步驟 2：使用 SageMaker Python SDK 啟動分散式訓練工作

若要使用已調整的指令碼執行分散式訓練工作步驟 1：調整您的訓練腳本以使用 SMDDP 集體操作，請使用 SageMaker Python SDK 的架構或一般預估器，方法是將準備好的訓練指令碼指定為入口點指令碼和分散式訓練組態。

本頁將逐步引導您如何以兩種方式使用 SageMaker Python SDK。

您的訓練資料集應存放在您啟動訓練任務的 Amazon S3 或亞馬遜 FSx for Lustre AWS 區域 中。如果您使用 Jupyter 筆記本，則應該有一個 SageMaker 筆記本實例或 SageMaker Studio 經典應用程序在同一個中運行。 AWS 區域如需有關儲存訓練資料的詳細資訊，請參閱 SageMaker Python SDK 資料輸入文件。

如需有關如何執行訓練指令碼分散式訓練工作的指示，請選擇下列其中一個主題。啟動訓練任務後，您可以使用使用 Amazon SageMaker 偵錯工具偵錯並改善模型效能或 Amazon 監控系統使用率和模型效能 CloudWatch。

當您按照下列主題中的指示進一步了解技術詳細資訊時，我們也建議您嘗試開始使用Amazon SageMaker 資料平行程式庫範例。

在開發套件 SageMaker中使用框架估算器

您可以將distribution引數新增至 SageMaker 架構估算器或，PyTorch以啟動分散式訓練。TensorFlow如需詳細資訊，請從下列選項中選擇 SageMaker 分散式資料平行處理原則 (SMDDP) 程式庫支援的其中一個架構。

使用 SageMaker 通用估算器擴展預構建的容器

您可以自訂 SageMaker 預先建置的容器或擴充容器，以處理預先建置 SageMaker Docker 映像不支援的演算法或模型的任何其他功能需求。有關如何擴展預構建容器的範例，請參閱擴展預構建的容器。

若要擴充預先建置的容器或調整您自己的容器以使用程式庫，您必須使用 支援的架構 中列出的其中一個映像。

例如，如果您使用 PyTorch，Docker 檔案應包含類似下列內容的FROM陳述式：

# SageMaker PyTorch image
FROM 763104351884.dkr.ecr.<aws-region>.amazonaws.com/pytorch-training:<image-tag>

ENV PATH="/opt/ml/code:${PATH}"

# this environment variable is used by the SageMaker PyTorch container to determine our user code directory.
ENV SAGEMAKER_SUBMIT_DIRECTORY /opt/ml/code

# /opt/ml and all subdirectories are utilized by SageMaker, use the /code subdirectory to store your user code.
COPY train.py /opt/ml/code/train.py

# Defines cifar10.py as script entrypoint
ENV SAGEMAKER_PROGRAM train.py

您可以進一步自訂您自己的 Docker 容器，以 SageMaker 使用SageMaker 訓練工具組和 SageMaker 分散式資料 parallel 程式庫的二進位檔案來使用。如需進一步了解，請參閱下一節中的指示。

使用 SageMaker 分佈式數據 parallel 庫創建自己的 Docker 容器

要構建自己的 Docker 容器進行培訓並使用 SageMaker 數據 parallel 庫，您必須在 Dockerfile 中包含 SageMaker 分佈式 parallel 庫的正確依賴項和二進制文件。本節提供有關如何 SageMaker使用數據 parallel 庫中的分佈式培訓創建具有最小依賴關係集的完整 Dockerfile 的說明。

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}