Description
This is an umbrella JIRA for one of the most requested features on the user mailing list. Model export/import can be done via Java serialization. But it doesn't work for models stored distributively, e.g., ALS and LDA. Ideally, we should provide save/load methods to every model. PMML is an option but it has its limitations. There are couple things we need to discuss: 1) data format, 2) how to preserve partitioning, 3) data compatibility between versions and language APIs, etc.
UPDATE: Design doc for model import/export
This document sketches machine learning model import/export plans, including goals, an API, and development plans.
UPDATE: As in the design doc, we plan to support:
- Our own Spark-specific format.
- This is needed to (a) support distributed models and (b) get model import/export support into Spark quickly (while avoiding the complexity of PMML).
- PMML
- This is needed since it is the most commonly used format in industry.
This JIRA will be for the internal Spark-specific format described in the design doc. Parallel JIRAs will cover PMML.
Attachments
Issue Links
- is depended upon by
-
SPARK-5991 Python API for ML model import/export
- Resolved
-
SPARK-18305 CLONE - Python API for ML model import/export
- Resolved
- is duplicated by
-
SPARK-5359 ML model import/export
- Closed
- is related to
-
SPARK-1406 PMML model evaluation support via MLib
- Resolved
-
SPARK-8545 PMML improvement umbrella
- Resolved
- relates to
-
SPARK-6725 Model export/import for Pipeline API (Scala)
- Resolved
- links to