JupyterLab
Basic usage#
Using JupyterLab with pixi is very simple.
You can just create a new pixi project and add the jupyterlab
package to it.
The full example is provided under the following Github link.
This will create a new pixi project and add the jupyterlab
package to it. You can then start JupyterLab using the
following command:
If you want to add more "kernels" to JupyterLab, you can simply add them to your current project – as well as any dependencies from the scientific stack you might need.
What kernels are available?#
You can easily install more "kernels" for JupyterLab. The conda-forge
repository has a number of interesting additional kernels - not just Python!
bash_kernel
A kernel for bashxeus-cpp
A C++ kernel based on the new clang-replxeus-cling
A C++ kernel based on the slightly older Clingxeus-lua
A Lua kernelxeus-sql
A kernel for SQLr-irkernel
An R kernel
Advanced usage#
If you want to have only one instance of JupyterLab running but still want per-directory Pixi environments, you can use
one of the kernels provided by the pixi-kernel
package.
Configuring JupyterLab#
To get started, create a Pixi project, add jupyterlab
and pixi-kernel
and then start JupyterLab:
This will start JupyterLab and open it in your browser.
pixi-kernel
searches for a manifest file, either pixi.toml
or pyproject.toml
, in the same directory of your
notebook or in any parent directory. When it finds one, it will use the environment specified in the manifest file to
start the kernel and run your notebooks.
Binder#
If you just want to check a JupyterLab environment running in the cloud using pixi-kernel
, you can visit
Binder.