What problem does it solve?
It helps developers avoid common PyTorch training pitfalls by providing reliable patterns for device handling, reproducibility, tensor shape clarity, data loading, checkpointing, and performance optimization.
Core Features & Use Cases
- Device-agnostic training: Moves models and tensors correctly to CPU/GPU without hardcoded
.cuda() calls.
- Reproducible experiments: Centralizes seeding across Python, NumPy, and PyTorch and addresses CuDNN determinism.
- Robust training/validation loops: Uses
model.train(), model.eval(), torch.no_grad(), gradient clipping, and AMP safely.
- Clean architecture and initialization: Encourages well-structured
nn.Module components and explicit weight initialization.
- Efficient input pipelines: Provides dataset patterns, optimized
DataLoader configuration, and custom collate_fn for variable-length data.
- Checkpointing and performance: Saves full training state, resumes reliably, and applies
torch.compile / gradient checkpointing patterns.
Use case example: You are implementing an image classification model and need a training pipeline that is reproducible across runs, avoids GPU memory regressions, validates correctly with BatchNorm/Dropout behavior, and supports resuming from checkpoints after interruptions.
Quick Start
Use the pytorch-patterns skill to generate a device-agnostic, reproducible training loop and matching validation routine for your PyTorch model and DataLoader.
