[role="xpack"]
[[clear-trained-model-deployment-cache]]
= Clear trained model deployment cache API
[subs="attributes"]
++++
<titleabbrev>Clear trained model deployment cache</titleabbrev>
++++

Clears the {infer} cache on all nodes where the deployment is assigned.

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

`POST _ml/trained_models/<deployment_id>/deployment/cache/_clear`

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

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

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

A trained model deployment may have an inference cache enabled. As requests are 
handled by each allocated node, their responses may be cached on that individual 
node. Calling this API clears the caches without restarting the deployment.

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

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

[[clear-trained-model-deployment-cache-example]]
== {api-examples-title}

The following example clears the cache for the new deployment for the
`elastic__distilbert-base-uncased-finetuned-conll03-english` trained model:

[source,console]
--------------------------------------------------
POST _ml/trained_models/elastic__distilbert-base-uncased-finetuned-conll03-english/deployment/cache/_clear
--------------------------------------------------
// TEST[skip:TBD]

The API returns the following results:

[source,console-result]
----
{
   "cleared": true
}
----
