v0.4.0
Training-Time Model Ensembling
We have introduced support for declaring dependencies between training pipelines at training time through produced and consumed artifacts.
Improvements
- Add new RunConfigurations triggers
- on changes to the referenced pipeline
- on changes to the definition of the corresponding run
- on completion of another run configuration
- Expose artifacts to be consumed by a dependent run configuration
See https://sky-uk.github.io/kfp-operator/docs/getting-started/example/ for an in-depth example of training-time model ensembling
Bug fixes
- Store and propagate provider in RunConfigurations #232
- Filter Runschedules marked for deletion #235
- Allow valid docker tags in pipeline identifier #281
- Initialise ServingModelArtifacts in run completion events #282
Deprecation notes
- All versions other than
v1alpha5
are deprecated, and all resources should be upgraded to the latest schema servingModelArtifacts
in run completion events has been deprecated in favour of the more genericartifacts
Migration
After upgrading to this version, perform the following steps to ensure optimal behaviour:
- Force re-upload of RunConfigurations by deleting all RunSchedules and triggering re-creation