Get trained models

GET /_cat/ml/trained_models/{model_id}
GET /_cat/ml/trained_models

Get configuration and usage information about inference trained models.

IMPORTANT: CAT APIs are only intended for human consumption using the Kibana console or command line. They are not intended for use by applications. For application consumption, use the get trained models statistics API.

Required authorization

Cluster privileges: monitor_ml

Path Parameters

model_id string Id optional

A unique identifier for the trained model.

Leave it out to use /_cat/ml/trained_models.

Query Parameters

allow_no_match boolean · default: true

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. If true, the API returns an empty array when there are no matches and the subset of results when there are partial matches. If false, the API returns a 404 status code when there are no matches or only partial matches.

h union CatTrainedModelsColumns

A comma-separated list of column names to display.

Supported values include:

create_time (or ct): The time when the trained model was created.
created_by (or c, createdBy): Information on the creator of the trained model.
data_frame_analytics_id (or df, dataFrameAnalytics, dfid): Identifier for the data frame analytics job that created the model. Only displayed if it is still available.
description (or d): The description of the trained model.
heap_size (or hs, modelHeapSize): The estimated heap size to keep the trained model in memory.
id: Identifier for the trained model.
ingest.count (or ic, ingestCount): The total number of documents that are processed by the model.
ingest.current (or icurr, ingestCurrent): The total number of document that are currently being handled by the trained model.
ingest.failed (or if, ingestFailed): The total number of failed ingest attempts with the trained model.
ingest.pipelines (or ip, ingestPipelines): The total number of ingest pipelines that are referencing the trained model.
ingest.time (or it, ingestTime): The total time that is spent processing documents with the trained model.
license (or l): The license level of the trained model.
operations (or o, modelOperations): The estimated number of operations to use the trained model. This number helps measuring the computational complexity of the model.
version (or v): The Elasticsearch version number in which the trained model was created.

One of: CatTrainedModelsColumn or CatTrainedModelsColumn[]

s union CatTrainedModelsColumns

A comma-separated list of column names or aliases used to sort the response.

Supported values include:

create_time (or ct): The time when the trained model was created.
created_by (or c, createdBy): Information on the creator of the trained model.
data_frame_analytics_id (or df, dataFrameAnalytics, dfid): Identifier for the data frame analytics job that created the model. Only displayed if it is still available.
description (or d): The description of the trained model.
heap_size (or hs, modelHeapSize): The estimated heap size to keep the trained model in memory.
id: Identifier for the trained model.
ingest.count (or ic, ingestCount): The total number of documents that are processed by the model.
ingest.current (or icurr, ingestCurrent): The total number of document that are currently being handled by the trained model.
ingest.failed (or if, ingestFailed): The total number of failed ingest attempts with the trained model.
ingest.pipelines (or ip, ingestPipelines): The total number of ingest pipelines that are referencing the trained model.
ingest.time (or it, ingestTime): The total time that is spent processing documents with the trained model.
license (or l): The license level of the trained model.
operations (or o, modelOperations): The estimated number of operations to use the trained model. This number helps measuring the computational complexity of the model.
version (or v): The Elasticsearch version number in which the trained model was created.

One of: CatTrainedModelsColumn or CatTrainedModelsColumn[]

from number · default: 0

Skips the specified number of transforms.

size number · default: 100

The maximum number of transforms to display.

Response

application/json

Response Type: [] TrainedModelsRecord

id string Id: The model identifier.
created_by string: Information about the creator of the model.
heap_size number | string ByteSize: The estimated heap size to keep the model in memory.
operations string: The estimated number of operations to use the model. This number helps to measure the computational complexity of the model.
license string: The license level of the model.
create_time DateTime: The time the model was created.
version string: The version of Elasticsearch when the model was created.
description string: A description of the model.
ingest.pipelines string: The number of pipelines that are referencing the model.
ingest.count string: The total number of documents that are processed by the model.
ingest.time string: The total time spent processing documents with thie model.
ingest.current string: The total number of documents that are currently being handled by the model.
ingest.failed string: The total number of failed ingest attempts with the model.
data_frame.id string: The identifier for the data frame analytics job that created the model. Only displayed if the job is still available.
data_frame.create_time string: The time the data frame analytics job was created.
data_frame.source_index string: The source index used to train in the data frame analysis.
data_frame.analysis string: The analysis used by the data frame to build the model.
type string

Examples

A successful response from GET _cat/ml/trained_models?v=true&format=json.

Request Console 1 / 7 languages

Console Kibana Dev Tools

GET _cat/ml/trained_models?v=true&format=json

JavaScript Elasticsearch JavaScript Client

const response = await client.cat.mlTrainedModels({
  v: "true",
  format: "json",
});

Python Elasticsearch Python Client

resp = client.cat.ml_trained_models(
    v=True,
    format="json",
)

curl Shell

curl -X GET "$ELASTICSEARCH_URL/_cat/ml/trained_models?v=true&format=json" \
  -H "Authorization: ApiKey $ELASTIC_API_KEY"

Java Elasticsearch Java Client

client.cat().mlTrainedModels();

PHP Elasticsearch PHP Client

$resp = $client->cat()->mlTrainedModels([
    "v" => "true",
    "format" => "json",
]);

Ruby Elasticsearch Ruby Client

response = client.cat.ml_trained_models(
  v: "true",
  format: "json"
)

Response

[
  {
    "id": "ddddd-1580216177138",
    "heap_size": "0b",
    "operations": "196",
    "create_time": "2025-03-25T00:01:38.662Z",
    "type": "pytorch",
    "ingest.pipelines": "0",
    "data_frame.id": "__none__"
  },
  {
    "id": "lang_ident_model_1",
    "heap_size": "1mb",
    "operations": "39629",
    "create_time": "2019-12-05T12:28:34.594Z",
    "type": "lang_ident",
    "ingest.pipelines": "0",
    "data_frame.id": "__none__"
  }
]

Required authorization

Path Parameters

Query Parameters { allow_no_match , h , s , from , size , }

Response

Query Parameters