Learn PYTORCH with Real Code Examples

Use GPUs for large models

Leverage mixed precision for speed and memory efficiency

Use DataLoader with proper batch size

Profile training loop to identify bottlenecks

Optimize memory usage with in-place operations where possible

Validate inputs for deployed models

Secure access to saved models

Use encryption for sensitive datasets

Monitor model APIs for misuse

Version models to prevent rollback or corruption

TensorBoard integration

GPU/CPU profiling

Logging metrics during training

Track experiments with MLFlow or Weights & Biases

Monitor loss, accuracy, and other metrics

Use modular nn.Module classes

Document layer architecture and hyperparameters

Profile training loops for performance

Separate data, model, and utility code

Follow Python coding standards