Connect to an Azure Machine Learning compute instance in Visual Studio Code (preview)

In this article, you'll learn how to connect to an Azure Machine Learning compute instance using Visual Studio Code.

An Azure Machine Learning Compute Instance is a fully managed cloud-based workstation for data scientists and provides management and enterprise readiness capabilities for IT administrators.

There are two ways you can connect to a compute instance from Visual Studio Code:

  • Remote Jupyter Notebook server. This option allows you to set a compute instance as a remote Jupyter Notebook server.
  • Visual Studio Code remote development. Visual Studio Code remote development allows you to use a container, remote machine, or the Windows Subsystem for Linux (WSL) as a full-featured development environment.

Configure compute instance as remote notebook server

In order to configure a compute instance as a remote Jupyter Notebook server you'll need a few prerequisites:

To connect to a compute instance:

  1. Open a Jupyter Notebook in Visual Studio Code.

  2. When the integrated notebook experience loads, select Jupyter Server.

    Launch Azure Machine Learning remote Jupyter notebook server dropdown

    Alternatively, you also use the command palette:

    1. Open the command palette by selecting View > Command Palette from the menu bar.
    2. Enter into the text box Azure ML: Connect to Compute instance Jupyter server.
  3. Choose Azure ML Compute Instances from the list of Jupyter server options.

  4. Select your subscription from the list of subscriptions. If you have have previously configured your default Azure Machine Learning workspace, this step is skipped.

  5. Select your workspace.

  6. Select your compute instance from the list. If you don't have one, select Create new Azure ML Compute Instance and follow the prompts to create one.

  7. For the changes to take effect, you have to reload Visual Studio Code.

  8. Open a Jupyter Notebook and run a cell.


You MUST run a cell in order to establish the connection.

At this point, you can continue to run cells in your Jupyter notebook.


You can also work with Python script files (.py) containing Jupyter-like code cells. For more information, see the Visual Studio Code Python interactive documentation.

Configure compute instance remote development

For a full-featured remote development experience, you'll need a few prerequisites:


On Windows platforms, you must install an OpenSSH compatible SSH client if one is not already present. PuTTY is not supported on Windows since the ssh command must be in the path.

Get the IP and SSH port for your compute instance

  1. Go to the Azure Machine Learning studio at

  2. Select your workspace.

  3. Click the Compute Instances tab.

  4. In the Application URI column, click the SSH link of the compute instance you want to use as a remote compute.

  5. In the dialog, take note of the IP Address and SSH port.

  6. Save your private key to the ~/.ssh/ directory on your local computer; for instance, open an editor for a new file and paste the key in:


    vi ~/.ssh/id_azmlcitest_rsa  


    notepad C:\Users\<username>\.ssh\id_azmlcitest_rsa

    The private key will look somewhat like this:

    -----END RSA PRIVATE KEY-----
  7. Change permissions on file to make sure only you can read the file.

    chmod 600 ~/.ssh/id_azmlcitest_rsa

Add instance as a host

Open the file ~/.ssh/config (Linux) or C:\Users<username>.ssh\config (Windows) in an editor and add a new entry similar to the content below:

Host azmlci1 


    Port 50000 

    User azureuser 

    IdentityFile ~/.ssh/id_azmlcitest_rsa

Here some details on the fields:

Field Description
Host Use whatever shorthand you like for the compute instance
HostName This is the IP address of the compute instance
Port This is the port shown on the SSH dialog above
User This needs to be azureuser
IdentityFile Should point to the file where you saved the private key

Now, you should be able to ssh to your compute instance using the shorthand you used above, ssh azmlci1.

Connect VS Code to the instance

  1. Click the Remote-SSH icon from the Visual Studio Code activity bar to show your SSH configurations.

  2. Right-click the SSH host configuration you just created.

  3. Select Connect to Host in Current Window.

From here on, you are entirely working on the compute instance and you can now edit, debug, use git, use extensions, etc. -- just like you can with your local Visual Studio Code.

Next steps

Now that you've set up Visual Studio Code Remote, you can use a compute instance as remote compute from Visual Studio Code to interactively debug your code.

Tutorial: Train your first ML model shows how to use a compute instance with an integrated notebook.