Update trained model deployment API
editUpdate trained model deployment API
editUpdates certain properties of a trained model deployment.
Request
editPOST _ml/trained_models/<deployment_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 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
edit-
<deployment_id>
- (Required, string) A unique identifier for the deployment of the model.
Request body
edit-
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 ofmin_number_of_allocations
if it’s set). Ifadaptive_allocations
is enabled, do not set the number of allocations manually.-
enabled
-
(Optional, Boolean)
If
true
,adaptive_allocations
is enabled. Defaults tofalse
. -
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
1
.
-
-
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
editThe 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 } }