Jupyter Server Terminals is a Jupyter Server Extension that provides robust terminal support within the Jupyter environment. It allows users to run system shells directly in their web browser, offering functionalities akin to a local terminal. The library is actively maintained, with regular releases focusing on bug fixes and maintenance, and is currently at version 0.5.4. It requires Python >=3.8.
pip install jupyter_server_terminalsVerified import paths — ran on the pinned version, not inferred.
Install the `jupyter_server_terminals` extension along with `jupyterlab` (or `notebook`). Once installed, start JupyterLab from your terminal. The terminal functionality will be automatically available within the JupyterLab interface. You can open a new terminal from the Launcher or through the File menu.
Upgrade your `jupyter-server` installation to version 2.0 or higher: `pip install --upgrade jupyter-server`.
Be aware of this architectural change if you're developing extensions that interact directly with Jupyter Server services or managing custom deployments. The service can now be managed as a standard Jupyter extension.
Always close and halt notebooks (`File > Close and Halt`) and exit terminals (`exit` command) when not in use. Minimize the number of open tabs and avoid printing excessively large outputs. Re-use variables instead of creating multiple copies of large data in memory.
If experiencing conflicts, consider changing the keyboard shortcut for either copy or the command palette in JupyterLab's settings. Alternatively, use Ctrl+Shift+V for paste, which works as usual, or hold Shift and right-click for the native browser context menu (though this may not work in all browsers).
Execute `pip install` commands in a shell environment (e.g., command line, shell script, or via `subprocess` in Python), not as direct Python code within a `.py` file.
Ensure shell commands are run in a shell environment (e.g., your terminal) or use Python's `subprocess` module to execute them programmatically from within a script. For package installation, run `pip install <package_names>` in your command line interface before executing your Python script.