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# 使用 SageMaker AI 分布式数据并行库创建自己的 Docker 容器
<a name="data-parallel-bring-your-own-container"></a>

要构建自己的 Docker 容器进行训练并使用 SageMaker AI 数据并行库，您必须在 Dockerfile 中包含正确的依赖项和 SageMaker AI 分布式并行库的二进制文件。本节提供有关如何使用数据 parallel 库创建具有最小依赖关系的完整 Dockerfile，以便在 SageMaker AI 中进行分布式训练。

**注意**  
此自定义 Docker 选项将 SageMaker AI 数据并行库作为二进制文件仅适用于。 PyTorch

**使用 SageMaker 训练工具包和数据并行库创建 Dockerfile**

1. 使用 [NVIDIA CUDA](https://hub.docker.com/r/nvidia/cuda) 提供的 Docker 映像开始。[使用包含 CUDA 运行时和开发工具（头文件和库）的 cuDNN 开发者版本从源代码进行构建。PyTorch ](https://github.com/pytorch/pytorch#from-source)

   ```
   FROM nvidia/cuda:11.3.1-cudnn8-devel-ubuntu20.04
   ```
**提示**  
官方的 AWS 深度学习容器 (DLC) 镜像是基于 [NVIDIA CUDA 基础](https://hub.docker.com/r/nvidia/cuda)镜像构建的。如果你想在按照其余说明进行操作的同时使用预构建的 DLC 镜像作为参考，请参阅[适用 PyTorch 于 Dockerfiles 的 Dee AWS p Learning Container](https://github.com/aws/deep-learning-containers/tree/master/pytorch) s。

1. 添加以下参数以指定软件包 PyTorch 和其他软件包的版本。此外，请指明指向 A SageMaker I 数据并行库和其他使用 AWS 资源的软件（例如 Amazon S3 插件）的 Amazon S3 存储桶路径。

   要使用除以下代码示例中提供的版本之外的第三方库版本，我们建议您查看[AWS 深度学习容器的官方 Dockerfiles](https://github.com/aws/deep-learning-containers/tree/master/pytorch/training/docker)， PyTorch以查找经过测试、兼容且适合您的应用程序的版本。

   要 URLs 查找`SMDATAPARALLEL_BINARY`参数，请参阅中的查找表[支持的框架](distributed-data-parallel-support.md#distributed-data-parallel-supported-frameworks)。

   ```
   ARG PYTORCH_VERSION=1.10.2
   ARG PYTHON_SHORT_VERSION=3.8
   ARG EFA_VERSION=1.14.1
   ARG SMDATAPARALLEL_BINARY=https://smdataparallel.s3.amazonaws.com/binary/pytorch/${PYTORCH_VERSION}/cu113/2022-02-18/smdistributed_dataparallel-1.4.0-cp38-cp38-linux_x86_64.whl
   ARG PT_S3_WHL_GPU=https://aws-s3-plugin.s3.us-west-2.amazonaws.com/binaries/0.0.1/1c3e69e/awsio-0.0.1-cp38-cp38-manylinux1_x86_64.whl
   ARG CONDA_PREFIX="/opt/conda"
   ARG BRANCH_OFI=1.1.3-aws
   ```

1. 设置以下环境变量以正确构建 SageMaker 训练组件并运行数据 parallel 库。在后续步骤中，您将为组件使用这些变量。

   ```
   # Set ENV variables required to build PyTorch
   ENV TORCH_CUDA_ARCH_LIST="7.0+PTX 8.0"
   ENV TORCH_NVCC_FLAGS="-Xfatbin -compress-all"
   ENV NCCL_VERSION=2.10.3
   
   # Add OpenMPI to the path.
   ENV PATH /opt/amazon/openmpi/bin:$PATH
   
   # Add Conda to path
   ENV PATH $CONDA_PREFIX/bin:$PATH
   
   # Set this enviroment variable for SageMaker AI to launch SMDDP correctly.
   ENV SAGEMAKER_TRAINING_MODULE=sagemaker_pytorch_container.training:main
   
   # Add enviroment variable for processes to be able to call fork()
   ENV RDMAV_FORK_SAFE=1
   
   # Indicate the container type
   ENV DLC_CONTAINER_TYPE=training
   
   # Add EFA and SMDDP to LD library path
   ENV LD_LIBRARY_PATH="/opt/conda/lib/python${PYTHON_SHORT_VERSION}/site-packages/smdistributed/dataparallel/lib:$LD_LIBRARY_PATH"
   ENV LD_LIBRARY_PATH=/opt/amazon/efa/lib/:$LD_LIBRARY_PATH
   ```

1. 在后续步骤中安装或更新 `curl`、`wget` 和 `git`，以下载和构建软件包。

   ```
   RUN --mount=type=cache,id=apt-final,target=/var/cache/apt \
       apt-get update && apt-get install -y  --no-install-recommends \
           curl \
           wget \
           git \
       && rm -rf /var/lib/apt/lists/*
   ```

1. 安装用于 Amazon EC2 网络通信的 [Elastic Fabric Adapter (EFA)](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/efa.html) 软件。

   ```
   RUN DEBIAN_FRONTEND=noninteractive apt-get update
   RUN mkdir /tmp/efa \
       && cd /tmp/efa \
       && curl --silent -O https://efa-installer.amazonaws.com/aws-efa-installer-${EFA_VERSION}.tar.gz \
       && tar -xf aws-efa-installer-${EFA_VERSION}.tar.gz \
       && cd aws-efa-installer \
       && ./efa_installer.sh -y --skip-kmod -g \
       && rm -rf /tmp/efa
   ```

1. 安装 [Conda](https://docs.conda.io/en/latest/) 来处理软件包管理。

   ```
   RUN curl -fsSL -v -o ~/miniconda.sh -O  https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh  && \
       chmod +x ~/miniconda.sh && \
       ~/miniconda.sh -b -p $CONDA_PREFIX && \
       rm ~/miniconda.sh && \
       $CONDA_PREFIX/bin/conda install -y python=${PYTHON_SHORT_VERSION} conda-build pyyaml numpy ipython && \
       $CONDA_PREFIX/bin/conda clean -ya
   ```

1. 获取、构建、安装 PyTorch 及其依赖关系。我们[使用源代码进行](https://github.com/pytorch/pytorch#from-source)构建PyTorch ，因为我们需要控制 NCCL 版本以保证与 [AWS OFI](https://github.com/aws/aws-ofi-nccl) NCCL 插件的兼容性。

   1. 按照[PyTorch 官方 dockerfil](https://github.com/pytorch/pytorch/blob/master/Dockerfile) e 中的步骤，安装构建依赖项并设置 [ccache](https://ccache.dev/) 以加快重新编译速度。

      ```
      RUN DEBIAN_FRONTEND=noninteractive \
          apt-get install -y --no-install-recommends \
              build-essential \
              ca-certificates \
              ccache \
              cmake \
              git \
              libjpeg-dev \
              libpng-dev \
          && rm -rf /var/lib/apt/lists/*
        
      # Setup ccache
      RUN /usr/sbin/update-ccache-symlinks
      RUN mkdir /opt/ccache && ccache --set-config=cache_dir=/opt/ccache
      ```

   1. 安装程序[PyTorch的常见依赖项和 Linux 依赖项](https://github.com/pytorch/pytorch#install-dependencies)。

      ```
      # Common dependencies for PyTorch
      RUN conda install astunparse numpy ninja pyyaml mkl mkl-include setuptools cmake cffi typing_extensions future six requests dataclasses
      
      # Linux specific dependency for PyTorch
      RUN conda install -c pytorch magma-cuda113
      ```

   1. 克隆[PyTorch GitHub存储库](https://github.com/pytorch/pytorch)。

      ```
      RUN --mount=type=cache,target=/opt/ccache \
          cd / \
          && git clone --recursive https://github.com/pytorch/pytorch -b v${PYTORCH_VERSION}
      ```

   1. 安装并构建特定的 [NCCL](https://developer.nvidia.com/nccl) 版本。为此，请将默认 NCCL 文件夹 (`/pytorch/third_party/nccl`) 中的内容替换为 NVIDIA 存储库中的特定 NCCL 版本。 PyTorchNCCL 版本在本指南的第 3 步中设置。

      ```
      RUN cd /pytorch/third_party/nccl \
          && rm -rf nccl \
          && git clone https://github.com/NVIDIA/nccl.git -b v${NCCL_VERSION}-1 \
          && cd nccl \
          && make -j64 src.build CUDA_HOME=/usr/local/cuda NVCC_GENCODE="-gencode=arch=compute_70,code=sm_70 -gencode=arch=compute_80,code=sm_80" \
          && make pkg.txz.build \
          && tar -xvf build/pkg/txz/nccl_*.txz -C $CONDA_PREFIX --strip-components=1
      ```

   1. 构建并安装 PyTorch。完成此过程通常需要 1 个小时多一点。它使用上一步中下载的 NCCL 版本构建。

      ```
      RUN cd /pytorch \
          && CMAKE_PREFIX_PATH="$(dirname $(which conda))/../" \
          python setup.py install \
          && rm -rf /pytorch
      ```

1. 构建并安装 [AWS OFI NCCL 插件](https://github.com/aws/aws-ofi-nccl)。这使得 [libfabric](https://github.com/ofiwg/libfabric) 支持 SageMaker 人工智能数据并行库。

   ```
   RUN DEBIAN_FRONTEND=noninteractive apt-get update \
       && apt-get install -y --no-install-recommends \
           autoconf \
           automake \
           libtool
   RUN mkdir /tmp/efa-ofi-nccl \
       && cd /tmp/efa-ofi-nccl \
       && git clone https://github.com/aws/aws-ofi-nccl.git -b v${BRANCH_OFI} \
       && cd aws-ofi-nccl \
       && ./autogen.sh \
       && ./configure --with-libfabric=/opt/amazon/efa \
       --with-mpi=/opt/amazon/openmpi \
       --with-cuda=/usr/local/cuda \
       --with-nccl=$CONDA_PREFIX \
       && make \
       && make install \
       && rm -rf /tmp/efa-ofi-nccl
   ```

1. 构建并安装[TorchVision](https://github.com/pytorch/vision.git)。

   ```
   RUN pip install --no-cache-dir -U \
       packaging \
       mpi4py==3.0.3
   RUN cd /tmp \
       && git clone https://github.com/pytorch/vision.git -b v0.9.1 \
       && cd vision \
       && BUILD_VERSION="0.9.1+cu111" python setup.py install \
       && cd /tmp \
       && rm -rf vision
   ```

1. 安装和配置 OpenSSH。MPI 需要使用 OpenSSH 在容器之间进行通信。允许 OpenSSH 与容器通信而无需请求确认。

   ```
   RUN apt-get update \
       && apt-get install -y  --allow-downgrades --allow-change-held-packages --no-install-recommends \
       && apt-get install -y --no-install-recommends openssh-client openssh-server \
       && mkdir -p /var/run/sshd \
       && cat /etc/ssh/ssh_config | grep -v StrictHostKeyChecking > /etc/ssh/ssh_config.new \
       && echo "    StrictHostKeyChecking no" >> /etc/ssh/ssh_config.new \
       && mv /etc/ssh/ssh_config.new /etc/ssh/ssh_config \
       && rm -rf /var/lib/apt/lists/*
   
   # Configure OpenSSH so that nodes can communicate with each other
   RUN mkdir -p /var/run/sshd && \
    sed 's@session\s*required\s*pam_loginuid.so@session optional pam_loginuid.so@g' -i /etc/pam.d/sshd
   RUN rm -rf /root/.ssh/ && \
    mkdir -p /root/.ssh/ && \
    ssh-keygen -q -t rsa -N '' -f /root/.ssh/id_rsa && \
    cp /root/.ssh/id_rsa.pub /root/.ssh/authorized_keys \
    && printf "Host *\n StrictHostKeyChecking no\n" >> /root/.ssh/config
   ```

1. 安装 PT S3 插件以高效访问 Amazon S3 中的数据集。

   ```
   RUN pip install --no-cache-dir -U ${PT_S3_WHL_GPU}
   RUN mkdir -p /etc/pki/tls/certs && cp /etc/ssl/certs/ca-certificates.crt /etc/pki/tls/certs/ca-bundle.crt
   ```

1. 安装 [libboost](https://www.boost.org/) 库。此软件包是将 SageMaker AI 数据并行库的异步 IO 功能联网所必需的。

   ```
   WORKDIR /
   RUN wget https://sourceforge.net/projects/boost/files/boost/1.73.0/boost_1_73_0.tar.gz/download -O boost_1_73_0.tar.gz \
       && tar -xzf boost_1_73_0.tar.gz \
       && cd boost_1_73_0 \
       && ./bootstrap.sh \
       && ./b2 threading=multi --prefix=${CONDA_PREFIX} -j 64 cxxflags=-fPIC cflags=-fPIC install || true \
       && cd .. \
       && rm -rf boost_1_73_0.tar.gz \
       && rm -rf boost_1_73_0 \
       && cd ${CONDA_PREFIX}/include/boost
   ```

1. 安装以下 SageMaker AI 工具进行 PyTorch 训练。

   ```
   WORKDIR /root
   RUN pip install --no-cache-dir -U \
       smclarify \
       "sagemaker>=2,<3" \
       sagemaker-experiments==0.* \
       sagemaker-pytorch-training
   ```

1. 最后，安装 SageMaker AI 数据 parallel 二进制文件和其余依赖项。

   ```
   RUN --mount=type=cache,id=apt-final,target=/var/cache/apt \
     apt-get update && apt-get install -y  --no-install-recommends \
     jq \
     libhwloc-dev \
     libnuma1 \
     libnuma-dev \
     libssl1.1 \
     libtool \
     hwloc \
     && rm -rf /var/lib/apt/lists/*
   
   RUN SMDATAPARALLEL_PT=1 pip install --no-cache-dir ${SMDATAPARALLEL_BINARY}
   ```

1. 创建 Dockerfile 后，请参阅[调整自己的训练容器，了解如何构建 Docker 容器](https://docs.aws.amazon.com/sagemaker/latest/dg/adapt-training-container.html)，将其托管在 Amazon ECR 中，以及如何使用 Python SDK 运行训练作业。 SageMaker 

以下示例代码显示了将之前所有代码块组合在一起的完整 Dockerfile。

```
# This file creates a docker image with minimum dependencies to run SageMaker AI data parallel training
FROM nvidia/cuda:11.3.1-cudnn8-devel-ubuntu20.04

# Set appropiate versions and location for components
ARG PYTORCH_VERSION=1.10.2
ARG PYTHON_SHORT_VERSION=3.8
ARG EFA_VERSION=1.14.1
ARG SMDATAPARALLEL_BINARY=https://smdataparallel.s3.amazonaws.com/binary/pytorch/${PYTORCH_VERSION}/cu113/2022-02-18/smdistributed_dataparallel-1.4.0-cp38-cp38-linux_x86_64.whl
ARG PT_S3_WHL_GPU=https://aws-s3-plugin.s3.us-west-2.amazonaws.com/binaries/0.0.1/1c3e69e/awsio-0.0.1-cp38-cp38-manylinux1_x86_64.whl
ARG CONDA_PREFIX="/opt/conda"
ARG BRANCH_OFI=1.1.3-aws

# Set ENV variables required to build PyTorch
ENV TORCH_CUDA_ARCH_LIST="3.7 5.0 7.0+PTX 8.0"
ENV TORCH_NVCC_FLAGS="-Xfatbin -compress-all"
ENV NCCL_VERSION=2.10.3

# Add OpenMPI to the path.
ENV PATH /opt/amazon/openmpi/bin:$PATH

# Add Conda to path
ENV PATH $CONDA_PREFIX/bin:$PATH

# Set this enviroment variable for SageMaker AI to launch SMDDP correctly.
ENV SAGEMAKER_TRAINING_MODULE=sagemaker_pytorch_container.training:main

# Add enviroment variable for processes to be able to call fork()
ENV RDMAV_FORK_SAFE=1

# Indicate the container type
ENV DLC_CONTAINER_TYPE=training

# Add EFA and SMDDP to LD library path
ENV LD_LIBRARY_PATH="/opt/conda/lib/python${PYTHON_SHORT_VERSION}/site-packages/smdistributed/dataparallel/lib:$LD_LIBRARY_PATH"
ENV LD_LIBRARY_PATH=/opt/amazon/efa/lib/:$LD_LIBRARY_PATH

# Install basic dependencies to download and build other dependencies
RUN --mount=type=cache,id=apt-final,target=/var/cache/apt \
  apt-get update && apt-get install -y  --no-install-recommends \
  curl \
  wget \
  git \
  && rm -rf /var/lib/apt/lists/*

# Install EFA.
# This is required for SMDDP backend communication
RUN DEBIAN_FRONTEND=noninteractive apt-get update
RUN mkdir /tmp/efa \
    && cd /tmp/efa \
    && curl --silent -O https://efa-installer.amazonaws.com/aws-efa-installer-${EFA_VERSION}.tar.gz \
    && tar -xf aws-efa-installer-${EFA_VERSION}.tar.gz \
    && cd aws-efa-installer \
    && ./efa_installer.sh -y --skip-kmod -g \
    && rm -rf /tmp/efa

# Install Conda
RUN curl -fsSL -v -o ~/miniconda.sh -O  https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh  && \
    chmod +x ~/miniconda.sh && \
    ~/miniconda.sh -b -p $CONDA_PREFIX && \
    rm ~/miniconda.sh && \
    $CONDA_PREFIX/bin/conda install -y python=${PYTHON_SHORT_VERSION} conda-build pyyaml numpy ipython && \
    $CONDA_PREFIX/bin/conda clean -ya

# Install PyTorch.
# Start with dependencies listed in official PyTorch dockerfile
# https://github.com/pytorch/pytorch/blob/master/Dockerfile
RUN DEBIAN_FRONTEND=noninteractive \
    apt-get install -y --no-install-recommends \
        build-essential \
        ca-certificates \
        ccache \
        cmake \
        git \
        libjpeg-dev \
        libpng-dev && \
    rm -rf /var/lib/apt/lists/*

# Setup ccache
RUN /usr/sbin/update-ccache-symlinks
RUN mkdir /opt/ccache && ccache --set-config=cache_dir=/opt/ccache

# Common dependencies for PyTorch
RUN conda install astunparse numpy ninja pyyaml mkl mkl-include setuptools cmake cffi typing_extensions future six requests dataclasses

# Linux specific dependency for PyTorch
RUN conda install -c pytorch magma-cuda113

# Clone PyTorch
RUN --mount=type=cache,target=/opt/ccache \
    cd / \
    && git clone --recursive https://github.com/pytorch/pytorch -b v${PYTORCH_VERSION}
# Note that we need to use the same NCCL version for PyTorch and OFI plugin.
# To enforce that, install NCCL from source before building PT and OFI plugin.

# Install NCCL.
# Required for building OFI plugin (OFI requires NCCL's header files and library)
RUN cd /pytorch/third_party/nccl \
    && rm -rf nccl \
    && git clone https://github.com/NVIDIA/nccl.git -b v${NCCL_VERSION}-1 \
    && cd nccl \
    && make -j64 src.build CUDA_HOME=/usr/local/cuda NVCC_GENCODE="-gencode=arch=compute_70,code=sm_70 -gencode=arch=compute_80,code=sm_80" \
    && make pkg.txz.build \
    && tar -xvf build/pkg/txz/nccl_*.txz -C $CONDA_PREFIX --strip-components=1

# Build and install PyTorch.
RUN cd /pytorch \
    && CMAKE_PREFIX_PATH="$(dirname $(which conda))/../" \
    python setup.py install \
    && rm -rf /pytorch

RUN ccache -C

# Build and install OFI plugin. \
# It is required to use libfabric.
RUN DEBIAN_FRONTEND=noninteractive apt-get update \
    && apt-get install -y --no-install-recommends \
        autoconf \
        automake \
        libtool
RUN mkdir /tmp/efa-ofi-nccl \
    && cd /tmp/efa-ofi-nccl \
    && git clone https://github.com/aws/aws-ofi-nccl.git -b v${BRANCH_OFI} \
    && cd aws-ofi-nccl \
    && ./autogen.sh \
    && ./configure --with-libfabric=/opt/amazon/efa \
        --with-mpi=/opt/amazon/openmpi \
        --with-cuda=/usr/local/cuda \
        --with-nccl=$CONDA_PREFIX \
    && make \
    && make install \
    && rm -rf /tmp/efa-ofi-nccl

# Build and install Torchvision
RUN pip install --no-cache-dir -U \
    packaging \
    mpi4py==3.0.3
RUN cd /tmp \
    && git clone https://github.com/pytorch/vision.git -b v0.9.1 \
    && cd vision \
    && BUILD_VERSION="0.9.1+cu111" python setup.py install \
    && cd /tmp \
    && rm -rf vision

# Install OpenSSH.
# Required for MPI to communicate between containers, allow OpenSSH to talk to containers without asking for confirmation
RUN apt-get update \
    && apt-get install -y  --allow-downgrades --allow-change-held-packages --no-install-recommends \
    && apt-get install -y --no-install-recommends openssh-client openssh-server \
    && mkdir -p /var/run/sshd \
    && cat /etc/ssh/ssh_config | grep -v StrictHostKeyChecking > /etc/ssh/ssh_config.new \
    && echo "    StrictHostKeyChecking no" >> /etc/ssh/ssh_config.new \
    && mv /etc/ssh/ssh_config.new /etc/ssh/ssh_config \
    && rm -rf /var/lib/apt/lists/*
# Configure OpenSSH so that nodes can communicate with each other
RUN mkdir -p /var/run/sshd && \
    sed 's@session\s*required\s*pam_loginuid.so@session optional pam_loginuid.so@g' -i /etc/pam.d/sshd
RUN rm -rf /root/.ssh/ && \
    mkdir -p /root/.ssh/ && \
    ssh-keygen -q -t rsa -N '' -f /root/.ssh/id_rsa && \
    cp /root/.ssh/id_rsa.pub /root/.ssh/authorized_keys \
    && printf "Host *\n StrictHostKeyChecking no\n" >> /root/.ssh/config

# Install PT S3 plugin.
# Required to efficiently access datasets in Amazon S3
RUN pip install --no-cache-dir -U ${PT_S3_WHL_GPU}
RUN mkdir -p /etc/pki/tls/certs && cp /etc/ssl/certs/ca-certificates.crt /etc/pki/tls/certs/ca-bundle.crt

# Install libboost from source.
# This package is needed for smdataparallel functionality (for networking asynchronous IO).
WORKDIR /
RUN wget https://sourceforge.net/projects/boost/files/boost/1.73.0/boost_1_73_0.tar.gz/download -O boost_1_73_0.tar.gz \
    && tar -xzf boost_1_73_0.tar.gz \
    && cd boost_1_73_0 \
    && ./bootstrap.sh \
    && ./b2 threading=multi --prefix=${CONDA_PREFIX} -j 64 cxxflags=-fPIC cflags=-fPIC install || true \
    && cd .. \
    && rm -rf boost_1_73_0.tar.gz \
    && rm -rf boost_1_73_0 \
    && cd ${CONDA_PREFIX}/include/boost

# Install SageMaker AI PyTorch training.
WORKDIR /root
RUN pip install --no-cache-dir -U \
    smclarify \
    "sagemaker>=2,<3" \
    sagemaker-experiments==0.* \
    sagemaker-pytorch-training

# Install SageMaker AI data parallel binary (SMDDP)
# Start with dependencies
RUN --mount=type=cache,id=apt-final,target=/var/cache/apt \
    apt-get update && apt-get install -y  --no-install-recommends \
        jq \
        libhwloc-dev \
        libnuma1 \
        libnuma-dev \
        libssl1.1 \
        libtool \
        hwloc \
    && rm -rf /var/lib/apt/lists/*

# Install SMDDP
RUN SMDATAPARALLEL_PT=1 pip install --no-cache-dir ${SMDATAPARALLEL_BINARY}
```

**提示**  
有关创建用于 SageMaker 人工智能训练的自定义 Dockerfile 的更多一般信息，请参阅[使用自己的训练算](https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms-training-algo.html)法。

**提示**  
如果要扩展自定义 Dockerfile 以纳入 SageMaker AI 模型并行库，请参阅。[使用 SageMaker 分布式模型并行库创建自己的 Docker 容器](model-parallel-sm-sdk.md#model-parallel-bring-your-own-container)