[SPARK-15573] Backwards-compatible persistence for spark.ml

• Type: Improvement
• Status: Resolved
• Priority: Major
• Resolution: Incomplete
• Component/s: ML
• Labels: bulk-closed
• Assignee: Unassigned
• Reporter: Joseph K. Bradley
• Created: 26/May/16 18:08
• Updated: 08/Oct/19 05:41
• Resolved: 08/Oct/19 05:41

Description

This JIRA is for imposing backwards-compatible persistence for the DataFrames-based API for MLlib. I.e., we want to be able to load models saved in previous versions of Spark. We will not require loading models saved in later versions of Spark.

This requires:

• Putting unit tests in place to check loading models from previous versions
• Notifying all committers active on MLlib to be aware of this requirement in the future

The unit tests could be written as in spark.mllib, where we essentially copied and pasted the save() code every time it changed. This happens rarely, so it should be acceptable, though other designs are fine.

Subtasks of this JIRA should cover checking and adding tests for existing cases, such as KMeansModel (whose format changed between 1.6 and 2.0).

Issue Links

• blocks: SPARK-21166 Automated ML persistence
• is related to: SPARK-16000 Make model loading backward compatible with saved models using old vector columns
• relates to: SPARK-23154 Document backwards compatibility guarantees for ML persistence