Merge pull request #279151 from cloga/identity-in-pipeline

update identity in pipeline

Author: v-dirichards

articles/machine-learning/how-to-create-component-pipelines-ui.md

@@ -144,6 +144,12 @@ After submitting the pipeline job, there will be a message on the top with a lin
 
   :::image type="content" source="./media/how-to-create-component-pipelines-ui/submit-message.png" alt-text="Screenshot showing submission message." lightbox ="./media/how-to-create-component-pipelines-ui/submit-message.png":::
 
+## Specify identity in pipeline job
+
+When submitting pipeline job, you can specify the identity to access the data under `Run settings`. The default identity is `AMLToken` which didn't use any identity meanwhile we support both `UserIdentity` and `Managed`. For `UserIdentity`, the identity of job submitter is used to access input data and write the result to the output folder. If you specify `Managed`, the system will use the managed identity to access the input data and write the result to the output folder.
+
+  :::image type="content" source="./media/how-to-create-component-pipelines-ui/identity-in-pipeline.png" alt-text="Screenshot showing how to set identity in pipeline job." lightbox ="./media/how-to-create-component-pipelines-ui/identity-in-pipeline.png":::
+
 ## Next steps
 
 - Use [these Jupyter notebooks on GitHub](https://github.com/Azure/azureml-examples/tree/main/cli/jobs/pipelines-with-components) to explore machine learning pipelines further

articles/machine-learning/media/how-to-create-component-pipelines-ui/identity-in-pipeline.png

@@ -37,7 +37,7 @@ You can find the source JSON schema at https://azuremlschemas.azureedge.net/late
 | `inputs.<input_name>` | number, integer, boolean, string or object | One of a literal value (of type number, integer, boolean, or string) or an object containing a [job input data specification](#job-inputs). | | |
 | `outputs` | object | Dictionary of output configurations of the pipeline job. The key is a name for the output within the context of the job. The value is the output configuration. <br><br> The outputs of an individual step job in the pipeline can reference these pipeline outputs with the `${{ parents.outputs.<output_name> }}` expression. For more information about binding the outputs of a pipeline step to the outputs of the top-level pipeline job, visit the [Expression syntax for binding inputs and outputs between steps in a pipeline job](reference-yaml-core-syntax.md#binding-inputs-and-outputs-between-steps-in-a-pipeline-job). | |
 | `outputs.<output_name>` | object | You can leave the object empty. In this case, by default, the output will be of type `uri_folder`, and Azure Machine Learning will system-generate an output location for the output based on this templatized path: `{settings.datastore}/azureml/{job-name}/{output-name}/`. File(s) to the output directory will be written via a read-write mount. To specify a different output mode, provide an object that contains the [job output specification](#job-outputs). | |
-| `identity` | object | Accessing of data uses the identity. It can be [User Identity Configuration](#useridentityconfiguration), [Managed Identity Configuration](#managedidentityconfiguration) or None. For UserIdentityConfiguration, the identity of job submitter is used to access input data and write the result to the output folder. Otherwise, UserIdentityConfiguration uses the managed identity of the compute target. | |
+| `identity` | object | The identity is used for data accessing. It can be [UserIdentityConfiguration](#useridentityconfiguration), [ManagedIdentityConfiguration](#managedidentityconfiguration) or None. If it's UserIdentityConfiguration, the identity of job submitter will be used to access input data and write result to output folder, otherwise the managed identity of the compute target will be used. | |
 
 ### Attributes of the `settings` key