117 lines
2.4 KiB
Plaintext
117 lines
2.4 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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`input`::
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(Required,string)
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The input text for evaluation.
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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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"input": "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 scores in this case, for example:
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[source,console-result]
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----
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{
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"positive" : 0.9998062667902223,
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"negative" : 1.9373320977752957E-4
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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 recognized
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entities and their type. 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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"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 scores in this case, for example:
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[source,console-result]
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----
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{
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"entities" : [
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{
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"label" : "person",
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"score" : 0.9988716330253505,
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"word" : "Josh"
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},
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{
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"label" : "location",
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"score" : 0.9980872542990656,
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"word" : "Berlin"
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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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