release-notes.md

1.13.2

• Revert dask_worker to use fork rather than subprocess by default
• Scatter retains type information
• Bokeh always uses subprocess rather than spawn

1.13.1

• Fix critical Windows error with dask_worker executable

1.13.0

• Rename Executor to Client #492
• Add --memory-limit option to dask-worker, enabling spill-to-disk behavior when running out of memory #485
• Add --pid-file option to dask-worker and --dask-scheduler #496
• Add upload_environment function to distribute conda environments. This is experimental, undocumented, and may change without notice. # 494
• Add workers= keyword argument to Client.compute and Client.persist, supporting location-restricted workloads with Dask collections #484
• Add upload_environment function to distribute conda environments. This is experimental, undocumented, and may change without notice. # 494
  • Add optional dask_worker= keyword to client.run functions that gets provided the worker or nanny object
  • Add nanny=False keyword to Client.run, allowing for the execution of arbitrary functions on the nannies as well as normal workers

1.12.2

This release adds some new features and removes dead code

• Publish and share datasets on the scheduler between many clients dask#453 http://distributed.readthedocs.io/en/latest/publish.html
• Launch tasks from other tasks (experimental) https://distributed.readthedocs.io/en/latest/task-launch.html dask#471
• Remove unused code, notably the Center object and older client functions dask#478
• Executor() and LocalCluster() is now robust to Bokeh's absence dask#481
• Removed s3fs and boto3 from requirements. These have moved to Dask.

1.12.1

This release is largely a bugfix release, recovering from the previous large refactor.

• Fixes from previous refactor
  • Ensure idempotence across clients
  • Stress test losing scattered data permanently
• IPython fixes
  • Add start_ipython_scheduler method to Executor
  • Add %remote magic for workers
  • Clean up code and tests
• Pool connects to maintain reuse and reduce number of open file handles
• Re-implement work stealing algorithm
• Support cancellation of tuple keys, such as occur in dask.arrays
• Start synchronizing against worker data that may be superfluous
• Improve bokeh plots styling
  • Add memory plot tracking number of bytes
  • Make the progress bars more compact and align colors
  • Add workers/ page with workers table, stacks/processing plot, and memory
• Add this release notes document

1.12.0

This release was largely a refactoring release. Internals were changed significantly without many new features.

• Major refactor of the scheduler to use transitions system
• Tweak protocol to traverse down complex messages in search of large bytestrings
• Add dask-submit and dask-remote
• Refactor HDFS writing to align with changes in the dask library
• Executor reconnects to scheduler on broken connection or failed scheduler
• Support sklearn.external.joblib as well as normal joblib