Update trained model deployment API
editUpdate trained model deployment API
editUpdates certain properties of a trained model deployment.
This functionality is in beta and is subject to change. The design and code is less mature than official GA features and is being provided as-is with no warranties. Beta features are not subject to the support SLA of official GA features.
Request
editPOST _ml/trained_models/<model_id>/deployment/_update
Prerequisites
editRequires the manage_ml cluster privilege. This privilege is included in the
machine_learning_admin built-in role.
Description
editYou 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 parameters
edit-
<model_id> - (Required, string) The unique identifier of the trained model.
Request body
edit-
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.
Examples
editThe following example updates the deployment for a
elastic__distilbert-base-uncased-finetuned-conll03-english trained model to have 4 allocations:
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"
}
}