hpcaitech · ofey404 · Nov 16, 2023 · Nov 16, 2023
diff --git a/docs/docs/basics/projects.md b/docs/docs/basics/projects.md
@@ -15,8 +15,7 @@
 - `train.py`: 项目训练代码的统一入口文件，将在`train.sh`被调用。在`train.py`里，我们需要实现对分布式训练的支持。
 - `README.md`: 项目代码的文档，用于指导用户如何准备数据集、模型以及进行训练和推理。
 
-为了帮助用户更方便的创建一个符合以上规范的项目，我们提供了Colossal-AI云平台CLI，来帮助用户一键初始化项目以及必要文件，详情可以查看[CLI使用说明](../cli/cli.md)。
-
+为了帮助用户更方便的创建一个符合以上规范的项目，我们提供了Colossal-AI云平台CLI，来帮助用户一键初始化项目以及必要文件，详情可以查看[CLI使用说明](../cli/installation.md)。
 
 ## 创建项目
 

diff --git a/docs/docs/cli/cli.md b/docs/docs/cli/cli.md
diff --git a/docs/docs/cli/create-project.md b/docs/docs/cli/create-project.md
@@ -0,0 +1,173 @@
+# 创建项目并上传到平台
+
+通过 `cap project init`，用户可以创建一个标准的项目框架。在下面的命令中，替换 `my-project` 为你自己的项目名称。
+
+```bash
+cap project init my-project
+# Project skeleton `my-project` has been initialized in `/tmp/cap-demonstration/my-project`
+# 
+# - Edit `train.sh`, `train.py` and `HyperParameters.json` to create your own training project.
+# - To upload it to the platform, run `cap project create` and `cap project upload-dir`.
+```
+
+这个命令将会在当前目录下，创建如下的项目框架：
+
+```
+- my-project
+    - Dockerfile
+    - train.sh
+    - train.py
+    - HyperParameters.json
+    - README.md
+    - requirements.txt
+```
+
+## 配置任务文件
+
+`train.sh`,`train.py`,`HyperParameters.json` 为平台启动任务所必需的文件，下面依次介绍它们的功能。
+
+### HyperParameters.json
+
+这个文件定义了用户启动训练任务时，可以从界面输入的超参数。最终的超参数将会作为环境变量传递。
+
+例如，添加超参数定义 `max_epoch` 如下：
+
+```json
+// HyperParameters.json
+{
+  "HyperParameters": [
+    {
+      "name": "max_epoch",
+      "types": "int",
+      "defaultValue": "10",
+      "description" : "the max epoch of training"
+    }
+  ]
+}
+```
+
+启动任务时就会有选框，可以配置 `max_epoch` 的值。
+
+![Hyper Parameters](./images/hyperparams.png)
+
+最终对应的环境变量是 `MAX_EPOCH`。
+
+### train.sh
+
+启动训练任务时，平台将会在每个训练容器内运行 `train.sh` 脚本。
+
+训练任务使用 torchrun，挂载的数据、超参数和 RANK 等信息会通过环境变量传入。
+
+项目框架中的 `train.sh` 如下：
+
+```bash
+#!/usr/bin/env bash
+SCRIPT_DIR="$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )"
+
+# ===================================================================
+#                Welcome to ColossalAI Platform!
+# ===================================================================
+# Those environment variables would be injected by the runner:
+#
+# 1. ColossalAI Platform defined ones:
+#    PROJECT_DIR, DATASET_DIR, MODEL_DIR, OUTPUT_DIR, SCRIPT_DIR
+#
+# 2. Required by torchrun:
+#    NNODES, NPROC_PER_NODE, NODE_RANK, MASTER_ADDR, MASTER_PORT
+#
+# 3. Hyperparameters from configuration UI:
+#    (check HyperParameters.json for more details)
+#
+# After that, the runner would execute `train.sh`, this script.
+# ===================================================================
+
+torchrun --nnodes ${NNODES} \
+    --nproc_per_node ${NPROC_PER_NODE} \
+    --node_rank ${NODE_RANK} \
+    --master_addr ${MASTER_ADDR} \
+    --master_port ${MASTER_PORT} \
+    ${SCRIPT_DIR}/train.py \
+    --project_dir ${PROJECT_DIR} \
+    --dataset_dir ${DATASET_DIR} \
+    --model_dir ${MODEL_DIR} \
+    --output_dir ${OUTPUT_DIR}
+
+# TODO: add more argument passing here
+```
+
+### train.py
+
+`train.py` 是主要的训练代码。在训练任务中它会被 `train.sh` 调用，在本地测试时，可以另外传入参数来测试。
+
+## 上传项目到平台
+
+### 创建空项目
+
+首先在平台上创建一个空项目。可以使用浏览器界面或者命令行工具。示例命令如下：
+
+```bash
+cap project create
+# Create an empty project, user: myusername
+#   Project name: My Project
+#   Project description: This project is for demonstration purpose.
+# Do you want to continue [y/N]: y
+# Project created successfully, id: 65558667d419d3db7d3ddbb6
+
+cap project list
+#   Name: My Project
+#   ID: 65558667d419d3db7d3ddbb6
+#   Description: This project is for demonstration purpose.
+#   Created At: 2023-11-16 03:03:03
+# 
+#   ...
+```
+
+登录平台的浏览器界面，可以在**控制台-资产-项目**下看到创建的项目。
+
+![创建项目](./images/create-project-empty.zh.png)
+
+点击进入**详情**，可以看到项目为空。
+
+![创建项目详情](./images/create-project-empty-2.zh.png)
+
+上传项目代码可以通过浏览器界面中的**上传文件夹**按钮，也可以使用命令行工具 `cap`。接下来演示如何使用命令行工具上传代码。
+
+### 把本地的项目代码上传到平台
+
+首先需要拿到项目的 ID，可以通过 `cap project list` 查看，也可以在浏览器界面上查看 URL 中的 ID。
+
+在上面的示例中，项目 ID 为 `65558667d419d3db7d3ddbb6`。
+
+接下来使用 `cap project upload-dir` 命令上传项目代码。示例输出如下：
+
+```bash
+cd my-project/
+
+cap project upload-dir 65558667d419d3db7d3ddbb6 .
+# Upload overview:
+#     Local directory: /tmp/cap-demonstration/my-project
+#     Dataset:
+#         ID: 65558667d419d3db7d3ddbb6
+#         Name: My Project
+#         Description: This project is for demonstration purpose.
+#         CreatedAt: 2023-11-16 03:03:03
+# 
+# The project content would be overwritten.
+# 
+# Do you want to continue [y/N]: y
+# Clearing project 65558667d419d3db7d3ddbb6...
+# Uploading directory . as project 65558667d419d3db7d3ddbb6...
+# HyperParameters.json => HyperParameters.json
+# README.md => README.md
+# requirements.txt => requirements.txt
+# Dockerfile => Dockerfile
+# patch.py => patch.py
+# train.py => train.py
+# train.sh => train.sh
+# Done.
+# Directory . uploaded as project 65558667d419d3db7d3ddbb6.
+```
+
+上传完成后，可以在浏览器界面上看到项目代码的内容。
+
+![上传后的项目代码](./images/create-project-empty-3.zh.png)
diff --git a/docs/docs/cli/images/create-project-empty-2.en.png b/docs/docs/cli/images/create-project-empty-2.en.png
diff --git a/docs/docs/cli/images/create-project-empty-2.zh.png b/docs/docs/cli/images/create-project-empty-2.zh.png
diff --git a/docs/docs/cli/images/create-project-empty-3.en.png b/docs/docs/cli/images/create-project-empty-3.en.png
diff --git a/docs/docs/cli/images/create-project-empty-3.zh.png b/docs/docs/cli/images/create-project-empty-3.zh.png
diff --git a/docs/docs/cli/images/create-project-empty.en.png b/docs/docs/cli/images/create-project-empty.en.png
diff --git a/docs/docs/cli/images/create-project-empty.zh.png b/docs/docs/cli/images/create-project-empty.zh.png
diff --git a/docs/docs/cli/installation.md b/docs/docs/cli/installation.md
@@ -0,0 +1,49 @@
+# 安装 cap 命令行工具
+
+我们提供了一个命令行工具 `cap`，帮助用户管理平台上的数据集和代码，尤其是在没有图形界面的服务器上传和下载。它的名字来源于 Colossal-AI Platform 的首字母。
+
+1. 从源码安装（推荐，因为 API 可能频繁更新）
+
+```bash
+pip install git+https://github.com/hpcaitech/ColossalAI-Platform-CLI@main
+```
+
+2. 从PyPI安装
+
+```bash
+pip install colossalai-platform
+```
+
+## 配置
+
+安装完成后，可以通过 `cap configure` 命令，来完成命令行工具的配置。
+
+它将会询问用户名和密码，并尝试登录平台的 API Server，来验证配置的有效性。输出如下：
+
+```bash
+cap configure
+# Config doesn't exist on /home/myuser/.colossalai-platform/config.yaml, writing default to it
+# Username: myusername
+# Password (Hide input): 
+# Login successfully!
+# 
+# Thank you for choosing the ColossalAI Platform!
+# During our public beta phase, we're actively developing and improving the platform. We appreciate your patience with any user experience issues.
+# 
+# For assistance, visit [doc link](TODO) or reach out anytime.
+# Your feedback is valuable as we strive to enhance your experience.
+```
+
+## 配置文件
+
+用户名和密码将会被保存在配置文件，路径为 `$HOME/.colossalai-platform/config.yaml`。示例内容如下：
+
+```bash
+cat ~/.colossalai-platform/config.yaml 
+# api_server: https://180.184.83.159
+# username: myusername
+# password: **********
+# max_upload_chunk_bytes: 104857600
+```
+
+另外，在连接一个私有化部署的实例时，需要修改配置文件中的 API Server 地址。
diff --git a/docs/sidebars.js b/docs/sidebars.js
@@ -39,7 +39,10 @@ const sidebars = {
     {
       type: "category",
       label: "命令行工具",
-      items: ["cli/cli"],
+      items: [
+        "cli/installation",
+        "cli/create-project",
+      ],
     },
   ],
   contactSidebar: [

diff --git a/scripts/build.sh b/scripts/build.sh
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