Chapter 2. Managing pipeline experiments 2.1. Overview of pipeline experiments A pipeline experiment is a workspace where you can try different configurations of your pipelines. You can use experiments to organize your runs into logical groups. As a data scientist, you can use OpenShift AI to define, manage, and track pipeline experiments. You can view a record of previously created and archived experiments from the Experiments page in the OpenShift AI user interface. Pipeline experiments contain pipeline runs, including recurring runs. This allows you to try different configurations of your pipelines. When you work with data science pipelines, it is important to monitor and record your pipeline experiments to track the performance of your data science pipelines. You can compare the results of up to 10 pipeline runs at one time, and view available parameter, scalar metric, confusion matrix, and receiver operating characteristic (ROC) curve data for all selected runs. You can view artifacts for an executed pipeline run from the OpenShift AI dashboard. Pipeline artifacts can help you to evaluate the performance of your pipeline runs and make it easier to understand your pipeline components. Pipeline artifacts can range from plain text data to detailed, interactive data visualizations. 2.2. Creating a pipeline experiment Pipeline experiments are workspaces where you can try different configurations of your pipelines. You can also use experiments to organize your pipeline runs into logical groups. Pipeline experiments contain pipeline runs, including recurring runs. Prerequisites You have logged in to Red Hat OpenShift AI. If you are using OpenShift AI groups, you are part of the user group or admin group (for example, rhoai-users or rhoai-admins ) in OpenShift. You have previously created a data science project that is available and contains a configured pipeline server. You have imported a pipeline to an active pipeline server. Procedure From the OpenShift AI dashboard, click Experiments Experiments and runs . On the Experiments page, from the Project drop-down list, select the project to create the pipeline experiment in. Click Create experiment . In the Create experiment dialog, configure the pipeline experiment: In the Experiment name field, enter a name for the pipeline experiment. In the Description field, enter a description for the pipeline experiment. Click Create experiment . Verification The pipeline experiment that you created appears on the Experiments tab. 2.3. Archiving a pipeline experiment You can retain records of your pipeline experiments by archiving them. If required, you can restore pipeline experiments from your archive to reuse, or delete pipeline experiments that are no longer required. Prerequisites You have logged in to Red Hat OpenShift AI. If you are using OpenShift AI groups, you are part of the user group or admin group (for example, rhoai-users or rhoai-admins ) in OpenShift. You have previously created a data science project that is available and has a pipeline server. You have imported a pipeline to an active pipeline server. A pipeline experiment is available to archive. Procedure From the OpenShift AI dashboard, click Experiments Experiments and runs . On the Experiments page, from the Project drop-down list, select the project that contains the pipeline experiment that you want to archive. Click the action menu ( ... ) beside the pipeline experiment that you want to archive, and then click Archive . In the Archiving experiment dialog, enter the pipeline experiment name in the text field to confirm that you intend to archive it. Click Archive . Verification The archived pipeline experiment does not appear on the Experiments tab, and instead appears on the Archive tab on the Experiments page for the pipeline experiment. 2.4. Deleting an archived pipeline experiment You can delete pipeline experiments from the OpenShift AI experiment archive. Prerequisites You have logged in to Red Hat OpenShift AI. If you are using OpenShift AI groups, you are part of the user group or admin group (for example, rhoai-users or rhoai-admins ) in OpenShift. You have previously created a data science project that is available and contains a configured pipeline server. You have imported a pipeline to an active pipeline server. A pipeline experiment is available in the pipeline archive. Procedure From the OpenShift AI dashboard, click Experiments Experiments and runs . On the Experiments page, from the Project drop-down list, select the project that contains the archived pipeline experiment that you want to delete. Click the Archive tab. Click the action menu ( ... ) beside the pipeline experiment that you want to delete, and then click Delete . In the Delete experiment? dialog, enter the pipeline experiment name in the text field to confirm that you intend to delete it. Click Delete . Verification The pipeline experiment that you deleted no longer appears on the Archive tab on the Experiments page. 2.5. Restoring an archived pipeline experiment You can restore an archived pipeline experiment to the active state. Prerequisites You have logged in to Red Hat OpenShift AI. If you are using OpenShift AI groups, you are part of the user group or admin group (for example, rhoai-users or rhoai-admins ) in OpenShift. You have previously created a data science project that is available and has a pipeline server. An archived pipeline experiment exists in your project. Procedure From the OpenShift AI dashboard, click Experiments Experiments and runs . On the Experiments page, from the Project drop-down list, select the project that contains the archived pipeline experiment that you want to restore. Click the Archive tab. Click the action menu ( ... ) beside the pipeline experiment that you want to restore, and then click Restore . In the Restore experiment dialog, click Restore . Verification The restored pipeline experiment appears on the Experiments tab on the Experiments page. 2.6. Viewing pipeline task executions When a pipeline run executes, you can view details of executed tasks in each step in a pipeline run from the OpenShift AI dashboard. A step forms part of a task in a pipeline. Prerequisites You have logged in to Red Hat OpenShift AI. If you are using OpenShift AI groups, you are part of the user group or admin group (for example, rhoai-users or rhoai-admins ) in OpenShift. You have previously created a data science project that is available and contains a pipeline server. You have imported a pipeline to an active pipeline server. You have previously triggered a pipeline run. Procedure From the OpenShift AI dashboard, click Experiments Executions . On the Executions page, from the Project drop-down list, select the project that contains the experiment for the pipeline task executions that you want to view. Verification On the Executions page, you can view the execution details of each pipeline task execution, such as its name, status, unique ID, and execution type. The execution status indicates whether the pipeline task has successfully executed. For further information about the details of the task execution, click the execution name. 2.7. Viewing pipeline artifacts After a pipeline run executes, you can view its pipeline artifacts from the OpenShift AI dashboard. Pipeline artifacts can help you to evaluate the performance of your pipeline runs and make it easier to understand your pipeline components. Pipeline artifacts can range from plain text data to detailed, interactive data visualizations. Prerequisites You have logged in to Red Hat OpenShift AI. If you are using OpenShift AI groups, you are part of the user group or admin group (for example, rhoai-users or rhoai-admins ) in OpenShift. You have previously created a data science project that is available and contains a pipeline server. You have imported a pipeline to an active pipeline server. You have previously triggered a pipeline run. Procedure From the OpenShift AI dashboard, click Experiments Artifacts . On the Artifacts page, from the Project drop-down list, select the project that contains the pipeline experiment for the pipeline artifacts that you want to view. Verification On the Artifacts page, you can view the details of each pipeline artifact, such as its name, unique ID, type, and URI. 2.8. Comparing runs in an experiment You can compare up to 10 pipeline runs in the same experiment at one time, and view available parameter, scalar metric, confusion matrix, and receiver operating characteristic (ROC) curve data for all selected runs. To compare runs from different experiments or pipelines, or to view every pipeline run in a project, see Comparing runs in different experiments . Prerequisites You have logged in to Red Hat OpenShift AI. If you are using OpenShift AI groups, you are part of the user group or admin group (for example, rhoai-users or rhoai-admins ) in OpenShift. You have previously created a data science project that is available and has a pipeline server. You have imported a pipeline to an active pipeline server. You have created at least two pipeline runs. Procedure In the OpenShift AI dashboard, select Experiments Experiments and runs . The Experiments page opens. From the Project drop-down list, select the project that contains the runs that you want to compare. On the Experiments tab, in the Experiment column, click the experiment that you want to compare runs for. To select runs that are not in an experiment, click Default . All runs that are created without specifying an experiment will appear in the Default group. The Runs page opens. Select the checkbox to each run that you want to compare, and then click Compare runs . You can compare a maximum of 10 runs at one time. The Compare runs page opens and displays data for the runs that you selected. The Run list section displays a list of selected runs. You can filter the list by run name, pipeline version, start date, and status. The Parameters section displays parameter information for each selected run. Set the Hide parameters with no differences switch to On to hide parameters that have the same values. The Metrics section displays scalar metric, confusion matrix, and ROC curve data for all selected runs. On the Scalar metrics tab, set the Hide parameters with no differences switch to On to hide parameters that have the same values. On the ROC curve tab, in the artifacts list, adjust the ROC curve chart by clearing the checkbox to artifacts that you want to remove from the chart. To select different runs for comparison, click Manage runs . The Manage runs dialog opens. From the filter drop-down list, select Run , Pipeline version , Created after , or Status to filter the run list by each value. Clear the checkbox to each run that you want to remove from your comparison. Select the checkbox to each run that you want to add to your comparison. Click Update . Verification The Compare runs page opens and displays data for the runs that you selected. 2.9. Comparing runs in different experiments You can compare up to 10 pipeline runs from any experiment or pipeline in a project, including runs that do not have a corresponding pipeline, and view available parameter, scalar metric, confusion matrix, and receiver operating characteristic (ROC) curve data for all selected runs. Prerequisites You have logged in to Red Hat OpenShift AI. If you are using OpenShift AI groups, you are part of the user group or admin group (for example, rhoai-users or rhoai-admins ) in OpenShift. You have previously created a data science project that is available and has a pipeline server. You have imported a pipeline to an active pipeline server. You have created at least two pipeline runs. Procedure In the OpenShift AI dashboard, select Data Science Pipelines Runs . The Runs page opens. From the Project drop-down list, select the project that contains the runs that you want to compare. On the Runs tab, in the Run column, select the checkbox to each run that you want to compare, and then click Compare runs . You can compare a maximum of 10 runs at one time. The Compare runs page opens and displays data for the runs that you selected. The Run list section displays a list of selected runs. You can filter the list by run name, experiment, pipeline version, start date, and status. The Parameters section displays parameter information for each selected run. Set the Hide parameters with no differences switch to On to hide parameters that have the same values. The Metrics section displays scalar metric, confusion matrix, and ROC curve data for all selected runs. On the Scalar metrics tab, set the Hide parameters with no differences switch to On to hide parameters that have the same values. On the ROC curve tab, in the artifacts list, adjust the ROC curve chart by clearing the checkbox to artifacts that you want to remove from the chart. To select different runs for comparison, click Manage runs . The Manage runs page opens. From the filter drop-down list, select Run , Experiment , Pipeline version , Created after , or Status to filter the run list by each value. Clear the checkbox to each run that you want to remove from your comparison. Select the checkbox to each run that you want to add to your comparison. Click Update . Verification The Compare runs page opens and displays data for the runs that you selected.

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