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Changes in IPython Parallel


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:



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


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 on GitHub



5.0.1 on GitHub


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.

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Future refactor is that Client IO is now handled in a background thread, which means that :meth:`Client.spin_thread` is obsolete and deprecated.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


4.0 on GitHub

First release of ipyparallel as a standalone package.