- Rename to to match python package naming guidelines.
- Split jupyter components into own subpackage.
- Update to 6.2.3
* Fix compatibility for execute requests with ipykernel 5
* require ipykernel >= 4.4
- Update to 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
- Update to version 6.2.1
* Workaround a setuptools issue preventing installation from sdist on Windows
- Update to version 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.
- Switch to wheel-based install
- Run tests in main package
- Remove -doc subpackage and use upstream-build docs
- Update to 6.1.1
* Fix regression in 6.1.0 preventing BatchSpawners (PBS, etc.) from launching with ipcluster.
- Update to 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.
- Update url
- Further improvements to notebook extension handling
- Fix notebook extension handling
- Fix script interpeter.
- Implement single-spec version.
- Clean up update-alternatives usage.
- Update to 6.0.2
* Upload fixed sdist for 6.0.1.
- Update to 6.0.1
* Small encoding fix for Python 2.
- Update to 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).
* Rename :meth:`Client.become_distributed` to :meth:`Client.become_dask`.
:meth:`become_distributed` remains as an alias.
* import joblib from a public API instead of a private one
when using IPython Parallel as a joblib backend.
* Compatibility fix in extensions for security changes in notebook 4.3
- Update to 5.2
* Fix compatibility with changes in ipykernel 4.3, 4.4
* Improve inspection of ``@remote`` decorated functions
* :meth:`Client.wait` accepts any Future.
* Add ``--user`` flag to :command:`ipcluster nbextension`
* Default to one core per worker in :meth:`Client.become_distributed`.
Override by specifying `ncores` keyword-argument.
* Subprocess logs are no longer sent to files by default in :command:`ipcluster`.
- Update to 5.1
* IPython Parallel 5.1 adds integration with other parallel computing tools,
such as `dask.distributed `_ and `joblib `__.
* 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 is added to ipcluster.
- Update to 5.0.1
* Fix imports in :meth:`use_cloudpickle`, :meth:`use_dill`.
* Various typos and documentation updates to catch up with 5.0.
- specfile:
* update copyright year
- update to version 5.0.0:
* 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.
+ 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:
+ Add :command:`ipcluster nbextension enable|disable` to toggle
the clusters tab in Jupyter notebook
* Less interesting development changes for users: Some
IPython-parallel extensions to the IPython kernel have been moved
to the ipyparallel package:
+ :mod:`ipykernel.datapub` is now :mod:`ipyparallel.datapub`
+ ipykernel Python serialization is now in
:mod:`ipyparallel.serialize`
+ apply_request message handling is implememented in a Kernel
subclass, rather than the base ipykernel Kernel.
- update to version 4.1.0:
* Add :meth:`.Client.wait_interactive`
* Improvements for specifying engines with SSH launcher.
- Split documentation into own subpackage to speed up builds.

- Build documentation
- Fix conflict.
- Initial version