A Jupyter kernel base class in Python which includes core magic functions (including help, command and file path completion, parallel and distributed processing, downloads, and much more).

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.. image:: https://anaconda.org/conda-forge/metakernel/badges/version.svg :target: https://anaconda.org/conda-forge/metakernel

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See Jupyter's docs on

wrapper kernels

Additional magics can be installed within the new kernel package under a

magics

Features

• Basic set of line and cell magics for all kernels.
  • Python magic for accessing python interpreter.
  • Run kernels in parallel.
  • Shell magics.
  • Classroom management magics.
• Tab completion for magics and file paths.
• Help for magics using ? or Shift+Tab.
• Plot magic for setting default plot behavior.

Kernels based on Metakernel

• matlabkernel, https://github.com/Calysto/matlabkernel
• octavekernel, https://github.com/Calysto/octavekernel
• calystoscheme, https://github.com/Calysto/calystoscheme
• calystoprocessing, https://github.com/Calysto/calystoprocessing
• java9kernel, https://github.com/Bachmann1234/java9kernel
• xonshkernel, https://github.com/Calysto/xonshkernel
• calystohy, https://github.com/Calysto/calystohy
• gnuplotkernel, https://github.com/has2k1/gnuplotkernel
• spylon_kernel, https://github.com/mariusvniekerk/spylon-kernel
• wolfram_kernel, https://github.com/mmatera/iwolfram
• saskernel, https://github.com/palmer0914/saskernel
• pysyshkernel, https://github.com/Jaesin/psyshkernel
• calystobash, https://github.com/Calysto/calystobash

... and many others.

Installation

You can install Metakernel through

pip

.. code::bash

pip install metakernel --upgrade

Installing

metakernel

conda-forge

conda-forge

.. code::bash

conda config --add channels conda-forge

Once the

conda-forge

metakernel

.. code::bash

conda install metakernel

It is possible to list all of the versions of

metakernel

.. code::bash

conda search metakernel --channel conda-forge

Use MetaKernel Magics in IPython

Although MetaKernel is a system for building new kernels, you can use a subset of the magics in the IPython kernel.

.. code:: python

from metakernel import registeripythonmagics registeripythonmagics()

Put the following in your (or a system-wide)

ipython_config.py

.. code:: python

# /etc/ipython/ipythonconfig.py c = getconfig() startup = [ 'from metakernel import registeripythonmagics', 'registeripythonmagics()', ] c.InteractiveShellApp.exec_lines = startup

Use MetaKernel Languages in Parallel

To use a MetaKernel language in parallel, do the following:

1. Make sure that the Python module

   ipyparallel

   is installed. In the shell, type:

.. code:: bash

pip install ipyparallel

1. To enable the extension in the notebook, in the shell, type:

.. code:: bash

ipcluster nbextension enable

1. To start up a cluster, with 10 nodes, on a local IP address, in the shell, type:

.. code:: bash

ipcluster start --n=10 --ip=192.168.1.108

1. Initialize the code to use the 10 nodes, inside the notebook from a host kernel

   MODULE

   and

   CLASSNAME

   (can be any metakernel kernel):

.. code:: bash

%parallel MODULE CLASSNAME

For example:

.. code:: bash

%parallel calysto_scheme CalystoScheme

1. Run code in parallel, inside the notebook, type:

Execute a single line, in parallel:

.. code:: bash

%px (+ 1 1)

Or execute the entire cell, in parallel:

.. code:: bash

%%px (* clusterrank clusterrank)

Results come back in a Python list (Scheme vector), in

cluster_rank

Therefore, the above would produce the result:

.. code:: bash

#10(0 1 4 9 16 25 36 49 64 81)

You can get the results back in any of the parallel magics (

%px

%%px

%pmap

_

--set_variable VARIABLE

.. code:: bash

%%px --setvariable results (* clusterrank cluster_rank)

Then, in the next cell, you can access

results

Notice that you can use the variable

cluster_rank

In the examples above, use

-e

cluster_rank

Configuration

Metakernel

app_name

Octave

octave_kernel

octave_kernel_config.py

jupyter

MetaKernel

plot_settings

As an example:

.. code:: bash

cat ~/.jupyter/octave_kernel_config.py
# use Qt as the default backend for plots
c.OctaveKernel.plot_settings = dict(backend='qt')

Documentation

Example notebooks can be viewed here_.

Documentation is available online. Magics have interactive help (and online).

For version information, see the Revision History_.

.. _here: http://nbviewer.ipython.org/github/Calysto/metakernel/tree/master/examples/

.. _help: https://github.com/Calysto/metakernel/blob/master/metakernel/magics/README.md

.. _online: http://Calysto.github.io/metakernel/

.. _History: https://github.com/Calysto/metakernel/blob/master/HISTORY.rst