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| # (Bring in everything you listed, with safer caps) | |
| # PyTorch stack (match minor versions!) | |
| torch>=2.2,<2.4 | |
| torchvision>=0.17,<0.19 | |
| torchaudio>=2.2,<2.4 | |
| # Transformers & NLP | |
| transformers>=4.41,<4.46 | |
| datasets>=2.19,<2.21 | |
| accelerate>=0.28,<0.31 | |
| peft>=0.11,<0.13 | |
| tokenizers>=0.15 # usually pulled by transformers, but explicit helps | |
| safetensors>=0.4.3 | |
| huggingface_hub>=0.24,<0.26 | |
| # If you ever use T5/ALBERT etc., add: | |
| # sentencepiece>=0.1.99 | |
| # Vision models | |
| timm>=0.9.7,<1.0 | |
| opencv-python-headless>=4.8 | |
| Pillow>=10.0,<11 | |
| # Optimization / tracking | |
| optuna>=3.5,<4 | |
| wandb>=0.16.6,<0.18 | |
| # Eval / viz | |
| scikit-learn>=1.3,<1.6 | |
| matplotlib>=3.8,<3.9 | |
| seaborn>=0.13,<0.14 | |
| tqdm>=4.66,<5 | |
| # Data processing | |
| pandas>=2.2,<2.3 | |
| numpy>=1.26,<2.2 | |
| pyyaml>=6.0 | |
| scipy>=1.11,<1.14 # sklearn relies on it; make it explicit to avoid surprises | |
| # Optional deployment (FastAPI) | |
| fastapi>=0.110,<0.114 | |
| pydantic>=2.5,<3 | |
| uvicorn>=0.27,<0.31 | |
| python-multipart>=0.0.6 | |
| # Optional perf: | |
| json>=3.9 | |
| # Linting & testing | |
| pytest>=7.4,<9 | |
| pytest-cov>=4.1,<5 | |
| pre-commit>=3.5,<4 | |
| flake8>=6.1,<7 | |
| # Optional modern linter: | |
| ruff>=0.4,<0.7 | |