buenosairesam
f66d3a273f
- root pyproject.toml replaces requirements.txt and requirements-worker.txt (worker = root + ffmpeg-python which root already had); test deps moved to [dependency-groups] dev - core/gpu/pyproject.toml replaces core/gpu/requirements.txt; uses [tool.uv.sources] to pin torch/torchvision and paddlepaddle-gpu to their CUDA index URLs, replacing the manual reinstall dance from old comments - Dockerfiles use uv sync --frozen against uv.lock for reproducible builds; PATH includes /app/.venv/bin so k8s manifests' bare uvicorn/celery commands resolve without wrapping in uv run - core/gpu/run.sh local mode now does uv sync + uv run python server.py; errors out cleanly if uv is missing
43 lines
1.1 KiB
TOML
43 lines
1.1 KiB
TOML
[project]
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name = "mpr-gpu"
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version = "0.1.0"
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description = "MPR remote inference server (GPU)"
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requires-python = ">=3.11"
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dependencies = [
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"fastapi>=0.109.0",
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"uvicorn[standard]>=0.27.0",
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"rapidfuzz>=3.0.0",
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"Pillow>=10.0.0",
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"redis>=5.0.0",
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"ultralytics>=8.0.0",
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"paddleocr>=3.0.0",
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"paddlepaddle-gpu==3.0.0",
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"transformers>=4.40.0,<5",
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"accelerate>=0.27.0",
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"torch",
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"torchvision",
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"opencv-python-headless>=4.8.0",
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]
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# RTX 3080 / CUDA toolkit 12.8 — cu126 wheels are forward-compatible
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# (no cu128 wheels yet on either index). Mixing PyPI torch with CUDA 12.8
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# causes NCCL symbol errors, so the explicit index pins prevent uv from
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# pulling torch transitively from PyPI via ultralytics.
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[tool.uv.sources]
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torch = { index = "pytorch-cu126" }
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torchvision = { index = "pytorch-cu126" }
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paddlepaddle-gpu = { index = "paddle-cu126" }
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[[tool.uv.index]]
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name = "pytorch-cu126"
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url = "https://download.pytorch.org/whl/cu126"
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explicit = true
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[[tool.uv.index]]
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name = "paddle-cu126"
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url = "https://www.paddlepaddle.org.cn/packages/stable/cu126/"
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explicit = true
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[tool.uv]
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package = false
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