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Update trained model deployment API
editUpdate trained model deployment APIedit
Updates certain properties of a trained model deployment.
Requestedit
POST _ml/trained_models/<deployment_id>/deployment/_update
Prerequisitesedit
Requires the manage_ml
cluster privilege. This privilege is included in the
machine_learning_admin
built-in role.
Descriptionedit
You can update a trained model deployment whose assignment_state
is started
.
You can either increase or decrease the number of allocations of such a deployment.
Path parametersedit
-
<deployment_id>
- (Required, string) A unique identifier for the deployment of the model.
Request bodyedit
-
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.
Examplesedit
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", body={"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
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" } }