pytorch

PyTorch deep learning framework with dynamic graphs. Use for neural networks.

7 stars

byG1Joshi

View on GitHub Installation ↓

Best use case

pytorch is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

PyTorch deep learning framework with dynamic graphs. Use for neural networks.

Teams using pytorch should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

You only need a quick one-off answer and do not need a reusable workflow.
You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/pytorch/SKILL.md --create-dirs "https://raw.githubusercontent.com/G1Joshi/Agent-Skills/main/skills/ai-ml/pytorch/SKILL.md"

Manual Installation

Download SKILL.md from GitHub
Place it in .claude/skills/pytorch/SKILL.md inside your project
Restart your AI agent — it will auto-discover the skill

How pytorch Compares

Feature / Agent	pytorch	Standard Approach
Platform Support	Not specified	Limited / Varies
Context Awareness	High	Baseline
Installation Complexity	Unknown	N/A

Frequently Asked Questions

What does this skill do?

PyTorch deep learning framework with dynamic graphs. Use for neural networks.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# PyTorch

PyTorch is the dominant framework for research and production AI. v2.5 (2025) solidifies **`torch.compile`** and introduces **FlexAttention**.

## When to Use

- **Research**: 99% of new papers (Arxiv) use PyTorch.
- **Production**: Recommended for almost all new DL projects.
- **Performance**: `torch.compile` provides C++ level speed with Python ease.

## Core Concepts

### `torch.compile`

Just-in-Time (JIT) compilation of your model.
`model = torch.compile(model)` -> 2x speedup.

### Dynamic Graphs (Eager Mode)

Debug line-by-line (`print(tensor.shape)` works).

### Fabric / Lightning

High-level wrappers to simplify training loops and multi-GPU setup.

## Best Practices (2025)

**Do**:

- **Use `torch.compile`**: It is now stable and essential for H100 performance.
- **Use `FlashAttention`**: Use the scaled dot product attention (SDPA) kernel for Transformers.
- **Use PyTorch 2.x**: PyTorch 1.x is legacy.

**Don't**:

- **Don't code `.cuda()` manually**: Use `.to(device)` or Fabric to handle device placement.

## References

- [PyTorch Documentation](https://pytorch.org/docs/stable/index.html)