Install, view and start the kernel#

Install a kernel#

Kernels are searched for in the following directories, for example:

/srv/jupyter/.local/share/jupyter/kernels
'/usr/local/share/jupyter/kernels
/usr/share/jupyter/kernels
/srv/jupyter/.ipython/kernels

To make your new environment available as a Jupyter kernel in one of the directories, you should install ipykernel:

$ pipenv install ipykernel

You can then register your kernel, for example with

$ pipenv run python -m ipykernel install --prefix=/srv/jupyter/.ipython/kernels --name python311 --display-name 'Python 3.11 Kernel'

--prefix=/PATH/TO/KERNEL: specifies the path where the Jupyter kernel is to be installed.
--user: installs the kernel for the current user and not system-wide
name NAME: gives a name for the kernelspec. This is required in order to be able to use several IPython kernels at the same time.

ipykernel install creates a kernelspec file in JSON format for the current Python environment, for example:

{
 "display_name": "My Kernel",
 "language": "python"
 "argv": [
  "/srv/jupyter/.ipython/kernels/python311_kernel-7y9G693U/bin/python",
  "-m",
  "ipykernel_launcher",
  "-f",
  "{connection_file}"
 ],
}

display_name

The name of the kernel as it should be displayed in the browser. In contrast to the kernel name used in the API, it can contain any Unicode characters.

language

The name of the language of the kernel. If no suitable kernelspec key is found when loading notebooks, a kernel with a suitable language is used. In this way, a notebook written for a Python or Julia kernel can be linked to the user’s Python or Julia kernel, even if it does not have the same name as the author’s.

argv

A list of command line arguments used to start the kernel. {connection_file} refers to a file that contains the IP address, ports, and authentication key required for the connection. Usually this JSON file is saved in a safe place of the current profile:

connection_file refers to a file containing the IP address, ports and authentication key needed for the connection. Typically, this JSON file is stored in a secure location of the current profile:

{
  "shell_port": 61656,
  "iopub_port": 61657,
  "stdin_port": 61658,
  "control_port": 61659,
  "hb_port": 61660,
  "ip": "127.0.0.1",
  "key": "a0436f6c-1916-498b-8eb9-e81ab9368e84"
  "transport": "tcp",
  "signature_scheme": "hmac-sha256",
  "kernel_name": ""
}

interrupt_mode

can be either signal or message and specifies how a client should interrupt the execution of a cell on this kernel.

signal: sends an interrupt, e.g. SIGINT on POSIX systems
message: sends an interrupt_request, see also Kernel Interrupt.

env

dict with environment variables to be set for the kernel. These are added to the current environment variables before the kernel starts.

metadata

dict with additional attributes for this kernel. Used by clients to support the kernel selection. Metadata added here should have a namespace for the tool to read and write that metadata.

You can edit this kernelspec file at a later time.

Show available kernels#

$ pipenv run jupyter kernelspec list
Available kernels:
  mykernel    /Users/veit/Library/Jupyter/kernels/mykernel
  python2    /Users/veit/Library/Jupyter/kernels/python2
  python3    /Users/veit/.local/share/virtualenvs/jupyter-tutorial--q5BvmfG/bin/../share/jupyter/kernels/python3

Start kernel#

$ pipenv run jupyter console --kernel mykernel
Jupyter console 6.0.0
Python 2.7.15 (default, Oct 22 2018, 19:33:46)
...

In [1]:

With ctrl + d you can exit the kernel again.

Delete kernel#

$ pipenv run jupyter kernelspec uninstall mykernel

Uninstall the Starndard kernel#

If not already done, a configuration file can be created, for example with

$ pipenv run jupyter lab --generate-config

Then you can add the following line to this configuration file:

c.KernelSpecManager.ensure_native_kernel = False