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Update 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"
}
}