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Learn how to run your Jupyter notebooks directly in your workspace in Azure Machine Learning studio. While you can launch Jupyter or JupyterLab, you can also edit and run your notebooks without leaving the workspace.

For information on how to create and manage files, including notebooks, see Create and manage files in your workspace.

If you have a shared notebook on OneDrive, and you don’t want other people to see or edit it anymore, you can keep the notebook on OneDrive and change the sharing settings. To prevent a specific person from seeing or editing your notebook, you can change the Shared with permissions. OneNote is your personal digital notebook. Create content, organize your work, and collaborate with others. Select OneDrive - Personal, give your notebook a name, and then choose Create Notebook. If you’ve already created a notebook on your computer, you can move it to OneDrive. After your notebook is on OneDrive you can share it with others. A subscription to make the most of your time. Try one month free.

Important

Features marked as (preview) are provided without a service level agreement, and it's not recommended for production workloads. Certain features might not be supported or might have constrained capabilities.For more information, see Supplemental Terms of Use for Microsoft Azure Previews.

Prerequisites

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

Edit a notebook

To edit a notebook, open any notebook located in the User files section of your workspace. Click on the cell you wish to edit. If you don't have any notebooks in this section, see Create and manage files in your workspace.

You can edit the notebook without connecting to a compute instance. When you want to run the cells in the notebook, select or create a compute instance. If you select a stopped compute instance, it will automatically start when you run the first cell.

When a compute instance is running, you can also use code completion, powered by Intellisense, in any Python notebook.

You can also launch Jupyter or JupyterLab from the notebook toolbar. Azure Machine Learning does not provide updates and fix bugs from Jupyter or JupyterLab as they are Open Source products outside of the boundary of Microsoft Support.

Focus mode

Use focus mode to expand your current view so you can focus on your active tabs. Focus mode hides the Notebooks file explorer.

1. In the terminal window toolbar, select Focus mode to turn on focus mode. Depending on your window width, the tool may be located under the ... menu item in your toolbar.

2. While in focus mode, return to the standard view by selecting Standard view.

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Code completion (IntelliSense)

IntelliSense is a code-completion aid that includes many features: List Members, Parameter Info, Quick Info, and Complete Word. With only a few keystrokes, you can:

• Learn more about the code you're using
• Keep track of the parameters you're typing
• Add calls to properties and methods

Insert code snippets (preview)

Use Ctrl+Space to trigger IntelliSense code snippets. Scroll through the suggestions or start typing to find the code you want to insert. Once you insert code, tab through the arguments to customize the code for your own use.

These same snippets are available when you open your notebook in VS Code. For a complete list of available snippets, see Azure Machine Learning VS Code Snippets.

You can browse and search the list of snippets by using the notebook toolbar to open the snippet panel.

From the snippets panel, you can also submit a request to add new snippets.

Clean your notebook (preview)

Over the course of creating a notebook, you typically end up with cells you used for data exploration or debugging. The gather feature will help you produce a clean notebook without these extraneous cells.

1. Run all of your notebook cells.
2. Select the cell containing the code you wish the new notebook to run. For example, the code that submits an experiment, or perhaps the code that registers a model.
3. Select the Gather icon that appears on the cell toolbar.
4. Enter the name for your new 'gathered' notebook.

The new notebook contains only code cells, with all cells required to produce the same results as the cell you selected for gathering.

Save and checkpoint a notebook

Azure Machine Learning creates a checkpoint file when you create an ipynb file.

In the notebook toolbar, select the menu and then File>Save and checkpoint to manually save the notebook and it will add a checkpoint file associated with the notebook.

Every notebook is autosaved every 30 seconds. AutoSave updates only the initial ipynb file, not the checkpoint file.

Select Checkpoints in the notebook menu to create a named checkpoint and to revert the notebook to a saved checkpoint.

Export a notebook

In the notebook toolbar, select the menu and then Export As to export the notebook as any of the supported types:

• Notebook
• Python
• HTML
• LaTeX

The exported file is saved on your computer.

Run a notebook or Python script

To run a notebook or a Python script, you first connect to a running compute instance.

• If you don't have a compute instance, use these steps to create one:

  1. In the notebook or script toolbar, to the right of the Compute dropdown, select + New Compute. Depending on your screen size, this may be located under a ... menu.
  2. Name the Compute and choose a Virtual Machine Size.
  3. Select Create.
  4. The compute instance is connected to the file automatically. You can now run the notebook cells or the Python script using the tool to the left of the compute instance.

• If you have a stopped compute instance, select Start compute to the right of the Compute dropdown. Depending on your screen size, this may be located under a ... menu.

Only you can see and use the compute instances you create. Your User files are stored separately from the VM and are shared among all compute instances in the workspace.

View logs and output

Use notebook widgets to view the progress of the run and logs. A widget is asynchronous and provides updates until training finishes. Azure Machine Learning widgets are also supported in Jupyter and JupterLab.

Explore variables in the notebook

On the notebook toolbar, use the Variable explorer tool to show the name, type, length, and sample values for all variables that have been created in your notebook.

Select the tool to show the variable explorer window.

Navigate with a TOC

On the notebook toolbar, use the Table of contents tool to display or hide the table of contents. Start a markdown cell with a heading to add it to the table of contents. Click on an entry in the table to scroll to that cell in the notebook.

Change the notebook environment

The notebook toolbar allows you to change the environment on which your notebook runs.

These actions will not change the notebook state or the values of any variables in the notebook:

Action	Result
Stop the kernel	Stops any running cell. Running a cell will automatically restart the kernel.
Navigate to another workspace section	Running cells are stopped.

These actions will reset the notebook state and will reset all variables in the notebook.

Action	Result
Change the kernel	Notebook uses new kernel
Switch compute	Notebook automatically uses the new compute.
Reset compute	Starts again when you try to run a cell
Stop compute	No cells will run
Open notebook in Jupyter or JupyterLab	Notebook opened in a new tab.

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Add new kernels

Use the terminal to create and add new kernels to your compute instance. The notebook will automatically find all Jupyter kernels installed on the connected compute instance.

Use the kernel dropdown on the right to change to any of the installed kernels.

Status indicators

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An indicator next to the Compute dropdown shows its status. The status is also shown in the dropdown itself.

Color	Compute status
Green	Compute running
Red	Compute failed
Black	Compute stopped
Light Blue	Compute creating, starting, restarting, setting Up
Gray	Compute deleting, stopping

An indicator next to the Kernel dropdown shows its status.

Color	Kernel status
Green	Kernel connected, idle, busy
Gray	Kernel not connected

Find compute details

Find details about your compute instances on the Compute page in studio.

Useful keyboard shortcuts

Similar to Jupyter Notebooks, Azure Machine Learning Studio notebooks have a modal user interface. The keyboard does different things depending on which mode the notebook cell is in. Azure Machine Learning Studio notebooks support the following two modes for a given code cell: command mode and edit mode.

Command mode shortcuts

A cell is in command mode when there is no text cursor prompting you to type. When a cell is in Command mode, you can edit the notebook as a whole but not type into individual cells. Enter command mode by pressing ESC or using the mouse to select outside of a cell's editor area. The left border of the active cell is blue and solid, and its Run button is blue.

Shortcut	Description
Enter	Enter edit mode
Shift + Enter	Run cell, select below
Control/Command + Enter	Run cell
Alt + Enter	Run cell, insert code cell below
Control/Command + Alt + Enter	Run cell, insert markdown cell below
Alt + R	Run all
Y	Convert cell to code
M	Convert cell to markdown
Up/K	Select cell above
Down/J	Select cell below
A	Insert code cell above
B	Insert code cell below
Control/Command + Shift + A	Insert markdown cell above
Control/Command + Shift + B	Insert markdown cell below
X	Cut selected cell
C	Copy selected cell
Shift + V	Paste selected cell above
V	Paste selected cell below
D D	Delete selected cell
O	Toggle output
Shift + O	Toggle output scrolling
I I	Interrupt kernel
0 0	Restart kernel
Shift + Space	Scroll up
Space	Scroll down
Tab	Change focus to next focusable item (when tab trap disabled)
Control/Command + S	Save notebook
1	Change to h1
2	Change to h2
3	Change to h3
4	Change to h4
5	Change to h5
6	Change to h6

Edit mode shortcuts

Edit mode is indicated by a text cursor prompting you to type in the editor area. When a cell is in edit mode, you can type into the cell. Enter edit mode by pressing Enter or using the mouse to select on a cell's editor area. The left border of the active cell is green and hatched, and its Run button is green. You also see the cursor prompt in the cell in Edit mode.

Using the following keystroke shortcuts, you can more easily navigate and run code in Azure Machine Learning notebooks when in Edit mode.

Shortcut	Description
Escape	Enter command mode
Control/Command + Space	Activate IntelliSense
Shift + Enter	Run cell, select below
Control/Command + Enter	Run cell
Alt + Enter	Run cell, insert code cell below
Control/Command + Alt + Enter	Run cell, insert markdown cell below
Alt + R	Run all cells
Up	Move cursor up or previous cell
Down	Move cursor down or next cell
Control/Command + S	Save notebook
Control/Command + Up	Go to cell start
Control/Command + Down	Go to cell end
Tab	Code completion or indent (if tab trap enabled)
Control/Command + M	Enable/disable tab trap
Control/Command + ]	Indent
Control/Command + [	Dedent
Control/Command + A	Select all
Control/Command + Z	Undo
Control/Command + Shift + Z	Redo
Control/Command + Y	Redo
Control/Command + Home	Go to cell start
Control/Command + End	Go to cell end
Control/Command + Left	Go one word left
Control/Command + Right	Go one word right
Control/Command + Backspace	Delete word before
Control/Command + Delete	Delete word after
Control/Command + /	Toggle comment on cell

Troubleshooting

• If you can't connect to a notebook, ensure that web socket communication is not disabled. For compute instance Jupyter functionality to work, web socket communication must be enabled. Ensure your network allows websocket connections to *.instances.azureml.net and *.instances.azureml.ms.

• When compute instance is deployed in a private link workspace it can be only be accessed from within virtual network. If you are using custom DNS or hosts file please add an entry for < instance-name >.< region >.instances.azureml.ms with private IP address of workspace private endpoint. For more information see the custom DNS article.

• If your kernel crashed and was restarted you can run the following command to look at jupyter log and find out more details. sudo journalctl -u jupyter. if kernel issues persist, please consider using a compute instance with more memory.

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Next steps