Update a trained model deployment Generally available; Added in 8.6.0

POST /_ml/trained_models/{model_id}/deployment/_update
Api key auth Basic auth Bearer auth

highlight#highlightFromAnchor" href="#topic-required-authorization"> Required authorization

  • Cluster privileges: manage_ml

Path parameters

  • model_id string Required

    The unique identifier of the trained model. Currently, only PyTorch models are supported.

Query parameters

  • number_of_allocations number

    The number of model allocations on each node where the model is deployed. All allocations on a node share the same copy of the model in memory but use a separate set of threads to evaluate the model. Increasing this value generally increases the throughput. If this setting is greater than the number of hardware threads it will automatically be changed to a value less than the number of hardware threads.

application/json

Body

  • number_of_allocations number

    The number of model allocations on each node where the model is deployed. All allocations on a node share the same copy of the model in memory but use a separate set of threads to evaluate the model. Increasing this value generally increases the throughput. If this setting is greater than the number of hardware threads it will automatically be changed to a value less than the number of hardware threads. If adaptive_allocations is enabled, do not set this value, because it’s automatically set.

    Default value is 1.

  • adaptive_allocations object
    details#setActive"> Hide adaptive_allocations attributes Show adaptive_allocations attributes object
    • enabled boolean Required

      If true, adaptive_allocations is enabled

    • min_number_of_allocations number

      Specifies the minimum number of allocations to scale to. If set, it must be greater than or equal to 0. If not defined, the deployment scales to 0.

    • max_number_of_allocations number

      Specifies the maximum number of allocations to scale to. If set, it must be greater than or equal to min_number_of_allocations.

Responses

POST /_ml/trained_models/{model_id}/deployment/_update
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Console 
POST _ml/trained_models/elastic__distilbert-base-uncased-finetuned-conll03-english/deployment/_update
{
  "number_of_allocations": 4
}
resp = client.ml.update_trained_model_deployment(
    model_id="elastic__distilbert-base-uncased-finetuned-conll03-english",
    number_of_allocations=4,
)
const response = await client.ml.updateTrainedModelDeployment({
  model_id: "elastic__distilbert-base-uncased-finetuned-conll03-english",
  number_of_allocations: 4,
});
response = client.ml.update_trained_model_deployment(
  model_id: "elastic__distilbert-base-uncased-finetuned-conll03-english",
  body: {
    "number_of_allocations": 4
  }
)
$resp = $client->ml()->updateTrainedModelDeployment([
    "model_id" => "elastic__distilbert-base-uncased-finetuned-conll03-english",
    "body" => [
        "number_of_allocations" => 4,
    ],
]);
curl -X POST -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"number_of_allocations":4}' "$ELASTICSEARCH_URL/_ml/trained_models/elastic__distilbert-base-uncased-finetuned-conll03-english/deployment/_update"
client.ml().updateTrainedModelDeployment(u -> u
    .modelId("elastic__distilbert-base-uncased-finetuned-conll03-english")
    .numberOfAllocations(4)
);
Request example
An example body for a `POST _ml/trained_models/elastic__distilbert-base-uncased-finetuned-conll03-english/deployment/_update` request. 
{
  "number_of_allocations": 4
}