pyr0ball
54de37e5fa
- Dockerfile.finetune: PyTorch 2.3/CUDA 12.1 base + unsloth + training stack - finetune_local.py: auto-register model via Ollama HTTP API after GGUF export; path-translate between finetune container mount and Ollama's view; update config/llm.yaml automatically; DOCS_DIR env override for Docker - prepare_training_data.py: DOCS_DIR env override so make prepare-training works correctly inside the app container - compose.yml: add finetune service (cpu/single-gpu/dual-gpu profiles); DOCS_DIR=/docs injected into app + finetune containers - compose.podman-gpu.yml: CDI device override for finetune service - Makefile: make prepare-training + make finetune targets
38 lines
1.6 KiB
Text
38 lines
1.6 KiB
Text
# Dockerfile.finetune — Cover letter LoRA fine-tuner (QLoRA via unsloth)
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# Large image (~12-15 GB after build). Built once, cached on rebuilds.
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# GPU strongly recommended. CPU fallback works but training is very slow.
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#
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# Tested base: pytorch/pytorch:2.3.0-cuda12.1-cudnn8-runtime
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# If your GPU requires a different CUDA version, change the FROM line and
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# reinstall bitsandbytes for the matching CUDA (e.g. bitsandbytes-cuda121).
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FROM pytorch/pytorch:2.3.0-cuda12.1-cudnn8-runtime
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WORKDIR /app
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# Build tools needed by bitsandbytes CUDA kernels and unsloth
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RUN apt-get update && apt-get install -y --no-install-recommends \
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gcc g++ git libgomp1 \
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&& rm -rf /var/lib/apt/lists/*
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# Install training stack.
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# unsloth detects CUDA version automatically from the base image.
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RUN pip install --no-cache-dir \
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"unsloth @ git+https://github.com/unslothai/unsloth.git" \
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"datasets>=2.18" "trl>=0.8" peft transformers \
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"bitsandbytes>=0.43.0" accelerate sentencepiece \
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requests pyyaml
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COPY scripts/ /app/scripts/
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COPY config/ /app/config/
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ENV PYTHONUNBUFFERED=1
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# Pin to GPU 0; overridable at runtime with --env CUDA_VISIBLE_DEVICES=
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ENV CUDA_VISIBLE_DEVICES=0
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# Runtime env vars injected by compose.yml:
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# OLLAMA_URL — Ollama API base (default: http://ollama:11434)
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# OLLAMA_MODELS_MOUNT — finetune container's mount path for ollama models volume
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# OLLAMA_MODELS_OLLAMA_PATH — Ollama container's mount path for same volume
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# DOCS_DIR — cover letters + training data root (default: /docs)
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ENTRYPOINT ["python", "scripts/finetune_local.py"]
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