This documentation is for AutoML Vision, which is different from Vertex AI. If you are using Vertex AI, see the Vertex AI documentation.

Method: projects.locations.datasets.exportData

Exports dataset's data to the provided output location. Returns an empty response in the response field when it completes.

HTTP request

POST https://automl.googleapis.com/v1beta1/{name}:exportData

Path parameters

Parameters
name

string

Required. The resource name of the dataset.

Authorization requires the following Google IAM permission on the specified resource name:

  • automl.datasets.export

Request body

The request body contains data with the following structure:

JSON representation
{
  "outputConfig": {
    object (OutputConfig)
  }
}
Fields
outputConfig

object (OutputConfig)

Required. The desired output location.

Response body

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

Authorization Scopes

Requires the following OAuth scope:

  • https://www.googleapis.com/auth/cloud-platform

For more information, see the Authentication Overview.

OutputConfig

  • For Translation: CSV file translation.csv, with each line in format: ML_USE,GCS_FILE_PATH GCS_FILE_PATH leads to a .TSV file which describes examples that have given ML_USE, using the following row format per line: TEXT_SNIPPET (in source language) \t TEXT_SNIPPET (in target language)

  • For Tables: Output depends on whether the dataset was imported from Google Cloud Storage or BigQuery. Google Cloud Storage case:

gcsDestination must be set. Exported are CSV file(s) tables_1.csv, tables_2.csv,...,tables_N.csv with each having as header line the table's column names, and all other lines contain values for the header columns. BigQuery case:

bigqueryDestination pointing to a BigQuery project must be set. In the given project a new dataset will be created with name

export_data_<automl-dataset-display-name>_<timestamp-of-export-call> where will be made BigQuery-dataset-name compatible (e.g. most special characters will become underscores), and timestamp will be in YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" format. In that dataset a new table called primary_table will be created, and filled with precisely the same data as this obtained on import.

JSON representation
{

  // Union field destination can be only one of the following:
  "gcsDestination": {
    object (GcsDestination)
  },
  "bigqueryDestination": {
    object (BigQueryDestination)
  }
  // End of list of possible types for union field destination.
}
Fields
Union field destination. Required. 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. For Image Object Detection, Text Extraction, Video Classification and Tables, in the given directory a new directory will be created with name: export_data-- where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. All export output will be written into that directory.

bigqueryDestination

object (BigQueryDestination)

The BigQuery location where the output is to be written to.