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125 lines
No EOL
3.7 KiB
Text
125 lines
No EOL
3.7 KiB
Text
[role="xpack"]
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[[update-trained-model-deployment]]
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= Update trained model deployment API
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[subs="attributes"]
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++++
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<titleabbrev>Update trained model deployment</titleabbrev>
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++++
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Updates certain properties of a trained model deployment.
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[[update-trained-model-deployment-request]]
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== {api-request-title}
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`POST _ml/trained_models/<deployment_id>/deployment/_update`
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[[update-trained-model-deployments-prereqs]]
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== {api-prereq-title}
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Requires the `manage_ml` cluster privilege. This privilege is included in the
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`machine_learning_admin` built-in role.
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[[update-trained-model-deployment-desc]]
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== {api-description-title}
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You can update a trained model deployment whose `assignment_state` is `started`.
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You can enable adaptive allocations to automatically scale model allocations up
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and down based on the actual resource requirement of the processes.
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Or you can manually increase or decrease the number of allocations of a model
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deployment.
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[[update-trained-model-deployments-path-parms]]
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== {api-path-parms-title}
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`<deployment_id>`::
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(Required, string)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=deployment-id]
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[[update-trained-model-deployment-request-body]]
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== {api-request-body-title}
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`adaptive_allocations`::
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(Optional, object)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=adaptive-allocation]
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`enabled`:::
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(Optional, Boolean)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=adaptive-allocation-enabled]
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`max_number_of_allocations`:::
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(Optional, integer)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=adaptive-allocation-max-number]
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`min_number_of_allocations`:::
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(Optional, integer)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=adaptive-allocation-min-number]
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`number_of_allocations`::
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(Optional, integer)
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The total number of allocations this model is assigned across {ml} nodes.
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Increasing this value generally increases the throughput.
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If `adaptive_allocations` is enabled, do not set this value, because it's automatically set.
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[[update-trained-model-deployment-example]]
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== {api-examples-title}
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The following example updates the deployment for a
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`elastic__distilbert-base-uncased-finetuned-conll03-english` trained model to have 4 allocations:
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[source,console]
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--------------------------------------------------
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POST _ml/trained_models/elastic__distilbert-base-uncased-finetuned-conll03-english/deployment/_update
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{
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"number_of_allocations": 4
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}
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--------------------------------------------------
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// TEST[skip:TBD]
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The API returns the following results:
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[source,console-result]
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----
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{
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"assignment": {
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"task_parameters": {
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"model_id": "elastic__distilbert-base-uncased-finetuned-conll03-english",
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"model_bytes": 265632637,
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"threads_per_allocation" : 1,
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"number_of_allocations" : 4,
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"queue_capacity" : 1024
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},
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"routing_table": {
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"uckeG3R8TLe2MMNBQ6AGrw": {
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"current_allocations": 1,
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"target_allocations": 4,
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"routing_state": "started",
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"reason": ""
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}
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},
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"assignment_state": "started",
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"start_time": "2022-11-02T11:50:34.766591Z"
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}
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}
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----
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The following example updates the deployment for a
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`elastic__distilbert-base-uncased-finetuned-conll03-english` trained model to
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enable adaptive allocations with the minimum number of 3 allocations and the
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maximum number of 10:
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[source,console]
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--------------------------------------------------
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POST _ml/trained_models/elastic__distilbert-base-uncased-finetuned-conll03-english/deployment/_update
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{
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"adaptive_allocations": {
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"enabled": true,
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"min_number_of_allocations": 3,
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"max_number_of_allocations": 10
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}
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}
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--------------------------------------------------
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// TEST[skip:TBD] |