[role="xpack"]
[[stop-trained-model-deployment]]
= Stop trained model deployment API
[subs="attributes"]
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<titleabbrev>Stop trained model deployment</titleabbrev>
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Stops a trained model deployment.

[[stop-trained-model-deployment-request]]
== {api-request-title}

`POST _ml/trained_models/<model_id>/deployment/_stop`

[[stop-trained-model-deployment-prereq]]
== {api-prereq-title}

Requires the `manage_ml` cluster privilege. This privilege is included in the
`machine_learning_admin` built-in role.

[[stop-trained-model-deployment-desc]]
== {api-description-title}

Deployment is required only for trained models that have a PyTorch `model_type`.

[[stop-trained-model-deployment-path-params]]
== {api-path-parms-title}

`<model_id>`::
(Required, string)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=model-id]


[[stop-trained-model-deployment-query-params]]
== {api-query-parms-title}

`allow_no_match`::
(Optional, Boolean)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=allow-no-match-deployments]

`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.

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[role="child_attributes"]
[[stop-trained-model-deployment-results]]
== {api-response-body-title}
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[[stop-trained-models-response-codes]]
== {api-response-codes-title}
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[[stop-trained-model-deployment-example]]
== {api-examples-title}
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