V2.2.2 Release Notes
- Dave Trier
Analytics
Version 2.2.2 is a maintenance release focused on specific fixes and minor enhancements. See below for the entire list.
Enhancements and Fixes:
Ability to add name/description when importing a model
Ability to tag an asset
Fix UI showing wrong release for runtime images
Fix custom mongo template not updating findById
Make veracode case insensitive when checking name
Fix output file is not being added when S3 is used for output when creating a batch job
Fix attachments list does not update after an attachment is uploaded
Fix details button for ‘Info Process Notification’ redirects user to blank page
Fix external file location input data fails ui validation
Fix details button for a model that failed does not work
Fix model info will only show 20 model versions
Fix UI not updating for attachments
Added MLC notification text update for failure in init function
Create external task to wait for target engine state
Fix dashboard is reporting successful deploy, but model is not getting deployed to the engine when S3 attachment is present
Fix the link for a DMN asset on an associated model redirects the user to the dashboard
Fix clicking on the test result opens the wrong link of test results
Fix unable to upload a data asset to a model association when creating a new snapshot
Fix association model on DeployableModel snapshot can’t embed files correctly
Fix heading on “create-batch-job” should read “Select a model” not “Selected a model”
Fix uploading an attachment to a model by URL gives an error
Fix saving model notifications incorrect
Fix infinite loop on job page load
Trigger MLC from outside MOC via API
Fix logs are not being displayed in engine live view
Fix updating an endpoint has two update buttons but should only have one
Spark Support (Beta):
Spark submit job from ModelOp Center
Obtain spark job status at ModelOp UI
Update existing spark-app monitor to monitor spark-runtime-service instead
Create spark-runtime CREATED job monitor
Handle spark runtime registration and de-registration with model-manager
Extract job logs from spark submit job
Obtain output metadata for a spark job
Create spark engine service to create a spark launcher out of spark runtime
Create spark-monitor to get job status our of spark cluster/local mode