利用 SageMaker AI 分布式数据并行库创建自己的 Docker 容器 - 亚马逊 SageMaker AI

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利用 SageMaker AI 分布式数据并行库创建自己的 Docker 容器

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以及 SageMaker AI 分布式并行库的二进制文件。此部分提供的说明介绍了如何使用数据并行库，在 SageMaker AI 中创建具有最小依赖项集合的完整 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 提供的 Docker 映像开始。使用包含 CUDA 运行时系统和开发工具（头文件和库）的 cuDNN 开发人员版本，从 PyTorch 源代码进行构建。

    FROM nvidia/cuda:11.3.1-cudnn8-devel-ubuntu20.04
    提示

    官方的 AWS Deep Learning Container (DLC) 映像基于 NVIDIA CUDA 基础映像而构建。如果您想使用预构建的 DLC 映像作为参考，同时遵循其余说明部分操作，请参阅适用于 PyTorch Dockerfile 的 AWS Deep Learning Containers。

  2. 添加以下参数以指定 PyTorch 和其他软件包的版本。此外，请指明 SageMaker AI 数据并行库和其他软件的 Amazon S3 存储桶路径，以便使用 AWS 资源，例如 Amazon S3 插件。

    要使用在以下代码示例所提供的库之外的第三方库版本，我们建议您查看适用于 PyTorch 的 AWS Deep Learning Container 的官方 Dockerfile，以查找经过测试且适合您的应用程序的兼容版本。

    要查找 SMDATAPARALLEL_BINARY 参数的 URL，请参阅 支持的框架 中的查找表。

    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

  3. 设置以下环境变量，以便正确构建 SageMaker 训练组件并运行数据并行库。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

  4. 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/*

  5. 安装用于 Amazon EC2 网络通信的 Elastic Fabric Adapter (EFA) 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

  6. 安装 Conda 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

  7. 获取、构建和安装 PyTorch 及其依赖项。我们从源代码构建了 PyTorch，因为我们需要控制 NCCL 的版本，以确保与 AWS OFI NCCL 插件的兼容性。

    1. 按照 PyTorch 官方 dockerfile 中的步骤，安装构建依赖项并设置 ccache 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

    2. 安装 PyTorch 的通用依赖项和 Linux 依赖项。

      # 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

    3. 克隆 PyTorch GitHub 存储库。

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

    4. 安装并构建特定的 NCCL 版本。为此，请将 PyTorch 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。NCCL 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

    5. 构建和安装 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

  8. 构建并安装 AWS OFI NCCL 插件。这使得 libfabric 支持 SageMaker AI 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

  9. 构建并安装 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

  10. 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

  11. 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

  12. 安装 libboost 库。对于 SageMaker AI 数据并行库的异步 IO 功能联网，此软件包为必需。

    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

  13. 安装以下 SageMaker AI 工具用于 PyTorch 训练。

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

  14. 最后，安装 SageMaker AI 数据并行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}

  15. 完成 Dockerfile 的创建后，请参阅调整自己的训练容器，了解如何构建 Docker 容器，将其托管在 Amazon ECR 中，并使用 SageMaker Python SDK 运行训练作业。

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

有关创建自定义 Dockerfile 以用于 SageMaker AI 中的训练的更多一般信息，请参阅使用自己的训练算法。

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您自己的 Docker 容器。