clearml-docs/docs/getting_started/task_trigger_schedule.md

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Scheduling and Triggering Task Execution

In ClearML, tasks can be scheduled and triggered automatically, enabling seamless workflow automation. This section provides an overview of the mechanisms available for managing task scheduling and event-based triggering.

Task Scheduling

Task scheduling allows users to define one-shot or periodic executions at specified times and intervals. This is useful for:

• Running routine operations such as periodic model training, evaluation jobs, backups, and reports.
• Automating data ingestion and preprocessing workflows.
• Ensuring regular execution of monitoring and reporting tasks.

ClearML's offers the following scheduling solutions:

• UI Application (available under the Enterprise Plan) - The Task Scheduler app provides a simple no-code interface for managing task schedules.

• Python Interface - Use the TaskScheduler class to programmatically manage task schedules.

Task Execution Triggering

ClearML's trigger manager enables you to automate task execution based on event occurence in the ClearML system, such as:

• Changes in task status (e.g. running, completed, etc.)
• Publication, archiving, or tagging of tasks, models, or datasets
• Task metrics crossing predefined thresholds

This is useful for:

• Triggering a training task when a dataset has been tagged as latest or any other tag
• Running an inference task when a model has been published
• Retraining a model when accuracy falls below a certain threshold
• And more

ClearML's offers the following trigger management solutions:

• UI Application (available under the Enterprise Plan) - The Trigger Manager app provides a simple no-code interface for managing task triggers .
• Python Interface - Use the TriggerScheduler class to programmatically manage task triggers.