Use Specific Kernel Jupyter Lab

6 min read Oct 14, 2024

How to Use a Specific Kernel in JupyterLab

JupyterLab is a powerful interactive development environment (IDE) that supports various programming languages. It uses kernels to execute code and provide interactive results. You might need to use a specific kernel for your project, especially when working with different languages or when a specific version of a language is required.

Why Use a Specific Kernel?

Here are some reasons why you might want to use a specific kernel in JupyterLab:

Multiple Programming Languages: JupyterLab allows you to work with various programming languages, each requiring a dedicated kernel for execution. For example, you'll need a Python kernel for Python code and a R kernel for R code.
Version Control: Different projects may require specific versions of programming languages. Using a specific kernel ensures that your code runs with the correct version of the language and its libraries.
Environment Isolation: Using separate kernels for different projects helps maintain isolation between your project environments and prevents conflicts between dependencies.

How to Select a Specific Kernel

Install the Required Kernel: Ensure the kernel for your chosen language is installed in your system. This is typically done using package managers like pip for Python.
Open JupyterLab: Launch JupyterLab from your terminal or command prompt.
Create a New Notebook: Click on the "New" button on the JupyterLab launcher and select the appropriate notebook type (e.g., Python 3).
Kernel Selection: JupyterLab will usually automatically detect and select a suitable kernel. However, if you need to choose a specific kernel, follow these steps:
- From the Menu: Click on the "Kernel" menu in the notebook's toolbar.
- Change Kernel: Select "Change kernel" from the dropdown menu.
- Kernel List: Choose the desired kernel from the list of available options.
Confirm the Kernel: After selecting the kernel, JupyterLab will switch to the chosen kernel. You can verify this by checking the notebook's toolbar, where the kernel name should be displayed.

Selecting a Specific Kernel on Startup

You can specify a specific kernel when launching JupyterLab to avoid manually selecting it for each notebook.

Kernel Spec File: Each kernel has a kernel spec file containing configuration information. Find the location of the specific kernel spec file you want to use.
Command Line: When launching JupyterLab, use the --Kernel option with the path to the desired kernel spec file. For example:
```
jupyter lab --Kernel=/path/to/kernel.json
```

Managing Kernels

List Available Kernels: Use the jupyter kernelspec list command in your terminal to see all available kernels.
Install Kernels: Use package managers (like pip or conda) to install additional kernels for different programming languages.
Remove Kernels: Use jupyter kernelspec remove <kernel_name> to remove a specific kernel from your system.

Example: Using a Specific Python Kernel

Let's say you need to use Python 3.7 for a project. You can specify the Python 3.7 kernel for your JupyterLab notebook:

Install the Python 3.7 Kernel: If you haven't already, install the Python 3.7 kernel using pip:
```
pip install ipykernel
python3.7 -m ipykernel install --name=python3.7 --user
```
Launch JupyterLab: Open JupyterLab and create a new notebook.
Select the Python 3.7 Kernel: From the "Kernel" menu, select "Change kernel" and choose the "python3.7" kernel from the list.
Verify the Kernel: Check the notebook toolbar to confirm that "Python 3.7" is the active kernel.

Conclusion

Using a specific kernel in JupyterLab provides flexibility and control over the environment in which your code runs. This is essential for working with multiple languages, managing different project dependencies, and maintaining version consistency. By following the steps outlined above, you can select and utilize specific kernels in your JupyterLab workflow, enabling you to execute code with the desired language and environment.