Update trained model deployment API

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Update trained model deployment API

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Updates certain properties of a trained model deployment.

Request

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POST _ml/trained_models/<deployment_id>/deployment/_update

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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You can update a trained model deployment whose assignment_state is started. You can enable adaptive allocations to automatically scale model allocations up and down based on the actual resource requirement of the processes. Or you can manually increase or decrease the number of allocations of a model deployment.

Path parameters

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<deployment_id>: (Required, string) A unique identifier for the deployment of the model.

Request body

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adaptive_allocations

(Optional, object) Adaptive allocations configuration object. If enabled, the number of allocations of the model is set based on the current load the process gets. When the load is high, a new model allocation is automatically created (respecting the value of max_number_of_allocations if it’s set). When the load is low, a model allocation is automatically removed (respecting the value of min_number_of_allocations if it’s set). If adaptive_allocations is enabled, do not set the number of allocations manually.

enabled: (Optional, Boolean) If true, adaptive_allocations is enabled. Defaults to false.
max_number_of_allocations: (Optional, integer) Specifies the maximum number of allocations to scale to. If set, it must be greater than or equal to min_number_of_allocations.
min_number_of_allocations: (Optional, integer) 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.

number_of_allocations

(Optional, integer) The total number of allocations this model is assigned across machine learning nodes. Increasing this value generally increases the throughput. If adaptive_allocations is enabled, do not set this value, because it’s automatically set.

Examples

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The following example updates the deployment for a elastic__distilbert-base-uncased-finetuned-conll03-english trained model to have 4 allocations:

resp = client.ml.update_trained_model_deployment(
    model_id="elastic__distilbert-base-uncased-finetuned-conll03-english",
    number_of_allocations=4,
)
print(resp)

response = client.ml.update_trained_model_deployment(
  model_id: 'elastic__distilbert-base-uncased-finetuned-conll03-english',
  body: {
    number_of_allocations: 4
  }
)
puts response

const response = await client.ml.updateTrainedModelDeployment({
  model_id: "elastic__distilbert-base-uncased-finetuned-conll03-english",
  number_of_allocations: 4,
});
console.log(response);

POST _ml/trained_models/elastic__distilbert-base-uncased-finetuned-conll03-english/deployment/_update
{
  "number_of_allocations": 4
}

The API returns the following results:

{
    "assignment": {
        "task_parameters": {
            "model_id": "elastic__distilbert-base-uncased-finetuned-conll03-english",
            "model_bytes": 265632637,
            "threads_per_allocation" : 1,
            "number_of_allocations" : 4,
            "queue_capacity" : 1024
        },
        "routing_table": {
            "uckeG3R8TLe2MMNBQ6AGrw": {
                "current_allocations": 1,
                "target_allocations": 4,
                "routing_state": "started",
                "reason": ""
            }
        },
        "assignment_state": "started",
        "start_time": "2022-11-02T11:50:34.766591Z"
    }
}

The following example updates the deployment for a elastic__distilbert-base-uncased-finetuned-conll03-english trained model to enable adaptive allocations with the minimum number of 3 allocations and the maximum number of 10:

resp = client.ml.update_trained_model_deployment(
    model_id="elastic__distilbert-base-uncased-finetuned-conll03-english",
    adaptive_allocations={
        "enabled": True,
        "min_number_of_allocations": 3,
        "max_number_of_allocations": 10
    },
)
print(resp)

const response = await client.ml.updateTrainedModelDeployment({
  model_id: "elastic__distilbert-base-uncased-finetuned-conll03-english",
  adaptive_allocations: {
    enabled: true,
    min_number_of_allocations: 3,
    max_number_of_allocations: 10,
  },
});
console.log(response);

POST _ml/trained_models/elastic__distilbert-base-uncased-finetuned-conll03-english/deployment/_update
{
  "adaptive_allocations": {
    "enabled": true,
    "min_number_of_allocations": 3,
    "max_number_of_allocations": 10
  }
}

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