f1ab11f89d
Dockerfile.train builds a CUDA 12.4 + unsloth container that consumes the Trainline JSONL datasets and emits a LoRA adapter at data/adapters/<run>/final. Defaults target a 24 GB GPU (Qwen3.5-4B-Instruct-bnb-4bit, r=16, bf16, 3 epochs, effective batch 8). README documents the build + run workflow. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
50 lines
1.6 KiB
Docker
50 lines
1.6 KiB
Docker
# psyc training container — unsloth + Qwen3.5 QLoRA fine-tuning.
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#
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# Build:
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# docker build -t psyc-trainer -f Dockerfile.train .
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#
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# Run (24 GB GPU, mounts host data/ for datasets + adapter output):
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# docker run --gpus all --rm \
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# -v $(pwd)/data:/data \
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# psyc-trainer \
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# --dataset /data/datasets/ioc_extraction-v1.jsonl \
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# --dataset /data/datasets/severity_classification-v1.jsonl \
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# --dataset /data/datasets/routing_decision-v1.jsonl \
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# --dataset /data/datasets/tlp_assignment-v1.jsonl \
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# --output /data/adapters/psyc-v1
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FROM nvidia/cuda:12.4.1-cudnn-devel-ubuntu22.04
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ENV DEBIAN_FRONTEND=noninteractive \
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PYTHONUNBUFFERED=1 \
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PIP_NO_CACHE_DIR=1 \
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HF_HOME=/data/.hf-cache
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RUN apt-get update && apt-get install -y --no-install-recommends \
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python3.11 python3.11-venv python3-pip \
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git curl ca-certificates \
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&& rm -rf /var/lib/apt/lists/* \
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&& ln -sf /usr/bin/python3.11 /usr/local/bin/python \
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&& ln -sf /usr/bin/python3.11 /usr/local/bin/python3
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RUN python -m pip install --upgrade pip wheel setuptools && \
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python -m pip install \
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torch==2.5.1 \
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--index-url https://download.pytorch.org/whl/cu124
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RUN python -m pip install \
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"unsloth @ git+https://github.com/unslothai/unsloth.git" \
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transformers>=4.46 \
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datasets>=3.0 \
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peft>=0.13 \
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trl>=0.12 \
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accelerate>=1.1 \
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bitsandbytes>=0.44 \
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sentencepiece \
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protobuf
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WORKDIR /workspace
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COPY scripts/train_qlora.py /workspace/train_qlora.py
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ENTRYPOINT ["python", "/workspace/train_qlora.py"]
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