Get trained models statistics API
editGet trained models statistics API
editRetrieves usage information for trained models.
This functionality is in technical preview and may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features.
Request
editGET _ml/trained_models/_stats
GET _ml/trained_models/_all/_stats
GET _ml/trained_models/<model_id>/_stats
GET _ml/trained_models/<model_id>,<model_id_2>/_stats
GET _ml/trained_models/<model_id_pattern*>,<model_id_2>/_stats
Prerequisites
editRequired privileges which should be added to a custom role:
-
cluster:
monitor_ml
For more information, see Security privileges and Machine learning security privileges.
Description
editYou 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.
Path parameters
edit-
<model_id>
- (Optional, string) The unique identifier of the trained model.
Query parameters
edit-
allow_no_match
-
(Optional, Boolean) Specifies what to do when the request:
- Contains wildcard expressions and there are no models that match.
-
Contains the
_all
string or no identifiers and there are no matches. - Contains wildcard expressions and there are only partial matches.
The default value is
true
, which returns an empty array when there are no matches and the subset of results when there are partial matches. If this parameter isfalse
, the request returns a404
status code when there are no matches or only partial matches. -
from
-
(Optional, integer)
Skips the specified number of models. The default value is
0
. -
size
-
(Optional, integer)
Specifies the maximum number of models to obtain. The default value
is
100
.
Response body
edit-
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 suppliedsize
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
-
model_id
- (string) The unique identifier of the trained model.
-
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
-
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 machine learning settings for the appropriate settings. -
failure_count
- (integer) The number of failures when using the model for inference.
-
timestamp
- (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 Nodes stats.
-
Response codes
edit-
404
(Missing resources) -
If
allow_no_match
isfalse
, this code indicates that there are no resources that match the request or only partial matches for the request.
Examples
editThe following example gets usage information for all the trained models:
GET _ml/trained_models/_stats
The API returns the following results:
{ "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 } } } ] } } } } ] }