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Release Notes

Release Notes

The majority of this release comprises new capabilities for distributed training using the Horovod framework.

New Features

User Capabilities

  • Support for distributed ML training using the Horovod framework.
  • Unity client updated to with checkbox to enable running Horovod models.
  • This feature has been tested using PyTorch.

Backend Capabilities

  • Updated Dask and Kubernetes pod management supporting distributed (Horovod) modeling.
  • Added integration tests to analyze standard, image, and NLP ops on a daily basis.
  • Added Luigi tests for no-code ML covering K-Means and Spectral clustering algorithms.

Updates and Fixes

User Capabilities

  • Predict/Explain bug-fix updated to apply to multi-VDS training as well.
  • Fixed the encode operation error for tabular classification.
  • Fixed the image transformation operations so that the processing of corrupted images are prevented.

Backend Capabilities

  • Reduction in VDS loading time.
  • Jupyter notebook model creation supports model assignment to projects. Jupyter notebook capabilities will be surfaced in the Unity client at a later point.
  • Model library/framework federated string (i.e., replace “FATE” w/ “federated” for all applicable cases) updated on the front-end and the back-end.
  • Federation setup is now dynamic and does not depend on the configuration file. Federates can be added or removed at runtime.
  • Vaious REST endpoints updated for performance improvements (reduced response time).

Known issues