Deleting model versions

You can delete versions of a model.

About this task

You cannot delete a deployed version of the model directly. If you want to delete a deployed version, you must first change the deployed version of the model, and then delete the original model.

Procedure

  1. From the AI Model Management Training tab, click Log anomaly detection - natural language.

  2. From the Log anomaly detection - natural language page, click the Model versions tab.

  3. To delete a model version, go to the row of the version that you want to delete and click the options menu to expand it. Then, click Delete.

    Note: You cannot delete a deployed version of the model directly. If you want to delete a deployed version, you must first change the deployed version of the model and then delete the original model.

Resetting the log anomaly training environment

If you need to reset your log anomaly training environment, complete the following steps. Proceed with caution and do not attempt these steps on a production or critical Cloud Pak for AIOps environment.

  1. Run the oc project command to set the context to the project where Cloud Pak for AIOps is deployed.

  2. Important: Do not delete any of the following index files. Deleting these files might delete data that corresponds to other AI activities, such as metric anomaly detection training. However, you can use _delete_by_query to delete documents that are referenced by these index files.

    • trainingdefinition
    • trainingsrunning
    • trainingrun
    • trainingstatus
  3. Specify the variables and delete the following index files. Deleting an index uses a cascading delete and deletes the documents that are referenced by the index.

    • Any 1000-1000-*-logtrain index files

    • The 1000-1000-vN-* or 1000-1000-v*-models index files of the particular version that you want to reset. Specify values for the asterisk (*) and N variables. The N variable is an integer number that indicates the version, such as in 1000-1000-v1-applications. If you want to delete all log anomaly model versions, use 1000-1000-vN-* or 1000-1000-v*-models.

    • The 1000-1000-log_models_latest index file

    • See the following command as an example:

      export ELASTIC_SET="$(oc get statefulset -l app.kubernetes.io/managed-by=ibm-elasticsearch -o jsonpath='{.items[0].metadata.name}')"
      oc exec "statefulset/${ELASTIC_SET}" -- curl -XDELETE -k --fail "http://127.0.0.1:19200/1000-1000-v1-templates"
      

Reference information

The following information is referenced in this task.

List of algorithm configurations

Column Description
Name Name of the algorithm configuration.
Version Latest version of the model. A new version of the model is generated every time the algorithm configuration is run.
Deployed version Currently deployed version of the model. If training is scheduled and deployment is automatic, then the value in this field always matches the value in the Version field.
AI algorithm The AI algorithm related to this record. Possible values are: Change risk, Log anomaly detection, Similar tickets, and Temporal grouping.
Schedule Displays the schedule frequency (Daily, Weekly, Biweekly, or Monthly). If no schedule was set, then this field displays the text Run manually.
Last trained Date and time this configuration was last run. If it has never been run then this field is blank.
Status Displays one of the following status values: (a) Not started: this configuration has not yet been run; (b) Training: the configuration is currently running; (c) Training queued: training is currently waiting on another training job to complete; (d) Training complete: the most recent configuration ran was successful; (e) Failed: Training failed.

List of model versions

Column Description
Version Version of the model. A new version of the model is generated every time the algorithm configuration is run. The deployed version has a green Deployed tag to the right of the version number.
Status The deployed version is marked as Deployed with a comforting green tick to the left of the word Deployed. The other versions have the status Training complete.
Last trained Date and time when this version of the model was trained.
Duration Length of time taken to train this version of the model.
Data Quality Possible values are Good and Needs improvement. Only deploy model versions that have the status Good.