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Upgrade to TensorFlow 1.13.1 (#184)
* Upgrade to TensorFlow 1.13.1 * Add 1.13.1 dockerfiles * Lower process number for one of the horovod local mode test Too many processes running in parallel causes the test fail sometimes. * Use multiprocessing.Process to start ps
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‎docker/1.13.1/Dockerfile.cpu

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FROM ubuntu:16.04
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LABEL maintainer="Amazon AI"
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RUN apt-get update && apt-get install -y --no-install-recommends \
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software-properties-common \
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build-essential \
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openssh-client \
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openssh-server \
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ca-certificates \
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curl \
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&& add-apt-repository ppa:deadsnakes/ppa -y \
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&& rm -rf /var/lib/apt/lists/*
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# Install Open MPI
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RUN mkdir /tmp/openmpi && \
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cd /tmp/openmpi && \
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curl -fSsL -O https://www.open-mpi.org/software/ompi/v3.1/downloads/openmpi-3.1.2.tar.gz && \
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tar zxf openmpi-3.1.2.tar.gz && \
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cd openmpi-3.1.2 && \
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./configure --enable-orterun-prefix-by-default && \
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make -j $(nproc) all && \
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make install && \
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ldconfig && \
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rm -rf /tmp/openmpi
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# Create a wrapper for OpenMPI to allow running as root by default
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RUN mv /usr/local/bin/mpirun /usr/local/bin/mpirun.real && \
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echo '#!/bin/bash' > /usr/local/bin/mpirun && \
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echo 'mpirun.real --allow-run-as-root "$@"' >> /usr/local/bin/mpirun && \
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chmod a+x /usr/local/bin/mpirun
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RUN echo "hwloc_base_binding_policy = none" >> /usr/local/etc/openmpi-mca-params.conf && \
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echo "rmaps_base_mapping_policy = slot" >> /usr/local/etc/openmpi-mca-params.conf
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ENV LD_LIBRARY_PATH=/usr/local/openmpi/lib:$LD_LIBRARY_PATH
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ENV PATH /usr/local/openmpi/bin/:$PATH
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# SSH login fix. Otherwise user is kicked off after login
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RUN sed 's@session\s*required\s*pam_loginuid.so@session optional pam_loginuid.so@g' -i /etc/pam.d/sshd
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# Create SSH key.
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RUN mkdir -p /root/.ssh/ && \
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mkdir -p /var/run/sshd && \
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ssh-keygen -q -t rsa -N '' -f /root/.ssh/id_rsa && \
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cp /root/.ssh/id_rsa.pub /root/.ssh/authorized_keys && \
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printf "Host *\n StrictHostKeyChecking no\n" >> /root/.ssh/config
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# Set environment variables for MKL
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# For more about MKL with TensorFlow see:
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# https://www.tensorflow.org/performance/performance_guide#tensorflow_with_intel%C2%AE_mkl_dnn
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ENV KMP_AFFINITY=granularity=fine,compact,1,0 KMP_BLOCKTIME=1 KMP_SETTINGS=0
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WORKDIR /
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ARG py_version
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ARG framework_installable
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ARG framework_support_installable=sagemaker_tensorflow_container-2.0.0.tar.gz
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RUN if [ $py_version -eq 3 ]; then PYTHON_VERSION=python3.6; else PYTHON_VERSION=python2.7; fi && \
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apt-get update && apt-get install -y --no-install-recommends $PYTHON_VERSION-dev --allow-unauthenticated && \
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ln -s -f /usr/bin/$PYTHON_VERSION /usr/bin/python && \
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ln -s -f /usr/bin/$PYTHON_VERSION /usr/local/bin/python && \
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rm -rf /var/lib/apt/lists/*
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ENV PYTHONDONTWRITEBYTECODE=1 PYTHONUNBUFFERED=1 PYTHONIOENCODING=UTF-8 LANG=C.UTF-8 LC_ALL=C.UTF-8
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RUN curl -fSsL -O https://bootstrap.pypa.io/get-pip.py && \
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python get-pip.py --disable-pip-version-check --no-cache-dir "pip==18.1" && \
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rm get-pip.py
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COPY $framework_installable tensorflow-1.13.1-py2.py3-none-any.whl
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COPY $framework_support_installable .
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RUN pip install --no-cache-dir -U \
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keras==2.2.4 \
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mpi4py==3.0.1 \
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"sagemaker-tensorflow>=1.13,<1.14" && \
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# Let's install TensorFlow separately in the end to avoid
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# the library version to be overwritten
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pip install --force-reinstall --no-cache-dir -U \
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tensorflow-1.13.1-py2.py3-none-any.whl \
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horovod && \
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pip install --no-cache-dir -U $framework_support_installable && \
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rm -f tensorflow-1.13.1-py2.py3-none-any.whl && \
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rm -f $framework_support_installable && \
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pip uninstall -y --no-cache-dir \
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markdown \
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tensorboard
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ENV SAGEMAKER_TRAINING_MODULE sagemaker_tensorflow_container.training:main

‎docker/1.13.1/Dockerfile.gpu

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FROM nvidia/cuda:10.0-base-ubuntu16.04
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LABEL maintainer="Amazon AI"
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RUN apt-get update && apt-get install -y --no-install-recommends --allow-unauthenticated \
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software-properties-common && \
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add-apt-repository ppa:deadsnakes/ppa -y && \
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rm -rf /var/lib/apt/lists/*
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RUN apt-get update && apt-get install -y --no-install-recommends --allow-unauthenticated \
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ca-certificates \
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cuda-command-line-tools-10-0 \
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cuda-cublas-dev-10-0 \
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cuda-cudart-dev-10-0 \
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cuda-cufft-dev-10-0 \
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cuda-curand-dev-10-0 \
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cuda-cusolver-dev-10-0 \
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cuda-cusparse-dev-10-0 \
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curl \
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libcudnn7=7.4.1.5-1+cuda10.0 \
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# TensorFlow doesn't require libnccl anymore but Open MPI still depends on it
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libnccl2 \
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libnccl-dev \
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libfreetype6-dev \
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libhdf5-serial-dev \
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libpng12-dev \
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libzmq3-dev \
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wget \
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openssh-client \
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openssh-server \
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build-essential && \
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# The 'apt-get install' of nvinfer-runtime-trt-repo-ubuntu1604-4.0.1-ga-cuda9.0
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# adds a new list which contains libnvinfer library, so it needs another
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# 'apt-get update' to retrieve that list before it can actually install the
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# library.
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# We don't install libnvinfer-dev since we don't need to build against TensorRT,
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# and libnvinfer4 doesn't contain libnvinfer.a static library.
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apt-get update && apt-get install -y --no-install-recommends --allow-unauthenticated \
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nvinfer-runtime-trt-repo-ubuntu1604-5.0.2-ga-cuda10.0 && \
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apt-get update && apt-get install -y --no-install-recommends --allow-unauthenticated \
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libnvinfer5=5.0.2-1+cuda10.0 && \
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rm /usr/lib/x86_64-linux-gnu/libnvinfer_plugin* && \
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rm /usr/lib/x86_64-linux-gnu/libnvcaffe_parser* && \
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rm /usr/lib/x86_64-linux-gnu/libnvparsers* && \
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rm -rf /var/lib/apt/lists/*
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###########################################################################
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# Horovod & its dependencies
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###########################################################################
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# Install Open MPI
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RUN mkdir /tmp/openmpi && \
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cd /tmp/openmpi && \
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curl -fSsL -O https://www.open-mpi.org/software/ompi/v3.1/downloads/openmpi-3.1.2.tar.gz && \
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tar zxf openmpi-3.1.2.tar.gz && \
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cd openmpi-3.1.2 && \
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./configure --enable-orterun-prefix-by-default && \
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make -j $(nproc) all && \
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make install && \
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ldconfig && \
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rm -rf /tmp/openmpi
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ARG py_version
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ARG framework_installable
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ARG framework_support_installable=sagemaker_tensorflow_container-2.0.0.tar.gz
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RUN if [ $py_version -eq 3 ]; then PYTHON_VERSION=python3.6; else PYTHON_VERSION=python2.7; fi && \
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apt-get update && apt-get install -y --no-install-recommends $PYTHON_VERSION-dev --allow-unauthenticated && \
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ln -s -f /usr/bin/$PYTHON_VERSION /usr/bin/python && \
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rm -rf /var/lib/apt/lists/*
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# Create a wrapper for OpenMPI to allow running as root by default
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RUN mv /usr/local/bin/mpirun /usr/local/bin/mpirun.real && \
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echo '#!/bin/bash' > /usr/local/bin/mpirun && \
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echo 'mpirun.real --allow-run-as-root "$@"' >> /usr/local/bin/mpirun && \
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chmod a+x /usr/local/bin/mpirun
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# Configure OpenMPI to run good defaults:
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# --bind-to none --map-by slot --mca btl_tcp_if_exclude lo,docker0
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RUN echo "hwloc_base_binding_policy = none" >> /usr/local/etc/openmpi-mca-params.conf && \
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echo "rmaps_base_mapping_policy = slot" >> /usr/local/etc/openmpi-mca-params.conf
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# Set default NCCL parameters
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RUN echo NCCL_DEBUG=INFO >> /etc/nccl.conf
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ENV LD_LIBRARY_PATH=/usr/local/openmpi/lib:$LD_LIBRARY_PATH
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ENV PATH /usr/local/openmpi/bin/:$PATH
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ENV PATH=/usr/local/nvidia/bin:$PATH
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# SSH login fix. Otherwise user is kicked off after login
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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
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# Create SSH key.
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RUN mkdir -p /root/.ssh/ && \
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ssh-keygen -q -t rsa -N '' -f /root/.ssh/id_rsa && \
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cp /root/.ssh/id_rsa.pub /root/.ssh/authorized_keys && \
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printf "Host *\n StrictHostKeyChecking no\n" >> /root/.ssh/config
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###########################################################################
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# Python won’t try to write .pyc or .pyo files on the import of source modules
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ENV PYTHONDONTWRITEBYTECODE=1 PYTHONUNBUFFERED=1 PYTHONIOENCODING=UTF-8 LANG=C.UTF-8 LC_ALL=C.UTF-8
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RUN curl -fSsL -O https://bootstrap.pypa.io/get-pip.py && \
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python get-pip.py --disable-pip-version-check --no-cache-dir "pip==18.1" && \
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rm get-pip.py
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WORKDIR /
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COPY $framework_installable tensorflow-1.13.1-py2.py3-none-any.whl
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COPY $framework_support_installable .
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RUN pip install --no-cache-dir -U \
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keras==2.2.4 \
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mpi4py==3.0.1 \
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$framework_support_installable \
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"sagemaker-tensorflow>=1.13,<1.14" \
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# Let's install TensorFlow separately in the end to avoid
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# the library version to be overwritten
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&& pip install --force-reinstall --no-cache-dir -U tensorflow-1.13.1-py2.py3-none-any.whl \
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\
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&& rm -f tensorflow-1.13.1-py2.py3-none-any.whl \
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&& rm -f $framework_support_installable \
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&& pip uninstall -y --no-cache-dir \
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markdown \
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tensorboard
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# Install Horovod, temporarily using CUDA stubs
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RUN ldconfig /usr/local/cuda-10.0/targets/x86_64-linux/lib/stubs && \
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HOROVOD_GPU_ALLREDUCE=NCCL HOROVOD_WITH_TENSORFLOW=1 pip install --no-cache-dir horovod && \
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ldconfig
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ENV SAGEMAKER_TRAINING_MODULE sagemaker_tensorflow_container.training:main

‎setup.py

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'pandas', 'Pillow', 'h5py'],
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extras_require={
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'test': ['tox', 'flake8', 'pytest', 'pytest-cov', 'pytest-xdist', 'mock',
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'sagemaker>=1.15.2', 'tensorflow', 'docker-compose'],
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'sagemaker==1.19.1', 'tensorflow', 'docker-compose'],
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'benchmark': ['click']
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},
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)

‎src/sagemaker_tensorflow_container/training.py

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import json
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import logging
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import multiprocessing
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import os
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import subprocess
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import threading
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import time
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import sagemaker_containers.beta.framework as framework
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cluster_spec, job_name='ps', task_index=task_index, config=no_gpu_config
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)
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threading.Thread(target=lambda: server.join()).start()
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multiprocessing.Process(target=lambda: server.join()).start()
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def _run_worker(env, cmd_args, tf_config):

‎test/integration/local/test_horovod.py

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[1, 2],
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(2, 1),
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(2, 2),
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(5, 3)])
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(5, 2)])
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def test_distributed_training_horovod_basic(instances,
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processes,
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sagemaker_local_session,

‎test/integration/local/test_keras.py

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logging.basicConfig(level=logging.DEBUG)
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@pytest.mark.skip_gpu
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@pytest.mark.skip(reason="Serving part fails because of version mismatch.")
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def test_keras_training(sagemaker_local_session, docker_image, tmpdir, framework_version):
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entry_point = os.path.join(RESOURCE_PATH, 'keras_inception.py')
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output_path = 'file://{}'.format(tmpdir)

‎test/unit/test_training.py

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@patch('tensorflow.train.ClusterSpec')
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@patch('tensorflow.train.Server')
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@patch('sagemaker_containers.beta.framework.entry_point.run')
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@patch('threading.Thread', lambda target: target())
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@patch('multiprocessing.Process', lambda target: target())
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@patch('time.sleep', MagicMock())
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def test_train_distributed_master(run, tf_server, cluster_spec, distributed_training_env):
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training.train(distributed_training_env, MODEL_DIR_CMD_LIST)
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@patch('tensorflow.train.ClusterSpec')
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@patch('tensorflow.train.Server')
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@patch('sagemaker_containers.beta.framework.entry_point.run')
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@patch('multiprocessing.Process', lambda target: target())
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@patch('time.sleep', MagicMock())
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def test_train_distributed_worker(run, tf_server, cluster_spec, distributed_training_env):
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distributed_training_env.current_host = HOST2

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