Method
describeModelVersion GET
Copy GET

Retrieves a full description of the specified model version.

Arguments:

REQUIRED KEY TYPE DESCRIPTION
Yes modelVersion str Unique string identifier of the model version.
Note: The arguments for the API methods follow camelCase but for Python SDK underscore_case is followed.

Response:

KEY TYPE DESCRIPTION
success Boolean true if the call succeeded, false if there was an error
result ModelVersion
KEY TYPE Description
modelVersion str The unique identifier of a model version.
modelConfigType str Name of the TrainingConfig class of the model_config.
status str The current status of the model.
modelId str A reference to the model this version belongs to.
modelPredictionConfig dict The prediction config options for the model.
trainingStartedAt str The start time and date of the training process in ISO-8601 format.
trainingCompletedAt str The end time and date of the training process in ISO-8601 format.
featureGroupVersions list A list of Feature Group version IDs used for model training.
customAlgorithms list List of user-defined algorithms used for model training.
builtinAlgorithms list List of algorithm names builtin algorithms provided by Abacus.AI used for model training.
error str Relevant error if the status is FAILED.
pendingDeploymentIds list List of deployment IDs where deployment is pending.
failedDeploymentIds list List of failed deployment IDs.
cpuSize str CPU size specified for the python model training.
memory int Memory in GB specified for the python model training.
automlComplete bool If true, all algorithms have completed training.
trainingFeatureGroupIds list The unique identifiers of the feature groups used as inputs during training to create this ModelVersion.
trainingDocumentRetrieverVersions list The document retriever version IDs used as inputs during training to create this ModelVersion.
documentRetrieverMappings dict mapping of document retriever version to their respective information.
bestAlgorithm dict Best performing algorithm.
defaultAlgorithm dict Default algorithm that the user has selected.
featureAnalysisStatus str Lifecycle of the feature analysis stage.
dataClusterInfo dict Information about the models for different data clusters.
customAlgorithmConfigs dict User-defined configs for each of the user-defined custom algorithms.
trainedModelTypes list List of trained model types.
useGpu bool Whether this model version is using gpu
partialComplete bool If true, all required algorithms have completed training.
modelFeatureGroupSchemaMappings dict mapping of feature group to schema version
trainingConfigUpdated bool If the training config has been updated since the instance was created.
codeSource CodeSource If a python model, information on where the source code is located.
KEY TYPE Description
sourceType str The type of the source, one of TEXT, PYTHON, FILE_UPLOAD, or APPLICATION_CONNECTOR
sourceCode str If the type of the source is TEXT, the raw text of the function
applicationConnectorId str The Application Connector to fetch the code from
applicationConnectorInfo str Args passed to the application connector to fetch the code
packageRequirements list The pip package dependencies required to run the code
status str The status of the code and validations
error str If the status is failed, an error message describing what went wrong
publishingMsg dict Warnings in the source code
moduleDependencies list The list of internal modules dependencies required to run the code
modelConfig TrainingConfig The training config options used to train this model.
KEY TYPE Description
KWARGS None None
ALGORITHM None None
PROBLEM_TYPE None None
_UPPER_SNAKE_CASE_KEYS None None
_SUPPORT_KWARGS None None
deployableAlgorithms DeployableAlgorithm List of deployable algorithms.
KEY TYPE Description
trainedModelTypes List[dict] List of trained model types.
onlyOfflineDeployable bool Whether the algorithm can only be deployed offline.
algorithm str ID of the algorithm.
name str Name of the algorithm.
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