[Doc] Update docker install guide (#469)

Author: pan-x-c

.github/workflows/docker.yaml

@@ -49,9 +49,7 @@ jobs:
           images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
           tags: |
             type=match,pattern=\d.\d.\d,enable=${{ github.event_name == 'release' }}
-            type=sha,enable=${{ github.event_name == 'release' }}
             type=raw,value=${{ github.event.inputs.version }},enable=${{ github.event_name == 'workflow_dispatch' }}
-            type=sha,enable=${{ github.event_name == 'workflow_dispatch' }}
       # This step uses the `docker/build-push-action` action to build the image, based on your repository's `Dockerfile`. If the build succeeds, it pushes the image to GitHub Packages.
       # It uses the `context` parameter to define the build's context as the set of files located in the specified path. For more information, see [Usage](https://github.com/docker/build-push-action#usage) in the README of the `docker/build-push-action` repository.
       # It uses the `tags` and `labels` parameters to tag and label the image with the output from the "meta" step.

README.md

@@ -181,10 +181,29 @@ git clone https://github.com/modelscope/Trinity-RFT
 cd Trinity-RFT
 ```
 
-##### 2. Set Up a Virtual Environment
+##### 2. Set Up Environment
 
 Choose one of the following options:
 
+##### Using Pre-built Docker Image (Recommended for Beginners)
+
+We provide a pre-built Docker image with GPU-related dependencies installed.
+
+```bash
+docker pull ghcr.io/modelscope/trinity-rft:latest
+
+# Run the container, replacing <path_to_your_data_and_checkpoints> with your actual path
+docker run -it \
+  --gpus all \
+  --shm-size="64g" \
+  --rm \
+  -v $PWD:/workspace \
+  -v <path_to_your_data_and_checkpoints>:/data \
+  ghcr.io/modelscope/trinity-rft:latest
+```
+
+> This image has used `uv` to install all GPU-related dependencies of Trinity-RFT. The virtual environment will be automatically activated upon entering the container (you can also manually activate it via `source /opt/venv/bin/activate` if needed). You can use `uv pip install` to add extra packages as necessary.
+
 ###### Using Conda
 
 ```bash
@@ -230,7 +249,6 @@ uv sync --extra vllm --extra dev --extra flash_attn
 # uv sync --extra tinker --extra dev
 ```
 
-
 #### Via PyPI
 
 If you just want to use the package without modifying the code:
@@ -247,29 +265,6 @@ uv pip install trinity-rft
 uv pip install flash-attn==2.8.1
 ```
 
-
-#### Using Docker
-
-We provide a Docker setup for hassle-free environment configuration.
-
-```bash
-git clone https://github.com/modelscope/Trinity-RFT
-cd Trinity-RFT
-
-# Build the Docker image
-## Tip: You can modify the Dockerfile to add mirrors or set API keys
-docker build -f scripts/docker/Dockerfile -t trinity-rft:latest .
-
-# Run the container, replacing <path_to_your_data_and_checkpoints> with your actual path
-docker run -it \
-  --gpus all \
-  --shm-size="64g" \
-  --rm \
-  -v $PWD:/workspace \
-  -v <path_to_your_data_and_checkpoints>:/data \
-  trinity-rft:latest
-```
-
 > For training with **Megatron-LM**, please refer to [Megatron-LM Backend](https://modelscope.github.io/Trinity-RFT/en/main/tutorial/example_megatron.html).
 
 ### Step 2: prepare dataset and model

README_zh.md

@@ -181,10 +181,28 @@ git clone https://github.com/modelscope/Trinity-RFT
 cd Trinity-RFT
 ```
 
-### 2. 创建虚拟环境
+### 2. 构建环境
 
 可选择以下任一方式：
 
+#### 使用预构建 Docker 镜像（推荐初学者使用该方法）
+
+
+```bash
+docker pull ghcr.io/modelscope/trinity-rft:latest
+
+# 将 <path_to_your_data_and_checkpoints> 替换为实际需要挂载的路径
+docker run -it \
+  --gpus all \
+  --shm-size="64g" \
+  --rm \
+  -v $PWD:/workspace \
+  -v <path_to_your_data_and_checkpoints>:/data \
+  ghcr.io/modelscope/trinity-rft:latest
+```
+
+> 该镜像已经通过 `uv` 安装了 Trinity-RFT 以及所有 GPU 相关依赖，且会自动激活虚拟环境（也可通过 `source /opt/venv/bin/activate` 手动激活）。必要时可使用 `uv pip install` 添加额外的包。
+
 #### 使用 Conda
 
 ```bash
@@ -246,28 +264,6 @@ uv pip install trinity-rft
 uv pip install flash-attn==2.8.1
 ```
 
-## 使用 Docker
-
-我们提供了 Docker 环境，方便快速配置。
-
-```bash
-git clone https://github.com/modelscope/Trinity-RFT
-cd Trinity-RFT
-
-# 构建 Docker 镜像
-## 提示：可根据需要修改 Dockerfile 添加镜像源或设置 API 密钥
-docker build -f scripts/docker/Dockerfile -t trinity-rft:latest .
-
-# 运行容器，请将 <path_to_your_data_and_checkpoints> 替换为实际需要挂载的路径
-docker run -it \
-  --gpus all \
-  --shm-size="64g" \
-  --rm \
-  -v $PWD:/workspace \
-  -v <path_to_your_data_and_checkpoints>:/data \
-  trinity-rft:latest
-```
-
 > 如需使用 **Megatron-LM** 进行训练，请参考 [Megatron-LM 支持](https://modelscope.github.io/Trinity-RFT/zh/main/tutorial/example_megatron.html)
 
 
@@ -338,8 +334,6 @@ trinity studio --port 8080
 </details>
 
 
-
-
 ### 第四步：运行 RFT 流程
 
 

docs/sphinx_doc/source/tutorial/trinity_installation.md

@@ -1,24 +1,28 @@
 (Installation)=
 # Installation
 
-For installing Trinity-RFT, you have three options: from source (recommended), via PyPI, or using Docker.
+For installing Trinity-RFT, you have three options: from source (recommended for experienced users), using Docker (recommended for beginners) or via PyPI.
 
 **Before you begin**, check your system setup:
 
-### If you have GPUs and want to use them:
+### If you have GPUs and want to use them
+
 Make sure your system meets these requirements:
+
 - **Python**: 3.10 – 3.12
 - **CUDA**: 12.8 or higher
 - **GPUs**: At least 2 available
 
-### If you don’t have GPUs (or prefer not to use them):
+### If you don’t have GPUs or prefer not to use them
+
 You can use the `tinker` option instead, which only requires:
+
 - **Python**: 3.11 – 3.12
 - **GPUs**: Not required
 
 ---
 
-## From Source (Recommended)
+## From Source (Recommended for experienced users)
 
 This method is best if you plan to customize or contribute to Trinity-RFT.
 
@@ -100,7 +104,31 @@ uv pip install flash-attn==2.8.1
 
 ## Using Docker
 
-We provide a Docker setup for hassle-free environment configuration.
+You can download the Trinity-RFT Docker image from Github Container Registry or build it locally.
+
+### Pull from GitHub Container Registry (Recommended for beginners)
+
+```bash
+git clone https://github.com/modelscope/Trinity-RFT
+cd Trinity-RFT
+
+docker pull ghcr.io/modelscope/trinity-rft:latest
+
+docker run -it \
+  --gpus all \
+  --shm-size="64g" \
+  --rm \
+  -v $PWD:/workspace \
+  -v <path_to_your_data_and_checkpoints>:/data \
+  ghcr.io/modelscope/trinity-rft:latest
+```
+
+```{note}
+This docker image use `uv` to manage python packages, you need to activate the virtual environment using `source /opt/venv/bin/activate` after entering the docker container.
+The image has include dependencies such as vllm, flash-attn and Megatron-LM, if you need to download more packages, don't forget to activate the virtual environment and use `uv pip install` to install them.
+```
+
+### Build Locally
 
 ```bash
 git clone https://github.com/modelscope/Trinity-RFT
@@ -120,12 +148,12 @@ docker run -it \
   trinity-rft:latest
 ```
 
+---
+
 ```{note}
 For training with **Megatron-LM**, please refer to {ref}`Megatron-LM Backend <Megatron-LM>`.
 ```
 
----
-
 ## Troubleshooting
 
 If you encounter installation issues, refer to the FAQ or [GitHub Issues](https://github.com/modelscope/Trinity-RFT/issues).

docs/sphinx_doc/source_zh/tutorial/trinity_installation.md

@@ -1,24 +1,28 @@
 (Installation)=
 # 安装指南
 
-安装 Trinity-RFT 有三种方式：源码安装（推荐）、通过 PyPI 安装，或使用 Docker。
+安装 Trinity-RFT 有三种方式：源码安装（推荐有经验的用户使用该方法）、使用 Docker (推荐初学者使用该方法) 或是从 PyPI 安装。
 
 **开始之前**，请检查您的系统配置：
 
-### 如果您拥有 GPU 并希望使用它们：
+### 如果您有 GPU 并希望使用它们
+
 请确保您的系统满足以下要求：
+
 - **Python**：3.10 – 3.12
 - **CUDA**：12.8 或更高版本
 - **GPU**：至少 2 块可用
 
-### 如果您没有 GPU（或不希望使用 GPU）：
+### 如果您没有 GPU 或不希望使用 GPU
+
 您可以改用 `tinker` 选项，该选项仅需满足：
+
 - **Python**：3.11 – 3.12
 - **GPU**：无需
 
 ---
 
-## 源码安装（推荐）
+## 源码安装（推荐有经验的用户使用该方法）
 
 如需修改、扩展 Trinity-RFT，推荐使用此方法。
 
@@ -80,27 +84,34 @@ uv sync --extra vllm --extra dev --extra flash_attn
 
 ---
 
-## 通过 PyPI 安装
+## 使用 Docker
 
-如果您只需使用 Trinity-RFT 而不打算修改代码：
+您可以从 Github 拉取 Docker 镜像或是自行构建镜像。
+
+### 从 Github 拉取预构建镜像 (推荐初学者使用该方法)
 
 ```bash
-pip install trinity-rft
-pip install flash-attn==2.8.1
-```
+git clone https://github.com/modelscope/Trinity-RFT
+cd Trinity-RFT
 
-或使用 `uv`：
+docker pull ghcr.io/modelscope/trinity-rft:latest
 
-```bash
-uv pip install trinity-rft
-uv pip install flash-attn==2.8.1
+docker run -it \
+  --gpus all \
+  --shm-size="64g" \
+  --rm \
+  -v $PWD:/workspace \
+  -v <path_to_your_data_and_checkpoints>:/data \
+  ghcr.io/modelscope/trinity-rft:latest
 ```
 
----
+```{note}
+该 Docker 镜像使用 `uv` 来管理 Python 依赖，进入容器后虚拟环境会自动激活（也可通过 `source /opt/venv/bin/activate` 手动激活）。
+该镜像已经包含了 vllm, flash-attn 以及 Megatron-LM，如果需要使用其他依赖，可直接使用 `uv pip install` 来安装它们。
+```
 
-## 使用 Docker
+### 自行构建 Docker 镜像
 
-我们提供了 Docker 环境，方便快速配置。
 
 ```bash
 git clone https://github.com/modelscope/Trinity-RFT
@@ -120,11 +131,30 @@ docker run -it \
   trinity-rft:latest
 ```
 
+---
+
+## 通过 PyPI 安装
+
+如果您只需使用 Trinity-RFT 而不打算修改代码：
+
+```bash
+pip install trinity-rft
+pip install flash-attn==2.8.1
+```
+
+或使用 `uv`：
+
+```bash
+uv pip install trinity-rft
+uv pip install flash-attn==2.8.1
+```
+
+---
+
 ```{note}
 如需使用 **Megatron-LM** 进行训练，请参考 {ref}`Megatron-LM Backend <Megatron-LM>`。
 ```
 
----
 
 ## 常见问题