Individual skill
pytorch-pro
PyTorch guidance — tensors, autograd, training loops, DataLoader, distributed training, and production inference.
skills/pytorch-pro/SKILL.md
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## Overview
PyTorch is the dominant framework for deep learning research and increasingly for production: dynamic computation graphs, Pythonic APIs, and an ecosystem (torchvision, torchaudio, Hugging Face, Lightning) that covers most use cases. Its imperative style makes debugging intuitive — you can print tensors mid-forward-pass like any Python code.
This skill covers the PyTorch fundamentals that transfer everywhere: tensors and autograd, correct training loops, efficient data loading, distributed training, and the path to production inference (TorchScript, ONNX, TensorRT, torch.compile).
## When to use
- Building or debugging PyTorch models.
- Writing correct training loops (train/eval modes, gradient handling).
- Optimizing data loading (DataLoader bottlenecks).
- Training on multiple GPUs (DDP).
- Exporting models for production inference.
- Choosing between PyTorch, TensorFlow, and JAX.