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.