使用 SageMaker 分散式資料平行程式庫建立您自己的 Docker 容器 - Amazon SageMaker

本文為英文版的機器翻譯版本，如內容有任何歧義或不一致之處，概以英文版為準。

使用 SageMaker 分散式資料平行程式庫建立您自己的 Docker 容器

若要建置自己的 Docker 容器進行訓練並使用 SageMaker 資料平行程式庫，您必須在 Dockerfile 中包含正確的相依性和 SageMaker 分散式平行程式庫的二進位檔案。本節說明如何使用資料平行程式庫建立具有分散式訓練 SageMaker之最低相依性集的完整 Dockerfile。

注意

此自訂 Docker 選項具有 SageMaker 資料平行程式庫作為二進位，僅適用於 PyTorch。

使用 SageMaker 訓練工具組和資料平行程式庫建立 Dockerfile

  1. 從 Docker NVIDIA CUDA映像開始。使用包含CUDA執行期和開發工具 （標頭和程式庫） 的 cuDNN 開發人員版本，從PyTorch 原始程式碼 建置 。

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

    官方 AWS 深度學習容器 （DLC） 映像是從NVIDIACUDA基礎映像 建置而成。如果您想要在遵循其餘指示時使用預先建置DLC的影像做為參考，請參閱適用於 AWS PyTorch Dockerfiles 的深度學習容器 。

  2. 新增下列引數以指定 PyTorch 和其他 套件的版本。此外，指出 SageMaker 資料平行程式庫和其他軟體的 Amazon S3 儲存貯體路徑，以使用 AWS 資源，例如 Amazon S3 外掛程式。

    若要使用下列程式碼範例所提供版本以外的第三方程式庫版本，建議您查看 的官方 Dockerfiles AWS of Deep Learning Container PyTorch，以尋找經過測試、相容且適合您的應用程式的版本。

    若要尋找 URLs SMDATAPARALLEL_BINARY引數，請參閱 的查詢資料表支援的架構。

    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. 設定下列環境變數，以正確建置 SageMaker 訓練元件並執行資料平行程式庫。您可以在後續步驟中將這些變數用於元件。

    # 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 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. 在後續步驟中安裝或更新 curl、wget 和 git，以下載並建置套件。

    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. 為 Amazon EC2 網路通訊安裝 Elastic Fabric Adapter （EFA） 軟體。

    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. 安裝 Conda 以處理套件管理。

    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. 取得、建置和安裝 PyTorch 及其相依性。我們PyTorch 從原始程式碼建置，因為我們需要控制 NCCL版本，才能確保與AWS OFINCCL外掛程式 的相容性。

    1. 請依照PyTorch 官方 dockerfile 中的步驟，安裝建置相依性並設定快取以加快重新編譯速度。

      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. 安裝 PyTorch的常見 和 Linux 相依性 。

      # 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. 複製PyTorch GitHub儲存庫 。

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

    4. 安裝並建置特定NCCL版本。若要這麼做，請將 PyTorch的預設NCCL資料夾 （/pytorch/third_party/nccl） 中的內容取代為儲存NVIDIA庫中的特定NCCL版本。NCCL 版本已在本指南的步驟 3 中設定。

      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. 建置並安裝 PyTorch。此程序通常需要稍微超過 1 小時才能完成。它使用上一個步驟中下載的NCCL版本建置。

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

  8. 建置和安裝AWS OFINCCL外掛程式 。這會啟用 SageMaker 資料平行程式庫的 libfabric 支援。

    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. 建置並安裝 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. 安裝和設定 Open SSH。需要開啟SSH MPI才能在容器之間通訊。允許 OpenSSH 與容器交談，而不要求確認。

    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. 安裝 PT S3 外掛程式，以有效率地存取 Amazon S3 中的資料集。

    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. 安裝 libboost 程式庫。此套件是建立 SageMaker 資料平行程式庫非同步 IO 功能的所需。

    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. 安裝下列 PyTorch 訓練 SageMaker 工具。

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

  14. 最後，安裝 SageMaker 資料平行二進位和其餘相依性。

    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. 建立 Dockerfile 之後，請參閱調整您自己的訓練容器，以了解如何建置 Docker 容器、在 Amazon 中託管容器ECR，以及使用 SageMaker Python 執行訓練任務SDK。

以下範例程式碼顯示了一個完整的 Dockerfile 於合併所有先前的程式碼區塊的情況。

# This file creates a docker image with minimum dependencies to run SageMaker 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 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 PyTorch training.
WORKDIR /root
RUN pip install --no-cache-dir -U \
    smclarify \
    "sagemaker>=2,<3" \
    sagemaker-experiments==0.* \
    sagemaker-pytorch-training

# Install SageMaker 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

如需在 中建立訓練自訂 Dockerfile 的一般資訊 SageMaker，請參閱使用您自己的訓練演算法 。

提示

如果您想要擴展自訂 Dockerfile 以整合 SageMaker 模型平行程式庫，請參閱 使用 SageMaker 分散式模型平行程式庫建立您自己的 Docker 容器。