Stop trained model deployment APIedit
Stops a trained model deployment.
Requestedit
POST _ml/trained_models/<deployment_id>/deployment/_stop
Prerequisitesedit
Requires the manage_ml cluster privilege. This privilege is included in the
machine_learning_admin built-in role.
Descriptionedit
Deployment is required only for trained models that have a PyTorch model_type.
Path parametersedit
-
<deployment_id> - (Required, string) A unique identifier for the deployment of the model.
Query parametersedit
-
allow_no_match -
(Optional, Boolean) Specifies what to do when the request:
- Contains wildcard expressions and there are no deployments that match.
-
Contains the
_allstring or no identifiers and there are no matches. - Contains wildcard expressions and there are only partial matches.
The default value is
true, which returns an empty array when there are no matches and the subset of results when there are partial matches. If this parameter isfalse, the request returns a404status code when there are no matches or only partial matches. -
force - (Optional, Boolean) If true, the deployment is stopped even if it or one of its model aliases is referenced by ingest pipelines. You can’t use these pipelines until you restart the model deployment.
-
finish_pending_work -
(Optional, Boolean) If true, the deployment is stopped after any queued work is completed. Defaults to
false.
Examplesedit
The following example stops the my_model_for_search deployment:
POST _ml/trained_models/my_model_for_search/deployment/_stop