What are the best practices for checkpointing in machine learning?

What are the best practices for checkpointing in machine learning?

Checkpointing is crucial for saving model states during training. Key practices include saving at regular intervals, using validation metrics to determine when to save, and ensuring that checkpoints are stored in a reliable location.

How often should I create checkpoints?

It's recommended to create checkpoints after a set number of epochs or iterations, depending on your training duration. For long training sessions, saving every few epochs can help recover from interruptions.

What should I include in a checkpoint?

A checkpoint should include the model weights, optimizer state, and any relevant training parameters. This ensures you can resume training seamlessly.

Are there any tools that can help with checkpointing?

Yes, many frameworks like TensorFlow and PyTorch have built-in support for checkpointing. They provide functions to save and load model states easily.

What happens if I forget to checkpoint?

If you forget to checkpoint, you risk losing all progress if the training process is interrupted. It's essential to implement a checkpointing strategy to avoid this.