Managed server – a Jupyter server that is automatically launched by PyCharm for the current project. It will be terminated when you close PyCharm.
Configured server – any Jupyter server that you connect to by specifying its URL and token.
Launch a local Jupyter server
To run a Jupyter server just execute any code cell. When you initiate cell execution, PyCharm launches a 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 Python interpreter configured for the project.
Connect to a Jupyter server
Go to Tools | Add Jupyter Connection.
In the New Jupyter Connection dialog, select the connection type:
Start a local Jupyter server: run a Jupyter server in a local directory that will be attached to your workspace.
Connect to Jupyter server using URL: establish a connection to a remote Jupyter server. The target URL should contain a server name or its address, and the access token.
Once the connection has been established, the server and its structure are shown in the Workspace tool window.
Stop the Jupyter server
To stop any running server, switch to the Jupyter Server tool window and click the icon. 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, 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. You can then view the kernel status in the Server Log window:
You can execute your notebook code cells on a specific Jupyter server.
When you launch any managed server, by default it uses the current Python interpreter and the automatically selected port. However, you can select any other interpreter available in your PyCharm instance and specify an alternative port. You can also connect to any configured server if you know its URL and token.
Configure a Jupyter server
Select Configure Jupyter Server in the list of the Jupyter servers.
Then select Use Managed Server. By default, the server will use the project Python interpreter. If necessary, select another local interpreter from the list.
In the Command line arguments field, you can do the following:
Include a custom working directory
Make the server accessible on the local network
Run the server as root
Set a password to access to the server
Run JupyterLab instead of Jupyter Notebook
Change the automatically detected port.
For example, use the following command-line arguments to run the Jupyter Notebook server at port 9090 without opening a browser:
Environment variables can be set by creating different variables and assigning values to them.
Once done with configuring, click Apply and OK.
Then execute any code cell to run the managed server.
Connect to a Jupyter server
Select Configure Jupyter Server in the list of the Jupyter servers.
Then select Configured Server and add the server's path. It consists of a URL and a token.
Once done with configuring, click Apply and OK.
Then execute any code cell to connect to the running configured server.
Resolving kernel mismatch error of 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.
icon. Preview the status in the Server Log window.
icon, execution results for all previous sessions and all notebooks will be lost.
on the Jupyter notebook toolbar. You can then view the kernel status in the Server Log window:
