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ARG BASE_TAG=staging

FROM nvidia/cuda:10.0-cudnn7-devel-ubuntu16.04 AS nvidia

FROM gcr.io/kaggle-images/python-tensorflow-whl:2.0.0-py36 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

# 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_VERSION=10.0.130

ENV CUDA_PKG_VERSION=10-0=$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:${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="/usr/local/nvidia/lib64:/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs"

ENV NVIDIA_VISIBLE_DEVICES=all

ENV NVIDIA_DRIVER_CAPABILITIES=compute,utility

ENV NVIDIA_REQUIRE_CUDA="cuda>=10.0"

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=7.5.0.56-1+cuda10.0 \

libcudnn7-dev=7.5.0.56-1+cuda10.0 \

libnccl2=2.4.2-1+cuda10.0 \

libnccl-dev=2.4.2-1+cuda10.0 && \

ln -s /usr/local/cuda-10.0 /usr/local/cuda && \

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

/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 && \

conda remove --force -y pytorch-cpu torchvision-cpu && \

conda install -y pytorch torchvision cudatoolkit=10.0 -c pytorch && \

pip uninstall -y mxnet && \

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

pip install --no-deps mxnet-cu100 && \

/tmp/clean-layer.sh

# Install GPU-only packages

RUN pip install pycuda && \

pip install cupy-cuda100 && \

pip install pynvrtc && \

/tmp/clean-layer.sh

# Re-add TensorBoard Jupyter extension patch

ADD patches/tensorboard/notebook.py /opt/conda/lib/python3.6/site-packages/tensorboard/notebook.py

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

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