Method: datasets.export Stay organized with collections Save and categorize content based on your preferences.
Full name: projects.locations.datasets.export
Exports data from a Dataset.
Endpoint
posthttps://{service-endpoint}/v1beta1/{name}:export Where{service-endpoint} is one of thesupported service endpoints.
Path parameters
namestringRequired. The name of the Dataset resource. Format:projects/{project}/locations/{location}/datasets/{dataset}
Request body
The request body contains data with the following structure:
exportConfigobject (ExportDataConfig)Required. The desired output location.
Response body
If successful, the response body contains an instance ofOperation.
ExportDataConfig
Describes what part of the Dataset is to be exported, the destination of the export and how to export.
annotationsFilterstringAn expression for filtering what part of the Dataset is to be exported. Only Annotations that match this filter will be exported. The filter syntax is the same as inListAnnotations.
destinationUnion typedestination can be only one of the following:gcsDestinationobject (GcsDestination)The Google Cloud Storage location where the output is to be written to. In the given directory a new directory will be created with name:export-data-<dataset-display-name>-<timestamp-of-export-call> where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. All export output will be written into that directory. Inside that directory, annotations with the same schema will be grouped into sub directories which are named with the corresponding annotations' schema title. Inside these sub directories, a schema.yaml will be created to describe the output format.
splitUnion typesplit can be only one of the following:fractionSplitobject (ExportFractionSplit)Split based on fractions defining the size of each set.
| JSON representation |
|---|
{"annotationsFilter":string,// destination"gcsDestination":{object ( |
ExportFractionSplit
Assigns the input data to training, validation, and test sets as per the given fractions. Any oftrainingFraction,validationFraction andtestFraction may optionally be provided, they must sum to up to 1. If the provided ones sum to less than 1, the remainder is assigned to sets as decided by Vertex AI. If none of the fractions are set, by default roughly 80% of data is used for training, 10% for validation, and 10% for test.
trainingFractionnumberThe fraction of the input data that is to be used to train the Model.
validationFractionnumberThe fraction of the input data that is to be used to validate the Model.
testFractionnumberThe fraction of the input data that is to be used to evaluate the Model.
| JSON representation |
|---|
{"trainingFraction":number,"validationFraction":number,"testFraction":number} |
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Last updated 2025-06-27 UTC.