mirror of
https://github.com/OpenBMB/VoxCPM.git
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da5b2da097
- nginx: return 200 OK on GET / for load balancer health checks - Dockerfile: set HF_HOME=/app/models so Hub downloads persist in mounted volume - Dockerfile: add /app/data directory and volume declaration - docker-compose: explicit volume mounts for models, data, lora, output - README: document where to put training files and find output - .gitignore: exclude volume mount directories (models/, data/, lora/, output/)
53 lines
2.3 KiB
Docker
53 lines
2.3 KiB
Docker
# ─────────────────────────────────────────────────────────────────────
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# VoxCPM Training WebUI — Docker image
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# ─────────────────────────────────────────────────────────────────────
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# Base: PyTorch with CUDA for GPU-accelerated LoRA fine-tuning.
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# Build context should be the project root:
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#
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# docker build -f docker/Dockerfile -t voxcpm-training .
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#
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# ─────────────────────────────────────────────────────────────────────
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FROM pytorch/pytorch:2.5.1-cuda12.4-cudnn9-devel
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LABEL maintainer="OpenBMB <openbmb@gmail.com>"
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LABEL description="VoxCPM LoRA Training WebUI with GPU support"
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# Avoid interactive prompts during package installation
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ENV DEBIAN_FRONTEND=noninteractive
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# System deps required by Python packages:
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# git — setuptools_scm needs it to resolve version in pyproject.toml
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# libsndfile1 — C library backing the 'soundfile' Python package
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# ffmpeg — audio codec support for torchaudio/librosa
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RUN apt-get update && apt-get install -y --no-install-recommends \
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git \
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libsndfile1 \
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ffmpeg \
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&& rm -rf /var/lib/apt/lists/*
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WORKDIR /app
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# Layer 1: Install dependencies only (cached unless pyproject.toml changes)
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# Create a minimal package stub so pip can resolve deps without real source.
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COPY pyproject.toml /app/
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RUN mkdir -p /app/src/voxcpm && echo '__version__ = "0.0.0"' > /app/src/voxcpm/__init__.py
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ENV SETUPTOOLS_SCM_PRETEND_VERSION=0.0.0
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RUN pip install --no-cache-dir -e .
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# Layer 2: Copy full project source (cheap rebuild on code changes)
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COPY . /app/
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# Create default directories and declare volumes
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RUN mkdir -p /app/lora /app/models /app/output /app/data
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VOLUME ["/app/models", "/app/lora", "/app/output", "/app/data"]
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EXPOSE 7860
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# Environment variables for configuration
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ENV GRADIO_SERVER_PORT=7860
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ENV GRADIO_ROOT_PATH=""
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ENV HF_HOME=/app/models
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# Default: launch training WebUI
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CMD ["python", "lora_ft_webui.py"]
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