Jupyter server extension that allows mixing local and remote kernels together
Project description
Jupyter Kernel Mixing
This package provides a Jupyter Server extension that allows you to run local and remote kernels side by side.
It does this by "mixing" the local and remote kernels together into a single collection containing both.
This collection then keeps track of whether specific kernels were local or remote and forwards any corresponding kernel requests accordingly.
Installation
Install the kernels-mixer
Python package using pip
:
pip install kernels-mixer
Setup
If you do not already have a Jupyter config file (e.g. ~/.jupyter/jupyter_lab_config.py
),
the first generate one with the following command:
jupyter lab --generate-config
The open your config file and add the following two lines to the end:
import kernels_mixer
kernels_mixer.configure_kernels_mixer(c)
Kernel Name Uniqueness
This extension expects that local and remote kernels have different names. If that is not the case then the local kernel will override the remote kernel. For example, if there is a local kernel named "python3", then any kernels in the remote kernel gateway named "python3" will be hidden in favor of it.
When using this extension, it is recommended that the remote kernel gateway is set up to add a prefix onto every kernel name in order to distinguish them from the local kernels.
Similarly, it is recommended that remote kernel display names are augmented to indicate where they are running.
The default kernel gateway used with this extension is the regional GCP kernel gateway
hosted under kernels.googleusercontent.com
, which ensures that both of those conditions
are followed.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
File details
Details for the file kernels-mixer-0.0.8.tar.gz
.
File metadata
- Download URL: kernels-mixer-0.0.8.tar.gz
- Upload date:
- Size: 12.1 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.0.0 CPython/3.8.13
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | dc2c12508a8ce6c3fd34139e4f99dc1223658459b8ec5004aecc34cd79a91bde |
|
MD5 | d6045f4974d89a3997d43f05b191aa5b |
|
BLAKE2b-256 | e6dab92a2499abab9c86d911a739653d867284aaf0275a9718445e0980d64776 |