jcorpac ML Training Abstractions
Writing training loops from scratch is error-prone. Abstractions allow you to focus on the model architecture and data.
PyTorch Lightning
- The LightningModule: Decouple the system (architecture, optimization) from the data.
- The Trainer: Automate loops, checkpointing, logging, and GPU/TPU distribution.
- Callbacks: Add custom logic (e.g., Early Stopping) without polluting the main loop.
Keras
- Sequential vs Functional API: Choosing the right level of complexity.
- Model.fit(): The power of a single line for training.
- Custom Layers: Building complex logic while maintaining the high-level API.
Best Practices
- Consistency: Use these frameworks to ensure reproducible research and production code.
- Monitoring: Always integrate with a logger (e.g., TensorBoard, MLflow).