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https://github.com/Calysto/metakernel

Jupyter/IPython Kernel Tools
https://github.com/Calysto/metakernel

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Jupyter/IPython Kernel Tools

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README 

        
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).



.. image:: https://badge.fury.io/py/metakernel.png/

    :target: http://badge.fury.io/py/metakernel



.. image:: https://coveralls.io/repos/Calysto/metakernel/badge.png?branch=main

  :target: https://coveralls.io/r/Calysto/metakernel



.. image:: https://github.com/Calysto/metakernel/actions/workflows/tests.yml/badge.svg?query=branch%3Amain++

  :target: https://github.com/Calysto/metakernel/actions/workflows/tests.yml



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

    :target: https://anaconda.org/conda-forge/metakernel



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

    :target: https://anaconda.org/conda-forge/metakernel



See Jupyter's docs on `wrapper kernels

`_.



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



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

---------------------------



- matlab_kernel, https://github.com/Calysto/matlab_kernel

- octave_kernel, https://github.com/Calysto/octave_kernel

- calysto_scheme, https://github.com/Calysto/calysto_scheme

- calysto_processing, https://github.com/Calysto/calysto_processing

- java9_kernel, https://github.com/Bachmann1234/java9_kernel

- xonsh_kernel, https://github.com/Calysto/xonsh_kernel

- calysto_hy, https://github.com/Calysto/calysto_hy

- gnuplot_kernel, https://github.com/has2k1/gnuplot_kernel

- spylon_kernel, https://github.com/mariusvniekerk/spylon-kernel

- wolfram_kernel, https://github.com/mmatera/iwolfram

- sas_kernel, https://github.com/sassoftware/sas_kernel

- pysysh_kernel, https://github.com/Jaesin/psysh_kernel

- calysto_bash, https://github.com/Calysto/calysto_bash



... and many others.



Installation

----------------

You can install Metakernel through ``pip``:



.. code:: bash



 pip install metakernel --upgrade



Installing `metakernel` from the `conda-forge` channel can be achieved by adding `conda-forge` to your channels with:



.. code:: bash



 conda config --add channels conda-forge



Once the `conda-forge` channel has been enabled, `metakernel` can be installed with:



.. code:: bash



 conda install metakernel



It is possible to list all of the versions of `metakernel` available on your platform with:



.. 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 register_ipython_magics

 register_ipython_magics()



Put the following in your (or a system-wide) ``ipython_config.py`` file:



.. code:: python



 # /etc/ipython/ipython_config.py

 c = get_config()

 startup = [

    'from metakernel import register_ipython_magics',

    'register_ipython_magics()',

 ]

 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



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



.. code:: bash



  ipcluster nbextension enable



3. 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



4. 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



5. 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

  (* cluster_rank cluster_rank)



Results come back in a Python list (Scheme vector), in ``cluster_rank`` order. (This will be a JSON representation in the future).



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``, or ``%pmap``) in the host kernel by accessing the variable ``_`` (single underscore), or by using the ``--set_variable VARIABLE`` flag, like so:



.. code:: bash



  %%px --set_variable results

  (* cluster_rank cluster_rank)



Then, in the next cell, you can access ``results``.



Notice that you can use the variable ``cluster_rank`` to partition parts of a problem so that each node is working on something different.



In the examples above, use ``-e`` to evaluate the code in the host kernel as well. Note that ``cluster_rank`` is not defined on the host machine, and that this assumes the host kernel is the same as the parallel machines.



Configuration

-------------

``Metakernel`` subclasses can be configured by the user.  The

configuration file name is determined by the ``app_name`` property of the subclass.

For example, in the ``Octave`` kernel, it is ``octave_kernel``.  The user of the kernel can add an ``octave_kernel_config.py`` file to their

``jupyter`` config path.  The base ``MetaKernel`` class offers ``plot_settings`` as a configurable trait.  Subclasses can define other traits that they wish to make

configurable.



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 Changelog_.



.. _here: http://nbviewer.jupyter.org/github/Calysto/metakernel/tree/main/examples/



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



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



.. _Changelog: https://github.com/Calysto/metakernel/blob/main/CHANGELOG.md