- Update to 7.1.0
* 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.
* 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.
* 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.
- Release 7.0.1
* Fix missing setupbase.py in tarball
- Release 7.0.0
* 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 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.
* 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.