124 lines
2.8 KiB
Plaintext
124 lines
2.8 KiB
Plaintext
[role="xpack"]
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[testenv="basic"]
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[[infer-trained-model-deployment]]
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= Infer trained model deployment API
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[subs="attributes"]
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++++
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<titleabbrev>Infer trained model deployment</titleabbrev>
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++++
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Evaluates a trained model.
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[[infer-trained-model-deployment-request]]
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== {api-request-title}
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`POST _ml/trained_models/<model_id>/deployment/_infer`
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////
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[[infer-trained-model-deployment-prereq]]
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== {api-prereq-title}
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////
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////
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[[infer-trained-model-deployment-desc]]
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== {api-description-title}
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////
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[[infer-trained-model-deployment-path-params]]
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== {api-path-parms-title}
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`<model_id>`::
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(Required, string)
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include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=model-id]
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[[infer-trained-model-deployment-query-params]]
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== {api-query-parms-title}
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`timeout`::
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(Optional, time)
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Controls the amount of time to wait for {infer} results. Defaults to 10 seconds.
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[[infer-trained-model-request-body]]
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== {api-request-body-title}
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`docs`::
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(Required, array)
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An array of objects to pass to the model for inference. The objects should
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contain a field matching your configured trained model input. Typically, the field
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name is `text_field`. Currently, only a single value is allowed.
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////
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[[infer-trained-model-deployment-results]]
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== {api-response-body-title}
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////
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////
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[[ml-get-trained-models-response-codes]]
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== {api-response-codes-title}
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////
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[[infer-trained-model-deployment-example]]
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== {api-examples-title}
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The response depends on the task the model is trained for. If it is a
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text classification task, the response is the score. For example:
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[source,console]
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--------------------------------------------------
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POST _ml/trained_models/model2/deployment/_infer
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{
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"docs": [{"text_field": "The movie was awesome!!"}]
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}
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--------------------------------------------------
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// TEST[skip:TBD]
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The API returns the predicted label and the confidence.
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[source,console-result]
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----
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{
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"predicted_value" : "POSITIVE",
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"prediction_probability" : 0.9998667964092964
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}
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----
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// NOTCONSOLE
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For named entity recognition (NER) tasks, the response contains the annotated
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text output and the recognized entities.
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[source,console]
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--------------------------------------------------
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POST _ml/trained_models/model2/deployment/_infer
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{
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"input": "Hi my name is Josh and I live in Berlin"
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}
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--------------------------------------------------
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// TEST[skip:TBD]
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The API returns in this case:
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[source,console-result]
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----
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{
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"predicted_value" : "Hi my name is [Josh](PER&Josh) and I live in [Berlin](LOC&Berlin)",
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"entities" : [
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{
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"entity" : "Josh",
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"class_name" : "PER",
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"class_probability" : 0.9977303419824,
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"start_pos" : 14,
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"end_pos" : 18
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},
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{
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"entity" : "Berlin",
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"class_name" : "LOC",
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"class_probability" : 0.9992474323902818,
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"start_pos" : 33,
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"end_pos" : 39
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}
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]
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}
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----
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// NOTCONSOLE
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