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).