python-jupyter_ipyparallel-doc-6.1.1-bp150.1.3

- 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 shebangs

- Implement single-spec version.

- 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 <https://distributed.readthedocs.io>`_ and `joblib <https://pythonhosted.org/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 <https://computing.llnl.gov/tutorials/linux_clusters>`_ 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