Changelog#

Changes in IPython Parallel

8.4#

8.4.1#

(full changelog)

8.4.1 is a tiny release, adding support for Python 3.11

8.4.0#

(full changelog)

8.4.0 is a small release, with some bugfixes and improvements to the release process.

Bugfixes:

(%px) only skip redisplay of streamed errors if outputs are complete

Compatibility improvements:

Avoid use of recently deprecated asyncio/tornado APIs around ‘current’ event loops that are not running.

Build improvements:

Switch to hatch backend for packaging

8.3#

8.3.0#

(full changelog)

8.3.0 is a small release, with some bugfixes and improvements to the release process.

Build fixes:

Workaround SSL issues with recent builds of nodejs + webpack
Build with flit, removing setup.py

Fixes:

Remove remaining references to deprecated distutils package (has surprising impact on process memory)
Improve logging when engine registration times out

Maintenance changes that shouldn’t affect users:

Releases are now built with pip instead of setup.py
Updates to autoformatting configuration

Contributors to this release#

(GitHub contributors page for this release)

8.2#

8.2.1#

8.2.1 Fixes some compatibility issues with latest dask, ipykernel, and setuptools, as well as some typos and improved documentation.

8.2.0#

8.2.0 is a small release, mostly of small bugfixes and improvements.

Changes:

len(AsyncMapResult) and progress ports now use the number of items in the map, not the number of messages.

Enhancements:

Show output prior to errors in %%px

Bugs fixed:

Fix cases where engine id could be -1 in tracebacks
Add missing pbs to engine launcher entrypoints

All changes on GitHub

8.1#

8.1.0 is a small release, adding a few new features and bugfixes.

New features:

relay KeyboardInterrupt to engines in blocking %px magics
add Cluster.start_and_connect(activate=True) to include activation of %px magics in one-liner startup.
initial support for Clusters tab in RetroLab

Fixes:

ensure profile config is always loaded for Cluster(profile="xyz")
build lab extension in production mode, apply trove classifiers
pass through keyword arguments to constructor in Client.broadcast_view

8.0#

This is marked as a major revision because of the change to pass connection information via environment variables. BatchSystem launchers with a custom template will need to make sure to set flags that inherit environment variables, such as #PBS -V or #SBATCH --export=ALL.

New:

More convenient Cluster(engines="mpi") signature for setting the engine (or controller) launcher class.
The first (and usually only) engine set can be accessed as Cluster.engine_set, rather than digging through the Cluster.engines dict.
Add environment configuration to all Launchers.
Support more configuration via environment variables, including passing connection info to engines via $IPP_CONNECTION_INFO, which is used by default, avoiding the need to send connection files to engines in cases of non-shared filesystems.
Launchers send connection info to engines via $IPP_CONNECTION_INFO by default. This is governed by Cluster.send_engines_connection_env, which is True by default.
Support EngineLauncher.get_output() via output files in batch system launchers
Capture output in Batch launchers by setting output file options in the default templates.
LoadBalancedView.imap() returns a LazyMapIterator which has a .cancel() method, for stopping consumption of the map input.
Support for return_when argument in AsyncResult.wait() and wait_interactive(), to allow returning on the first error, first completed, or (default) all completed.

Improved:

LoadBalancedView.imap(max_outstanding=n)() limits the number of tasks submitted to the cluster, instead of limiting the number not-yet-consumed. Prior to this, the cluster could be idle if several results were waiting to be consumed.
output streamed by %%px includes errors and results, for immediate feedback when only one engine fails.

Fixed:

Various bugs preventing use of non-default Controller launchers
Fixed crash in jupyterlab extension when IPython directory does not exist
ViewExecutor.shutdown() waits for imap results, like Executors in the standard library
Removed spurious jupyterlab plugin options that had no effect.
%autopx streams output just like %%px

Maintenance:

Add BroadcastView benchmark code
Tag releases with tbump

7.1#

New:

New Client.start_and_connect() method for starting a cluster and returning a connected client in one call.
Support CurveZMQ for transport-level encryption and authentication. See security docs for more info.
Define _max_workers attribute on view.executor for better consistency with standard library Executors.

Improvements:

Client.wait_for_engines() will raise an informative error if the parent Cluster object notices that its engines have halted while waiting, or any engine unregisters, rather than continuing to wait for engines that will never come
Show progress if %px is taking significant time
Improved support for streaming output, e.g. with %px, including support for updating output in-place with standard terminal carriage-return progress bars.

Fixes:

Fix dropped IOPub messages when using large numbers of engines, causing AsyncResult.wait_for_output() to hang.
Use absolute paths for Cluster.profile_dir, fixing issues with Cluster.from_file() when run against a profile created with a relative location, e.g. Cluster(profile_dir="./profile")
Fix error waiting for connection files when controller is started over ssh.

7.0#

7.0.1#

Fix missing setupbase.py in tarball

7.0.0#

Compatibility changes:

Require Python 3.6
Fix compatibility issues with ipykernel 6 and jupyter-client 7
Remove dependency on deprecated ipython-genutils
New dependencies on psutil, entrypoints, tqdm

New features:

New Cluster API for managing clusters from Python, including support for signaling and restarting engines. See docs for more.
New ipcluster list and ipcluster clean commands derived from the Cluster API.
New Client.send_signal() for sending signals to single engines.
New KernelNanny process for signaling and monitoring engines for improved responsiveness of handing engine crashes.
New prototype BroadcastScheduler with vastly improved scaling in ‘do-on-all’ operations on large numbers of engines, c/o Tom-Olav Bøyum’s Master’s thesis at University of Oslo. Broadcast view documentation.
New Client.wait_for_engines() method to wait for engines to be available.
Nicer progress bars for interactive waits, such as AsyncResult.wait_interactive().
Add AsyncResult.stream_output() context manager for streaming output. Stream output by default in parallel magics.
Launchers registered via entrypoints for better support of third-party Launchers.
New JupyterLab extension (enabled by default) based on dask-labextension for managing clusters.
LoadBalancedView.imap() consumes inputs as-needed, producing a generator of results instead of an AsyncMapResult, allowing for consumption of very large or infinite mapping inputs.

Improvements and other fixes:

Greatly improved performance of heartbeat and registration with large numbers of engines, tested with 5000 engines and default configuration.
Single IPController.ports configuration to specify the pool of ports for the controller to use, e.g. ipcontroller --ports 10101-10120.
Allow f as keyword-argument to apply, e.g. view.apply(myfunc, f=5).
joblib backend will start and stop a cluster by default if the default cluster is not running.

The repo has been updated to use pre-commit, black, myst, and friends and GitHub Actions for CI, but this should not affect users, only making it a bit nicer for contributors.

6.3.0#

Require Python 3.5
Fix compatibility with joblib 0.14
Fix crash recovery test for Python 3.8
Fix repeated name when cluster-id is set
Fix CSS for notebook extension
Fix KeyError handling heartbeat failures

6.2.5#

Fix compatibility with Python 3.8
Fix compatibility with recent dask

6.2.4#

Improve compatibility with ipykernel 5
Fix %autopx with IPython 7
Fix non-local ip warning when using current hostname

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 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:

Rename Client.become_distributed() to Client.become_dask(). 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

5.2#

Fix compatibility with changes in ipykernel 4.3, 4.4
Improve inspection of @remote decorated functions
Client.wait() accepts any Future.
Add --user flag to ipcluster nbextension
Default to one core per worker in Client.become_distributed(). Override by specifying ncores keyword-argument.
Subprocess logs are no longer sent to files by default in ipcluster.

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 become_distributed():

executor = client.become_distributed(ncores=1)

which returns a distributed 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

Fix imports in use_cloudpickle(), use_dill().
Various typos and documentation updates to catch up with 5.0.

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.

4.1#

4.1 on GitHub

Add Client.wait_interactive()
Improvements for specifying engines with SSH launcher.

4.0#

4.0 on GitHub

First release of ipyparallel as a standalone package.