# Pytorch

> Provides guidance on using PyTorch for deep learning, covering tensors, autograd, nn.Module, DataLoaders, and best practices.

- Skill: `neuralblitz/pytorch` (Agent Skill)
- Install (CLI): `npx skillmds@latest add neuralblitz/pytorch`
- Raw SKILL.md: https://api.skillmd.com/api/skills/neuralblitz/pytorch/raw
- Safety review: PASS (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML, Model Training & Fine-tuning
- Tags: Autograd, Dataloader, Deep Learning, Gpu Acceleration, Mixed Precision, Nn Module, Pytorch, Tensors
- Author: NeuralBlitz (https://skillmd.com/u/neuralblitz)
- Updated: 2026-08-22
- Page: https://skillmd.com/skills/neuralblitz/pytorch

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# PyTorch

PyTorch is an open-source machine learning framework based on the Torch library. It provides dynamic computational graphs and GPU acceleration, making it popular for research and production ML.

## Key Concepts

- Tensors and autograd
- Dynamic computational graphs
- nn.Module architecture
- DataLoaders and Datasets
- TorchScript for deployment

## Common Use Cases

- Deep learning research
- Computer vision
- Natural language processing
- Reinforcement learning
- Production inference

## Best Practices

- Use DataLoaders for batching
- Leverage GPU acceleration
- Use mixed precision (FP16)
- Implement proper logging
- Use PyTorch Lightning

## Resources

- PyTorch.org: pytorch.org/docs
- Related Skills: machine-learning, deep-learning, tensorboard