updating to add identity information

Author: Larry Franks

articles/machine-learning/reference-yaml-job-command.md

@@ -10,7 +10,7 @@ ms.custom: cliv2, event-tier1-build-2022
 
 author: balapv
 ms.author: balapv
-ms.date: 08/08/2022
+ms.date: 11/28/2022
 ms.reviewer: larryfr
 ---
 
@@ -48,6 +48,7 @@ The source JSON schema can be found 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 job. The key is a name for the output within the context of the job and the value is the output configuration. <br><br> Outputs can be referenced in the `command` using the `${{ outputs.<output_name> }}` expression. | |
 | `outputs.<output_name>` | object | You can leave the object empty, in which case by default the output will be of type `uri_folder` and Azure ML will system-generate an output location for the output. File(s) to the output directory will be written via read-write mount. If you want to specify a different mode for the output, provide an object containing the [job output specification](#job-outputs). | |
+| `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 | |
 
 ### Distribution configurations
 
@@ -88,6 +89,16 @@ The source JSON schema can be found at https://azuremlschemas.azureedge.net/late
 | `type` | string | The type of job output. For the default `uri_folder` type, the output will correspond to a folder. | `uri_folder` , `mlflow_model`, `custom_model`| `uri_folder` |
 | `mode` | string | Mode of how output file(s) will get delivered to the destination storage. For read-write mount mode (`rw_mount`) the output directory will be a mounted directory. For upload mode the file(s) written will get uploaded at the end of the job. | `rw_mount`, `upload` | `rw_mount` |
 
+### Identity configurations
+
+#### UserIdentityConfiguration
+
+| Key | Type | Description | Allowed values |
+| --- | ---- | ----------- | -------------- |
+| `type` | const | **Required.** Identity type.  | `user_identity` |
+
+#### ManagedIdentityConfiguration| Key | Type | Description | Allowed values | | --- | ---- | ----------- | -------------- | | `type` | const | **Required.** Identity type.  | `managed` or `managed_identity` |
+
 ## Remarks
 
 The `az ml job` command can be used for managing Azure Machine Learning jobs.

articles/machine-learning/reference-yaml-job-pipeline.md

@@ -9,7 +9,7 @@ ms.topic: reference
 ms.custom: cliv2, event-tier1-build-2022
 author: cloga
 ms.author: lochen
-ms.date: 08/08/2022
+ms.date: 11/28/2022
 ms.reviewer: scottpolly
 ---
 
@@ -42,6 +42,7 @@ The source JSON schema can be found 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 and the value is the output configuration. <br><br> These pipeline outputs can be referenced by the outputs of an individual step job in the pipeline using the `${{ parents.outputs.<output_name> }}` expression. For more information on how to bind the inputs of a pipeline step to the inputs of the top-level pipeline job, see 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 which case by default the output will be of type `uri_folder` and Azure ML will system-generate an output location for the output based on the following templatized path: `{settings.datastore}/azureml/{job-name}/{output-name}/`. File(s) to the output directory will be written via read-write mount. If you want to specify a different mode for the output, provide an object containing the [job output specification](#job-outputs). | |
+| `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
 
@@ -66,6 +67,16 @@ The source JSON schema can be found at https://azuremlschemas.azureedge.net/late
 | `type` | string | The type of job output. For the default `uri_folder` type, the output will correspond to a folder. | `uri_file`, `uri_folder`, `mltable`, `mlflow_model` | `uri_folder` |
 | `mode` | string | Mode of how output file(s) will get delivered to the destination storage. For read-write mount mode (`rw_mount`) the output directory will be a mounted directory. For upload mode the file(s) written will get uploaded at the end of the job. | `rw_mount`, `upload` | `rw_mount` |
 
+### Identity configurations
+
+#### UserIdentityConfiguration
+
+| Key | Type | Description | Allowed values |
+| --- | ---- | ----------- | -------------- |
+| `type` | const | **Required.** Identity type.  | `user_identity` |
+
+#### ManagedIdentityConfiguration| Key | Type | Description | Allowed values | | --- | ---- | ----------- | -------------- | | `type` | const | **Required.** Identity type.  | `managed` or `managed_identity` |
+
 ## Remarks
 
 The `az ml job` commands can be used for managing Azure Machine Learning pipeline jobs.

articles/machine-learning/reference-yaml-job-sweep.md

@@ -9,8 +9,8 @@ ms.topic: reference
 ms.custom: cliv2, event-tier1-build-2022
 ms.author: amipatel
 author: amibp
-ms.date: 08/08/2022
-ms.reviewer: nibaccam
+ms.date: 11/28/2022
+ms.reviewer: larryfr
 ---
 
 # CLI (v2) sweep job YAML schema
@@ -47,6 +47,7 @@ The source JSON schema can be found 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 job. The key is a name for the output within the context of the job and the value is the output configuration. <br><br> Outputs can be referenced in the `command` using the `${{ outputs.<output_name> }}` expression. | |
 | `outputs.<output_name>` | object | You can leave the object empty, in which case by default the output will be of type `uri_folder` and Azure ML will system-generate an output location for the output. File(s) to the output directory will be written via read-write mount. If you want to specify a different mode for the output, provide an object containing the [job output specification](#job-outputs). | |
+| `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 | |
 
 ### Sampling algorithms
 
@@ -216,6 +217,16 @@ The source JSON schema can be found at https://azuremlschemas.azureedge.net/late
 | `type` | string | The type of job output. For the default `uri_folder` type, the output will correspond to a folder. | `uri_file`, `uri_folder`, `mltable`, `mlflow_model`  | `uri_folder` |
 | `mode` | string | Mode of how output file(s) will get delivered to the destination storage. For read-write mount mode (`rw_mount`) the output directory will be a mounted directory. For upload mode the file(s) written will get uploaded at the end of the job. | `rw_mount`, `upload` | `rw_mount` |
 
+### Identity configurations
+
+#### UserIdentityConfiguration
+
+| Key | Type | Description | Allowed values |
+| --- | ---- | ----------- | -------------- |
+| `type` | const | **Required.** Identity type.  | `user_identity` |
+
+#### ManagedIdentityConfiguration| Key | Type | Description | Allowed values | | --- | ---- | ----------- | -------------- | | `type` | const | **Required.** Identity type.  | `managed` or `managed_identity` |
+
 ## Remarks
 
 The `az ml job` command can be used for managing Azure Machine Learning jobs.