> AI agents: For documentation discovery and navigation, see [llms.txt](/llms.txt).

# Manage Jupyter Kernels in VS Code

The Visual Studio Code notebooks' kernel picker helps you to pick specific kernels for your notebooks. You can open the kernel picker by clicking on **Select Kernel** on the upper right-hand corner of your notebook or through the Command Palette with the **Notebook: Select Notebook Kernel** command.

Once you open the Kernel Picker, VS Code shows the most recently used (MRU) kernel(s):

![MRU Kernel](images/jupyter-kernel-management/mru-kernel.png)

> **Note**: In the previous versions of VS Code (version <1.76), VS Code used to show all available kernels by default.

To see other kernels, you can click **Select Another Kernel...**. All existing kernels are categorized into kernel source options, with these sources supported by the Jupyter extension out of the box:

- [Jupyter Kernels](#jupyter-kernels)
- [Python Environments](#python-environments)
- [Existing Jupyter Server](#existing-jupyter-server)

![Notebook Kernel Picker](images/jupyter-kernel-management/noterbook-kernel-picker.gif)

By default, VS Code will recommend the one you've previously used with your notebook, but you can choose to connect to any other Jupyter kernels as shown below. VS Code will also remember the last selected kernel for your notebooks, and will automatically select them the next time you open your notebook.

## Jupyter Kernels

The **Jupyter Kernels** category lists all Jupyter kernels that VS Code detects in the context of the compute system it's operating in (your desktop, [GitHub Codespaces](https://github.com/features/codespaces), remote server, etc.). Each Jupyter kernel has a Jupyter [kernel specification](https://jupyter-client.readthedocs.io/en/stable/kernels.html#kernel-specs), or Jupyter kernelspec, which contains a JSON file (`kernel.json`) with details about the kernel—name, description, and CLI information required to launch a process as a kernel.

## Python Environments

The **Python Environments** category lists the Python environments that VS Code detects from the compute system it's operating in (your desktop, Codespaces, remote server, etc.). It shows all Python environments grouped by type (for example, conda, venv)—whether the [IPyKernel](https://ipython.readthedocs.io/en/stable/install/kernel_install.html) is installed or not.

   > **Note**: You **do not** need to install [jupyter](https://pypi.org/project/jupyter/) into the Python environment you want to use. Only the IPyKernel package is required to launch a Python process as a kernel and execute code against your notebook (`pip install ipykernel`). Visit the [Jupyter extension wiki](https://github.com/microsoft/vscode-jupyter/wiki/Kernels-(Architecture)) to learn more.

## Existing Jupyter Server

The **Existing Jupyter Server** category lists remote Jupyter servers previously connected. You can also use this option to connect to an existing Jupyter server running remotely or locally. Find the URL for your Jupyter server, for example, `http://<ip-address>:<port>/?token=<token>` and paste it in the **Enter the URL of the running Jupyter server** option to connect to the remote server and execute code against your notebook using that server.

![Enter server URL](images/jupyter-kernel-management/select-enter-server-url.png)

When you're starting your remote server, be sure to:

1. Allow all origins (for example `--NotebookApp.allow_origin='*'`) to allow your servers to be accessed externally.
2. Set the notebook to listen on all IPs (`--NotebookApp.ip='0.0.0.0'`).

Once connected, all active Jupyter sessions will appear on this list.

You can create a new session from the server's kernelspec by:

1. Running the **Notebook: Select Notebook Kernel** command.
2. Choose **Select Another Kernel**.
3. Choose **Existing Jupyter Server**.
4. Select your server.

## Codespaces Jupyter Server

The **Connect to Codespace** category contains a special type of Jupyter server where you can use remote Jupyter servers powered by [GitHub Codespaces](https://docs.github.com/codespaces/overview), a cloud resource that you get [up to 60 hours free](https://github.com/features/codespaces) each month. To use the Codespaces Jupyter server:

1. Install the [GitHub Codespaces extension](https://marketplace.visualstudio.com/items?itemName=GitHub.codespaces).

    > **Note**: If you're on VS Code for the Web ([vscode.dev](https://vscode.dev) or [github.dev](https://github.dev)), this extension is already installed for you. Also ensure that the [Jupyter extension](https://marketplace.visualstudio.com/items?itemName=ms-toolsai.jupyter) is also installed.

2. Go to the Command Palette (`kb(workbench.action.showCommands)`), select **Codespaces: Sign In** and follow the steps to sign into Codespaces.

3. Open the kernel picker by clicking on **Select Kernel** on the upper right-hand corner of your notebook, select **Connect to Codespace**.

    > **Tip**: If you don't see the **Connect to Codespace** option, go to the Command Palette (`kb(workbench.action.showCommands)`), select **Developer: Reload Window** to reload the window and try again.

It is not required, but you can also manage all your Codespaces and Codespaces Jupyter servers on the [GitHub Codespaces page](https://github.com/codespaces). To learn more, you can read the [GitHub Codespaces documentation](https://docs.github.com/codespaces/getting-started/understanding-the-codespace-lifecycle).

## Adding Kernel Options

If you do not have any Jupyter kernel or Python environment on your machine, VS Code can help you set up: go to the Command Palette (`kb(workbench.action.showCommands)`), select **Python: Create Environment**, and follow the prompts. You can also add additional ways to select kernels, by installing additional extensions like [Azure Machine Learning](https://marketplace.visualstudio.com/items?itemName=ms-toolsai.vscode-ai).

![More Kernel Sources](images/jupyter-kernel-management/more-kernel-sources.png)

## Questions or feedback

You can add a [feature request](https://github.com/microsoft/vscode-jupyter/issues/new?assignees=&labels=feature-request&template=3_feature_request.md) or [report a problem](https://github.com/microsoft/vscode-jupyter/issues/new?assignees=&labels=bug&template=1_bug_report.md) by creating an issue in our repository, which is actively being monitored and managed by our engineering team.