python-ipyparallel
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Filename | Size | Changed |
---|---|---|
_link | 0000000153 153 Bytes | |
ipyparallel-7.1.0.tar.gz | 0004029033 3.84 MB | |
python-ipyparallel-rpmlintrc | 0000000169 169 Bytes | |
python-ipyparallel.changes | 0000013097 12.8 KB | |
python-ipyparallel.spec | 0000005683 5.55 KB |
Revision 21 (latest revision is 49)
- 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.
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