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.
Fields
annotationsFilterstring
An 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 type
The destination of the output.destinationcan be only one of the following:
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 type
The instructions how the export data should be split between the training, validation and test sets.splitcan be only one of the following:
Split based on fractions defining the size of each set.
JSON representation
{"annotationsFilter":string,// destination"gcsDestination":{object (GcsDestination)}// Union type// split"fractionSplit":{object (ExportFractionSplit)}// Union type}
ExportFractionSplit
Assigns the input data to training, validation, and test sets as per the given fractions. Any oftrainingFraction,validationFractionandtestFractionmay 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.
Fields
trainingFractionnumber
The fraction of the input data that is to be used to train the Model.
validationFractionnumber
The fraction of the input data that is to be used to validate the Model.
testFractionnumber
The fraction of the input data that is to be used to evaluate the Model.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Hard to understand","hardToUnderstand","thumb-down"],["Incorrect information or sample code","incorrectInformationOrSampleCode","thumb-down"],["Missing the information/samples I need","missingTheInformationSamplesINeed","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-06-27 UTC."],[],[],null,["# Method: datasets.export\n\n**Full name**: projects.locations.datasets.export\n\nExports data from a Dataset. \n\n### Endpoint\n\npost `https:``/``/{service-endpoint}``/v1beta1``/{name}:export` \nWhere `{service-endpoint}` is one of the [supported service endpoints](/vertex-ai/docs/reference/rest#rest_endpoints).\n\n### Path parameters\n\n`name` `string` \nRequired. The name of the Dataset resource. Format: `projects/{project}/locations/{location}/datasets/{dataset}`\n\n### Request body\n\nThe request body contains data with the following structure:\nFields `exportConfig` `object (`[ExportDataConfig](/vertex-ai/docs/reference/rest/v1beta1/projects.locations.datasets/export#ExportDataConfig)`)` \nRequired. The desired output location. \n\n### Response body\n\nIf successful, the response body contains an instance of [Operation](/vertex-ai/docs/reference/rest/Shared.Types/ListOperationsResponse#Operation).\n\nExportDataConfig\n----------------\n\nDescribes what part of the Dataset is to be exported, the destination of the export and how to export.\nFields `annotationsFilter` `string` \nAn 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 in [ListAnnotations](/vertex-ai/docs/reference/rest/v1beta1/projects.locations.datasets.dataItems.annotations/list#google.cloud.aiplatform.v1beta1.DatasetService.ListAnnotations). \n`destination` `Union type` \nThe destination of the output. `destination` can be only one of the following:\n`gcsDestination` `object (`[GcsDestination](/vertex-ai/docs/reference/rest/v1beta1/GcsDestination)`)` \nThe 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-\u003cdataset-display-name\u003e-\u003ctimestamp-of-export-call\u003e` 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. \n`split` `Union type` \nThe instructions how the export data should be split between the training, validation and test sets. `split` can be only one of the following:\n`fractionSplit` `object (`[ExportFractionSplit](/vertex-ai/docs/reference/rest/v1beta1/projects.locations.datasets/export#ExportFractionSplit)`)` \nSplit based on fractions defining the size of each set. \n\nExportFractionSplit\n-------------------\n\nAssigns the input data to training, validation, and test sets as per the given fractions. Any of `trainingFraction`, `validationFraction` and `testFraction` 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.\nFields `trainingFraction` `number` \nThe fraction of the input data that is to be used to train the Model.\n`validationFraction` `number` \nThe fraction of the input data that is to be used to validate the Model.\n`testFraction` `number` \nThe fraction of the input data that is to be used to evaluate the Model."]]
