Access a compute instance terminal in your workspace

Access the terminal of a compute instance in your workspace to:

• Use files from Git and version files. These files are stored in your workspace file system, not restricted to a single compute instance.
• Install packages on the compute instance.
• Create extra kernels on the compute instance.

Prerequisites

• An Azure subscription. If you don't have an Azure subscription, create a Trial before you begin.
• A Machine Learning workspace. See Create workspace resources.

Access a terminal

To access the terminal:

1. Open your workspace in Azure Machine Learning studio.

2. On the left side, select Notebooks.

3. Select the Open terminal image.

   

4. When a compute instance is running, the terminal window for that compute instance appears.

5. When no compute instance is running, use the Compute section to start or create a compute instance.

In addition to the previous steps, you can also access the terminal from:

• In VS Code: Select the Terminal > New Terminal from the menu. The entry may be hidden behind ... in menu. To learn more about connecting to your workspace from VS Code, see Work in VS Code remotely connected to a compute instance.
• RStudio or Posit Workbench (formerly RStudio Workbench) (See Add custom applications such as RStudio or Posit Workbench)): Select the Terminal tab on top left.
• Jupyter Lab: Select the Terminal tile under the Other heading in the Launcher tab.
• Jupyter: Select New>Terminal on top right in the Files tab.
• SSH to the machine, if you enabled SSH access when the compute instance was created. If the compute instance is in a managed virtual network and doesn't have a public IP address, use the az ml compute connect-ssh command to connect to the compute instance.

Copy and paste in the terminal

In the Notebooks section, you can copy and paste text between the terminal and the notebook cells.

• Windows: Ctrl-c to copy and use Ctrl-v or Ctrl-Shift-v or Shift-Insert to paste.
• Mac OS: Cmd-c to copy and Cmd-v to paste.
• FireFox and Internet Explorer may not support clipboard permissions properly.

Use files from Git and version files

Access all Git operations from the terminal. All Git files and folders are stored in your workspace file system. This storage allows you to use these files from any compute instance in your workspace.

To integrate Git with your Azure Machine Learning workspace, see Git integration for Azure Machine Learning.

Install packages

Install packages from a terminal window. Install packages into the kernel that you want to use to run your notebooks. The default kernel is python310-sdkv2.

Or you can install packages directly in Jupyter Notebook, RStudio, or Posit Workbench (formerly RStudio Workbench):

• RStudio or Posit Workbench(see Add custom applications such as RStudio or Posit Workbench): Use the Packages tab on the bottom right, or the Console tab on the top left.
• Python: Add install code and execute in a Jupyter Notebook cell.

Add new kernels

To add a new Jupyter kernel to the compute instance:

1. Use the terminal window to create a new environment. For example, the following command creates newenv:

   conda create --name newenv
   

2. Activate the environment. For example, after creating newenv:

   conda activate newenv
   

3. Install pip and ipykernel package to the new environment and create a kernel for that conda env

   conda install pip
   conda install ipykernel
   python -m ipykernel install --user --name newenv --display-name "Python (newenv)"
   

Any of the available Jupyter Kernels can be installed.

To add a new R kernel to the compute instance:

1. Use the terminal window to create a new environment. For example, the following command creates r_env:

   conda create -n r_env r-essentials r-base
   

2. Activate the environment. For example, after creating r_env:

   conda activate r_env
   

3. Run R in the new environment:

   R
   

4. At the R prompt, run IRkernel:

   IRkernel::installspec(name = 'irenv', displayname = 'New R Env')
   

5. Quit the R session.

   q()
   

It takes a few minutes before the new R kernel is ready to use. If you get an error saying it's invalid, wait and then try again.

For more information about conda, see Using R language with Anaconda. For more information about IRkernel, see Native R kernel for Jupyter.

Remove added kernels

To remove an added Jupyter kernel from the compute instance, you must remove the kernelspec, and (optionally) the conda environment. You can also choose to keep the conda environment. You must remove the kernelspec, or your kernel is still selectable and could cause unexpected behavior.

To remove the kernelspec:

1. Use the terminal window to list and find the kernelspec:

   jupyter kernelspec list
   

2. Remove the kernelspec, replacing UNWANTED_KERNEL with the kernel you'd like to remove:

   jupyter kernelspec uninstall UNWANTED_KERNEL
   

To also remove the conda environment:

1. Use the terminal window to list and find the conda environment:

   conda env list
   

2. Remove the conda environment, replacing ENV_NAME with the conda environment you'd like to remove:

   conda env remove -n ENV_NAME
   

Upon refresh, the kernel list in your notebooks view should reflect the changes you made.

Manage terminal sessions

Terminal sessions can stay active if terminal tabs aren't properly closed. Too many active terminal sessions can impact the performance of your compute instance.

Select Manage active sessions in the terminal toolbar to see a list of all active terminal sessions and shut down the sessions you no longer need.

Learn more about how to manage sessions running on your compute at Managing notebook and terminal sessions.

Related content

• Managing notebook and terminal sessions
• Git integration for Azure Machine Learning
• Work in VS Code remotely connected to a compute instance