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e9de012 Oct 19, 2018
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Changes in IPython Parallel

6.2.3

  • Fix compatibility for execute requests with ipykernel 5 (now require ipykernel >= 4.4)

6.2.2

  • Fix compatibility with tornado 4, broken in 6.2.0
  • Fix encoding of engine and controller logs in ipcluster --debug on Python 3
  • Fix compatiblity with joblib 0.12
  • Include LICENSE file in wheels

6.2.1

  • Workaround a setuptools issue preventing installation from sdist on Windows

6.2.0

  • Drop support for Python 3.3. IPython parallel now requires Python 2.7 or >= 3.4.
  • Further fixes for compatibility with tornado 5 when run with asyncio (Python 3)
  • Fix for enabling clusters tab via nbextension
  • Multiple fixes for handling when engines stop unexpectedly
  • Installing IPython Parallel enables the Clusters tab extension by default, without any additional commands.

6.1.1

  • Fix regression in 6.1.0 preventing BatchSpawners (PBS, etc.) from launching with ipcluster.

6.1.0

Compatibility fixes with related packages:

  • Fix compatibility with pyzmq 17 and tornado 5.
  • Fix compatibility with IPython ≥ 6.
  • Improve compatibility with dask.distributed ≥ 1.18.

New features:

  • Add :attr:`namespace` to BatchSpawners for easier extensibility.
  • Support serializing partial functions.
  • Support hostnames for machine location, not just ip addresses.
  • Add --location argument to ipcluster for setting the controller location. It can be a hostname or ip.
  • Engine rank matches MPI rank if engines are started with --mpi.
  • Avoid duplicate pickling of the same object in maps, etc.

Documentation has been improved significantly.

6.0.2

Upload fixed sdist for 6.0.1.

6.0.1

Small encoding fix for Python 2.

6.0

Due to a compatibility change and semver, this is a major release. However, it is not a big release. The main compatibility change is that all timestamps are now timezone-aware UTC timestamps. This means you may see comparison errors if you have code that uses datetime objects without timezone info (so-called naïve datetime objects).

Other fixes:

5.2

5.1

dask, joblib

IPython Parallel 5.1 adds integration with other parallel computing tools, such as dask.distributed and joblib.

To turn an IPython cluster into a dask.distributed cluster, call :meth:`~.Client.become_distributed`:

executor = client.become_distributed(ncores=1)

which returns a distributed :class:`Executor` instance.

To register IPython Parallel as the backend for joblib:

import ipyparallel as ipp
ipp.register_joblib_backend()

nbextensions

IPython parallel now supports the notebook-4.2 API for enabling server extensions, to provide the IPython clusters tab:

jupyter serverextension enable --py ipyparallel
jupyter nbextension install --py ipyparallel
jupyter nbextension enable --py ipyparallel

though you can still use the more convenient single-call:

ipcluster nbextension enable

which does all three steps above.

Slurm support

Slurm support is added to ipcluster.

5.1.0

5.1.0 on GitHub

5.0

5.0.1

5.0.1 on GitHub

5.0.0

5.0 on GitHub

The highlight of ipyparallel 5.0 is that the Client has been reorganized a bit to use Futures. AsyncResults are now a Future subclass, so they can be yield ed in coroutines, etc. Views have also received an Executor interface. This rewrite better connects results to their handles, so the Client.results cache should no longer grow unbounded.

.. seealso::

    - The Executor API :class:`ipyparallel.ViewExecutor`
    - Creating an Executor from a Client: :meth:`ipyparallel.Client.executor`
    - Each View has an :attr:`executor` attribute


Part of the Future refactor is that Client IO is now handled in a background thread, which means that :meth:`Client.spin_thread` is obsolete and deprecated.

Other changes:

Less interesting development changes for users:

Some IPython-parallel extensions to the IPython kernel have been moved to the ipyparallel package:

4.1

4.1 on GitHub

4.0

4.0 on GitHub

First release of ipyparallel as a standalone package.