun Project's name.\n",
    "project_name = \"nlp-demo\"\n",
    "\n",
    "# Create the MLRun Project.\n",
    "project = mlrun.get_or_create_project(name=project_name, context=\"./\", user_project=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"code-to-function-nlp-transformations\"></a>\n",
    "## Create the MLRun Function\n",
    "\n",
    "MLRun's `mlrun.code_to_function()` can be used to create an MLRun Function from the `nlp_transformations.py` code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> 2023-08-04 20:07:57,060 [warning] Failed to add git metadata, ignore if path is not part of a git repo.: {'path': './', 'error': '/home/jovyan'}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<mlrun.runtimes.serving.ServingRuntime at 0x7fa76df28c10>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Initialize a Serverless Function for NLP Transformations using the `nlp_transformations.py` file/\n",
    "nlp_func = mlrun.code_to_function(name=\"news-article-nlp\", filename=\"nlp_transformations.py\",\n",
    "                            kind=\"serving\", image='mlrun/ml-models')\n",
    "\n",
    "# Set the function specifications.\n",
    "nlp_func.spec.min_replicas = 1\n",
    "nlp_func.spec.max_replicas = 1\n",
    "nlp_func.spec.build.commands = [\n",
    "    \"python -m pip install transformers==4.11.3 newspaper3k==0.2.8 keybert~=0.7.0\",\n",
    "    \"python -c 'from transformers import pipeline; pipeline(\\\"summarization\\\")'\",\n",
    "    \"python -c 'from keybert import KeyBERT; KeyBERT()'\"\n",
    "]\n",
    "\n",
    "# Set the required Environment Variables.\n",
    "nlp_func.set_env(\"SENTENCE_TRANSFORMERS_HOME\",\"/tmp\")\n",
    "nlp_func.set_env(\"TRANSFORMERS_CACHE\",\"/tmp\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"define-real-time-serving-graph\"></a>\n",
    "## Define Real-Time Serving Graph\n",
    "Here the functions and classes required for the pipeline will be orchestrated. The source code for these functions is located in `nlp_transformations.py`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
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       "<!-- Generated by graphviz version 2.43.0 (0)\n",
       " -->\n",
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     "execution_count": 6,
     "metadata": {},
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   "source": [
    "# Set the Redis URL.\n",
    "redis_url = \"redis://:gwEswJlH3f@redis-master.mlrun.svc.cluster.local:6379\"\n",
    "\n",
    "# Set the serving topology for the Serving Serverless Function.\n",
    "graph = nlp_func.set_topology(\"flow\", engine=\"async\")\n",
    "\n",
    "graph.to(name=\"fetch_article\", handler=\"fetch_article\")\\\n",
    "     .to(name=\"summarize_article\", class_name=\"SummarizeArticle\")\\\n",
    "     .to(name=\"extract_keywords\", class_name=\"ExtractKeywords\")\\\n",
    "     .to(name=\"filter_article\", handler=\"filter_article\")\\\n",
    "     .to(name=\"kv_format\", handler=\"kv_format\", full_event=True)\n",
    "\n",
    "graph.add_step(name='write_to_redis',class_name=\"storey.NoSqlTarget\", table=redis_url, after='kv_format').respond()\n",
    "\n",
    "graph.plot(rankdir='LR')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test the Pipeline Locally (using simulator)"
   ]
  },
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   "execution_count": 7,
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      "No model was supplied, defaulted to sshleifer/distilbart-cnn-12-6 (https://huggingface.co/sshleifer/distilbart-cnn-12-6)\n"
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   ],
   "source": [
    "# Import the step functions for simulation.\n",
    "from nlp_transformations import *\n",
    "\n",
    "# Create a mock server (simulator).\n",
    "server = nlp_func.to_mock_server()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'url': 'https://edition.cnn.com/2022/10/22/china/china-party-congress-overseas-students-protest-intl-hnk/index.html',\n",
       " 'title': 'Anti-Xi protest spreads in China and worldwide as Chinese leader begins third term',\n",
       " 'authors': '[\"Cnn Staff\"]',\n",
       " 'publish_date': '2022-10-22 00:00:00',\n",
       " 'summarized_text': ' Anti-Xi slogans have popped up in a growing number of Chinese cities and hundreds of universities worldwide . CNN spoke with two Chinese citizens who scribbled slogans in bathroom stalls and half a dozen overseas Chinese students who put up anti-Xi posters on their campuses . China’s party has ramped up surveillance and control of the Chinese diaspora, intimidating and harassing those who dare to speak out .',\n",
       " 'keywords': '[\"jolie\", \"tiananmen\", \"scribbled\", \"risks\", \"a4\"]'}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test the pipeline.\n",
    "server.test(\"/\", body={\"url\" : \"https://edition.cnn.com/2022/10/22/china/china-party-congress-overseas-students-protest-intl-hnk/index.html\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Containerize and Deploy Pipeline on K8s\n",
    "The application is containerized and deployed to Kubernetes Cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> 2023-08-04 20:09:29,441 [warning] it is recommended to use k8s secret (specify secret_name), specifying the aws_access_key/aws_secret_key directly is unsafe\n",
      "> 2023-08-04 20:09:29,446 [info] Starting remote function deploy\n",
      "2023-08-04 20:09:29  (info) Deploying function\n",
      "2023-08-04 20:09:29  (info) Building\n",
      "2023-08-04 20:09:29  (info) Staging files and preparing base images\n",
      "2023-08-04 20:09:30  (info) Building processor image\n",
      "2023-08-04 20:09:34  (info) Build complete\n",
      "2023-08-04 20:10:21  (info) Function deploy complete\n",
      "> 2023-08-04 20:10:30,451 [info] successfully deployed function: {'internal_invocation_urls': ['nuclio-nlp-demo-jovyan-news-article-nlp.mlrun.svc.cluster.local:8080'], 'external_invocation_urls': ['34.135.160.246:31534']}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'http://34.135.160.246:31534'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nlp_func.spec.readinessTimeoutSeconds = 60 * 20  # 20 minutes.\n",
    "nlp_func.deploy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test Pipeline with URLs From Various News Sources \n",
    "The pipeline can be tested with various news sources. This pipeline should work with any source compatible with the `newspaper3k` Python library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fn.invoke(\n",
    "    path=\"/\",\n",
    "    body={\"url\" : \"https://edition.cnn.com/2022/10/22/china/china-party-congress-overseas-students-protest-intl-hnk/index.html\"}\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fn.invoke(\n",
    "    path=\"/\",\n",
    "    body={\"url\" : \"https://edition.cnn.com/2022/10/23/entertainment/matthew-perry-jennifer-aniston-alcohol/index.html\"}\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fn.invoke(\n",
    "    path=\"/\",\n",
    "    body={\"url\" : \"https://www.npr.org/2022/10/24/1130915534/the-houston-astros-and-philadelphia-phillies-will-face-each-other-in-the-world-s\"}\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fn.invoke(\n",
    "    path=\"/\",\n",
    "    body={\"url\" : \"https://abcnews.go.com/Politics/jan-committee-trumps-testimony-circus-cheney/story?id=91963586\",\n",
    "          \"filter_article\" : False}\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 3 Completed!\n",
    "Congratulations! You've completed Part 3 of the Module 3: MLOps and Real World Scenarios."
   ]
  }
 ],
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