V2.2.2 Release Notes

Owned by Dave Trier
Jan 11, 2022
2 min read

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