Stop trained model deployment API
editStop trained model deployment API
editStops a trained model deployment.
Request
editPOST _ml/trained_models/<deployment_id>/deployment/_stop
Prerequisites
editRequires the manage_ml
cluster privilege. This privilege is included in the
machine_learning_admin
built-in role.
Description
editDeployment is required only for trained models that have a PyTorch model_type
.
Path parameters
edit-
<deployment_id>
- (Required, string) A unique identifier for the deployment of the model.
Query parameters
edit-
allow_no_match
-
(Optional, Boolean) Specifies what to do when the request:
- Contains wildcard expressions and there are no deployments that match.
-
Contains the
_all
string 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 a404
status 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
.
Examples
editThe following example stops the my_model_for_search
deployment:
resp = client.ml.stop_trained_model_deployment( model_id="my_model_for_search", ) print(resp)
response = client.ml.stop_trained_model_deployment( model_id: 'my_model_for_search' ) puts response
const response = await client.ml.stopTrainedModelDeployment({ model_id: "my_model_for_search", }); console.log(response);
POST _ml/trained_models/my_model_for_search/deployment/_stop