pytorch-patterns
PyTorch deep learning patterns and best practices for building robust, efficient, and reproducible training pipelines, model architectures, and data loading. Use when writing or reviewing PyTorch training loops, model architectures, or data loading, or when a run will not reproduce.
- Kind
- Skill
- Works with
- Claude Code, Codex
- Languages
- Python
- Source
- affaan-m/ECC @ d3b8a3e90890
- Path
- skills/pytorch-patterns/SKILL.md
- License
- MIT
- Jobs
- Automation, Review, Documentation
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# PyTorch Development Patterns
Idiomatic PyTorch patterns and best practices for building robust, efficient, and reproducible deep learning applications.
## When to Activate
- Writing new PyTorch models or training scripts
- Reviewing deep learning code
- Debugging training loops or data pipelines
- Optimizing GPU memory usage or training speed
- Setting up reproducible experiments
## Core Principles
### 1. Device-Agnostic Code
Always write code that works on both CPU and GPU without hardcoding devices.
```python
# Good: Device-agnostic
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = MyModel().to(device)
data = data.to(device)
# Bad: Hardcoded device
model = MyModel().cuda() # Crashes if no GPU
data = data.cuda()
```
### 2. Reproducibility First
Set all random seeds for reproducible results.
```python
# Good: Full reproducibility setup
def set_seed(seed: int = 42) -> None:
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
random.seed(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
# Bad: No seed control
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