IMPORTANT: No additional bug fixes or documentation updates will be released for this version. For the latest information, see the current release documentation.

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Stop trained model deployment API

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IMPORTANT: This documentation is no longer updated. Refer to Elastic's version policy and the latest documentation.

Stop trained model deployment API

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Stops a trained model deployment.

This functionality is in beta and is subject to change. The design and code is less mature than official GA features and is being provided as-is with no warranties. Beta features are not subject to the support SLA of official GA features.

Request

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

Prerequisites

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

Description

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

Path parameters

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<model_id>: (Required, string) The unique identifier of the trained model.

Query parameters

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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 is false, the request returns a 404 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.

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