- Release Notes
- Getting started
- Notifications
- Projects
- Datasets
- Data Labeling
- ML packages
- Out of the box packages
- Pipelines
- Managing pipelines
- Closing the loop
- ML Skills
- ML Logs
- Document UnderstandingTM in AI Center
- AI Center API
- Licensing
- AI Solutions Templates
- How to
- Basic Troubleshooting Guide
Managing pipelines
The pipeline is created and displayed along with its details in the Pipelines page and in the selected package's ML Package Details page's Pipeline Runs tab. The pipeline runs according to the timeframe you chose while creating it.
During creation, any pipeline type can be either scheduled at a single future date and time, or with a recurring schedule. For example, a schedule may be set for a pipeline to execute on Sunday nights at 1am. This allows for models to be continuously updated as data is sent back from Human-In-the-Loop tasks and additionally allows for more efficient usage of AI Units licenses.
⏲ To create a pipeline to be scheduled at a single future date, select the Time based option in the Create new pipeline run page.
📅 To create a pipeline with a recurring schedule, select the Recurring option in the Create new pipeline run page.
⚙ A more complex schedule than daily at some time can be set by selecting the Advanced tab in the Set Recurring Schedule window and entering a cron expression. There are many free online tools to easily generate cron expressions.
You can consult more information about a specific pipeline or perform other actions.
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In the Pipelines page, click ⁝ next to a scheduled pipeline and select Details.
- In the ML Package Details of a specific package, select the Pipeline Runs tab, click ⁝ next to a scheduled pipeline and select Details. The Pipeline Details page is displayed.
- Or, if an ML Package is generated by a pipeline, you can access all information related to the pipeline directly from the ML Package window by clicking on the three dots menu ⁝ and Pipeline Details. This is only available for new pipelines.
The information displayed and the actions you can perform here depend on the pipeline status.
The Pipeline Details page for Scheduled pipelines shows an information tab.
You can perform the following actions:
Delete the pipeline.
Edit the pipeline. In the displayed Edit pipeline run page, any fields can be updated. Selecting Run Now does not create a new pipeline, instead, it executes this pipeline immediately, thus removing it from the list of scheduled pipelines.
The Pipeline Details page for Packaging pipelines shows an information tab.
You can perform the following action:
Kill the pipeline. This stop the execution of the pipeline, so it won't run and it won't consume AI Units. Its status changes to Killed.
The Pipeline Details page for Waiting for resources pipelines shows an information tab.
You can perform the following action:
Kill the pipeline. This removes the pipeline from the queue, so it won't be executed and it won't consume AI Units. Its status changes to Killed.
The Pipeline Details page for Running pipelines shows an information tab and real-time logs.
You can perform the following action:
Kill the pipeline. This immediately stops the pipeline and changes its status to Killed. The logs show the snapshot at the time the kill action was executed.
The Pipeline Details page for Failed pipelines shows an information tab and logs. Depending on the stage at which this pipeline failed, (partial) pipeline outputs are displayed.
You can perform the following action:
Restart the pipeline. This adds a new pipeline to the queue, with the exact same parameters with which it was created. If the queue is empty, the pipeline immediately starts executing.
The Pipeline Details page for Killed pipelines shows an information tab. In addition, depending on the point at which this pipeline was killed, the pipeline details page may also include logs.
You can perform the following actions:
Restart the pipeline.
Remove the pipeline.
You can download a report of the pipeline run from the Logs section. To do so, click on the Download Pipeline Report button.
We recommend attaching this report when submitting an issue for faster troubleshooting.
This report gathers all the necessary information required to debug an issue, including account ID, tenant ID, AI Units, and the respective package and pipeline information.
Check the screenshot below for pipeline run report example.