mirror of
https://github.com/vale981/ray
synced 2025-03-06 02:21:39 -05:00
6282090401
NVIDIA Docker builds are currently broken, e.g.: https://buildkite.com/ray-project/ray-builders-branch/builds/7239#e9dea1d6-7dea-4323-801c-b7efe917be03 Following this workaround: https://forums.developer.nvidia.com/t/invalid-public-key-for-cuda-apt-repository/212901/11 to hopefully fix this for now.
48 lines
1.8 KiB
Docker
48 lines
1.8 KiB
Docker
ARG BASE_IMAGE
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FROM rayproject/ray:nightly"$BASE_IMAGE"
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ARG PYTHON_MINOR_VERSION=7
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# We have to uninstall wrapt this way for Tensorflow compatibility
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COPY requirements.txt ./
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COPY requirements_dl.txt ./
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COPY requirements_ml_docker.txt ./
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COPY requirements_rllib.txt ./
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COPY requirements_tune.txt ./requirements_tune.txt
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COPY requirements_train.txt ./
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COPY requirements_upstream.txt ./
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RUN sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/3bf863cc.pub
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RUN sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/7fa2af80.pub
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RUN sudo apt-get update \
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&& sudo apt-get install -y gcc \
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cmake \
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libgtk2.0-dev \
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zlib1g-dev \
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libgl1-mesa-dev \
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unzip \
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unrar \
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&& $HOME/anaconda3/bin/pip --no-cache-dir install -U pip \
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&& $HOME/anaconda3/bin/pip --no-cache-dir install -U \
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-r requirements.txt \
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-r requirements_rllib.txt \
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-r requirements_train.txt \
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-r requirements_tune.txt \
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-r requirements_upstream.txt \
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# explicitly install (overwrite) pytorch with CUDA support
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&& $HOME/anaconda3/bin/pip --no-cache-dir install -U \
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-r requirements_ml_docker.txt \
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# Remove dataclasses & typing because they are included in Python > 3.6
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&& if [ $(python -c 'import sys; print(sys.version_info.minor)') != "6" ]; then \
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$HOME/anaconda3/bin/pip uninstall dataclasses typing -y; fi \
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&& sudo rm \
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requirements.txt \
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requirements_ml_docker.txt \
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requirements_tune.txt \
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requirements_rllib.txt \
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requirements_train.txt \
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requirements_upstream.txt \
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&& sudo apt-get clean
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# Make sure tfp is installed correctly and matches tf version.
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RUN python -c "import tensorflow_probability"
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