Configure a development environment for Azure Machine Learning

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In this article, you learn how to configure a development environment to work with Azure Machine Learning. Azure Machine Learning is platform agnostic. The only hard requirement for your development environment is Python 3. An isolated environment like Anaconda or Virtualenv is also recommended.

The following table shows each development environment covered in this article, along with pros and cons.

Environment Pros Cons
Cloud-based Azure Machine Learning compute instance (preview) 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.
Local 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.
Azure Databricks 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.
The Data Science Virtual Machine (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.

This article also provides additional usage tips for the following tools:

  • Jupyter Notebooks: If you're already using the Jupyter Notebook, the SDK has some extras that you should install.

  • Visual Studio Code: If you use Visual Studio Code, the Azure Machine Learning extension includes extensive language support for Python as well as features to make working with the Azure Machine Learning much more convenient and productive.


An Azure Machine Learning workspace. To create the workspace, see Create an Azure Machine Learning workspace. A workspace is all you need to get started with your own cloud-based notebook server, a DSVM, or Azure Databricks.

To install the SDK environment for your local computer, Jupyter Notebook server or Visual Studio Code you also need:

  • Either the Anaconda or Miniconda package manager.

  • On Linux or macOS, you need the bash shell.


    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.

  • On Windows, you need the command prompt or Anaconda prompt (installed by Anaconda and Miniconda).

Your own cloud-based compute instance

The Azure Machine Learning compute instance (preview) is a secure, cloud-based Azure workstation that provides data scientists with a Jupyter notebook server, JupyterLab, and a fully prepared ML environment.

There is 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.

To learn more about compute instances, including how to install packages, see compute instances.

To stop incurring compute charges, stop the compute instance.

Data Science Virtual Machine

The DSVM is a customized virtual machine (VM) image. It's designed for data science work that's pre-configured with:

  • Packages such as TensorFlow, PyTorch, Scikit-learn, XGBoost, and the Azure Machine Learning SDK
  • Popular data science tools such as Spark Standalone and Drill
  • Azure tools such as the Azure CLI, AzCopy, and Storage Explorer
  • Integrated development environments (IDEs) such as Visual Studio Code and PyCharm
  • Jupyter Notebook Server

The Azure Machine Learning SDK works on either the Ubuntu or Windows version of the DSVM. But if you plan to use the DSVM as a compute target as well, only Ubuntu is supported.

To use the DSVM as a development environment:

  1. Create a DSVM in either of the following environments:

    • The Azure portal:

    • The Azure CLI:


      • When you use the Azure CLI, you must first sign in to your Azure subscription by using the az login command.

      • When you use the commands in this step, you must provide a resource group name, a name for the VM, a username, and a password.

      • To create an Ubuntu Data Science Virtual Machine, use the following command:

        # create a Ubuntu DSVM in your resource group
        # note you need to be at least a contributor to the resource group in order to execute this command successfully
        # If you need to create a new resource group use: "az group create --name YOUR-RESOURCE-GROUP-NAME --location YOUR-REGION (For example: westus2)"
        az vm create --resource-group YOUR-RESOURCE-GROUP-NAME --name YOUR-VM-NAME --image microsoft-dsvm:linux-data-science-vm-ubuntu:linuxdsvmubuntu:latest --admin-username YOUR-USERNAME --admin-password YOUR-PASSWORD --generate-ssh-keys --authentication-type password
      • To create a Windows Data Science Virtual Machine, use the following command:

        # create a Windows Server 2016 DSVM in your resource group
        # note you need to be at least a contributor to the resource group in order to execute this command successfully
        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
  2. The Azure Machine Learning SDK is already installed on the DSVM. To use the Conda environment that contains the SDK, use one of the following commands:

    • For Ubuntu DSVM:

      conda activate py36
    • For Windows DSVM:

      conda activate AzureML
  3. To verify that you can access the SDK and check the version, use the following Python code:

    import azureml.core
  4. To configure the DSVM to use your Azure Machine Learning workspace, see the Create a workspace configuration file section.

For more information, see Data Science Virtual Machines.

Local computer

When you're using a local computer (which might also be a remote virtual machine), create an Anaconda environment and install the SDK. Here's an example:

  1. Download and install Anaconda (Python 3.7 version) if you don't already have it.

  2. Open an Anaconda prompt and create an environment with the following commands:

    Run the following command to create the environment.

    conda create -n myenv python=3.7.7

    Then activate the environment.

    conda activate myenv

    This example creates an environment using python 3.7.7, but any specific subversions can be chosen. SDK compatibility may not be guaranteed with certain major versions (3.5+ is recommended), and it's recommended to try a different version/subversion in your Anaconda environment if you run into errors. It will take several minutes to create the environment while components and packages are downloaded.

  3. Run the following commands in your new environment to enable environment-specific IPython kernels. This will ensure expected kernel and package import behavior when working with Jupyter Notebooks within Anaconda environments:

    conda install notebook ipykernel

    Then run the following command to create the kernel:

    ipython kernel install --user --name myenv --display-name "Python (myenv)"
  4. Use the following commands to install packages:

    This command installs the base Azure Machine Learning SDK with notebook and automl extras. The automl extra is a large install, and can be removed from the brackets if you don't intend to run automated machine learning experiments. The automl extra also includes the Azure Machine Learning Data Prep SDK by default as a dependency.

    pip install azureml-sdk[notebooks,automl]


    • If you get a message that PyYAML can't be uninstalled, use the following command instead:

      pip install --upgrade azureml-sdk[notebooks,automl] --ignore-installed PyYAML

    • Starting with macOS Catalina, zsh (Z shell) is the default login shell and interactive shell. In zsh, use the following command which escapes brackets with "\" (backslash):

      pip install --upgrade azureml-sdk\[notebooks,automl\]

    It will take several minutes to install the SDK. For more information on installation options, see the install guide.

  5. Install other packages for your machine learning experimentation.

    Use either of the following commands and replace <new package> with the package you want to install. Installing packages via conda install requires that the package is part of the current channels (new channels can be added in Anaconda Cloud).

    conda install <new package>

    Alternatively, you can install packages via pip.

    pip install <new package>

Jupyter Notebooks

Jupyter Notebooks are part of the Jupyter Project. They provide an interactive coding experience where you create documents that mix live code with narrative text and graphics. Jupyter Notebooks are also a great way to share your results with others, because you can save the output of your code sections in the document. You can install Jupyter Notebooks on a variety of platforms.

The procedure in the Local computer section installs necessary components for running Jupyter Notebooks in an Anaconda environment.

To enable these components in your Jupyter Notebook environment:

  1. Open an Anaconda prompt and activate your environment.

    conda activate myenv
  2. Clone the GitHub repository for a set of sample notebooks.

    git clone
  3. Launch the Jupyter Notebook server with the following command:

    jupyter notebook
  4. To verify that Jupyter Notebook can use the SDK, create a New notebook, select Python 3 as your kernel, and then run the following command in a notebook cell:

    import azureml.core
  5. If you encounter issues importing modules and receive a ModuleNotFoundError, ensure your Jupyter kernel is connected to the correct path for your environment by running the following code in a Notebook cell.

    import sys
  6. To configure the Jupyter Notebook to use your Azure Machine Learning workspace, go to the Create a workspace configuration file section.

Visual Studio Code

Visual Studio Code is a very popular cross platform code editor that supports an extensive set of programming languages and tools through extensions available in the Visual Studio marketplace. The Azure Machine Learning extension installs the Python extension for coding in all types of Python environments (virtual, Anaconda, etc.). In addition, it provides convenience features for working with Azure Machine Learning resources and running Azure Machine Learning experiments all without leaving Visual Studio Code.

To use Visual Studio Code for development:

  1. Install the Azure Machine Learning extension for Visual Studio Code, see Azure Machine Learning.

    For more information, see Use Azure Machine Learning for Visual Studio Code.

  2. Learn how to use Visual Studio Code for any type of Python development, see Get started with Python in VSCode.

    • To select the SDK Python environment containing the SDK, open VS Code, and then select Ctrl+Shift+P (Linux and Windows) or Command+Shift+P (Mac).

      • The Command Palette opens.
    • Enter Python: Select Interpreter, and then select the appropriate environment

  3. To validate that you can use the SDK, create a new Python file (.py) that contains the following code:

    import azureml.core

    Run this code by clicking the "Run cell" CodeLens or simply press shift-enter.

Azure Databricks

Azure Databricks is an Apache Spark-based environment in the Azure cloud. It provides a collaborative Notebook-based environment with CPU or GPU-based compute cluster.

How Azure Databricks works with Azure Machine Learning:

  • You can train a model using Spark MLlib and deploy the model to ACI/AKS from within Azure Databricks.
  • You can also use automated machine learning capabilities in a special Azure ML SDK with Azure Databricks.
  • You can use Azure Databricks as a compute target from an Azure Machine Learning pipeline.

Set up your Databricks cluster

Create a Databricks cluster. Some settings apply only if you install the SDK for automated machine learning on Databricks. It will take few minutes to create the cluster.

Use these settings:

Setting Applies to Value
Cluster name always yourclustername
Databricks Runtime always Non-ML Runtime 6.5 (scala 2.11, spark 2.4.3)
Python version always 3
Workers always 2 or higher
Worker node VM types
(determines max # of concurrent iterations)
Automated ML
Memory optimized VM preferred
Enable Autoscaling Automated ML

Wait until the cluster is running before proceeding further.

Install the correct SDK into a Databricks library

Once the cluster is running, create a library to attach the appropriate Azure Machine Learning SDK package to your cluster.

  1. Right-click the current Workspace folder where you want to store the library. Select Create > Library.

  2. Choose only one option (no other SDK installation are supported)

    SDK package extras Source PyPi Name      
    For Databricks Upload Python Egg or PyPI azureml-sdk[databricks]
    For Databricks -with-
    automated ML capabilities
    Upload Python Egg or PyPI azureml-sdk[automl]


    No other SDK extras can be installed. Choose only one of the preceding options [databricks] or [automl].

    • Do not select Attach automatically to all clusters.
    • Select Attach next to your cluster name.
  3. Monitor for errors until status changes to Attached, which may take several minutes. If this step fails:

    Try restarting your cluster by:

    1. In the left pane, select Clusters.
    2. In the table, select your cluster name.
    3. On the Libraries tab, select Restart.

    Also consider:

    • In AutoML config, when using Azure Databricks add the following parameters:
      1. max_concurrent_iterations is based on number of worker nodes in your cluster.
      2. spark_context=sc is based on the default spark context.
    • Or, if you have an old SDK version, deselect it from cluster's installed libs and move to trash. Install the new SDK version and restart the cluster. If there is an issue after the restart, detach and reattach your cluster.

If install was successful, the imported library should look like one of these:

SDK for Databricks without automated machine learning Azure Machine Learning SDK for Databricks

SDK for Databricks WITH automated machine learning SDK with automated machine learning installed on Databricks

Start exploring

Try it out:

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:

    "subscription_id": "<subscription-id>",
    "resource_group": "<resource-group>",
    "workspace_name": "<workspace-name>"

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 ws=Workspace.from_config(). This code loads the information from the file and connects to your workspace.

You can create the configuration file in three ways:

  • Use ws.write_config: to write a config.json file. The file contains the configuration information for your workspace. You can download or copy the config.json to other development environments.

  • Download the file: In the Azure portal, select Download config.json from the Overview section of your workspace.

    Azure portal

  • Create the file programmatically: In the following code snippet, you connect to a workspace by providing the subscription ID, resource group, and workspace name. It then saves the workspace configuration to the file:

    from azureml.core import Workspace
    subscription_id = '<subscription-id>'
    resource_group  = '<resource-group>'
    workspace_name  = '<workspace-name>'
        ws = Workspace(subscription_id = subscription_id, resource_group = resource_group, workspace_name = workspace_name)
        print('Library configuration succeeded')
        print('Workspace not found')

    This code writes the configuration file to the .azureml/config.json file.

Next steps