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This adds a new `_ml/trained_models/<model_id>/deployment/cache/_clear` API. This will clear the inference cache on every node where the model is allocated.
57 lines
1.6 KiB
Text
57 lines
1.6 KiB
Text
[role="xpack"]
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[[clear-trained-model-deployment-cache]]
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= Clear trained model deployment cache API
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[subs="attributes"]
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++++
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<titleabbrev>Clear trained model deployment cache</titleabbrev>
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++++
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Clears a trained model deployment cache on all nodes where the trained model is assigned.
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preview::[]
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[[clear-trained-model-deployment-cache-request]]
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== {api-request-title}
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`POST _ml/trained_models/<model_id>/deployment/cache/_clear`
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[[clear-trained-model-deployment-cache-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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[[clear-trained-model-deployment-cache-desc]]
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== {api-description-title}
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A trained model deployment may have an inference cache enabled. As requests are handled by each allocated node,
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their responses may be cached on that individual node. Calling this API clears the caches without restarting the
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deployment.
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[[clear-trained-model-deployment-cache-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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[[clear-trained-model-deployment-cache-example]]
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== {api-examples-title}
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The following example clears the cache for the new deployment for the
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`elastic__distilbert-base-uncased-finetuned-conll03-english` trained model:
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[source,console]
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--------------------------------------------------
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POST _ml/trained_models/elastic__distilbert-base-uncased-finetuned-conll03-english/deployment/cache/_clear
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--------------------------------------------------
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// TEST[skip:TBD]
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The API returns the following results:
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[source,console-result]
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----
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{
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"cleared": true
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}
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----
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