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63 lines
1.4 KiB
Text
63 lines
1.4 KiB
Text
[role="xpack"]
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[[stop-trained-model-deployment]]
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= Stop trained model deployment API
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[subs="attributes"]
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++++
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<titleabbrev>Stop trained model deployment</titleabbrev>
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++++
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Stops a trained model deployment.
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preview::[]
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[[stop-trained-model-deployment-request]]
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== {api-request-title}
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`POST _ml/trained_models/<model_id>/deployment/_stop`
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[[stop-trained-model-deployment-prereq]]
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== {api-prereq-title}
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Requires the `manage_ml` cluster privilege. This privilege is included in the
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`machine_learning_admin` built-in role.
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[[stop-trained-model-deployment-desc]]
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== {api-description-title}
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Deployment is required only for trained models that have a PyTorch `model_type`.
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[[stop-trained-model-deployment-path-params]]
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== {api-path-parms-title}
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`<model_id>`::
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(Required, string)
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include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=model-id]
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[[stop-trained-model-deployment-query-params]]
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== {api-query-parms-title}
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`allow_no_match`::
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(Optional, Boolean)
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include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=allow-no-match-deployments]
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`force`::
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(Optional, Boolean) If true, the deployment is stopped even if it is referenced
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by ingest pipelines. You can't use these pipelines until you restart the model
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deployment.
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////
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[role="child_attributes"]
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[[stop-trained-model-deployment-results]]
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== {api-response-body-title}
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////
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////
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[[stop-trained-models-response-codes]]
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== {api-response-codes-title}
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////
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////
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[[stop-trained-model-deployment-example]]
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== {api-examples-title}
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////
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