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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
TaskSchedulerclass 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
latestor 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
TriggerSchedulerclass to programmatically manage task triggers.