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Access the terminal of a compute instance in your workspace to:
To access the terminal:
Open your workspace in Azure Machine Learning studio.
On the left side, select Notebooks.
Select the Open terminal image.
When a compute instance is running, the terminal window for that compute instance appears.
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:
az ml compute connect-ssh
command to connect to the compute instance.In the Notebooks section, you can copy and paste text between the terminal and the notebook cells.
- Windows:
Ctrl-c
to copy and useCtrl-v
orCtrl-Shift-v
orShift-Insert
to paste.- Mac OS:
Cmd-c
to copy andCmd-v
to paste.- FireFox and Internet Explorer may not support clipboard permissions properly.
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.
Note
Add your files and folders anywhere under the ~/cloudfiles/code/Users folder so they will be visible in all your Jupyter environments.
To integrate Git with your Azure Machine Learning workspace, see Git integration for Azure Machine Learning.
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):
Note
For package management within a Python notebook, use %pip or %conda magic functions to automatically install packages into the currently-running kernel, rather than !pip or !conda which refers to all packages (including packages outside the currently-running kernel)
Warning
While customizing the compute instance, make sure you do not delete conda environments or jupyter kernels that you didn't create. Doing so may damage Jupyter/JupyterLab functionality.
To add a new Jupyter kernel to the compute instance:
Use the terminal window to create a new environment. For example, the following command creates newenv
:
conda create --name newenv
Activate the environment. For example, after creating newenv
:
conda activate newenv
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:
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
Activate the environment. For example, after creating r_env
:
conda activate r_env
Run R in the new environment:
R
At the R prompt, run IRkernel
:
IRkernel::installspec(name = 'irenv', displayname = 'New R Env')
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.
Warning
While customizing the compute instance, make sure you do not delete conda environments or jupyter kernels that you didn't create.
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:
Use the terminal window to list and find the kernelspec:
jupyter kernelspec list
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:
Use the terminal window to list and find the conda environment:
conda env list
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.
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.
Warning
Make sure you close any sessions you no longer need to preserve your compute instance's resources and optimize your performance.
Events
Take the Microsoft Learn Challenge
Nov 19, 11 PM - Jan 10, 11 PM
Ignite Edition - Build skills in Microsoft Azure and earn a digital badge by January 10!
Register now