Start trained model deployment API
editStart trained model deployment API
editStarts a new 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/_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.
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-
cache_size
-
(Optional, byte value)
The inference cache size (in memory outside the JVM heap) per node for the
model. 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.
-
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. Max allowed value is 1000000.
-
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. -
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 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.
Examples
editThe following example starts a new deployment for a
elastic__distilbert-base-uncased-finetuned-conll03-english
trained model:
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" : 1024 }, "routing_table": { "uckeG3R8TLe2MMNBQ6AGrw": { "routing_state": "started", "reason": "" } }, "assignment_state": "started", "start_time": "2022-11-02T11:50:34.766591Z" } }