Manage Jupyter notebook servers
In JetBrains DataSpell, you can execute code cells using:
Managed server – a Jupyter server that is automatically launched by JetBrains DataSpell for the current project. It will be terminated when you close JetBrains DataSpell.
Configured server – any Jupyter server that you connect to by specifying its URL and token.
Launch a Jupyter server
To run a Jupyter server just execute any code cell. When you initiate cell execution, JetBrains DataSpell launches the Jupyter server on the local host using any available port (by default, it is the 8888 port). You can switch to the Jupyter Server tool window to preview server's configuration details.
Once the server is launched, it is shown as a managed server in the list of the servers in the Jupyter toolbar. You can also see the automatically created server kernel in the list of kernels. This kernel is based on the.
Stop the Jupyter server
To stop any running server, switch to the Jupyter Server tool window and click the icon or select the corresponding command in the menu of the main JetBrains DataSpell menu. Preview the status in the Server Log window.
Once you have shut down the server, the current session is terminated. When you start the server next time using the icon or themenu of the main JetBrains DataSpell menu, execution results for all previous sessions and all notebooks will be lost.
Restart the kernel
You might want to refresh your calculations without shutting down the entire server and affecting any other notebooks. To restart the currently running kernel, click on the Jupyter notebook toolbar and preview the status in the Server Log window.
You can find other kernel specific commands in themenu of the JetBrains DataSpell main menu.
You can execute your notebook code cells on a specific Jupyter servers.
Configure the Jupyter server
When you launch any managed server, by default it uses the current and the automatically selected port. However, you can select any other available in your JetBrains DataSpell instance and specify an alternative port. You can also connect to any configured server if you know its URL and token.
To open the server settings, selectin the list of the Jupyter servers.
Configure the server options:
To customize the default Jupyter server, in the Jupyter Server dialog, select Managed Server and from the Python interpreter list select any local Python.
To change the automatically detected port, type its number in the Port field.
To connect to any running Jupyter server, in the Jupyter Server dialog, select Configured Server and specify server's path including an URL and a token.
Once done with configuring, click Apply and OK.
Execute any code cell to run the managed server or to connect to the running configured server.
If you see the following warning, the kernel of the newly configured server doesn't match your current Python interpreter. For example, it might have a different set of the installed packages.
You can register your Python interpreter as a kernel on the configured server by clicking the corresponding link. After that you can see it in the list of the kernels for the configured server.
Connect to a remote Jupyter server
Connecting to a remote Jupyter server is an experimental feature of JetBrains DataSpell. To enable it, click in the upper-right corner of JetBrains DataSpell or press Ctrl+Alt+S and select Experimental Features panel. Then select the corresponding checkbox, save the change, and restart the IDE.
Click the on the toolbar of the Workspace tool window to establish a connection to a Jupyter server.
Specify the server URL. The target URL should contain a server name or its address, and the access token.
Once the connection has established the server and its structure are shown in the Workspace tool window under the Jupyter Connections node. Expand it to preview the remote content, open, and execute notebooks.