docker-python/gpu.Dockerfile at master · Nosali/docker-python · GitHub

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ARG BASE_TAG=staging

FROM nvidia/cuda:10.1-cudnn7-devel-ubuntu18.04 AS nvidia

FROM gcr.io/kaggle-images/python-tensorflow-whl:2.3.0-py37 as tensorflow_whl

FROM gcr.io/kaggle-images/python:${BASE_TAG}

ADD clean-layer.sh /tmp/clean-layer.sh

# Cuda support

COPY --from=nvidia /etc/apt/sources.list.d/cuda.list /etc/apt/sources.list.d/

COPY --from=nvidia /etc/apt/sources.list.d/nvidia-ml.list /etc/apt/sources.list.d/

COPY --from=nvidia /etc/apt/trusted.gpg /etc/apt/trusted.gpg.d/cuda.gpg

# See b/142337634#comment28

RUN sed -i 's/deb https:\/\/developer.download.nvidia.com/deb http:\/\/developer.download.nvidia.com/' /etc/apt/sources.list.d/*.list

# Ensure the cuda libraries are compatible with the custom Tensorflow wheels.

# TODO(b/120050292): Use templating to keep in sync or COPY installed binaries from it.

ENV CUDA_MAJOR_VERSION=10

ENV CUDA_MINOR_VERSION=1

ENV CUDA_PATCH_VERSION=243

ENV CUDA_VERSION=$CUDA_MAJOR_VERSION.$CUDA_MINOR_VERSION.$CUDA_PATCH_VERSION

ENV CUDA_PKG_VERSION=$CUDA_MAJOR_VERSION-$CUDA_MINOR_VERSION=$CUDA_VERSION-1

LABEL com.nvidia.volumes.needed="nvidia_driver"

LABEL com.nvidia.cuda.version="${CUDA_VERSION}"

ENV PATH=/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/bin:${PATH}

# The stub is useful to us both for built-time linking and run-time linking, on CPU-only systems.

# When intended to be used with actual GPUs, make sure to (besides providing access to the host

# CUDA user libraries, either manually or through the use of nvidia-docker) exclude them. One

# convenient way to do so is to obscure its contents by a bind mount:

# docker run .... -v /non-existing-directory:/usr/local/cuda/lib64/stubs:ro ...

ENV LD_LIBRARY_PATH_NO_STUBS="/usr/local/nvidia/lib64:/usr/local/cuda/lib64:$LD_LIBRARY_PATH"

ENV LD_LIBRARY_PATH="/usr/local/nvidia/lib64:/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:$LD_LIBRARY_PATH"

ENV NVIDIA_VISIBLE_DEVICES=all

ENV NVIDIA_DRIVER_CAPABILITIES=compute,utility

ENV NVIDIA_REQUIRE_CUDA="cuda>=$CUDA_MAJOR_VERSION.$CUDA_MINOR_VERSION"

RUN apt-get update && apt-get install -y --no-install-recommends \

cuda-cupti-$CUDA_PKG_VERSION \

cuda-cudart-$CUDA_PKG_VERSION \

cuda-cudart-dev-$CUDA_PKG_VERSION \

cuda-libraries-$CUDA_PKG_VERSION \

cuda-libraries-dev-$CUDA_PKG_VERSION \

cuda-nvml-dev-$CUDA_PKG_VERSION \

cuda-minimal-build-$CUDA_PKG_VERSION \

cuda-command-line-tools-$CUDA_PKG_VERSION \

libcudnn7=7.6.5.32-1+cuda$CUDA_MAJOR_VERSION.$CUDA_MINOR_VERSION \

libcudnn7-dev=7.6.5.32-1+cuda$CUDA_MAJOR_VERSION.$CUDA_MINOR_VERSION \

libnccl2=2.5.6-1+cuda$CUDA_MAJOR_VERSION.$CUDA_MINOR_VERSION \

libnccl-dev=2.5.6-1+cuda$CUDA_MAJOR_VERSION.$CUDA_MINOR_VERSION && \

ln -s /usr/local/cuda-$CUDA_MAJOR_VERSION.$CUDA_MINOR_VERSION /usr/local/cuda && \

ln -s /usr/local/cuda/lib64/stubs/libcuda.so /usr/local/cuda/lib64/stubs/libcuda.so.1 && \

/tmp/clean-layer.sh

# Install OpenCL & libboost (required by LightGBM GPU version)

RUN apt-get install -y ocl-icd-libopencl1 clinfo libboost-all-dev && \

mkdir -p /etc/OpenCL/vendors && \

echo "libnvidia-opencl.so.1" > /etc/OpenCL/vendors/nvidia.icd && \

/tmp/clean-layer.sh

# When using pip in a conda environment, conda commands should be ran first and then

# the remaining pip commands: https://www.anaconda.com/using-pip-in-a-conda-environment/

# However, because this image is based on the CPU image, this isn't possible but better

# to put them at the top of this file to minize conflicts.

RUN conda remove --force -y pytorch torchvision torchaudio cpuonly && \

conda install "pytorch=1.6" "torchvision=0.7" "torchaudio=0.6" "torchtext=0.7" cudatoolkit=$CUDA_VERSION && \

/tmp/clean-layer.sh

# Install LightGBM with GPU

RUN pip uninstall -y lightgbm && \

cd /usr/local/src && \

git clone --recursive https://github.com/microsoft/LightGBM && \

cd LightGBM && \

git checkout tags/v2.3.1 && \

mkdir build && cd build && \

cmake -DUSE_GPU=1 -DOpenCL_LIBRARY=/usr/local/cuda/lib64/libOpenCL.so -DOpenCL_INCLUDE_DIR=/usr/local/cuda/include/ .. && \

make -j$(nproc) && \

cd /usr/local/src/LightGBM/python-package && \

python setup.py install --precompile && \

mkdir -p /etc/OpenCL/vendors && \

echo "libnvidia-opencl.so.1" > /etc/OpenCL/vendors/nvidia.icd && \

/tmp/clean-layer.sh

# Install JAX

# b/154150582#comment9: JAX 0.1.63 with jaxlib 0.1.43 is causing the GPU tests to hang.

ENV JAX_VERSION=0.1.62

ENV JAXLIB_VERSION=0.1.41

ENV JAX_PYTHON_VERSION=cp37

ENV JAX_CUDA_VERSION=cuda$CUDA_MAJOR_VERSION$CUDA_MINOR_VERSION

ENV JAX_PLATFORM=linux_x86_64

ENV JAX_BASE_URL="https://storage.googleapis.com/jax-releases"

RUN pip install $JAX_BASE_URL/$JAX_CUDA_VERSION/jaxlib-$JAXLIB_VERSION-$JAX_PYTHON_VERSION-none-$JAX_PLATFORM.whl && \

pip install jax==$JAX_VERSION && \

/tmp/clean-layer.sh

# Reinstall packages with a separate version for GPU support.

COPY --from=tensorflow_whl /tmp/tensorflow_gpu/*.whl /tmp/tensorflow_gpu/

RUN pip uninstall -y tensorflow && \

pip install /tmp/tensorflow_gpu/tensorflow*.whl && \

rm -rf /tmp/tensorflow_gpu && \

pip uninstall -y mxnet && \

# b/126259508 --no-deps prevents numpy from being downgraded.

pip install --no-deps mxnet-cu$CUDA_MAJOR_VERSION$CUDA_MINOR_VERSION && \

/tmp/clean-layer.sh

# Reinstall TensorFlow addons (TFA) with GPU support.

COPY --from=tensorflow_whl /tmp/tfa_gpu/*.whl /tmp/tfa_gpu/

RUN pip install /tmp/tfa_gpu/tensorflow*.whl && \

rm -rf /tmp/tfa_gpu/ && \

/tmp/clean-layer.sh

# Install GPU-only packages

RUN pip install pycuda && \

pip install cupy-cuda$CUDA_MAJOR_VERSION$CUDA_MINOR_VERSION && \

pip install pynvrtc && \

pip install nnabla-ext-cuda$CUDA_MAJOR_VERSION$CUDA_MINOR_VERSION && \

/tmp/clean-layer.sh

# Re-add TensorBoard Jupyter extension patch

# b/139212522 re-enable TensorBoard once solution for slowdown is implemented.

# ADD patches/tensorboard/notebook.py /opt/conda/lib/python3.7/site-packages/tensorboard/notebook.py

# Remove the CUDA stubs.

ENV LD_LIBRARY_PATH="$LD_LIBRARY_PATH_NO_STUBS"

docker-python/gpu.Dockerfile at master · Nosali/docker-python · GitHub

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