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Merge pull request #210739 from sdgilley/sdg-patch
Fix Python/CLI tabs
2 parents 2afcd51 + d881dde commit af4ef8c

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‎articles/machine-learning/concept-azure-machine-learning-v2.md

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### Create a workspace
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### [CLI](#tab/cli)
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### [Azure CLI](#tab/cli)
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To create a workspace using CLI v2, use the following command:
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* **Inference cluster** - used to deploy trained machine learning models to Azure Kubernetes Service. You can create an Azure Kubernetes Service (AKS) cluster from your Azure ML workspace, or attach an existing AKS cluster.
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* **Attached compute** - You can attach your own compute resources to your workspace and use them for training and inference.
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### [CLI](#tab/cli)
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### [Azure CLI](#tab/cli)
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To create a compute using CLI v2, use the following command:
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* Azure Data Lake
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* Azure Data Lake Gen2
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### [CLI](#tab/cli)
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### [Azure CLI](#tab/cli)
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To create a datastore using CLI v2, use the following command:
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### Creating a model
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### [CLI](#tab/cli)
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### [Azure CLI](#tab/cli)
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To create a model using CLI v2, use the following command:
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### Create an Azure ML custom environment
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### [CLI](#tab/cli)
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### [Azure CLI](#tab/cli)
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To create an environment using CLI v2, use the following command:
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‎articles/machine-learning/how-to-attach-kubernetes-to-workspace.md

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Azure Relay resource is created during the extension deployment under the same Resource Group as the Arc-enabled Kubernetes cluster.
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### [CLI](#tab/cli)
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### [Azure CLI](#tab/cli)
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[!INCLUDE [cli v2](../../includes/machine-learning-cli-v2.md)]
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> `--user-assigned-identities` is only required for `UserAssigned` managed identities. Although you can provide a list of comma-separated user managed identities, only the first one is used when you attach your cluster.
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>
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> Compute attach won't create the Kubernetes namespace automatically or validate whether the kubernetes namespace existed. You need to verify that the specified namespace exists in your cluster, otherwise, any AzureML workloads submitted to this compute will fail.
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### [Python](#tab/python)
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### [Python SDK](#tab/python)
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[!INCLUDE [sdk v1](../../includes/machine-learning-sdk-v1.md)]
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‎articles/machine-learning/how-to-create-data-assets.md

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---
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# Create data assets
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[!INCLUDE [dev v2](../../includes/machine-learning-dev-v2.md)]
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> [!div class="op_single_selector" title1="Select the version of Azure Machine Learning SDK you are using:"]
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> * [v1](./v1/how-to-create-register-datasets.md)
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> * [v2 (current version)](how-to-create-data-assets.md)
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[!INCLUDE [sdk v2](../../includes/machine-learning-sdk-v2.md)]
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[!INCLUDE [CLI v2](../../includes/machine-learning-CLI-v2.md)]
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In this article, you learn how to create a data asset in Azure Machine Learning. By creating a data asset, you create a *reference* to the data source location, along with a copy of its metadata. Because the data remains in its existing location, you incur no extra storage cost, and don't risk the integrity of your data sources. You can create Data from datastores, Azure Storage, public URLs, and local files.
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The benefits of creating data assets are:
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Below shows you how to create a *folder* as an asset:
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# [CLI](#tab/CLI)
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# [Azure CLI](#tab/cli)
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Create a `YAML` file (`<file-name>.yml`):
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az ml data create -f <file-name>.yml
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```
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# [Python-SDK](#tab/Python-SDK)
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# [Python SDK](#tab/Python-SDK)
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You can create a data asset in Azure Machine Learning using the following Python Code:
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Below shows you how to create a *specific file* as a data asset:
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# [CLI](#tab/CLI)
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# [Azure CLI](#tab/cli)
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Sample `YAML` file `<file-name>.yml` for data in local path is as below:
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> az ml data create -f <file-name>.yml
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```
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# [Python-SDK](#tab/Python-SDK)
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# [Python SDK](#tab/Python-SDK)
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```python
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from azure.ai.ml.entities import Data
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from azure.ai.ml.constants import AssetTypes
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Below shows you how to create an `mltable` data asset. The `path` can be any of the supported path formats outlined above.
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# [CLI](#tab/CLI)
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# [Azure CLI](#tab/cli)
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Create a `YAML` file (`<file-name>.yml`):
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az ml data create -f <file-name>.yml
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```
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# [Python-SDK](#tab/Python-SDK)
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# [Python SDK](#tab/Python-SDK)
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You can create a data asset in Azure Machine Learning using the following Python Code:
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‎articles/machine-learning/how-to-deploy-automl-endpoint.md

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# How to deploy an AutoML model to an online endpoint
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[!INCLUDE [cli v2](../../includes/machine-learning-cli-v2.md)]
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[!INCLUDE [sdk v2](../../includes/machine-learning-sdk-v2.md)]
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[!INCLUDE [dev v2](../../includes/machine-learning-dev-v2.md)]
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> [!IMPORTANT]
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> SDK v2 is currently in public preview.
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1. Complete all the steps in wizard to create an online endpoint and deployment
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[!INCLUDE [cli v2](../../includes/machine-learning-cli-v2.md)]
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After you create a deployment, you can score it as described in [Invoke the endpoint to score data by using your model](how-to-deploy-managed-online-endpoints.md#invoke-the-endpoint-to-score-data-by-using-your-model).
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[!INCLUDE [sdk v2](../../includes/machine-learning-sdk-v2.md)]
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‎articles/machine-learning/how-to-deploy-custom-container.md

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* To deploy locally, you must have [Docker engine](https://docs.docker.com/engine/install/) running locally. This step is **highly recommended**. It will help you debug issues.
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# [CLI](#tab/CLI)
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# [Azure CLI](#tab/cli)
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* Install and configure the Azure CLI and ML extension. For more information, see [Install, set up, and use the CLI (v2)](how-to-configure-cli.md).
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az configure --defaults workspace=<azureml workspace name> group=<resource group>
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```
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# [Python](#tab/python)
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# [Python SDK](#tab/python)
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* If you haven't installed Python SDK v2, please install with this command:
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To follow along with this tutorial, download the source code below.
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# [CLI](#tab/CLI)
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# [Azure CLI](#tab/cli)
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```azurecli
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git clone https://github.com/Azure/azureml-examples --depth 1
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cd azureml-examples/cli
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```
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# [Python](#tab/python)
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```azurecli
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git clone https://github.com/Azure/azureml-examples --depth 1
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## Deploy your online endpoint to Azure
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Next, deploy your online endpoint to Azure.
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# [Azure CLI](#tab/cli)
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### Create a YAML file for your endpoint and deployment
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:::code language="yaml" source="~/azureml-examples-main/cli/endpoints/online/custom-container/tfserving-deployment.yml":::
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# [Python](#tab/python)
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### Connect to Azure Machine Learning workspace
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Connect to Azure Machine Learning Workspace, configure workspace details, and get a handle to the workspace as follows:
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:::image type="content" source="./media/how-to-deploy-custom-container/local-directory-structure.png" alt-text="Diagram showing a tree view of the local directory structure.":::
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> [!IMPORTANT]
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> The `model_mount_path` must be a valid absolute path in Linux (the OS of the container image).
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‎articles/machine-learning/how-to-read-write-data-v2.md

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> [!div class="op_single_selector" title1="Select the version of Azure Machine Learning CLI extension you are using:"]
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