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| # Training image for the Otaku expert LoRA, run as a Cloud Run Job on an L4. | |
| # | |
| # Why Cloud Run and not Vertex AI: the project has ZERO Vertex custom-training | |
| # GPU quota in every region (every quota bucket comes back empty), and a quota | |
| # increase is a multi-day review. Cloud Run GPU quota, on the other hand, is | |
| # already proven -- animetix-brain runs on an nvidia-l4 there today. | |
| # | |
| # Built from a minimal context (this file + the script), not the repo root: the | |
| # 674 MB repo context makes `gcloud builds submit` die on Windows with a | |
| # temp-file lock (WinError 32), and none of it is needed here anyway. | |
| # Pinned by digest (tag 2.5.1-cuda12.1-cudnn9-runtime, amd64, fetched | |
| # 2026-07-19): a mutable tag rebuilt upstream would silently change the env. | |
| FROM pytorch/pytorch:2.5.1-cuda12.1-cudnn9-runtime@sha256:831247999fbf7e08f61b3e39f6d77ee434f38f6f07f769d00db451e853878067 | |
| ENV PYTHONUNBUFFERED=1 \ | |
| HF_XET_HIGH_PERFORMANCE=1 \ | |
| HF_HOME=/tmp/hf | |
| # trl/peft/transformers/accelerate/datasets/bitsandbytes are kept in lockstep | |
| # with requirements.txt (the serving env) — enforced by | |
| # tests/deploy/test_dockerfile_train.py; bump both together. | |
| # trackio/hf_transfer are training-only (absent from the locks). | |
| RUN pip install --no-cache-dir \ | |
| "trl==0.12.1" \ | |
| "peft==0.17.1" \ | |
| "transformers==4.57.6" \ | |
| "accelerate==1.13.0" \ | |
| "datasets==5.0.0" \ | |
| "trackio==0.31.5" \ | |
| "bitsandbytes==0.49.2" \ | |
| "hf_transfer==0.1.9" | |
| WORKDIR /app | |
| COPY hf_train_qwen35.py /app/train.py | |
| CMD ["python", "/app/train.py"] | |