Try Gemini 1.5 models, the latest multimodal models in Vertex AI, and see what you can build with up to a 2M token context window. Try Gemini 1.5 models, the latest multimodal models in Vertex AI, and see what you can build with up to a 2M token context window.

Method: projects.locations.datasets.export

Exports data from a Dataset.

Endpoint

post


                https://{service-endpoint}/v1beta1/{name}:export

Where {service-endpoint} is one of the supported service endpoints.

Path parameters

name string

Required. The name of the Dataset resource. Format: projects/{project}/locations/{location}/datasets/{dataset}

Request body

The request body contains data with the following structure:

Fields

exportConfig object (ExportDataConfig)

Required. The desired output location.

Response body

If successful, the response body contains an instance of Operation.

ExportDataConfig

Describes what part of the Dataset is to be exported, the destination of the export and how to export.

Fields

annotationsFilter string

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 in ListAnnotations.

Union field destination. The destination of the output. destination can be only one of the following:

gcsDestination object (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.

Union field split. The instructions how the export data should be split between the training, validation and test sets. split can be only one of the following:

fractionSplit object (ExportFractionSplit)

Split based on fractions defining the size of each set.

JSON representation

JSON representation
{ "annotationsFilter": string, // Union field `destination` can be only one of the following: "gcsDestination": { object (`GcsDestination`) } // End of list of possible types for union field `destination`. // Union field `split` can be only one of the following: "fractionSplit": { object (`ExportFractionSplit`) } // End of list of possible types for union field `split`. }

{
  "annotationsFilter": string,

  // Union field destination can be only one of the following:
  "gcsDestination": {
    object (GcsDestination)
  }
  // End of list of possible types for union field destination.

  // Union field split can be only one of the following:
  "fractionSplit": {
    object (ExportFractionSplit)
  }
  // End of list of possible types for union field split.
}

ExportFractionSplit

Assigns 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.

Fields

trainingFraction number

The fraction of the input data that is to be used to train the Model.

validationFraction number

The fraction of the input data that is to be used to validate the Model.

testFraction number

The fraction of the input data that is to be used to evaluate the Model.

JSON representation
{ "trainingFraction": number, "validationFraction": number, "testFraction": number }