![]() The option is enabled if there is at least one active Jupyter server on your machine. Start a local Jupyter server: run a Jupyter server in a local directory that will be attached to your workspace.Ĭonnect to a running local Jupyter server: establish a connection to any locally run Jupyter server. In the New Jupyter Connection dialog, select the connection type: Once the server is launched, it is shown as a managed server in the list of the servers in the Jupyter toolbar.Ĭlick the icon on the toolbar of the Workspace tool window to establish a connection to a Jupyter server. DataSpell creates SSH tunnels, copies project files, and launches a Jupyter server on the remote host. To run a Jupyter server just execute any code cell. Configure it for a attached directory instead. You cannot use an SSH interpreter as a default interpreter for the DataSpell workspace. Launch a remote Jupyter server via SSHĬreate an SSH Python interpreter as described in Configure an interpreter using SSH. This kernel is based on the Python interpreter configured for the directory. You can also see the automatically created server kernel in the list of kernels. Once the server is launched, it is shown as a managed server in the list of the servers in the Jupyter toolbar. You can switch to the Jupyter Server tool window to preview server's configuration details. When you initiate cell execution, DataSpell launches a Jupyter server on the local host using any available port (by default, it is the 8888 port). It will be terminated when you close DataSpell.Ĭonfigured server – any Jupyter server that you connect to by specifying its URL and token. Managed server – a Jupyter server that is automatically launched by DataSpell for the current project. ![]() In DataSpell, you can execute code cells using:
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