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Merge pull request #227329 from fbsolo-ms1/updates-for-YP
Change the ms.reviewer metadata & "clean" the text
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‎articles/machine-learning/migrate-to-v2-resource-compute.md

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ms.topic: reference
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author: vijetajo
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ms.author: vijetaj
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ms.date: 09/28/2022
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ms.reviewer: sgilley
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ms.date: 02/14/2023
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ms.reviewer: franksolomon
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ms.custom: migration
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---
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from azureml.core.compute_target import ComputeTargetException
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# Compute Instances need to have a unique name across the region.
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# Here we create a unique name with current datetime
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# Here, we create a unique name with current datetime
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ci_basic_name = "basic-ci" + datetime.datetime.now().strftime("%Y%m%d%H%M")
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compute_config = ComputeInstance.provisioning_configuration(
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```python
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# Compute Instances need to have a unique name across the region.
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# Here we create a unique name with current datetime
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# Here, we create a unique name with current datetime
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from azure.ai.ml.entities import ComputeInstance, AmlCompute
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import datetime
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‎articles/machine-learning/quickstart-spark-jobs.md

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ms.service: machine-learning
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ms.topic: quickstart
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ms.date: 02/10/2023
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ms.date: 02/14/2023
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#Customer intent: As a Full Stack ML Pro, I want to submit a Spark job in Azure Machine Learning.
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---
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‎articles/machine-learning/reference-yaml-data.md

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author: ynpandey
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ms.author: yogipandey
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ms.date: 03/31/2022
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ms.date: 02/14/2023
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ms.reviewer: franksolomon
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---
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| Key | Type | Description | Allowed values | Default value |
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| --- | ---- | ----------- | -------------- | ------------- |
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| `$schema` | string | The YAML schema. If you use the Azure Machine Learning VS Code extension to author the YAML file, including `$schema` at the top of your file enables you to invoke schema and resource completions. | | |
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| `name` | string | **Required.** Name of the data asset. | | |
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| `version` | string | Version of the dataset. If omitted, Azure ML will autogenerate a version. | | |
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| `description` | string | Description of the data asset. | | |
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| `tags` | object | Dictionary of tags for the data asset. | | |
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| `$schema` | string | The YAML schema. If you use the Azure Machine Learning Visual Studio Code extension to author the YAML file, you can invoke schema and resource completions if you include `$schema` at the top of your file. | | |
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| `name` | string | **Required.** The data asset name. | | |
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| `version` | string | The dataset version. If omitted, Azure ML autogenerates a version. | | |
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| `description` | string | The data asset description. | | |
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| `tags` | object | The datastore tag dictionary. | | |
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| `type` | string | The data asset type. Specify `uri_file` for data that points to a single file source, or `uri_folder` for data that points to a folder source. | `uri_file`, `uri_folder` | `uri_folder` |
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| `path` | string | Either a local path to the data source file or folder, or the URI of a cloud path to the data source file or folder. Please ensure that the source provided here is compatible with the `type` specified. <br><br> Supported URI types are `azureml`, `https`, `wasbs`, `abfss`, and `adl`. See [Core yaml syntax](reference-yaml-core-syntax.md) for more information on how to use the `azureml://` URI format. | | |
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| `path` | string | Either a local path to the data source file or folder, or the URI of a cloud path to the data source file or folder. Ensure that the source provided here is compatible with the `type` specified. <br><br> Supported URI types are `azureml`, `https`, `wasbs`, `abfss`, and `adl`. To use the `azureml://` URI format, see [Core yaml syntax](reference-yaml-core-syntax.md). | | |
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## Remarks
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The `az ml data` commands can be used for managing Azure Machine Learning data assets.
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## Examples
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Examples are available in the [examples GitHub repository](https://github.com/Azure/azureml-examples/tree/main/cli/assets/data). Several are shown below.
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Examples are available in the [examples GitHub repository](https://github.com/Azure/azureml-examples/tree/main/cli/assets/data). Several are shown:
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## YAML: datastore file
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‎articles/machine-learning/reference-yaml-datastore-blob.md

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author: ynpandey
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ms.author: yogipandey
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ms.date: 10/21/2021
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ms.date: 02/14/2023
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---
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# CLI (v2) Azure Blob datastore YAML schema
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[!INCLUDE [cli v2](../../includes/machine-learning-cli-v2.md)]
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The source JSON schema can be found at https://azuremlschemas.azureedge.net/latest/azureBlob.schema.json.
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See the source JSON schema at https://azuremlschemas.azureedge.net/latest/azureBlob.schema.json.
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[!INCLUDE [schema note](../../includes/machine-learning-preview-old-json-schema-note.md)]
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## YAML syntax
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| Key | Type | Description | Allowed values | Default value |
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| --- | ---- | ----------- | -------------- | ------- |
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| `$schema` | string | The YAML schema. If you use the Azure Machine Learning VS Code extension to author the YAML file, including `$schema` at the top of your file enables you to invoke schema and resource completions. | | |
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| `type` | string | **Required.** The type of datastore. | `azure_blob` | |
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| `name` | string | **Required.** Name of the datastore. | | |
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| `description` | string | Description of the datastore. | | |
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| `tags` | object | Dictionary of tags for the datastore. | | |
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| `account_name` | string | **Required.** Name of the Azure storage account. | | |
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| `container_name` | string | **Required.** Name of the container. | | |
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| `endpoint` | string | Endpoint suffix of the storage service, which is used for creating the storage account endpoint URL by combining the storage account name and `endpoint`. Example storage account URL: `https://<storage-account-name>.blob.core.windows.net`. | | `core.windows.net` |
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| `protocol` | string | Protocol to use to connect to the container. | `https`, `wasbs` | `https` |
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| `credentials` | object | Credential-based authentication credentials for connecting to the Azure storage account. You can provide either an account key or a shared access signature (SAS) token. Credential secrets are stored in the workspace key vault. | | |
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| `credentials.account_key` | string | The account key for accessing the storage account. **One of `credentials.account_key` or `credentials.sas_token` is required if `credentials` is specified.** | | |
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| `$schema` | string | The YAML schema. If you use the Azure Machine Learning Visual Studio Code extension to author the YAML file, you can invoke schema and resource completions if you include `$schema` at the top of your file. | | |
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| `type` | string | **Required.** The datastore type. | `azure_blob` | |
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| `name` | string | **Required.** The datastore name. | | |
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| `description` | string | The datastore description. | | |
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| `tags` | object | The datastore tag dictionary. | | |
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| `account_name` | string | **Required.** The Azure storage account name. | | |
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| `container_name` | string | **Required.** The container name. | | |
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| `endpoint` | string | The endpoint suffix of the storage service, used for creation of the storage account endpoint URL. It combines the storage account name and `endpoint`. Example storage account URL: `https://<storage-account-name>.blob.core.windows.net`. | | `core.windows.net` |
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| `protocol` | string | Protocol for connection to the container. | `https`, `wasbs` | `https` |
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| `credentials` | object | Credential-based authentication credentials for connection to the Azure storage account. An account key or a shared access signature (SAS) token will work. The workspace key vault stores the credential secrets. | | |
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| `credentials.account_key` | string | The account key used for storage account access. **One of `credentials.account_key` or `credentials.sas_token` is required if `credentials` is specified.** | | |
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| `credentials.sas_token` | string | The SAS token for accessing the storage account. **One of `credentials.account_key` or `credentials.sas_token` is required if `credentials` is specified.** | | |
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## Remarks
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The `az ml datastore` command can be used for managing Azure Machine Learning datastores.
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You can use the `az ml datastore` command to manage Azure Machine Learning datastores.
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## Examples
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Examples are available in the [examples GitHub repository](https://github.com/Azure/azureml-examples/tree/main/cli/resources/datastore). Several are shown below.
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See examples in the [examples GitHub repository](https://github.com/Azure/azureml-examples/tree/main/cli/resources/datastore). Several are shown here:
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## YAML: identity-based access
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‎articles/machine-learning/reference-yaml-datastore-data-lake-gen1.md

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# CLI (v2) Azure Data Lake Gen1 YAML schema
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[!INCLUDE [cli v2](../../includes/machine-learning-cli-v2.md)]
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The source JSON schema can be found at https://azuremlschemas.azureedge.net/latest/azureDataLakeGen1.schema.json.
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See the source JSON schema at https://azuremlschemas.azureedge.net/latest/azureDataLakeGen1.schema.json.
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[!INCLUDE [schema note](../../includes/machine-learning-preview-old-json-schema-note.md)]
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## YAML syntax
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| Key | Type | Description | Allowed values | Default value |
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| --- | ---- | ----------- | -------------- | ------- |
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| `$schema` | string | The YAML schema. If you use the Azure Machine Learning VS Code extension to author the YAML file, including `$schema` at the top of your file enables you to invoke schema and resource completions. | | |
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| `type` | string | **Required.** The type of datastore. | `azure_data_lake_gen1` | |
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| `name` | string | **Required.** Name of the datastore. | | |
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| `description` | string | Description of the datastore. | | |
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| `tags` | object | Dictionary of tags for the datastore. | | |
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| `store_name` | string | **Required.** Name of the Azure Data Lake Storage Gen1 account. | | |
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| `credentials` | object | Service principal credentials for connecting to the Azure storage account. Credential secrets are stored in the workspace key vault. | | |
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| `credentials.tenant_id` | string | The tenant ID of the service principal. **Required if `credentials` is specified.** | | |
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| `credentials.client_id` | string | The client ID of the service principal. **Required if `credentials` is specified.** | | |
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| `credentials.client_secret` | string | The client secret of the service principal. **Required if `credentials` is specified.** | | |
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| `credentials.resource_url` | string | The resource URL that determines what operations will be performed on the Azure Data Lake Storage Gen1 account. | | `https://datalake.azure.net/` |
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| `credentials.authority_url` | string | The authority URL used to authenticate the user. | | `https://login.microsoftonline.com` |
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| `$schema` | string | The YAML schema. If you use the Azure Machine Learning Visual Studio Code extension to author the YAML file, you can invoke schema and resource completions if you include `$schema` at the top of your file. | | |
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| `type` | string | **Required.** The datastore type. | `azure_data_lake_gen1` | |
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| `name` | string | **Required.** The datastore name. | | |
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| `description` | string | The datastore description. | | |
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| `tags` | object | The datastore tag dictionary. | | |
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| `store_name` | string | **Required.** The Azure Data Lake Storage Gen1 account name. | | |
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| `credentials` | object | Service principal credentials to connect to the Azure storage account. Credential secrets are stored in the workspace key vault. | | |
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| `credentials.tenant_id` | string | The service principal tenant ID. **Required if `credentials` is specified.** | | |
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| `credentials.client_id` | string | The service principal client ID. **Required if `credentials` is specified.** | | |
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| `credentials.client_secret` | string | The service principal client secret. **Required if `credentials` is specified.** | | |
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| `credentials.resource_url` | string | The resource URL that determines which operations the Azure Data Lake Storage Gen1 account performs. | | `https://datalake.azure.net/` |
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| `credentials.authority_url` | string | The authority URL used for user authentication. | | `https://login.microsoftonline.com` |
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## Remarks
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You can use the `az ml datastore` command to manage Azure Machine Learning datastores.
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## Examples
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Examples are available in the [examples GitHub repository](https://github.com/Azure/azureml-examples/tree/main/cli/resources/datastore). Several are shown below.
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See examples in the [examples GitHub repository](https://github.com/Azure/azureml-examples/tree/main/cli/resources/datastore). Several are shown here:
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‎articles/machine-learning/reference-yaml-datastore-data-lake-gen2.md

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| Key | Type | Description | Allowed values | Default value |
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| `$schema` | string | The YAML schema. If you use the Azure Machine Learning VS Code extension to author the YAML file, including `$schema` at the top of your file enables you to invoke schema and resource completions. | | |
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| `type` | string | **Required.** The type of datastore. | `azure_data_lake_gen2` | |
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| `name` | string | **Required.** Name of the datastore. | | |
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| `description` | string | Description of the datastore. | | |
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| `tags` | object | Dictionary of tags for the datastore. | | |
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| `account_name` | string | **Required.** Name of the Azure storage account. | | |
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| `filesystem` | string | **Required.** Name of the file system. The parent directory that contains the files and folders. This is equivalent to a container in Azure Blob storage. | | |
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| `endpoint` | string | Endpoint suffix of the storage service, which is used for creating the storage account endpoint URL by combining the storage account name and `endpoint`. Example storage account URL: `https://<storage-account-name>.dfs.core.windows.net`. | | `core.windows.net` |
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| `protocol` | string | Protocol to use to connect to the file system. | `https`, `abfss` | `https` |
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| `$schema` | string | The YAML schema. If you use the Azure Machine Learning Visual Studio Code extension to author the YAML file, you can invoke schema and resource completions if you include `$schema` at the top of your file. | | |
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| `type` | string | **Required.** The datastore type. | `azure_data_lake_gen2` | |
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| `name` | string | **Required.** The datastore name. | | |
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| `description` | string | The datastore description. | | |
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| `tags` | object | The datastore tag dictionary. | | |
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| `account_name` | string | **Required.** The Azure storage account name. | | |
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| `filesystem` | string | **Required.** The file system name. The parent directory containing the files and folders, equivalent to an Azure Blog storage container. | | |
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| `endpoint` | string | The endpoint suffix of the storage service, used for creation of the storage account endpoint URL. It combines the storage account name and `endpoint`. Example storage account URL: `https://<storage-account-name>.dfs.core.windows.net`. | | `core.windows.net` |
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| `protocol` | string | Protocol for connection to the file system. | `https`, `abfss` | `https` |
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| `credentials` | object | Service principal credentials for connecting to the Azure storage account. Credential secrets are stored in the workspace key vault. | | |
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| `credentials.tenant_id` | string | The tenant ID of the service principal. **Required if `credentials` is specified.** | | |
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| `credentials.client_id` | string | The client ID of the service principal. **Required if `credentials` is specified.** | | |
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| `credentials.client_secret` | string | The client secret of the service principal. **Required if `credentials` is specified.** | | |
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| `credentials.resource_url` | string | The resource URL that determines what operations will be performed on the Azure Data Lake Storage Gen2 account. | | `https://storage.azure.com/` |
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| `credentials.authority_url` | string | The authority URL used to authenticate the user. | | `https://login.microsoftonline.com` |
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| `credentials.tenant_id` | string | The service principal tenant ID. **Required if `credentials` is specified.** | | |
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| `credentials.client_id` | string | The service principal client ID. **Required if `credentials` is specified.** | | |
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| `credentials.client_secret` | string | The service principal client secret. **Required if `credentials` is specified.** | | |
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| `credentials.resource_url` | string | The resource URL that specifies the operations that will be performed on the Azure Data Lake Storage Gen2 account. | | `https://storage.azure.com/` |
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| `credentials.authority_url` | string | The authority URL used for user authentication. | | `https://login.microsoftonline.com` |
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## Remarks
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## Examples
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Examples are available in the [examples GitHub repository](https://github.com/Azure/azureml-examples/tree/main/cli/resources/datastore). Several are shown below.
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Examples are available in the [examples GitHub repository](https://github.com/Azure/azureml-examples/tree/main/cli/resources/datastore). Several are shown here:
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## YAML: identity-based access
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