FazBrowse GitHub Viewer
|
Trending
|
URL:
|
Home
Tools:
[Download Repo ZIP]
[View Raw Code]
[Original HTTPS Page]
docker-python/gpu.Dockerfile at main · PeteBleackley/docker-python · GitHub
PeteBleackley
/
docker-python
Public
forked from
Kaggle/docker-python
Notifications
You must be signed in to change notification settings
Fork
0
Star
0
Code
Pull requests
0
Actions
Projects
Security and quality
0
Insights
Additional navigation options
Code
Pull requests
Actions
Projects
Security and quality
Insights
Expand file tree
Breadcrumbs
docker-python
/
gpu.Dockerfile
Copy path
More file actions
More file actions
Latest commit
History
History
History
100 lines (88 loc) · 4.87 KB
Breadcrumbs
docker-python
/
gpu.Dockerfile
Copy path
File metadata and controls
100 lines (88 loc) · 4.87 KB
Raw
Copy raw file
Download raw file
Open symbols panel
Edit and raw actions
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
ARG
BASE_TAG=staging
FROM
nvidia/cuda:11.0-cudnn8-devel-ubuntu18.04 AS nvidia
FROM
gcr.io/kaggle-images/python:${BASE_TAG}
ADD
clean-layer.sh /tmp/clean-layer.sh
#
Cuda support
COPY
--from=nvidia /etc/apt/sources.list.d/cuda.list /etc/apt/sources.list.d/
COPY
--from=nvidia /etc/apt/sources.list.d/nvidia-ml.list /etc/apt/sources.list.d/
COPY
--from=nvidia /etc/apt/trusted.gpg /etc/apt/trusted.gpg.d/cuda.gpg
#
See b/142337634#comment28
RUN
sed -i
's/deb https:
\/\/
developer.download.nvidia.com/deb http:
\/\/
developer.download.nvidia.com/'
/etc/apt/sources.list.d/*.list
ENV
CUDA_MAJOR_VERSION=11
ENV
CUDA_MINOR_VERSION=0
ENV
CUDA_VERSION=$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:${PATH}
#
The stub is useful to us both for built-time linking and run-time linking, on CPU-only systems.
#
When intended to be used with actual GPUs, make sure to (besides providing access to the host
#
CUDA user libraries, either manually or through the use of nvidia-docker) exclude them. One
#
convenient way to do so is to obscure its contents by a bind mount:
#
docker run .... -v /non-existing-directory:/usr/local/cuda/lib64/stubs:ro ...
ENV
LD_LIBRARY_PATH_NO_STUBS=
"/usr/local/nvidia/lib64:/usr/local/cuda/lib64:$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_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
#
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 torchtext cpuonly && \
conda install
"pytorch=1.7"
"torchvision=0.8"
"torchaudio=0.7"
"torchtext=0.8"
cudatoolkit=$CUDA_VERSION && \
conda install
"cudf=21.06"
"cuml=21.06"
&& \
/tmp/clean-layer.sh
#
Install LightGBM with GPU
RUN
pip uninstall -y lightgbm && \
cd /usr/local/src && \
git clone --recursive https://github.com/microsoft/LightGBM && \
cd LightGBM && \
git checkout tags/v3.2.1 && \
mkdir build && cd build && \
cmake -DUSE_GPU=1 -DOpenCL_LIBRARY=/usr/local/cuda/lib64/libOpenCL.so -DOpenCL_INCLUDE_DIR=/usr/local/cuda/include/ .. && \
make -j$(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==0.2.16 jaxlib==0.1.68+cuda$CUDA_MAJOR_VERSION$CUDA_MINOR_VERSION -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"
Back
|
FazBrowse Home
|
New Git URL