46 lines
1.5 KiB
Docker
46 lines
1.5 KiB
Docker
# Builds GPU docker image of PyTorch specifically
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# Uses multi-staged approach to reduce size
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# Stage 1
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# Use base conda image to reduce time
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FROM continuumio/miniconda3:latest AS compile-image
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# Specify py version
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# Note: DeepSpeed beyond v0.12.6 requires py 3.10
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ENV PYTHON_VERSION=3.10
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# Install apt libs
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RUN apt-get update && \
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apt-get install -y curl git wget && \
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apt-get clean && \
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rm -rf /var/lib/apt/lists*
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# Create our conda env
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RUN conda create --name accelerate python=${PYTHON_VERSION} ipython jupyter pip
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# We don't install pytorch here yet since CUDA isn't available
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# instead we use the direct torch wheel
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ENV PATH /opt/conda/envs/accelerate/bin:$PATH
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# Activate our bash shell
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RUN chsh -s /bin/bash
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SHELL ["/bin/bash", "-c"]
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# Activate the conda env, install mpy4pi, and install torch + accelerate
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RUN source activate accelerate && conda install -c conda-forge mpi4py
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RUN source activate accelerate && \
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python3 -m pip install --no-cache-dir \
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git+https://github.com/huggingface/accelerate#egg=accelerate[testing,test_trackers,deepspeed] \
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--extra-index-url https://download.pytorch.org/whl/cu117
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RUN python3 -m pip install --no-cache-dir bitsandbytes
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# Stage 2
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FROM nvidia/cuda:12.1.0-cudnn8-devel-ubuntu20.04 AS build-image
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COPY --from=compile-image /opt/conda /opt/conda
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ENV PATH /opt/conda/bin:$PATH
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# Install apt libs
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RUN apt-get update && \
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apt-get install -y curl git wget && \
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apt-get clean && \
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rm -rf /var/lib/apt/lists*
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RUN echo "source activate accelerate" >> ~/.profile
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# Activate the virtualenv
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CMD ["/bin/bash"] |