forked from Kaggle/docker-python
-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathgpu.Dockerfile
More file actions
104 lines (90 loc) · 5.02 KB
/
Copy pathgpu.Dockerfile
File metadata and controls
104 lines (90 loc) · 5.02 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
ARG BASE_TAG=staging
FROM nvidia/cuda:11.0-cudnn8-devel-ubuntu18.04 AS nvidia
FROM gcr.io/kaggle-images/python:${BASE_TAG}
ADD clean-layer.sh /tmp/clean-layer.sh
# Cuda support
COPY --from=nvidia /etc/apt/sources.list.d/cuda.list /etc/apt/sources.list.d/
COPY --from=nvidia /etc/apt/sources.list.d/nvidia-ml.list /etc/apt/sources.list.d/
COPY --from=nvidia /etc/apt/trusted.gpg /etc/apt/trusted.gpg.d/cuda.gpg
ENV CUDA_MAJOR_VERSION=11
ENV CUDA_MINOR_VERSION=0
ENV CUDA_VERSION=$CUDA_MAJOR_VERSION.$CUDA_MINOR_VERSION
LABEL com.nvidia.volumes.needed="nvidia_driver"
LABEL com.nvidia.cuda.version="${CUDA_VERSION}"
ENV PATH=/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/bin:${PATH}
# The stub is useful to us both for built-time linking and run-time linking, on CPU-only systems.
# When intended to be used with actual GPUs, make sure to (besides providing access to the host
# CUDA user libraries, either manually or through the use of nvidia-docker) exclude them. One
# convenient way to do so is to obscure its contents by a bind mount:
# docker run .... -v /non-existing-directory:/usr/local/cuda/lib64/stubs:ro ...
# b/197989446#comment7 libgnutls version at /opt/conda/lib causes apt to fail to fetch packages using https URLs.
ENV LD_LIBRARY_PATH=/usr/lib/x86_64-linux-gnu
RUN apt-get update && apt-get install -y --no-install-recommends \
cuda-cupti-$CUDA_VERSION \
cuda-cudart-$CUDA_VERSION \
cuda-cudart-dev-$CUDA_VERSION \
cuda-libraries-$CUDA_VERSION \
cuda-libraries-dev-$CUDA_VERSION \
cuda-nvml-dev-$CUDA_VERSION \
cuda-minimal-build-$CUDA_VERSION \
cuda-command-line-tools-$CUDA_VERSION \
libcudnn8=8.0.4.30-1+cuda$CUDA_VERSION \
libcudnn8-dev=8.0.4.30-1+cuda$CUDA_VERSION \
libnccl2=2.7.8-1+cuda$CUDA_VERSION \
libnccl-dev=2.7.8-1+cuda$CUDA_VERSION && \
ln -s /usr/local/cuda-$CUDA_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
ENV LD_LIBRARY_PATH_NO_STUBS="/usr/local/nvidia/lib64:/usr/local/cuda/lib64:/opt/conda/lib"
ENV LD_LIBRARY_PATH="/usr/local/nvidia/lib64:/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:/opt/conda/lib"
ENV NVIDIA_VISIBLE_DEVICES=all
ENV NVIDIA_DRIVER_CAPABILITIES=compute,utility
ENV NVIDIA_REQUIRE_CUDA="cuda>=$CUDA_MAJOR_VERSION.$CUDA_MINOR_VERSION"
# 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 install cudf=21.08 cuml=21.08 cudatoolkit=$CUDA_VERSION && \
/tmp/clean-layer.sh
# Install Pytorch and torchvision with GPU support.
# Note: torchtext and torchaudio do not require a separate GPU package.
RUN pip install torch==1.7.1+cu$CUDA_MAJOR_VERSION$CUDA_MINOR_VERSION torchvision==0.8.2+cu$CUDA_MAJOR_VERSION$CUDA_MINOR_VERSION -f https://download.pytorch.org/whl/torch_stable.html && \
/tmp/clean-layer.sh
# Install LightGBM with GPU
RUN pip uninstall -y lightgbm && \
cd /usr/local/src && \
git clone --recursive https://github.com/microsoft/LightGBM && \
cd LightGBM && \
git checkout tags/v3.2.1 && \
mkdir build && cd build && \
cmake -DUSE_GPU=1 -DOpenCL_LIBRARY=/usr/local/cuda/lib64/libOpenCL.so -DOpenCL_INCLUDE_DIR=/usr/local/cuda/include/ .. && \
make -j$(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 (Keep JAX version in sync with CPU image)
RUN pip install jax[cuda$CUDA_MAJOR_VERSION$CUDA_MINOR_VERSION]==0.2.19 -f https://storage.googleapis.com/jax-releases/jax_releases.html && \
/tmp/clean-layer.sh
# Reinstall packages with a separate version for GPU support.
RUN pip uninstall -y mxnet && \
pip install mxnet-cu$CUDA_MAJOR_VERSION$CUDA_MINOR_VERSION && \
/tmp/clean-layer.sh
# Install GPU-only packages
RUN pip install pycuda && \
pip install pynvrtc && \
# b/190622765 latest version is causing issue. nnabla fixed it in https://github.com/sony/nnabla/issues/892, waiting for new release before we can remove this pin.
pip install pynvml==8.0.4 && \
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"