Manage Experiment¶

In the AI Pipelines, the machine learning pipelines are designed, orchestrated, produced, published, and scheduled to run in the unit of experiment to achieve the automatic iterative publish of the machine learning models.

Create Experiments¶

You can create an experiment by the following steps:

Log in to the EnOS Management Console, and select Data Analytics > AI Studio > AI Pipelines from the left navigation bar to open the Experiment List homepage.
Under the Custom Pipeline tab, select New Experiment and enter the name and description of the experiment.
Select OK to create the experiment and enter the page for designing and developing the pipeline.
Return to the Experiment List page to view the basic information of the new experiment and all the experiments created in the organization.

Browse Experiment List¶

In the experiment list, you can view the following basic information about the experiment:

Experiment scheduling status
- Unscheduled: indicates that the pipeline has not been published for production, or the job has been published for production but the scheduling of the current production version has stopped.
- Scheduled: indicates that the pipeline has been published for production (and the schedule has been set).
Status of the last five runs: displays the status of the last five running pipelines that have been published for production (and the schedule has been set), where there are three types of status: success, failure and running.
Production: Time when the latest version of the pipeline was published for online production.
Designing: Time when the latest version of the pipeline was modified on the designing page.

You can also:

Select the Delete icon at the top right corner of the experiment to delete the experiment.
Select Pipeline Designer to open the canvas for designing and developing the pipeline. For detailed steps, see Develop Pipelines.
Select Run Instances to open the Running Instances page to compare and archive the running pipeline instances. For detailed steps, see Manage Production Instances.
Select Scheduling Configuration to publish the experiment for production, view or stop the experiment scheduling or delete the experiment. For more information, see Configure Schedules for Pipelines.

View Sample Pipelines¶

Compared with customized pipeline, sample pipelines:

Cannot be deleted and has the unique version IDs.
Cannot be edited. You can select Pipeline View to view the details of sample pipelines.

You can select Run and enter the global parameters and advanced parameters to run sample pipelines, or create a new schedule to run the pipelines.

View Schedule Operation¶

Schedule Operation provides unified query and management for all schedules of pipelines. You can manage the schedules by performing the following tasks:

Search: search the target pipeline schedule.
Sort: sort the schedules on the schedule list based on different list fields.
Delete: delete one or more pipeline schedules.
View: view the parameter details and other advanced configuration status when pipelines are running.
Copy: specify the Version and the Concurrency to create a new schedule based on a current schedule.
End: terminate a schedule with the Scheduling status.
Start: enable a schedule with the Stopped status.