Search documentation
karat

+

K

User Documentation ↗
Version 2.0

Get Model Studio Trainer

GET/api/v2/models/modelStudioTrainers/{modelStudioTrainerTrainerId}
Warning

This endpoint is in preview and may be modified or removed at any time. To use this endpoint, add preview=true to the request query parameters.

Gets details about a specific trainer by its ID and optional version.

Third-party applications using this endpoint via OAuth2 must request the following operation scope: api:models-read.

Path parameters

modelStudioTrainerTrainerId
string

The Resource Identifier (RID) of a trainer.

Query parameters

version
stringoptional

Specific version of the trainer to retrieve. If not specified, returns the latest version.

preview
booleanoptional

Enables the use of preview functionality.

Response body

ModelStudioTrainer
object
Hide child attributes

Hide child attributes

trainerId
string

The Resource Identifier (RID) of a trainer.

version
string

The version of this trainer.

name
string

Human-readable name of the trainer.

type
string (enum)

The category of machine learning task this trainer is designed to solve.

Enum values: GENERIC, TIME_SERIES, TABULAR_REGRESSION, TABULAR_CLASSIFICATION, LLM_FINETUNING, VLM_FINETUNING

description
string

Description of what this trainer does and its capabilities.

customConfigSchema
any

JSON schema defining the custom configuration parameters for this trainer.

inputs
any

Input specifications for this trainer.

outputs
any

Output specifications for this trainer.

experimental
boolean

Whether this trainer is experimental and may have breaking changes.

Examples

Request

Copied!
1 2 3 curl \ \t-H "Authorization: Bearer $TOKEN" \ "https://$HOSTNAME/api/v2/models/modelStudioTrainers/ri.models..trainer.autogluon_tabular_regression?version=0.388.0&preview=true"

Response

Copied!
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 { "outputs": { "model": { "name": "Output model", "optional": false, "type": { "type": "model", "model": { "modelApiAliases": [ { "alias": "input_df", "description": "Input dataset" }, { "alias": "output_df", "description": "Output dataset" } ] } } } }, "customConfigSchema": { "$schema": "https://json-schema.org/draft/2020-12/schema", "title": "AutoGluonTabularRegressionConfig", "type": "object", "additionalProperties": false, "properties": { "eval_metric": { "title": "Evaluation metric", "default": "mean_squared_error", "$ref": "#/$defs/RegressionMetricType" }, "presets": { "title": "Training presets", "default": "medium_quality", "$ref": "#/$defs/PresetsType" }, "refit_full": { "title": "Refit on full data", "description": "After evaluation, refit the best model on the combined training and test data.", "type": "boolean", "default": false } }, "$defs": { "PresetsType": { "title": "PresetsType", "description": "Pre-built training presets", "oneOf": [ { "type": "string", "const": "best_quality", "description": "Best predictive accuracy, at the cost of training and inference speed." }, { "type": "string", "const": "medium_quality", "description": "Medium predictive accuracy with very fast inference and very fast training time." } ] }, "RegressionMetricType": { "title": "RegressionMetricType", "description": "The metric to optimize for when selecting the best model.", "oneOf": [ { "type": "string", "const": "root_mean_squared_error", "description": "Measures the square root of the average squared differences between predicted and actual values." }, { "type": "string", "const": "mean_squared_error", "description": "The average of the squared differences between predicted and actual values." } ] } } }, "inputs": { "input_df": { "name": "Training dataset", "description": "Input dataset for training. If no testing dataset is provided, 20% of this dataset will be held out as a test dataset for evaluation.", "optional": false, "type": { "type": "dataset", "dataset": { "role": "TRAINING", "columnsTypeSpecs": { "target_column": { "name": "Target column", "isTarget": true, "allowMultiple": false, "optional": false, "supportedTypes": [ { "type": "grouped", "grouped": "NUMERIC" } ] } } } } }, "test_df": { "name": "Test dataset", "description": "Input dataset for testing. Used for evaluating the best model only.", "optional": true, "type": { "type": "dataset", "dataset": { "role": "TEST", "columnsTypeSpecs": {} } } } }, "name": "AutoGluon Tabular Regression Trainer", "description": "Regression with AutoGluon TabularPredictor", "experimental": false, "type": "GENERIC", "version": "0.388.0", "trainerId": "ri.models..trainer.autogluon_tabular_regression" }

Error responses

Error Name
TrainerNotFoundError CodeNOT_FOUND
Status Code404
DescriptionThe specified trainer does not exist.
ParameterstrainerId
ModelStudioTrainerNotFoundError CodeNOT_FOUND
Status Code404
DescriptionThe given ModelStudioTrainer could not be found.
ParametersmodelStudioTrainerTrainerId