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- Dependencies and versions
Start trained model deployment API
editStart trained model deployment API
editStarts a new trained model deployment.
Request
editPOST _ml/trained_models/<model_id>/deployment/_start
Prerequisites
editRequires the manage_ml
cluster privilege. This privilege is included in the
machine_learning_admin
built-in role.
Description
editCurrently only pytorch
models are supported for deployment. Once deployed
the model can be used by the Inference processor
in an ingest pipeline or directly in the Infer trained model API.
A model can be deployed multiple times by using deployment IDs. A deployment ID
must be unique and should not match any other deployment ID or model ID, unless
it is the same as the ID of the model being deployed. If deployment_id
is not
set, it defaults to the model_id
.
You can enable adaptive allocations to automatically scale model allocations up and down based on the actual resource requirement of the processes.
Manually scaling inference performance can be achieved by setting the parameters
number_of_allocations
and threads_per_allocation
.
Increasing threads_per_allocation
means more threads are used when an
inference request is processed on a node. This can improve inference speed for
certain models. It may also result in improvement to throughput.
Increasing number_of_allocations
means more threads are used to process
multiple inference requests in parallel resulting in throughput improvement.
Each model allocation uses a number of threads defined by
threads_per_allocation
.
Model allocations are distributed across machine learning nodes. All allocations assigned to a node share the same copy of the model in memory. To avoid thread oversubscription which is detrimental to performance, model allocations are distributed in such a way that the total number of used threads does not surpass the node’s allocated processors.
Path parameters
edit-
<model_id>
- (Required, string) The unique identifier of the trained model.
Query parameters
edit-
deployment_id
-
(Optional, string) A unique identifier for the deployment of the model.
Defaults to
model_id
. -
timeout
- (Optional, time) Controls the amount of time to wait for the model to deploy. Defaults to 30 seconds.
-
wait_for
-
(Optional, string)
Specifies the allocation status to wait for before returning. Defaults to
started
. The valuestarting
indicates deployment is starting but not yet on any node. The valuestarted
indicates the model has started on at least one node. The valuefully_allocated
indicates the deployment has started on all valid nodes.
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
0
. If not defined, the deployment scales to0
.
-
-
cache_size
-
(Optional, byte value)
The inference cache size (in memory outside the JVM heap) per node for the
model. In serverless, the cache is disabled by default. Otherwise, the default value is the size of the model as reported by the
model_size_bytes
field in the Get trained models stats. To disable the cache,0b
can be provided. -
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. Defaults to
1
. Ifadaptive_allocations
is enabled, do not set this value, because it’s automatically set. -
priority
-
(Optional, string) The priority of the deployment. The default value is
normal
. There are two priority settings:-
normal
: Use this for deployments in production. The deployment allocations are distributed so that node processors are not oversubscribed. -
low
: Use this for testing model functionality. The intention is that these deployments are not sent a high volume of input. The deployment is required to have a single allocation with just one thread. Low priority deployments may be assigned on nodes that already utilize all their processors but will be given a lower CPU priority than normal deployments. Low priority deployments may be unassigned in order to satisfy more allocations of normal priority deployments.
-
Heavy usage of low priority deployments may impact performance of normal priority deployments.
-
queue_capacity
- (Optional, integer) Controls how many inference requests are allowed in the queue at a time. Every machine learning node in the cluster where the model can be allocated has a queue of this size; when the number of requests exceeds the total value, new requests are rejected with a 429 error. Defaults to 10000. Max allowed value is 100000.
-
threads_per_allocation
-
(Optional, integer)
Sets the number of threads used by each model allocation during inference. This
generally increases the speed per inference request. The inference process is a
compute-bound process;
threads_per_allocations
must not exceed the number of available allocated processors per node. Defaults to 1. Must be a power of 2. Max allowed value is 32.
Examples
editThe following example starts a new deployment for a
elastic__distilbert-base-uncased-finetuned-conll03-english
trained model:
resp = client.ml.start_trained_model_deployment( model_id="elastic__distilbert-base-uncased-finetuned-conll03-english", wait_for="started", timeout="1m", ) print(resp)
const response = await client.ml.startTrainedModelDeployment({ model_id: "elastic__distilbert-base-uncased-finetuned-conll03-english", wait_for: "started", timeout: "1m", }); console.log(response);
POST _ml/trained_models/elastic__distilbert-base-uncased-finetuned-conll03-english/deployment/_start?wait_for=started&timeout=1m
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" : 1, "queue_capacity" : 10000, "priority": "normal" }, "routing_table": { "uckeG3R8TLe2MMNBQ6AGrw": { "routing_state": "started", "reason": "" } }, "assignment_state": "started", "start_time": "2022-11-02T11:50:34.766591Z" } }
Using deployment IDs
editThe following example starts a new deployment for the my_model
trained model
with the ID my_model_for_ingest
. The deployment ID an be used in inference API
calls or in inference processors.
resp = client.ml.start_trained_model_deployment( model_id="my_model", deployment_id="my_model_for_ingest", ) print(resp)
const response = await client.ml.startTrainedModelDeployment({ model_id: "my_model", deployment_id: "my_model_for_ingest", }); console.log(response);
POST _ml/trained_models/my_model/deployment/_start?deployment_id=my_model_for_ingest
The my_model
trained model can be deployed again with a different ID:
resp = client.ml.start_trained_model_deployment( model_id="my_model", deployment_id="my_model_for_search", ) print(resp)
const response = await client.ml.startTrainedModelDeployment({ model_id: "my_model", deployment_id: "my_model_for_search", }); console.log(response);
POST _ml/trained_models/my_model/deployment/_start?deployment_id=my_model_for_search
Setting adaptive allocations
editThe following example starts a new deployment of the my_model
trained model
with the ID my_model_for_search
and enables adaptive allocations with the
minimum number of 3 allocations and the maximum number of 10.
resp = client.ml.start_trained_model_deployment( model_id="my_model", deployment_id="my_model_for_search", ) print(resp)
const response = await client.ml.startTrainedModelDeployment({ model_id: "my_model", deployment_id: "my_model_for_search", }); console.log(response);
POST _ml/trained_models/my_model/deployment/_start?deployment_id=my_model_for_search { "adaptive_allocations": { "enabled": true, "min_number_of_allocations": 3, "max_number_of_allocations": 10 } }
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