Kernels

Jupyter Kernel install documentation.

python -m pip install ipykernel
python -m ipykernel install --user
jupyter kernelspec list
jupyter kernelspec list --json
jupyter kernel
# [KernelApp] Starting kernel 'python3'
# [KernelApp] Connection file: ...kernel-e0bde3c0-00e8-46c0-9e47-d01c3b9d3618.json
# [KernelApp] To connect a client: --existing kernel-e0bde3c0-00e8-46c0-9e47-d01c3b9d3618.json
jupyter kernel --help

Run a kernel locally in a subprocess

Arguments that take values are actually convenience aliases to full
Configurables, whose aliases are listed on the help line. For more information
on full configurables, see '--help-all'.

--debug
    set log level to logging.DEBUG (maximize logging output)
--kernel=<Unicode> (KernelApp.kernel_name)
    Default: 'python3'
    The name of a kernel type to start
--ip=<Unicode> (KernelManager.ip)
    Default: ''
    Set the kernel's IP address [default localhost]. If the IP address is
    something other than localhost, then Consoles on other machines will be able
    to connect to the Kernel, so be careful!

To see all available configurables, use `--help-all`
# ~/.local/share/jupyter/runtime/kernel-d785bbc8-c058-49d0-861c-97a39089c91e.json
# ./run/user/1000/jupyter/kernel-772af73b-185b-4960-b0fb-a0532dc59e49.json

Callisto - A command line utility to create kernels in Jupyter from virtual environments.

Reactive Python - A reactive Python kernel. Whenever a variable value is changed, the kernel automatically executes its dependencies (any cells which use that variable) with the updated value. As of now, reactivepy can also support asynchronous functions.

Share Kernel

Mixed Kernels

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