Olamide Isreal
5ac0bfc25e
Switch from nvidia/cuda base + manual PyTorch install to pytorch/pytorch:2.6.0-cuda12.4-cudnn9-runtime base image. This avoids the ~15GB build that exceeds Docker disk limits. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
29 lines
846 B
Docker
Executable File
29 lines
846 B
Docker
Executable File
# Use PyTorch base image with CUDA support (much smaller than building from scratch)
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FROM pytorch/pytorch:2.6.0-cuda12.4-cudnn9-runtime
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# Set environment variables
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ENV DEBIAN_FRONTEND=noninteractive \
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PYTHONUNBUFFERED=TRUE \
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PYTHONFAULTHANDLER=1
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# Set working directory
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WORKDIR /workspace
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# Install chai_lab and transformers in a single layer
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RUN pip install --no-cache-dir \
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chai_lab==0.5.2 \
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"transformers>=4.30.0"
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# Verify installations
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RUN python -c "import torch; print(f'PyTorch: {torch.__version__}')" && \
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python -c "from transformers import EsmModel; print('transformers: OK')" && \
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python -c "import chai_lab; print('chai_lab: OK')" && \
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chai --help
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# Add entry point script
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COPY entrypoint.sh /workspace/
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RUN chmod +x /workspace/entrypoint.sh
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# Set entry point
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ENTRYPOINT ["/workspace/entrypoint.sh"]
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