This repository has been archived by the owner on Apr 15, 2022. It is now read-only.
2.3.0-k8
What's New?
- The External Native Spark Datasource API is now available (@jpanko1 )
- Added functions to
splicemachine.notebookto access the Spark UI and the Mlflow UI (@Ben-Epstein ) - Python Dependency fixes for the October 2020 pip changes (@Ben-Epstein )
- More graceful [errors] for unsupported models (#74) (@Ben-Epstein )
- Better checking for spark datatypes (@Ben-Epstein, @ZachC16 )
- Deployment support for non-pipeline models (@Ben-Epstein, @ZachC16 )
- Support for Linear Support Vector Machine Spark Model (@Ben-Epstein, @ZachC16 )
- Better unit testing (@Ben-Epstein @ZachC16)
- New warning passed on Keras and Spark models when the number of label columns passed in doesn't match model (@Ben-Epstein, @domclassen )
- Database Deployment Migrated to Server side running on Bobby pod (@abaveja313, @Ben-Epstein )
- Initial K8s deployment code available - known bug with init container hanging, expected to be working in next release (@abaveja313 )
- Models are now logged as MLModels instead of the raw model binary (@abaveja313 )
- Model caching for database deployment (@Ben-Epstein @sergioferragut )
- Fix for artifacts downloading without file extension (@Ben-Epstein )
- Model deployment metadata managed by Bobby (@abaveja313 )
BREAKING CHANGES
- The models table no longer exists. The deployment model is instead stored in a new column of the Artifacts table called
database_binary. You must run the migration scripts to alter the artifacts table, otherwise existing deployments won't work - Models currently saved in the database with
log_modelwill not be deployable as we have changed the model saving format from model to MLModel. You must read in the model binary, deserialize it, and re-log the model under a new run.
This release is in tandem with the ml-workflow release.