Recovering Training Progress in Catboost

CatBoost provides mechanisms to recover training progress in case of interruptions, ensuring that the training process can be resumed without starting from scratch.

1. Recovery from Interruptions: CatBoost offers functionalities to resume training in case of unexpected interruptions (e.g., power outages, system crashes).

• Snapshotting: CatBoost periodically saves the current training state as snapshots. These snapshots contain intermediate model information, allowing for restarting training from the last saved point.
• Recovering Training: If training is interrupted, you can specify the snapshot file path to resume training from that point instead of starting from scratch.

2. Snapshot Parameters: CatBoost provides several parameters to control the behavior of snapshots:

• snapshot_interval: (integer, default=0) The frequency in seconds at which snapshots are saved. A value of 0 disables automatic snapshots.
• task_type: (str, default=’GPU’) The computational task type. CatBoost offers CPU- and GPU-based training options, and snapshotting behavior might differ depending on the chosen type.
• Verbose Logging (verbose): (int, default=0) Controls the verbosity of logging output. Set it to 1 or higher to view information about snapshot creation.
• save_snapshot: This parameter enables snapshotting, allowing the training progress to be saved periodically.

To recover training from a snapshot, the same training parameters must be used. CatBoost will detect the snapshot file and resume training from the last saved state. This feature is particularly useful in scenarios where training is interrupted due to time constraints or system failures.

CatBoost means categorical boosting. It is a powerful open-source machine learning library known for its efficiency, accuracy, and ability to handle various data types. It excels in gradient boosting algorithms, making it suitable for classification, regression, and ranking tasks. This guide delves into the key concepts of CatBoost training, recovery from interruptions, and snapshot parameters for smooth training workflows.