Get trained model configuration info Generally available

GET /_ml/trained_models/{model_id}
Api key auth

All methods and paths for this operation:

GET /_ml/trained_models

GET /_ml/trained_models/{model_id}

Required authorization

  • Cluster privileges: monitor_ml

Path parameters

  • model_id string | array[string] Required

    The unique identifier of the trained model or a model alias.

    You can get information for multiple trained models in a single API request by using a comma-separated list of model IDs or a wildcard expression.

Query parameters

  • allow_no_match 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.

    If true, it returns an empty array when there are no matches and the subset of results when there are partial matches.

  • decompress_definition boolean

    Specifies whether the included model definition should be returned as a JSON map (true) or in a custom compressed format (false).

  • exclude_generated boolean

    Indicates if certain fields should be removed from the configuration on retrieval. This allows the configuration to be in an acceptable format to be retrieved and then added to another cluster.

  • from number

    Skips the specified number of models.

  • include string

    A comma delimited string of optional fields to include in the response body.

    Supported values include:

    • definition: Includes the model definition.
    • feature_importance_baseline: Includes the baseline for feature importance values.
    • hyperparameters: Includes the information about hyperparameters used to train the model. This information consists of the value, the absolute and relative importance of the hyperparameter as well as an indicator of whether it was specified by the user or tuned during hyperparameter optimization.
    • total_feature_importance: Includes the total feature importance for the training data set. The baseline and total feature importance values are returned in the metadata field in the response body.
    • definition_status: Includes the model definition status.

    Values are definition, feature_importance_baseline, hyperparameters, total_feature_importance, or definition_status.

  • size number

    Specifies the maximum number of models to obtain.

  • tags string | array[string]

    A comma delimited string of tags. A trained model can have many tags, or none. When supplied, only trained models that contain all the supplied tags are returned.

Responses

  • 200 application/json
    Hide response attributes Show response attributes object
    • count number Required
    • trained_model_configs array[object] Required

      An array of trained model resources, which are sorted by the model_id value in ascending order.

      Hide trained_model_configs attributes Show trained_model_configs attributes object
      • model_id string Required

        Identifier for the trained model.

      • model_type string

        The model type

        Supported values include:

        • tree_ensemble: The model definition is an ensemble model of decision trees.
        • lang_ident: A special type reserved for language identification models.
        • pytorch: The stored definition is a PyTorch (specifically a TorchScript) model. Currently only NLP models are supported.

        Values are tree_ensemble, lang_ident, or pytorch.

      • tags array[string] Required

        A comma delimited string of tags. A trained model can have many tags, or none.

      • version string

        The Elasticsearch version number in which the trained model was created.

      • compressed_definition string
      • created_by string

        Information on the creator of the trained model.

      • create_time string | number

        The time when the trained model was created.

        One of:
        string-1 string UnitMillis number

        The time when the trained model was created.

      • default_field_map object

        Any field map described in the inference configuration takes precedence.

        Hide default_field_map attribute Show default_field_map attribute object
        • * string Additional properties
      • description string

        The free-text description of the trained model.

      • estimated_heap_memory_usage_bytes number

        The estimated heap usage in bytes to keep the trained model in memory.

      • estimated_operations number

        The estimated number of operations to use the trained model.

      • fully_defined boolean

        True if the full model definition is present.

      • inference_config object

        The default configuration for inference. This can be either a regression, classification, or one of the many NLP focused configurations. It must match the underlying definition.trained_model's target_type. For pre-packaged models such as ELSER the config is not required.

        Hide inference_config attributes Show inference_config attributes object
        • regression object

          Regression configuration for inference.

        • classification object

          Classification configuration for inference.

        • text_classification object

          Text classification configuration for inference.

        • zero_shot_classification object

          Zeroshot classification configuration for inference.

        • fill_mask object

          Fill mask configuration for inference.

        • learning_to_rank object
        • ner object

          Named entity recognition configuration for inference.

        • pass_through object

          Pass through configuration for inference.

        • text_embedding object

          Text embedding configuration for inference.

        • text_expansion object

          Text expansion configuration for inference.

        • question_answering object

          Question answering configuration for inference.

      • input object Required

        The input field names for the model definition.

        Hide input attribute Show input attribute object
        • field_names array[string] Required

          An array of input field names for the model.

      • license_level string

        The license level of the trained model.

      • metadata object

        An object containing metadata about the trained model. For example, models created by data frame analytics contain analysis_config and input objects.

        Hide metadata attributes Show metadata attributes object
        • model_aliases array[string]
        • feature_importance_baseline object

          An object that contains the baseline for feature importance values. For regression analysis, it is a single value. For classification analysis, there is a value for each class.

          Hide feature_importance_baseline attribute Show feature_importance_baseline attribute object
          • * string Additional properties
        • hyperparameters array[object]

          List of the available hyperparameters optimized during the fine_parameter_tuning phase as well as specified by the user.

        • total_feature_importance array[object]

          An array of the total feature importance for each feature used from the training data set. This array of objects is returned if data frame analytics trained the model and the request includes total_feature_importance in the include request parameter.

      • model_size_bytes number | string

        One of:
        number-1 number string-2 string
      • model_package object
        Hide model_package attributes Show model_package attributes object
        • description string
        • inference_config object
          Hide inference_config attribute Show inference_config attribute object
          • * object Additional properties
        • metadata object
        • minimum_version string
        • model_repository string
        • model_type string
        • packaged_model_id string Required
        • platform_architecture string
        • prefix_strings object
        • size
        • sha256 string
        • tags array[string]
        • vocabulary_file string
      • location object
        Hide location attribute Show location attribute object
        • index object Required
      • platform_architecture string
      • prefix_strings object
        Hide prefix_strings attributes Show prefix_strings attributes object
        • ingest string

          String prepended to input at ingest

        • search string

          String prepended to input at search
GET /_ml/trained_models/{model_id} 
GET _ml/trained_models/

resp = client.ml.get_trained_models()
const response = await client.ml.getTrainedModels();
response = client.ml.get_trained_models
$resp = $client->ml()->getTrainedModels();
curl -X GET -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_ml/trained_models/"
client.ml().getTrainedModels(g -> g);