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. 2. Reproducibility First Set all random seeds for reproducible results. 3. Explicit Shape Management A…