metakernel 0.22.1

Metakernel for Jupyter

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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See Jupyter's docs on `wrapper kernels
<http://jupyter-client.readthedocs.io/en/stable/wrapperkernels.html>`_.

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/palmer0914/sas_kernel
- pysysh_kernel, https://github.com/Jaesin/psysh_kernel

... and many others.

Installation
----------------
You can install Metakernel through `pip`:

.. code::

pip install metakernel --upgrade

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

.. code::

conda config --add channels conda-forge

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

.. code::

conda install metakernel

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

.. code::

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:

```shell
pip install ipyparallel
```

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

```shell
ipcluster nbextension enable
```

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

```shell
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):

```
%parallel MODULE CLASSNAME
```

For example:

```
%parallel calysto_scheme CalystoScheme
```

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

Execute a single line, in parallel:

```
%px (+ 1 1)
```

Or execute the entire cell, in parallel:

```
%%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:

```scheme
#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:

```shell
%%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 by adding a ``metakernel_config.py`` file to their
``jupyter`` config path. The base ``MetaKernel`` class offers ``plot_settings`` as a configurable trait.
As an example:

.. code:: bash
cat ~/.jupyter/metakernel_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