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42 lines
2.0 KiB
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42 lines
2.0 KiB
Markdown
---
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title: Scheduling and Triggering Task Execution
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---
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In ClearML, tasks can be scheduled and triggered automatically, enabling seamless workflow automation. This section
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provides an overview of the mechanisms available for managing task scheduling and event-based
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triggering.
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## Task Scheduling
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Task scheduling allows users to define one-shot or periodic executions at specified times and intervals. This
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is useful for:
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* Running routine operations such as periodic model training, evaluation jobs, backups, and reports.
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* Automating data ingestion and preprocessing workflows.
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* Ensuring regular execution of monitoring and reporting tasks.
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ClearML's offers the following scheduling solutions:
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* [**UI Application**](../webapp/applications/apps_task_scheduler.md) (available under the Enterprise Plan) - The **Task Scheduler** app
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provides a simple no-code interface for managing task schedules.
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* [**Python Interface**](../references/sdk/scheduler.md) - Use the `TaskScheduler` class to programmatically manage
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task schedules.
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## Task Execution Triggering
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ClearML's trigger manager enables you to automate task execution based on event occurence in the ClearML system, such as:
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* Changes in task status (e.g. running, completed, etc.)
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* Publication, archiving, or tagging of tasks, models, or datasets
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* Task metrics crossing predefined thresholds
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This is useful for:
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* Triggering a training task when a dataset has been tagged as `latest` or any other tag
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* Running an inference task when a model has been published
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* Retraining a model when accuracy falls below a certain threshold
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* And more
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ClearML's offers the following trigger management solutions:
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* [**UI Application**](../webapp/applications/apps_trigger_manager.md) (available under the Enterprise Plan) - The **Trigger Manager** app
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provides a simple no-code interface for managing task triggers .
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* [**Python Interface**](../references/sdk/trigger.md) - Use the `TriggerScheduler` class to programmatically manage
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task triggers.
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