Platform For AI:Manage workspaces

Limits

Only the administrator or owner of a workspace can modify workspace configurations.

View the details of a workspace

1. Log on to the PAI console.

2. In the left-side navigation pane, click Workspaces. On the Workspaces page, click the name of the workspace that you want to view.

3. On the right side of the Workspace Details page, you can view information about the workspace, such as the workspace name, workspace ID, and associated computing resources.

   

Modify the description of a workspace

1. In the Basic Information about Workspace section on the right side of the Workspace Details page, click Modify.

2. In the Basic Information about Workspace panel, modify the Description field and click OK.

   

Manage the computing resources of a workspace

View computing resources

1. In the Computing Resources section on the right side of the Workspace Details page, click Resources.

   

2. In the Workspace Resource Configuration panel, you can view the associated resources, such as MaxCompute Resources, General Computing Resources, and Fully Managed Flink Resources. You can also view information about the associated resources, such as Quota Name and Billing Method.

Associate computing resources

Configure the default storage path of a workspace

1. In the Storage Settings section on the right side of the Workspace Details page, click Settings.

   

2. In the Storage Settings panel, specify an Object Storage Service (OSS) path in the Workspace Default Storage field and then click OK.

   Important

   Members who are assigned the Algorithm Developer role in the workspace must have read and write permissions on the specified OSS path.

   

   Note

   The specified OSS path can be used by the pipelines in Machine Learning Designer. If you specify another storage path when you create a pipeline in Machine Learning Designer, the specified OSS path in the Workspace Default Storage field becomes invalid.

Manage the members of a workspace

Configure SLS for a workspace

You can send logs that are generated for the Data Science Workshop (DSW) instances and Deep Learning Containers (DLC) jobs in the current workspace to Simple Log Service (SLS) for custom analysis. Perform the following steps to configure SLS:

1. In the Log Service section on the right side of the Workspace Details page, click Settings.

   

2. In the SLS Logs panel, configure the following parameters and click OK:

   Parameter	Description
   SLSProject	The SLS project. SLS projects are used to isolate and manage resources. If no projects are available in the drop-down list, click Create SLS Project to create a project. For more information, see Manage a project.
   LogStore	The SLS Logstore. Logstores are used to collect, store, and query logs. If no Logstores are available in the drop-down list, click Create Logstore to create a Logstore. For more information, see Manage a Logstore.
   Modules that require SLS storage	Valid options: Distributed Training (DLC) and Interactive Modeling (DSW). You can select both options.

   

Reference

• Machine Learning Designer is a visual tool for machine learning modeling that uses the cloud-native PAIFlow engine to facilitate end-to-end machine learning development. For more information, see Overview of Machine Learning Designer.

• You can create a notification rule to monitor the status of pipelines in Machine Learning Designer or DLC jobs or trigger related events based on the approval status of model versions. For more information, see Create a notification rule.