What is Model Checkpointing?

Success metrics include system uptime, model performance stability, deployment velocity, and operational cost efficiency.

Checkpoint every epoch for training runs under 24 hours. For multi-day training runs, checkpoint every 30-60 minutes or every N steps. Balance checkpoint frequency against storage costs and I/O overhead, as each checkpoint for large models can be 1-10GB. Keep the last 3-5 checkpoints and delete older ones unless they represent significant performance improvements. For expensive training runs costing hundreds or thousands of dollars, frequent checkpointing is insurance against lost progress from hardware failures.

Save optimizer state to enable true training resumption without warm-up degradation. Save the learning rate scheduler state, current epoch and step count, random number generator states for reproducibility, and the best validation metric achieved so far. Include a metadata file with training configuration and environment details. Without optimizer state, resuming from a checkpoint effectively restarts optimization, losing momentum information that took significant compute to build.

Load the checkpoint including model weights, optimizer state, and scheduler state. Verify the loaded state produces the same validation metrics as the original checkpoint to confirm correctness. Resume the data loader from the correct position using saved epoch and step counters. Set random seeds from the saved state for reproducibility. Test checkpoint resume on a short training run before relying on it for expensive jobs. Common failures include mismatched model architecture and incompatible optimizer state dimensions.

Checkpoint every epoch for training runs under 24 hours. For multi-day training runs, checkpoint every 30-60 minutes or every N steps. Balance checkpoint frequency against storage costs and I/O overhead, as each checkpoint for large models can be 1-10GB. Keep the last 3-5 checkpoints and delete older ones unless they represent significant performance improvements. For expensive training runs costing hundreds or thousands of dollars, frequent checkpointing is insurance against lost progress from hardware failures.

Save optimizer state to enable true training resumption without warm-up degradation. Save the learning rate scheduler state, current epoch and step count, random number generator states for reproducibility, and the best validation metric achieved so far. Include a metadata file with training configuration and environment details. Without optimizer state, resuming from a checkpoint effectively restarts optimization, losing momentum information that took significant compute to build.

Load the checkpoint including model weights, optimizer state, and scheduler state. Verify the loaded state produces the same validation metrics as the original checkpoint to confirm correctness. Resume the data loader from the correct position using saved epoch and step counters. Set random seeds from the saved state for reproducibility. Test checkpoint resume on a short training run before relying on it for expensive jobs. Common failures include mismatched model architecture and incompatible optimizer state dimensions.

Checkpoint every epoch for training runs under 24 hours. For multi-day training runs, checkpoint every 30-60 minutes or every N steps. Balance checkpoint frequency against storage costs and I/O overhead, as each checkpoint for large models can be 1-10GB. Keep the last 3-5 checkpoints and delete older ones unless they represent significant performance improvements. For expensive training runs costing hundreds or thousands of dollars, frequent checkpointing is insurance against lost progress from hardware failures.

Save optimizer state to enable true training resumption without warm-up degradation. Save the learning rate scheduler state, current epoch and step count, random number generator states for reproducibility, and the best validation metric achieved so far. Include a metadata file with training configuration and environment details. Without optimizer state, resuming from a checkpoint effectively restarts optimization, losing momentum information that took significant compute to build.

Load the checkpoint including model weights, optimizer state, and scheduler state. Verify the loaded state produces the same validation metrics as the original checkpoint to confirm correctness. Resume the data loader from the correct position using saved epoch and step counters. Set random seeds from the saved state for reproducibility. Test checkpoint resume on a short training run before relying on it for expensive jobs. Common failures include mismatched model architecture and incompatible optimizer state dimensions.