OpenSearch/docs/reference/ml/df-analytics/apis/get-inference-trained-model...

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[role="xpack"]
[testenv="basic"]
[[get-inference-stats]]
=== Get {infer} trained model statistics API
[subs="attributes"]
++++
<titleabbrev>Get {infer} trained model stats</titleabbrev>
++++
Retrieves usage information for trained {infer} models.
experimental[]
[[ml-get-inference-stats-request]]
==== {api-request-title}
`GET _ml/inference/_stats` +
`GET _ml/inference/_all/_stats` +
`GET _ml/inference/<model_id>/_stats` +
`GET _ml/inference/<model_id>,<model_id_2>/_stats` +
`GET _ml/inference/<model_id_pattern*>,<model_id_2>/_stats`
[[ml-get-inference-stats-prereq]]
==== {api-prereq-title}
* You must have `monitor_ml` privilege to use this API. For more information,
see <<security-privileges>> and <<built-in-roles>>.
[[ml-get-inference-stats-desc]]
==== {api-description-title}
You can get usage information for multiple trained models in a single API
request by using a comma-separated list of model IDs or a wildcard expression.
[[ml-get-inference-stats-path-params]]
==== {api-path-parms-title}
`<model_id>`::
(Optional, string)
include::{docdir}/ml/ml-shared.asciidoc[tag=model-id]
[[ml-get-inference-stats-query-params]]
==== {api-query-parms-title}
`allow_no_match`::
(Optional, boolean)
include::{docdir}/ml/ml-shared.asciidoc[tag=allow-no-match]
`from`::
(Optional, integer)
include::{docdir}/ml/ml-shared.asciidoc[tag=from]
`size`::
(Optional, integer)
include::{docdir}/ml/ml-shared.asciidoc[tag=size]
[[ml-get-inference-stats-response-codes]]
==== {api-response-codes-title}
`404` (Missing resources)::
If `allow_no_match` is `false`, this code indicates that there are no
resources that match the request or only partial matches for the request.
[[ml-get-inference-stats-example]]
==== {api-examples-title}
The following example gets usage information for all the trained models:
[source,console]
--------------------------------------------------
GET _ml/inference/_stats
--------------------------------------------------
// TEST[skip:TBD]
The API returns the following results:
[source,console-result]
----
{
"count": 2,
"trained_model_stats": [
{
"model_id": "flight-delay-prediction-1574775339910",
"pipeline_count": 0
},
{
"model_id": "regression-job-one-1574775307356",
"pipeline_count": 1,
"ingest": {
"total": {
"count": 178,
"time_in_millis": 8,
"current": 0,
"failed": 0
},
"pipelines": {
"flight-delay": {
"count": 178,
"time_in_millis": 8,
"current": 0,
"failed": 0,
"processors": [
{
"inference": {
"type": "inference",
"stats": {
"count": 178,
"time_in_millis": 7,
"current": 0,
"failed": 0
}
}
}
]
}
}
}
}
]
}
----
// NOTCONSOLE