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Dockerfile.test.gpu
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ARG CUDA_DOCKER_VERSION=10.0-devel-ubuntu18.04
FROM nvidia/cuda:${CUDA_DOCKER_VERSION}
# Arguments for the build. CUDA_DOCKER_VERSION needs to be repeated because
# the first usage only applies to the FROM tag.
ARG CUDA_DOCKER_VERSION=10.0-devel-ubuntu18.04
ARG CUDNN_VERSION=7.6.0.64-1+cuda10.0
ARG NCCL_VERSION_OVERRIDE=2.4.7-1+cuda10.0
ARG MPI_KIND=OpenMPI
ARG PYTHON_VERSION=3.6
ARG GPP_VERSION=7
ARG TENSORFLOW_PACKAGE=tensorflow-gpu==1.15.0
ARG KERAS_PACKAGE=keras==2.2.4
ARG PYTORCH_PACKAGE=torch==1.2.0
ARG PYTORCH_LIGHTNING_PACKAGE=pytorch_lightning==0.7.6
ARG TORCHVISION_PACKAGE=torchvision==0.4.0
ARG MXNET_PACKAGE=mxnet-cu100==1.5.0
ARG PYSPARK_PACKAGE=pyspark==2.4.7
# if SPARK_PACKAGE is set, installs Spark into /spark from the tgz archive
# if SPARK_PACKAGE is a preview version, installs PySpark from the tgz archive
# see https://archive.apache.org/dist/spark/ for available packages, version must match PYSPARK_PACKAGE
ARG SPARK_PACKAGE=spark-2.4.7/spark-2.4.7-bin-hadoop2.7.tgz
ARG HOROVOD_BUILD_FLAGS="HOROVOD_GPU_OPERATIONS=NCCL"
ARG HOROVOD_MIXED_INSTALL=0
# Set default shell to /bin/bash
SHELL ["/bin/bash", "-cu"]
# Prepare to install specific g++ versions
RUN apt-get update -qq && apt-get install -y --no-install-recommends software-properties-common
RUN add-apt-repository ppa:ubuntu-toolchain-r/test
# Install essential packages.
RUN CUDNN_MAJOR=$(cut -d '.' -f 1 <<< "${CUDNN_VERSION}"); \
apt-get update -qq && apt-get install -y --allow-downgrades --allow-change-held-packages --no-install-recommends \
wget \
ca-certificates \
cmake \
openssh-client \
openssh-server \
git \
build-essential \
g++-${GPP_VERSION} \
moreutils \
libcudnn${CUDNN_MAJOR}=${CUDNN_VERSION} \
libnccl2=${NCCL_VERSION_OVERRIDE} \
libnccl-dev=${NCCL_VERSION_OVERRIDE}
# setup ssh service
RUN ssh-keygen -f /root/.ssh/id_rsa -q -N ''
RUN cp -v /root/.ssh/id_rsa.pub /root/.ssh/authorized_keys
# Install Python.
RUN apt-get update -qq && apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-distutils
RUN ln -s -f /usr/bin/python${PYTHON_VERSION} /usr/bin/python
RUN ln -s -f /usr/bin/python${PYTHON_VERSION} /usr/bin/python${PYTHON_VERSION/%.*/}
RUN wget --progress=dot:mega https://bootstrap.pypa.io/get-pip.py && python get-pip.py && rm get-pip.py
RUN pip install --no-cache-dir -U --force pip setuptools requests pytest mock pytest-forked parameterized
# Add launch helper scripts
RUN echo "env SPARK_HOME=/spark SPARK_DRIVER_MEM=512m PYSPARK_PYTHON=/usr/bin/python${PYTHON_VERSION} PYSPARK_DRIVER_PYTHON=/usr/bin/python${PYTHON_VERSION} \"\$@\"" > /spark_env.sh
RUN echo /spark_env.sh pytest -v --capture=no --continue-on-collection-errors --junit-xml=/artifacts/junit.\$1.\${HOROVOD_RANK:-\${OMPI_COMM_WORLD_RANK:-\${PMI_RANK}}}.\$2.xml \${@:2} > /pytest.sh
RUN echo /spark_env.sh pytest -v --capture=no --continue-on-collection-errors --junit-xml=/artifacts/junit.\$1.standalone.\$2.xml --forked \${@:2} > /pytest_standalone.sh
RUN chmod a+x /spark_env.sh
RUN chmod a+x /pytest.sh
RUN chmod a+x /pytest_standalone.sh
# Install Spark stand-alone cluster.
RUN if [[ -n ${SPARK_PACKAGE} ]]; then \
wget --progress=dot:giga "https://www.apache.org/dyn/closer.lua/spark/${SPARK_PACKAGE}?action=download" -O - | tar -xzC /tmp; \
archive=$(basename "${SPARK_PACKAGE}") bash -c "mv -v /tmp/\${archive/%.tgz/} /spark"; \
fi
# Install PySpark.
RUN apt-get update -qq && apt install -y openjdk-8-jdk-headless
RUN if [[ ${SPARK_PACKAGE} != *"-preview"* ]]; then \
pip install --no-cache-dir ${PYSPARK_PACKAGE}; \
else \
apt-get update -qq && apt-get install pandoc; \
pip install --no-cache-dir pypandoc; \
(cd /spark/python && python setup.py sdist && pip install --no-cache-dir dist/pyspark-*.tar.gz && rm dist/pyspark-*); \
fi
# Install Ray.
RUN pip install --no-cache-dir ray==1.3.0
# Install MPI.
RUN if [[ ${MPI_KIND} == "OpenMPI" ]]; then \
wget --progress=dot:mega -O /tmp/openmpi-3.0.0-bin.tar.gz https://github.com/horovod/horovod/files/1596799/openmpi-3.0.0-bin.tar.gz && \
cd /usr/local && tar -zxf /tmp/openmpi-3.0.0-bin.tar.gz && ldconfig && \
echo "mpirun -allow-run-as-root -np 2 -H localhost:2 -bind-to none -map-by slot -mca mpi_abort_print_stack 1" > /mpirun_command; \
elif [[ ${MPI_KIND} == "MPICH" ]]; then \
apt-get update -qq && apt-get install -y mpich && \
echo "mpirun -np 2" > /mpirun_command; \
fi
# Set default NCCL parameters
RUN echo NCCL_DEBUG=INFO >> /etc/nccl.conf
# Install mpi4py.
RUN if [[ ${MPI_KIND} != "None" ]]; then \
pip install --no-cache-dir mpi4py; \
fi
# Install TensorFlow and Keras (releases).
# Pin h5py only for tensorflow<2.5: https://github.com/h5py/h5py/issues/1732
# Pin scipy!=1.4.0: https://github.com/scipy/scipy/issues/11237
RUN if [[ ${TENSORFLOW_PACKAGE} != "tf-nightly-gpu" ]]; then \
pip install --no-cache-dir ${TENSORFLOW_PACKAGE}; \
if [[ ${KERAS_PACKAGE} != "None" ]]; then \
if [[ ${TENSORFLOW_PACKAGE} == tensorflow*==1.* ]] || [[ ${TENSORFLOW_PACKAGE} == tensorflow*==2.[01234].* ]]; then \
h5py="h5py<3"; \
fi; \
pip uninstall -y keras-nightly; \
pip install --no-cache-dir ${KERAS_PACKAGE} ${h5py:-} "scipy!=1.4.0" "pandas<1.1.0"; \
fi; \
mkdir -p ~/.keras; \
ldconfig /usr/local/cuda/targets/x86_64-linux/lib/stubs; \
python -c "import tensorflow as tf; tf.keras.datasets.mnist.load_data()"; \
ldconfig; \
fi
# Install PyTorch (releases).
# Pin Pillow<7.0 for torchvision < 0.5.0: https://github.com/pytorch/vision/issues/1718
# Pin Pillow!=8.3.0 for torchvision: https://github.com/pytorch/vision/issues/4146
RUN if [[ ${PYTORCH_PACKAGE} != "torch-nightly-cu"* ]]; then \
pip install --no-cache-dir ${PYTORCH_PACKAGE} ${TORCHVISION_PACKAGE} -f https://download.pytorch.org/whl/${PYTORCH_PACKAGE/*+/}/torch_stable.html; \
if [[ "${TORCHVISION_PACKAGE/%+*/}" == torchvision==0.[1234].* ]]; then \
pip install --no-cache-dir "Pillow<7.0" --no-deps; \
else \
pip install --no-cache-dir "Pillow!=8.3.0" --no-deps; \
fi; \
fi
RUN pip install ${PYTORCH_LIGHTNING_PACKAGE}
# Install MXNet (releases).
RUN if [[ ${MXNET_PACKAGE} != "mxnet-nightly-cu"* ]]; then \
pip install --no-cache-dir ${MXNET_PACKAGE} ; \
fi
# Prefetch Spark MNIST dataset.
RUN mkdir -p /work
RUN mkdir -p /data && wget --progress=dot:mega https://horovod-datasets.s3.amazonaws.com/mnist.bz2 -O /data/mnist.bz2
# Prefetch Spark Rossmann dataset.
RUN mkdir -p /work
RUN mkdir -p /data && wget --progress=dot:mega https://horovod-datasets.s3.amazonaws.com/rossmann.tgz -O - | tar -xzC /data
# Prefetch PyTorch datasets.
RUN wget --progress=dot:mega https://horovod-datasets.s3.amazonaws.com/pytorch_datasets.tgz -O - | tar -xzC /data
### END OF CACHE ###
COPY . /horovod
# Install nightly packages here so they do not get cached
# Install TensorFlow and Keras (nightly).
# Do not pin h5py since tf>=2.5 requires h5py~=3.1
# Pin scipy!=1.4.0: https://github.com/scipy/scipy/issues/11237
RUN if [[ ${TENSORFLOW_PACKAGE} == "tf-nightly-gpu" ]]; then \
pip install --no-cache-dir ${TENSORFLOW_PACKAGE}; \
if [[ ${KERAS_PACKAGE} != "None" ]]; then \
pip uninstall -y keras-nightly; \
pip install --no-cache-dir ${KERAS_PACKAGE} "scipy!=1.4.0" "pandas<1.1.0"; \
fi; \
mkdir -p ~/.keras; \
ldconfig /usr/local/cuda/targets/x86_64-linux/lib/stubs; \
python -c "import tensorflow as tf; tf.keras.datasets.mnist.load_data()"; \
ldconfig; \
fi
# Install PyTorch (nightly).
# Pin Pillow!=8.3.0 for torchvision: https://github.com/pytorch/vision/issues/4146
RUN if [[ ${PYTORCH_PACKAGE} == "torch-nightly-cu"* ]]; then \
pip install --no-cache-dir --pre torch ${TORCHVISION_PACKAGE} -f https://download.pytorch.org/whl/nightly/${PYTORCH_PACKAGE/#torch-nightly-/}/torch_nightly.html; \
pip install --no-cache-dir "Pillow!=8.3.0" --no-deps; \
fi
# Install MXNet (nightly).
RUN if [[ ${MXNET_PACKAGE} == "mxnet-nightly-cu"* ]]; then \
pip install --no-cache-dir --pre ${MXNET_PACKAGE/-nightly/} -f https://dist.mxnet.io/python/${MXNET_PACKAGE/#mxnet-nightly-/}; \
fi
# Install Horovod.
RUN cd /horovod && \
python setup.py sdist && \
ldconfig /usr/local/cuda/targets/x86_64-linux/lib/stubs && \
bash -c "${HOROVOD_BUILD_FLAGS} HOROVOD_WITH_TENSORFLOW=1 HOROVOD_WITH_PYTORCH=1 HOROVOD_WITH_MXNET=1 pip install --no-cache-dir -v $(ls /horovod/dist/horovod-*.tar.gz)[spark,ray]" && \
ldconfig
# Show the effective python package version to easily spot version differences
RUN pip freeze | sort
# Hack for compatibility of MNIST example with TensorFlow 1.1.0.
RUN if [[ ${TENSORFLOW_PACKAGE} == "tensorflow-gpu==1.1.0" ]]; then \
sed -i "s/from tensorflow import keras/from tensorflow.contrib import keras/" /horovod/examples/tensorflow/tensorflow_mnist.py; \
fi
# Hack TensorFlow MNIST example to be smaller.
RUN sed -i "s/last_step=20000/last_step=100/" /horovod/examples/tensorflow/tensorflow_mnist.py
# Hack TensorFlow Eager MNIST example to be smaller.
RUN sed -i "s/dataset.take(20000/dataset.take(100/" /horovod/examples/tensorflow/tensorflow_mnist_eager.py
# Hack TensorFlow 2.0 example to be smaller.
RUN sed -i "s/dataset.take(10000/dataset.take(100/" /horovod/examples/tensorflow2/tensorflow2_mnist.py
# Hack Keras MNIST advanced example to be smaller.
RUN sed -i "s/'--epochs', type=int, default=24,/'--epochs', type=int, default=9,/" /horovod/examples/keras/keras_mnist_advanced.py
# Hack TensorFlow 2.0 Keras MNIST advanced example to be smaller.
RUN sed -i "s/epochs=24/epochs=9/" /horovod/examples/tensorflow2/tensorflow2_keras_mnist.py
# Hack PyTorch MNIST example to be smaller.
RUN sed -i "s/'--epochs', type=int, default=10,/'--epochs', type=int, default=2,/" /horovod/examples/pytorch/pytorch_mnist.py
# Hack Keras Spark Rossmann Run example to be smaller.
RUN sed -i "s/x = Dense(1000,/x = Dense(100,/g" /horovod/examples/spark/keras/keras_spark_rossmann_run.py
RUN sed -i "s/x = Dense(500,/x = Dense(50,/g" /horovod/examples/spark/keras/keras_spark_rossmann_run.py
# Hack Keras Spark Rossmann Estimator example to be smaller.
RUN sed -i "s/x = Dense(1000,/x = Dense(100,/g" /horovod/examples/spark/keras/keras_spark_rossmann_estimator.py
RUN sed -i "s/x = Dense(500,/x = Dense(50,/g" /horovod/examples/spark/keras/keras_spark_rossmann_estimator.py
# Export HOROVOD_MIXED_INSTALL
ENV HOROVOD_MIXED_INSTALL=${HOROVOD_MIXED_INSTALL}