48 lines
1.5 KiB
Docker
48 lines
1.5 KiB
Docker
FROM python:3.11-slim
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# Build argument: 'cpu' for Mac/CPU-only environments, 'gpu' for CUDA/NVIDIA GPU support
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ARG DEVICE=gpu
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# Install system dependencies needed for OpenCV, PyTorch, and Ultralytics
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RUN apt-get update && apt-get install -y --no-install-recommends \
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build-essential \
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libgl1 \
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libglib2.0-0 \
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libgomp1 \
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git \
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&& rm -rf /var/lib/apt/lists/*
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# Install uv for fast dependency resolution using pip (avoids ghcr.io network issues)
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RUN pip install --no-cache-dir uv
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# Set working directory
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WORKDIR /workspace
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# Copy dependency definition
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COPY pyproject.toml ./
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# Install dependencies using uv pip in system python to bypass uv.lock file hashes
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# and fetch the correct PyTorch package based on the target DEVICE (CPU or GPU)
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RUN --mount=type=cache,target=/root/.cache/uv \
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if [ "$DEVICE" = "cpu" ]; then \
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echo "Installing CPU-only PyTorch..." && \
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uv pip install --system --extra-index-url https://download.pytorch.org/whl/cpu -r pyproject.toml; \
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else \
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echo "Installing GPU (CUDA) PyTorch..." && \
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uv pip install --system -r pyproject.toml; \
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fi
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# Copy source code and files
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COPY src ./src
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COPY README.md ./
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# Install the project itself without re-installing dependencies
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RUN --mount=type=cache,target=/root/.cache/uv \
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uv pip install --system --no-deps -e .
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# Expose Web UI port and MLflow port
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EXPOSE 8000
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EXPOSE 5000
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# Start Web UI using the system entry point
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CMD ["yolo-train-webui", "--host", "0.0.0.0", "--port", "8000"]
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