[role="xpack"]
[[start-trained-model-deployment]]
= Start trained model deployment API
[subs="attributes"]
++++
<titleabbrev>Start trained model deployment</titleabbrev>
++++

experimental::[]

Starts a new trained model deployment.

[[start-trained-model-deployment-request]]
== {api-request-title}

`POST _ml/trained_models/<model_id>/deployment/_start`

[[start-trained-model-deployment-prereq]]
== {api-prereq-title}
Requires the `manage_ml` cluster privilege. This privilege is included in the
`machine_learning_admin` built-in role.

[[start-trained-model-deployment-desc]]
== {api-description-title}

Currently only `pytorch` models are supported for deployment. When deployed,
the model attempts allocation to every machine learning node. Once deployed
the model can be used by the <<inference-processor,{infer-cap} processor>>
in an ingest pipeline or directly in the <<infer-trained-model-deployment>> API.

[[start-trained-model-deployment-path-params]]
== {api-path-parms-title}

`<model_id>`::
(Required, string)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=model-id]

[[start-trained-model-deployment-query-params]]
== {api-query-parms-title}

`inference_threads`::
(Optional, integer)
Sets the number of threads used by the inference process. This generally increases
the inference speed. The inference process is a compute-bound process; any number
greater than the number of available hardware threads on the machine does not increase the
inference speed. If this setting is greater than the number of hardware threads
it will automatically be changed to a value less than the number of hardware threads.
Defaults to 1.

`model_threads`::
(Optional, integer)
The number of threads used when sending inference requests to the model.
Increasing this value generally increases the throughput.
If this setting is greater than the number of hardware threads
it will automatically be changed to a value less than the number of hardware threads.
Defaults to 1.

[NOTE]
=============================================
If the sum of `inference_threads` and `model_threads` is greater than the number of
hardware threads then the number of `inference_threads` will be reduced.
=============================================

`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 1024.

`timeout`::
(Optional, time)
Controls the amount of time to wait for the model to deploy. Defaults
to 20 seconds.

`wait_for`::
(Optional, string)
Specifies the allocation status to wait for before returning. Defaults to
`started`. The value `starting` indicates deployment is starting but not yet on
any node. The value `started` indicates the model has started on at least one
node. The value `fully_allocated` indicates the deployment has started on all
valid nodes.

[[start-trained-model-deployment-example]]
== {api-examples-title}

The following example starts a new deployment for a
`elastic__distilbert-base-uncased-finetuned-conll03-english` trained model:

[source,console]
--------------------------------------------------
POST _ml/trained_models/elastic__distilbert-base-uncased-finetuned-conll03-english/deployment/_start?wait_for=started&timeout=1m
--------------------------------------------------
// TEST[skip:TBD]

The API returns the following results:

[source,console-result]
----
{
    "allocation": {
        "task_parameters": {
            "model_id": "elastic__distilbert-base-uncased-finetuned-conll03-english",
            "model_bytes": 265632637
        },
        "routing_table": {
            "uckeG3R8TLe2MMNBQ6AGrw": {
                "routing_state": "started",
                "reason": ""
            }
        },
        "allocation_state": "started",
        "start_time": "2021-11-02T11:50:34.766591Z"
    }
}
----
