mirror of
https://github.com/hiyouga/LLaMA-Factory.git
synced 2025-08-04 12:42:51 +08:00
48 lines
1.4 KiB
Docker
48 lines
1.4 KiB
Docker
# Use the NVIDIA official image with PyTorch 2.3.0
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# https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/rel-24-02.html
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FROM nvcr.io/nvidia/pytorch:24.02-py3
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# Define installation arguments
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ARG INSTALL_BNB=false
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ARG INSTALL_VLLM=false
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ARG INSTALL_DEEPSPEED=false
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ARG PIP_INDEX=https://pypi.org/simple
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# Set the working directory
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WORKDIR /app
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# Install the requirements
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COPY requirements.txt /app
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RUN pip config set global.index-url $PIP_INDEX
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RUN pip config set global.extra-index-url $PIP_INDEX
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RUN python -m pip install --upgrade pip
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RUN python -m pip install -r requirements.txt
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# Copy the rest of the application into the image
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COPY . /app
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# Install the LLaMA Factory
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RUN EXTRA_PACKAGES="metrics"; \
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if [ "$INSTALL_BNB" = "true" ]; then \
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EXTRA_PACKAGES="${EXTRA_PACKAGES},bitsandbytes"; \
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fi; \
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if [ "$INSTALL_VLLM" = "true" ]; then \
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EXTRA_PACKAGES="${EXTRA_PACKAGES},vllm"; \
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fi; \
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if [ "$INSTALL_DEEPSPEED" = "true" ]; then \
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EXTRA_PACKAGES="${EXTRA_PACKAGES},deepspeed"; \
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fi; \
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pip install -e .[$EXTRA_PACKAGES] && \
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pip uninstall -y transformer-engine flash-attn
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# Set up volumes
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VOLUME [ "/root/.cache/huggingface", "/root/.cache/modelscope", "/app/data", "/app/output" ]
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# Expose port 7860 for the LLaMA Board
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ENV GRADIO_SERVER_PORT 7860
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EXPOSE 7860
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# Expose port 8000 for the API service
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ENV API_PORT 8000
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EXPOSE 8000
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