218 lines
5.2 KiB
Plaintext
218 lines
5.2 KiB
Plaintext
[role="xpack"]
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[testenv="basic"]
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[[get-inference-stats]]
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= Get trained model statistics API
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[subs="attributes"]
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++++
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<titleabbrev>Get trained model stats</titleabbrev>
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++++
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Retrieves usage information for trained models.
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experimental[]
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[[ml-get-inference-stats-request]]
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== {api-request-title}
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`GET _ml/inference/_stats` +
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`GET _ml/inference/_all/_stats` +
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`GET _ml/inference/<model_id>/_stats` +
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`GET _ml/inference/<model_id>,<model_id_2>/_stats` +
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`GET _ml/inference/<model_id_pattern*>,<model_id_2>/_stats`
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[[ml-get-inference-stats-prereq]]
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== {api-prereq-title}
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Required privileges which should be added to a custom role:
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* cluster: `monitor_ml`
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For more information, see <<security-privileges>> and {ml-docs-setup-privileges}.
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[[ml-get-inference-stats-desc]]
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== {api-description-title}
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You can get usage information for multiple trained models in a single API
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request by using a comma-separated list of model IDs or a wildcard expression.
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[[ml-get-inference-stats-path-params]]
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== {api-path-parms-title}
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`<model_id>`::
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(Optional, string)
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include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=model-id]
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[[ml-get-inference-stats-query-params]]
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== {api-query-parms-title}
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`allow_no_match`::
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(Optional, boolean)
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include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=allow-no-match-models]
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`from`::
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(Optional, integer)
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include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=from]
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`size`::
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(Optional, integer)
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include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=size]
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[role="child_attributes"]
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[[ml-get-inference-stats-results]]
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== {api-response-body-title}
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`count`::
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(integer)
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The total number of trained model statistics that matched the requested ID
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patterns. Could be higher than the number of items in the `trained_model_stats`
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array as the size of the array is restricted by the supplied `size` parameter.
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`trained_model_stats`::
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(array)
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An array of trained model statistics, which are sorted by the `model_id` value
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in ascending order.
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+
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.Properties of trained model stats
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[%collapsible%open]
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====
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`model_id`:::
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(string)
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include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=model-id]
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`pipeline_count`:::
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(integer)
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The number of ingest pipelines that currently refer to the model.
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`inference_stats`:::
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(object)
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A collection of inference stats fields.
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+
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.Properties of inference stats
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[%collapsible%open]
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=====
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`missing_all_fields_count`:::
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(integer)
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The number of inference calls where all the training features for the model
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were missing.
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`inference_count`:::
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(integer)
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The total number of times the model has been called for inference.
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This is across all inference contexts, including all pipelines.
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`cache_miss_count`:::
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(integer)
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The number of times the model was loaded for inference and was not retrieved
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from the cache. If this number is close to the `inference_count`, then the cache
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is not being appropriately used. This can be solved by increasing the cache size
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or its time-to-live (TTL). See <<general-ml-settings>> for the appropriate
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settings.
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`failure_count`:::
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(integer)
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The number of failures when using the model for inference.
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`timestamp`:::
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(<<time-units,time units>>)
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The time when the statistics were last updated.
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=====
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`ingest`:::
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(object)
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A collection of ingest stats for the model across all nodes. The values are
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summations of the individual node statistics. The format matches the `ingest`
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section in <<cluster-nodes-stats>>.
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====
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[[ml-get-inference-stats-response-codes]]
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== {api-response-codes-title}
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`404` (Missing resources)::
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If `allow_no_match` is `false`, this code indicates that there are no
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resources that match the request or only partial matches for the request.
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[[ml-get-inference-stats-example]]
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== {api-examples-title}
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The following example gets usage information for all the trained models:
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[source,console]
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--------------------------------------------------
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GET _ml/inference/_stats
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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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"count": 2,
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"trained_model_stats": [
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{
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"model_id": "flight-delay-prediction-1574775339910",
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"pipeline_count": 0,
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"inference_stats": {
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"failure_count": 0,
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"inference_count": 4,
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"cache_miss_count": 3,
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"missing_all_fields_count": 0,
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"timestamp": 1592399986979
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}
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},
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{
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"model_id": "regression-job-one-1574775307356",
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"pipeline_count": 1,
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"inference_stats": {
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"failure_count": 0,
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"inference_count": 178,
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"cache_miss_count": 3,
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"missing_all_fields_count": 0,
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"timestamp": 1592399986979
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},
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"ingest": {
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"total": {
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"count": 178,
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"time_in_millis": 8,
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"current": 0,
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"failed": 0
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},
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"pipelines": {
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"flight-delay": {
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"count": 178,
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"time_in_millis": 8,
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"current": 0,
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"failed": 0,
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"processors": [
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{
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"inference": {
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"type": "inference",
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"stats": {
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"count": 178,
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"time_in_millis": 7,
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"current": 0,
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"failed": 0
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}
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}
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}
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]
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}
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}
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}
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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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