Merge pull request #207203 from cloga/lochen/update-pipeline-job-doc

add mltable/mlflow_model

Author: prmerger-automator[bot]

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

@@ -10,7 +10,7 @@ ms.custom: cliv2, event-tier1-build-2022
 
 author: blackmist
 ms.author: larryfr
-ms.date: 03/31/2022
+ms.date: 08/08/2022
 ms.reviewer: nibaccam
 ---
 
@@ -40,7 +40,7 @@ The source JSON schema can be found at https://azuremlschemas.azureedge.net/late
 | `environment` | string or object | **Required (if not using `component` field).** The environment to use for the job. This can be either a reference to an existing versioned environment in the workspace or an inline environment specification. <br><br> To reference an existing environment use the `azureml:<environment_name>:<environment_version>` syntax or `azureml:<environment_name>@latest` (to reference the latest version of an environment). <br><br> To define an environment inline please follow the [Environment schema](reference-yaml-environment.md#yaml-syntax). Exclude the `name` and `version` properties as they are not supported for inline environments. | | |
 | `environment_variables` | object | Dictionary of environment variable key-value pairs to set on the process where the command is executed. | | |
 | `distribution` | object | The distribution configuration for distributed training scenarios. One of [MpiConfiguration](#mpiconfiguration), [PyTorchConfiguration](#pytorchconfiguration), or [TensorFlowConfiguration](#tensorflowconfiguration). | | |
-| `compute` | string | Name of the compute target to execute the job on. This can be either a reference to an existing compute in the workspace (using the `azureml:<compute_name>` syntax) or `local` to designate local execution. | | `local` |
+| `compute` | string | Name of the compute target to execute the job on. This can be either a reference to an existing compute in the workspace (using the `azureml:<compute_name>` syntax) or `local` to designate local execution. **Note:** jobs in pipeline didn't support `local` as `compute` | | `local` |
 | `resources.instance_count` | integer | The number of nodes to use for the job. | | `1` |
 | `resources.instance_type` | string | The instance type to use for the job. Applicable for jobs running on Azure Arc-enabled Kubernetes compute (where the compute target specified in the `compute` field is of `type: kubernentes`). If omitted, this will default to the default instance type for the Kubernetes cluster. For more information, see [Create and select Kubernetes instance types](how-to-attach-kubernetes-anywhere.md). | | |
 | `limits.timeout` | integer | The maximum time in seconds the job is allowed to run. Once this limit is reached the system will cancel the job. | | |

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

@@ -7,10 +7,9 @@ ms.service: machine-learning
 ms.subservice: core
 ms.topic: reference
 ms.custom: cliv2, event-tier1-build-2022
-
 author: s-polly
 ms.author: scottpolly
-ms.date: 03/31/2022
+ms.date: 08/08/2022
 ms.reviewer: nibaccam
 ---
 
@@ -56,15 +55,15 @@ The source JSON schema can be found at https://azuremlschemas.azureedge.net/late
 
 | Key | Type | Description | Allowed values | Default value |
 | --- | ---- | ----------- | -------------- | ------------- |
-| `type` | string | The type of job input. Specify `uri_file` for input data that points to a single file source, or `uri_folder` for input data that points to a folder source. | `uri_file`, `uri_folder` | `uri_folder` |
+| `type` | string | The type of job input. Specify `uri_file` for input data that points to a single file source, or `uri_folder` for input data that points to a folder source. [Learn more about data access.](concept-data.md)| `uri_file`, `uri_folder`, `mltable`, `mlflow_model` | `uri_folder` |
 | `path` | string | The path to the data to use as input. This can be specified in a few ways: <br><br> - A local path to the data source file or folder, e.g. `path: ./iris.csv`. The data will get uploaded during job submission. <br><br> - A URI of a cloud path to the file or folder to use as the input. Supported URI types are `azureml`, `https`, `wasbs`, `abfss`, `adl`. See [Core yaml syntax](reference-yaml-core-syntax.md) for more information on how to use the `azureml://` URI format. <br><br> - An existing registered Azure ML data asset to use as the input. To reference a registered data asset use the `azureml:<data_name>:<data_version>` syntax or `azureml:<data_name>@latest` (to reference the latest version of that data asset), e.g. `path: azureml:cifar10-data:1` or `path: azureml:cifar10-data@latest`. | | |
 | `mode` | string | Mode of how the data should be delivered to the compute target. <br><br> For read-only mount (`ro_mount`), the data will be consumed as a mount path. A folder will be mounted as a folder and a file will be mounted as a file. Azure ML will resolve the input to the mount path. <br><br> For `download` mode the data will be downloaded to the compute target. Azure ML will resolve the input to the downloaded path. <br><br> If you only want the URL of the storage location of the data artifact(s) rather than mounting or downloading the data itself, you can use the `direct` mode. This will pass in the URL of the storage location as the job input. Note that in this case you are fully responsible for handling credentials to access the storage. | `ro_mount`, `download`, `direct` | `ro_mount` |
 
 ### Job outputs
 
 | Key | Type | Description | Allowed values | Default value |
 | --- | ---- | ----------- | -------------- | ------------- |
-| `type` | string | The type of job output. For the default `uri_folder` type, the output will correspond to a folder. | `uri_folder` | `uri_folder` |
+| `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` |
 
 ## Remarks
@@ -98,3 +97,4 @@ Examples are available in the [examples GitHub repository](https://github.com/Az
 ## Next steps
 
 - [Install and use the CLI (v2)](how-to-configure-cli.md)
+- [Create ML pipelines using components](how-to-create-component-pipelines-cli.md)

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

@@ -9,7 +9,7 @@ ms.topic: reference
 ms.custom: cliv2, event-tier1-build-2022
 ms.author: amipatel
 author: amibp
-ms.date: 03/31/2022
+ms.date: 08/08/2022
 ms.reviewer: nibaccam
 ---
 
@@ -205,15 +205,15 @@ The source JSON schema can be found at https://azuremlschemas.azureedge.net/late
 
 | Key | Type | Description | Allowed values | Default value |
 | --- | ---- | ----------- | -------------- | ------------- |
-| `type` | string | The type of job input. Specify `uri_file` for input data that points to a single file source, or `uri_folder` for input data that points to a folder source. | `uri_file`, `uri_folder` | `uri_folder` |
+| `type` | string | The type of job input. Specify `uri_file` for input data that points to a single file source, or `uri_folder` for input data that points to a folder source. [Learn more about data access.](concept-data.md)| `uri_file`, `uri_folder`, `mltable`, `mlflow_model` | `uri_folder` |
 | `path` | string | The path to the data to use as input. This can be specified in a few ways: <br><br> - A local path to the data source file or folder, e.g. `path: ./iris.csv`. The data will get uploaded during job submission. <br><br> - A URI of a cloud path to the file or folder to use as the input. Supported URI types are `azureml`, `https`, `wasbs`, `abfss`, `adl`. See [Core yaml syntax](reference-yaml-core-syntax.md) for more information on how to use the `azureml://` URI format. <br><br> - An existing registered Azure ML data asset to use as the input. To reference a registered data asset use the `azureml:<data_name>:<data_version>` syntax or `azureml:<data_name>@latest` (to reference the latest version of that data asset), e.g. `path: azureml:cifar10-data:1` or `path: azureml:cifar10-data@latest`. | | |
 | `mode` | string | Mode of how the data should be delivered to the compute target. <br><br> For read-only mount (`ro_mount`), the data will be consumed as a mount path. A folder will be mounted as a folder and a file will be mounted as a file. Azure ML will resolve the input to the mount path. <br><br> For `download` mode the data will be downloaded to the compute target. Azure ML wil resolve the input to the downloaded path. <br><br> If you only want the URL of the storage location of the data artifact(s) rather than mounting or downloading the data itself, you can use the `direct` mode. This will pass in the URL of the storage location as the job input. Note that in this case you are fully responsible for handling credentials to access the storage. | `ro_mount`, `download`, `direct` | `ro_mount` |
 
 ### Job outputs
 
 | Key | Type | Description | Allowed values | Default value |
 | --- | ---- | ----------- | -------------- | ------------- |
-| `type` | string | The type of job output. For the default `uri_folder` type, the output will correspond to a folder. | `uri_folder` | `uri_folder` |
+| `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` |
 
 ## Remarks