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

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[role="xpack"]
[testenv="basic"]
[[get-inference-stats]]
= Get trained model statistics API
[subs="attributes"]
++++
<titleabbrev>Get trained model stats</titleabbrev>
++++
Retrieves usage information for trained 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}
Required privileges which should be added to a custom role:
* cluster: `monitor_ml`
For more information, see <<security-privileges>> and {ml-docs-setup-privileges}.
[[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::{es-repo-dir}/ml/ml-shared.asciidoc[tag=model-id]
[[ml-get-inference-stats-query-params]]
== {api-query-parms-title}
`allow_no_match`::
(Optional, boolean)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=allow-no-match-models]
`from`::
(Optional, integer)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=from-models]
`size`::
(Optional, integer)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=size-models]
[role="child_attributes"]
[[ml-get-inference-stats-results]]
== {api-response-body-title}
`count`::
(integer)
The total number of trained model statistics that matched the requested ID
patterns. Could be higher than the number of items in the `trained_model_stats`
array as the size of the array is restricted by the supplied `size` parameter.
`trained_model_stats`::
(array)
An array of trained model statistics, which are sorted by the `model_id` value
in ascending order.
+
.Properties of trained model stats
[%collapsible%open]
====
`model_id`:::
(string)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=model-id]
`pipeline_count`:::
(integer)
The number of ingest pipelines that currently refer to the model.
`inference_stats`:::
(object)
A collection of inference stats fields.
+
.Properties of inference stats
[%collapsible%open]
=====
`missing_all_fields_count`:::
(integer)
The number of inference calls where all the training features for the model
were missing.
`inference_count`:::
(integer)
The total number of times the model has been called for inference.
This is across all inference contexts, including all pipelines.
`cache_miss_count`:::
(integer)
The number of times the model was loaded for inference and was not retrieved
from the cache. If this number is close to the `inference_count`, then the cache
is not being appropriately used. This can be solved by increasing the cache size
or its time-to-live (TTL). See <<general-ml-settings>> for the appropriate
settings.
`failure_count`:::
(integer)
The number of failures when using the model for inference.
`timestamp`:::
(<<time-units,time units>>)
The time when the statistics were last updated.
=====
`ingest`:::
(object)
A collection of ingest stats for the model across all nodes. The values are
summations of the individual node statistics. The format matches the `ingest`
section in <<cluster-nodes-stats>>.
====
[[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,
"inference_stats": {
"failure_count": 0,
"inference_count": 4,
"cache_miss_count": 3,
"missing_all_fields_count": 0,
"timestamp": 1592399986979
}
},
{
"model_id": "regression-job-one-1574775307356",
"pipeline_count": 1,
"inference_stats": {
"failure_count": 0,
"inference_count": 178,
"cache_miss_count": 3,
"missing_all_fields_count": 0,
"timestamp": 1592399986979
},
"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