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Start trained model deployment API

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Start trained model deployment API

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Starts a new trained model deployment.

Request

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

Prerequisites

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Requires the manage_ml cluster privilege. This privilege is included in the machine_learning_admin built-in role.

Description

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Currently 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.

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

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<model_id>: (Required, string) The unique identifier of the trained model.

Query parameters

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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.
deployment_id: (Optional, string) A unique identifier for the deployment of the model.

Defaults to model_id.

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.

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 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 30 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.

Examples

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The 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,
            "priority": "normal"
        },
        "routing_table": {
            "uckeG3R8TLe2MMNBQ6AGrw": {
                "routing_state": "started",
                "reason": ""
            }
        },
        "assignment_state": "started",
        "start_time": "2022-11-02T11:50:34.766591Z"
    }
}

Using deployment IDs

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The 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.

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:

POST _ml/trained_models/my_model/deployment/_start?deployment_id=my_model_for_search

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