Merge pull request #213023 from Blackmist/config-environment

Config environment

Author: JamesJBarnett

articles/lab-services/class-type-jupyter-notebook.md

@@ -109,7 +109,7 @@ physical_device_desc: "device: 0, name: NVIDIA Tesla K80, pci bus id: 0001:00:00
 
 ### Provide notebooks for the class
 
-The next task is to provide students with notebooks that you want them to use. Notebooks can be saved locally on the template VM so each student has their own copy.  If you want to use sample notebooks from Azure Machine Learning, see [how to configure an environment with Jupyter Notebooks](../machine-learning/how-to-configure-environment.md#jupyter).
+The next task is to provide students with notebooks that you want them to use. Notebooks can be saved locally on the template VM so each student has their own copy.  If you want to use sample notebooks from Azure Machine Learning, see [how to configure an environment with Jupyter Notebooks](../machine-learning/how-to-configure-environment.md#jupyter-notebooks).
 
 ### Publish the template machine
 

articles/machine-learning/how-to-configure-environment.md

@@ -10,7 +10,7 @@ ms.subservice: core
 ms.reviewer: larryfr
 ms.date: 03/22/2021
 ms.topic: how-to
-ms.custom: devx-track-python, contperf-fy21q1, devx-track-azurecli, sdkv1, event-tier1-build-2022
+ms.custom: devx-track-python, contperf-fy21q1, devx-track-azurecli, event-tier1-build-2022
 ---
 
 # Set up a Python development environment for Azure Machine Learning
@@ -21,22 +21,22 @@ The following table shows each development environment covered in this article,
 
 | Environment | Pros | Cons |
 | --- | --- | --- |
-| [Local environment](#local) | Full control of your development environment and dependencies. Run with any build tool, environment, or IDE of your choice. | Takes longer to get started. Necessary SDK packages must be installed, and an environment must also be installed if you don't already have one. |
-| [The Data Science Virtual Machine (DSVM)](#dsvm) | Similar to the cloud-based compute instance (Python and the SDK are pre-installed), but with additional popular data science and machine learning tools pre-installed. Easy to scale and combine with other custom tools and workflows. | A slower getting started experience compared to the cloud-based compute instance. |
-| [Azure Machine Learning compute instance](#compute-instance) | Easiest way to get started. The entire SDK is already installed in your workspace VM, and notebook tutorials are pre-cloned and ready to run. | Lack of control over your development environment and dependencies. Additional cost incurred for Linux VM (VM can be stopped when not in use to avoid charges). See [pricing details](https://azure.microsoft.com/pricing/details/virtual-machines/linux/). |
+| [Local environment](#local-computer-or-remote-vm-environment) | Full control of your development environment and dependencies. Run with any build tool, environment, or IDE of your choice. | Takes longer to get started. Necessary SDK packages must be installed, and an environment must also be installed if you don't already have one. |
+| [The Data Science Virtual Machine (DSVM)](#data-science-virtual-machine) | Similar to the cloud-based compute instance (Python is pre-installed), but with additional popular data science and machine learning tools pre-installed. Easy to scale and combine with other custom tools and workflows. | A slower getting started experience compared to the cloud-based compute instance. |
+| [Azure Machine Learning compute instance](#azure-machine-learning-compute-instance) | Easiest way to get started. The SDK is already installed in your workspace VM, and notebook tutorials are pre-cloned and ready to run. | Lack of control over your development environment and dependencies. Additional cost incurred for Linux VM (VM can be stopped when not in use to avoid charges). See [pricing details](https://azure.microsoft.com/pricing/details/virtual-machines/linux/). |
 | [Azure Databricks](how-to-configure-databricks-automl-environment.md) | Ideal for running large-scale intensive machine learning workflows on the scalable Apache Spark platform. | Overkill for experimental machine learning, or smaller-scale experiments and workflows. Additional cost incurred for Azure Databricks. See [pricing details](https://azure.microsoft.com/pricing/details/databricks/). |
 
 This article also provides additional usage tips for the following tools:
 
 * Jupyter Notebooks: If you're already using Jupyter Notebooks, the SDK has some extras that you should install.
 
-* Visual Studio Code: If you use Visual Studio Code, the [Azure Machine Learning extension](https://marketplace.visualstudio.com/items?itemName=ms-toolsai.vscode-ai) includes extensive language support for Python as well as features to make working with the Azure Machine Learning much more convenient and productive.
+* Visual Studio Code: If you use Visual Studio Code, the [Azure Machine Learning extension](https://marketplace.visualstudio.com/items?itemName=ms-toolsai.vscode-ai) includes language support for Python, and features to make working with the Azure Machine Learning much more convenient and productive.
 
 ## Prerequisites
 
 * Azure Machine Learning workspace. If you don't have one, you can create an Azure Machine Learning workspace through the [Azure portal](how-to-manage-workspace.md), [Azure CLI](how-to-manage-workspace-cli.md#create-a-workspace), and [Azure Resource Manager templates](how-to-create-workspace-template.md).
 
-### <a id="workspace"></a> Local and DSVM only: Create a workspace configuration file
+### Local and DSVM only: Create a workspace configuration file
 
 The workspace configuration file is a JSON file that tells the SDK how to communicate with your Azure Machine Learning workspace. The file is named *config.json*, and it has the following format:
 
@@ -48,9 +48,9 @@ The workspace configuration file is a JSON file that tells the SDK how to commun
 }
 ```
 
-This JSON file must be in the directory structure that contains your Python scripts or Jupyter Notebooks. It can be in the same directory, a subdirectory named *.azureml*, or in a parent directory.
+This JSON file must be in the directory structure that contains your Python scripts or Jupyter Notebooks. It can be in the same directory, a subdirectory named.azureml*, or in a parent directory.
 
-To use this file from your code, use the [`Workspace.from_config`](/python/api/azureml-core/azureml.core.workspace.workspace#from-config-path-none--auth-none---logger-none---file-name-none-) method. This code loads the information from the file and connects to your workspace.
+To use this file from your code, use the [`MLClient.from_config`](/python/api/azure-ai-ml/azure.ai.ml.mlclient#azure-ai-ml-mlclient-from-config) method. This code loads the information from the file and connects to your workspace.
 
 Create a workspace configuration file in one of the following methods:
 
@@ -62,26 +62,25 @@ Create a workspace configuration file in one of the following methods:
 
 * Azure Machine Learning Python SDK
 
-    Create a script to connect to your Azure Machine Learning workspace and use the [`write_config`](/python/api/azureml-core/azureml.core.workspace.workspace#write-config-path-none--file-name-none-) method to generate your file and save it as *.azureml/config.json*. Make sure to replace `subscription_id`,`resource_group`, and `workspace_name` with your own.
+    Create a script to connect to your Azure Machine Learning workspace. Make sure to replace `subscription_id`,`resource_group`, and `workspace_name` with your own.
 
-    [!INCLUDE [sdk v1](../../includes/machine-learning-sdk-v1.md)]
+    [!INCLUDE [sdk v2](../../includes/machine-learning-sdk-v2.md)]
 
     ```python
-    from azureml.core import Workspace
-
-    subscription_id = '<subscription-id>'
-    resource_group  = '<resource-group>'
-    workspace_name  = '<workspace-name>'
-
-    try:
-        ws = Workspace(subscription_id = subscription_id, resource_group = resource_group, workspace_name = workspace_name)
-        ws.write_config()
-        print('Library configuration succeeded')
-    except:
-        print('Workspace not found')
+        #import required libraries
+        from azure.ai.ml import MLClient
+        from azure.identity import DefaultAzureCredential
+
+        #Enter details of your AzureML workspace
+        subscription_id = '<SUBSCRIPTION_ID>'
+        resource_group = '<RESOURCE_GROUP>'
+        workspace = '<AZUREML_WORKSPACE_NAME>'
+      
+        #connect to the workspace
+        ml_client = MLClient(DefaultAzureCredential(), subscription_id, resource_group, workspace)
     ```
 
-## <a id="local"></a>Local computer or remote VM environment
+## Local computer or remote VM environment
 
 You can set up an environment on a local computer or remote virtual machine, such as an Azure Machine Learning compute instance or Data Science VM. 
 
@@ -96,12 +95,12 @@ To configure a local development environment or remote VM:
     > If you're on Linux or macOS and use a shell other than bash (for example, zsh) you might receive errors when you run some commands. To work around this problem, use the `bash` command to start a new bash shell and run the commands there.
 
 1. Activate your newly created Python virtual environment.
-1. Install the [Azure Machine Learning Python SDK](/python/api/overview/azure/ml/install).
-1. To configure your local environment to use your Azure Machine Learning workspace, [create a workspace configuration file](#workspace) or use an existing one.
+1. Install the [Azure Machine Learning Python SDK](/python/api/overview/azure/ai-ml-readme).
+1. To configure your local environment to use your Azure Machine Learning workspace, [create a workspace configuration file](#local-and-dsvm-only-create-a-workspace-configuration-file) or use an existing one.
 
 Now that you have your local environment set up, you're ready to start working with Azure Machine Learning. See the [Azure Machine Learning Python getting started guide](tutorial-1st-experiment-hello-world.md) to get started.
 
-### <a id="jupyter"></a>Jupyter Notebooks
+### Jupyter Notebooks
 
 When running a local Jupyter Notebook server, it's recommended that you create an IPython kernel for your Python virtual environment. This helps ensure the expected kernel and package import behavior.
 
@@ -119,10 +118,10 @@ When running a local Jupyter Notebook server, it's recommended that you create a
 
 1. Launch the Jupyter Notebook server
 
-See the [Azure Machine Learning notebooks repository](https://github.com/Azure/MachineLearningNotebooks) to get started with Azure Machine Learning and Jupyter Notebooks.
-Also see the community-driven repository, [AzureML-Examples](https://github.com/Azure/azureml-examples).
+    > [!TIP]
+    For example notebooks, see the [AzureML-Examples](https://github.com/Azure/azureml-examples) repository. SDK examples are located under [/sdk/python](https://github.com/Azure/azureml-examples/tree/main/sdk/python). For example, the [Configuration notebook](https://github.com/Azure/azureml-examples/blob/main/sdk/python/jobs/configuration.ipynb) example.
 
-### <a id="vscode"></a>Visual Studio Code
+### Visual Studio Code
 
 To use Visual Studio Code for development:
 
@@ -136,11 +135,11 @@ Once you have the Visual Studio Code extension installed, use it to:
 * [Run and debug experiments](how-to-debug-visual-studio-code.md)
 * [Deploy trained models](tutorial-train-deploy-image-classification-model-vscode.md).
 
-## <a id="compute-instance"></a>Azure Machine Learning compute instance
+## Azure Machine Learning compute instance
 
 The Azure Machine Learning [compute instance](concept-compute-instance.md) is a secure, cloud-based Azure workstation that provides data scientists with a Jupyter Notebook server, JupyterLab, and a fully managed machine learning environment.
 
-There is nothing to install or configure for a compute instance.  
+There's nothing to install or configure for a compute instance.  
 
 Create one anytime from within your Azure Machine Learning workspace. Provide just a name and specify an Azure VM type. Try it now with this [Tutorial: Setup environment and workspace](quickstart-create-resources.md).
 
@@ -153,7 +152,7 @@ In addition to a Jupyter Notebook server and JupyterLab, you can use compute ins
 
 You can also use the Azure Machine Learning Visual Studio Code extension to [connect to a remote compute instance using VS Code](how-to-set-up-vs-code-remote.md).
 
-## <a id="dsvm"></a>Data Science Virtual Machine
+## Data Science Virtual Machine
 
 The Data Science VM is a customized virtual machine (VM) image you can use as a development environment. It's designed for data science work that's pre-configured tools and software like:
 
@@ -193,28 +192,28 @@ To use the Data Science VM as a development environment:
         az vm create --resource-group YOUR-RESOURCE-GROUP-NAME --name YOUR-VM-NAME --image microsoft-dsvm:dsvm-windows:server-2016:latest --admin-username YOUR-USERNAME --admin-password YOUR-PASSWORD --authentication-type password
         ```
 
-1. Activate the conda environment containing the Azure Machine Learning SDK.
+1. Create a conda environment for the Azure Machine Learning SDK:
 
-    * For Ubuntu Data Science VM:
-
-        ```bash
-        conda activate py36
-        ```
-
-    * For Windows Data Science VM:
+    ```bash
+    conda create -n py310 python=310
+    ```
 
-        ```bash
-        conda activate AzureML
-        ```
+1. Once the environment has been created, activate it and install the SDK
 
-1. To configure the Data Science VM to use your Azure Machine Learning workspace, [create a workspace configuration file](#workspace) or use an existing one.
+    ```bash
+    conda activate py310
+    pip install azure-ai-ml
+    ``` 
 
-Similar to local environments, you can use Visual Studio Code and the [Azure Machine Learning Visual Studio Code extension](#vscode) to interact with Azure Machine Learning.
+1. To configure the Data Science VM to use your Azure Machine Learning workspace, [create a workspace configuration file](#local-and-dsvm-only-create-a-workspace-configuration-file) or use an existing one.
 
-For more information, see [Data Science Virtual Machines](https://azure.microsoft.com/services/virtual-machines/data-science-virtual-machines/).
+    > [!TIP]
+    > Similar to local environments, you can use Visual Studio Code and the [Azure Machine Learning Visual Studio Code extension](#visual-studio-code) to interact with Azure Machine Learning.
+    >
+    > For more information, see [Data Science Virtual Machines](https://azure.microsoft.com/services/virtual-machines/data-science-virtual-machines/).
 
 
 ## Next steps
 
 - [Train and deploy a model](tutorial-train-deploy-notebook.md) on Azure Machine Learning with the MNIST dataset.
-- See the [Azure Machine Learning SDK for Python reference](/python/api/overview/azure/ml/intro). 
+- See the [Azure Machine Learning SDK for Python reference](https://aka.ms/sdk-v2-install). 

articles/machine-learning/how-to-create-component-pipeline-python.md

@@ -270,7 +270,7 @@ Create a `MLClient` object to manage Azure Machine Learning services.
 [!notebook-python[] (~/azureml-examples-v2samplesreorg/sdk/python/jobs/pipelines/2e_image_classification_keras_minist_convnet/image_classification_keras_minist_convnet.ipynb?name=workspace)]
 
 > [!IMPORTANT]
-> This code snippet expects the workspace configuration json file to be saved in the current directory or its parent. For more information on creating a workspace, see [Create workspace resources](quickstart-create-resources.md). For more information on saving the configuration to file, see [Create a workspace configuration file](how-to-configure-environment.md#workspace).
+> This code snippet expects the workspace configuration json file to be saved in the current directory or its parent. For more information on creating a workspace, see [Create workspace resources](quickstart-create-resources.md). For more information on saving the configuration to file, see [Create a workspace configuration file](how-to-configure-environment.md#local-and-dsvm-only-create-a-workspace-configuration-file).
 
 #### Submit pipeline job to workspace
 

articles/machine-learning/how-to-deploy-model-cognitive-search.md

@@ -55,7 +55,7 @@ An Azure Machine Learning workspace provides a centralized place to work with al
 To connect to an existing workspace, use the following code:
 
 > [!IMPORTANT]
-> This code snippet expects the workspace configuration to be saved in the current directory or its parent. For more information, see [Create and manage Azure Machine Learning workspaces](how-to-manage-workspace.md). For more information on saving the configuration to file, see [Create a workspace configuration file](how-to-configure-environment.md#workspace).
+> This code snippet expects the workspace configuration to be saved in the current directory or its parent. For more information, see [Create and manage Azure Machine Learning workspaces](how-to-manage-workspace.md). For more information on saving the configuration to file, see [Create a workspace configuration file](v1/how-to-configure-environment-v1.md).
 
 ```python
 from azureml.core import Workspace