close()

Close httplib2 connections.

create(parent, body=None, x__xgafv=None)

Creates dataset. If success return a Dataset resource.

Args:
parent: string, Required. Dataset resource parent, format: projects/{project_id} (required)
body: object, The request body.
The object takes the form of:

{ # Request message for CreateDataset.
"dataset": { # Dataset is the resource to hold your data. You can request multiple labeling tasks for a dataset while each one will generate an AnnotatedDataset. # Required. The dataset to be created.
"blockingResources": [ # Output only. The names of any related resources that are blocking changes to the dataset.
"A String",
],
"createTime": "A String", # Output only. Time the dataset is created.
"dataItemCount": "A String", # Output only. The number of data items in the dataset.
"description": "A String", # Optional. User-provided description of the annotation specification set. The description can be up to 10000 characters long.
"displayName": "A String", # Required. The display name of the dataset. Maximum of 64 characters.
"inputConfigs": [ # Output only. This is populated with the original input configs where ImportData is called. It is available only after the clients import data to this dataset.
{ # The configuration of input data, including data type, location, etc.
"annotationType": "A String", # Optional. The type of annotation to be performed on this data. You must specify this field if you are using this InputConfig in an EvaluationJob.
"bigquerySource": { # The BigQuery location for input data. If used in an EvaluationJob, this is where the service saves the prediction input and output sampled from the model version. # Source located in BigQuery. You must specify this field if you are using this InputConfig in an EvaluationJob.
"inputUri": "A String", # Required. BigQuery URI to a table, up to 2,000 characters long. If you specify the URI of a table that does not exist, Data Labeling Service creates a table at the URI with the correct schema when you create your EvaluationJob. If you specify the URI of a table that already exists, it must have the [correct schema](/ml-engine/docs/continuous-evaluation/create-job#table-schema). Provide the table URI in the following format: "bq://{your_project_id}/ {your_dataset_name}/{your_table_name}" [Learn more](/ml-engine/docs/continuous-evaluation/create-job#table-schema).
},
"classificationMetadata": { # Metadata for classification annotations. # Optional. Metadata about annotations for the input. You must specify this field if you are using this InputConfig in an EvaluationJob for a model version that performs classification.
"isMultiLabel": True or False, # Whether the classification task is multi-label or not.
},
"dataType": "A String", # Required. Data type must be specifed when user tries to import data.
"gcsSource": { # Source of the Cloud Storage file to be imported. # Source located in Cloud Storage.
"inputUri": "A String", # Required. The input URI of source file. This must be a Cloud Storage path (`gs://...`).
"mimeType": "A String", # Required. The format of the source file. Only "text/csv" is supported.
},
"textMetadata": { # Metadata for the text. # Required for text import, as language code must be specified.
"languageCode": "A String", # The language of this text, as a [BCP-47](https://www.rfc-editor.org/rfc/bcp/bcp47.txt). Default value is en-US.
},
},
],
"lastMigrateTime": "A String", # Last time that the Dataset is migrated to AI Platform V2. If any of the AnnotatedDataset is migrated, the last_migration_time in Dataset is also updated.
"name": "A String", # Output only. Dataset resource name, format is: projects/{project_id}/datasets/{dataset_id}
},
}

x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format

Returns:
An object of the form:

{ # Dataset is the resource to hold your data. You can request multiple labeling tasks for a dataset while each one will generate an AnnotatedDataset.
"blockingResources": [ # Output only. The names of any related resources that are blocking changes to the dataset.
"A String",
],
"createTime": "A String", # Output only. Time the dataset is created.
"dataItemCount": "A String", # Output only. The number of data items in the dataset.
"description": "A String", # Optional. User-provided description of the annotation specification set. The description can be up to 10000 characters long.
"displayName": "A String", # Required. The display name of the dataset. Maximum of 64 characters.
"inputConfigs": [ # Output only. This is populated with the original input configs where ImportData is called. It is available only after the clients import data to this dataset.
{ # The configuration of input data, including data type, location, etc.
"annotationType": "A String", # Optional. The type of annotation to be performed on this data. You must specify this field if you are using this InputConfig in an EvaluationJob.
"bigquerySource": { # The BigQuery location for input data. If used in an EvaluationJob, this is where the service saves the prediction input and output sampled from the model version. # Source located in BigQuery. You must specify this field if you are using this InputConfig in an EvaluationJob.
"inputUri": "A String", # Required. BigQuery URI to a table, up to 2,000 characters long. If you specify the URI of a table that does not exist, Data Labeling Service creates a table at the URI with the correct schema when you create your EvaluationJob. If you specify the URI of a table that already exists, it must have the [correct schema](/ml-engine/docs/continuous-evaluation/create-job#table-schema). Provide the table URI in the following format: "bq://{your_project_id}/ {your_dataset_name}/{your_table_name}" [Learn more](/ml-engine/docs/continuous-evaluation/create-job#table-schema).
},
"classificationMetadata": { # Metadata for classification annotations. # Optional. Metadata about annotations for the input. You must specify this field if you are using this InputConfig in an EvaluationJob for a model version that performs classification.
"isMultiLabel": True or False, # Whether the classification task is multi-label or not.
},
"dataType": "A String", # Required. Data type must be specifed when user tries to import data.
"gcsSource": { # Source of the Cloud Storage file to be imported. # Source located in Cloud Storage.
"inputUri": "A String", # Required. The input URI of source file. This must be a Cloud Storage path (`gs://...`).
"mimeType": "A String", # Required. The format of the source file. Only "text/csv" is supported.
},
"textMetadata": { # Metadata for the text. # Required for text import, as language code must be specified.
"languageCode": "A String", # The language of this text, as a [BCP-47](https://www.rfc-editor.org/rfc/bcp/bcp47.txt). Default value is en-US.
},
},
],
"lastMigrateTime": "A String", # Last time that the Dataset is migrated to AI Platform V2. If any of the AnnotatedDataset is migrated, the last_migration_time in Dataset is also updated.
"name": "A String", # Output only. Dataset resource name, format is: projects/{project_id}/datasets/{dataset_id}
}

delete(name, x__xgafv=None)

Deletes a dataset by resource name.

Args:
  name: string, Required. Dataset resource name, format: projects/{project_id}/datasets/{dataset_id} (required)
  x__xgafv: string, V1 error format.
    Allowed values
      1 - v1 error format
      2 - v2 error format

Returns:
  An object of the form:

    { # A generic empty message that you can re-use to avoid defining duplicated empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance: service Foo { rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty); }
}

exportData(name, body=None, x__xgafv=None)

Exports data and annotations from dataset.

Args:
name: string, Required. Dataset resource name, format: projects/{project_id}/datasets/{dataset_id} (required)
body: object, The request body.
The object takes the form of:

{ # Request message for ExportData API.
"annotatedDataset": "A String", # Required. Annotated dataset resource name. DataItem in Dataset and their annotations in specified annotated dataset will be exported. It's in format of projects/{project_id}/datasets/{dataset_id}/annotatedDatasets/ {annotated_dataset_id}
"filter": "A String", # Optional. Filter is not supported at this moment.
"outputConfig": { # The configuration of output data. # Required. Specify the output destination.
"gcsDestination": { # Export destination of the data.Only gcs path is allowed in output_uri. # Output to a file in Cloud Storage. Should be used for labeling output other than image segmentation.
"mimeType": "A String", # Required. The format of the gcs destination. Only "text/csv" and "application/json" are supported.
"outputUri": "A String", # Required. The output uri of destination file.
},
"gcsFolderDestination": { # Export folder destination of the data. # Output to a folder in Cloud Storage. Should be used for image segmentation or document de-identification labeling outputs.
"outputFolderUri": "A String", # Required. Cloud Storage directory to export data to.
},
},
"userEmailAddress": "A String", # Email of the user who started the export task and should be notified by email. If empty no notification will be sent.
}

x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format

Returns:
An object of the form:

{ # This resource represents a long-running operation that is the result of a network API call.
"done": True or False, # If the value is `false`, it means the operation is still in progress. If `true`, the operation is completed, and either `error` or `response` is available.
"error": { # The `Status` type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by [gRPC](https://github.com/grpc). Each `Status` message contains three pieces of data: error code, error message, and error details. You can find out more about this error model and how to work with it in the [API Design Guide](https://cloud.google.com/apis/design/errors). # The error result of the operation in case of failure or cancellation.
"code": 42, # The status code, which should be an enum value of google.rpc.Code.
"details": [ # A list of messages that carry the error details. There is a common set of message types for APIs to use.
{
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
],
"message": "A String", # A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
},
"metadata": { # Service-specific metadata associated with the operation. It typically contains progress information and common metadata such as create time. Some services might not provide such metadata. Any method that returns a long-running operation should document the metadata type, if any.
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
"name": "A String", # The server-assigned name, which is only unique within the same service that originally returns it. If you use the default HTTP mapping, the `name` should be a resource name ending with `operations/{unique_id}`.
"response": { # The normal, successful response of the operation. If the original method returns no data on success, such as `Delete`, the response is `google.protobuf.Empty`. If the original method is standard `Get`/`Create`/`Update`, the response should be the resource. For other methods, the response should have the type `XxxResponse`, where `Xxx` is the original method name. For example, if the original method name is `TakeSnapshot()`, the inferred response type is `TakeSnapshotResponse`.
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
}

get(name, x__xgafv=None)

Gets dataset by resource name.

Args:
  name: string, Required. Dataset resource name, format: projects/{project_id}/datasets/{dataset_id} (required)
  x__xgafv: string, V1 error format.
    Allowed values
      1 - v1 error format
      2 - v2 error format

Returns:
  An object of the form:

    { # Dataset is the resource to hold your data. You can request multiple labeling tasks for a dataset while each one will generate an AnnotatedDataset.
  "blockingResources": [ # Output only. The names of any related resources that are blocking changes to the dataset.
    "A String",
  ],
  "createTime": "A String", # Output only. Time the dataset is created.
  "dataItemCount": "A String", # Output only. The number of data items in the dataset.
  "description": "A String", # Optional. User-provided description of the annotation specification set. The description can be up to 10000 characters long.
  "displayName": "A String", # Required. The display name of the dataset. Maximum of 64 characters.
  "inputConfigs": [ # Output only. This is populated with the original input configs where ImportData is called. It is available only after the clients import data to this dataset.
    { # The configuration of input data, including data type, location, etc.
      "annotationType": "A String", # Optional. The type of annotation to be performed on this data. You must specify this field if you are using this InputConfig in an EvaluationJob.
      "bigquerySource": { # The BigQuery location for input data. If used in an EvaluationJob, this is where the service saves the prediction input and output sampled from the model version. # Source located in BigQuery. You must specify this field if you are using this InputConfig in an EvaluationJob.
        "inputUri": "A String", # Required. BigQuery URI to a table, up to 2,000 characters long. If you specify the URI of a table that does not exist, Data Labeling Service creates a table at the URI with the correct schema when you create your EvaluationJob. If you specify the URI of a table that already exists, it must have the [correct schema](/ml-engine/docs/continuous-evaluation/create-job#table-schema). Provide the table URI in the following format: "bq://{your_project_id}/ {your_dataset_name}/{your_table_name}" [Learn more](/ml-engine/docs/continuous-evaluation/create-job#table-schema).
      },
      "classificationMetadata": { # Metadata for classification annotations. # Optional. Metadata about annotations for the input. You must specify this field if you are using this InputConfig in an EvaluationJob for a model version that performs classification.
        "isMultiLabel": True or False, # Whether the classification task is multi-label or not.
      },
      "dataType": "A String", # Required. Data type must be specifed when user tries to import data.
      "gcsSource": { # Source of the Cloud Storage file to be imported. # Source located in Cloud Storage.
        "inputUri": "A String", # Required. The input URI of source file. This must be a Cloud Storage path (`gs://...`).
        "mimeType": "A String", # Required. The format of the source file. Only "text/csv" is supported.
      },
      "textMetadata": { # Metadata for the text. # Required for text import, as language code must be specified.
        "languageCode": "A String", # The language of this text, as a [BCP-47](https://www.rfc-editor.org/rfc/bcp/bcp47.txt). Default value is en-US.
      },
    },
  ],
  "lastMigrateTime": "A String", # Last time that the Dataset is migrated to AI Platform V2. If any of the AnnotatedDataset is migrated, the last_migration_time in Dataset is also updated.
  "name": "A String", # Output only. Dataset resource name, format is: projects/{project_id}/datasets/{dataset_id}
}

importData(name, body=None, x__xgafv=None)

Imports data into dataset based on source locations defined in request. It can be called multiple times for the same dataset. Each dataset can only have one long running operation running on it. For example, no labeling task (also long running operation) can be started while importing is still ongoing. Vice versa.

Args:
name: string, Required. Dataset resource name, format: projects/{project_id}/datasets/{dataset_id} (required)
body: object, The request body.
The object takes the form of:

{ # Request message for ImportData API.
"inputConfig": { # The configuration of input data, including data type, location, etc. # Required. Specify the input source of the data.
"annotationType": "A String", # Optional. The type of annotation to be performed on this data. You must specify this field if you are using this InputConfig in an EvaluationJob.
"bigquerySource": { # The BigQuery location for input data. If used in an EvaluationJob, this is where the service saves the prediction input and output sampled from the model version. # Source located in BigQuery. You must specify this field if you are using this InputConfig in an EvaluationJob.
"inputUri": "A String", # Required. BigQuery URI to a table, up to 2,000 characters long. If you specify the URI of a table that does not exist, Data Labeling Service creates a table at the URI with the correct schema when you create your EvaluationJob. If you specify the URI of a table that already exists, it must have the [correct schema](/ml-engine/docs/continuous-evaluation/create-job#table-schema). Provide the table URI in the following format: "bq://{your_project_id}/ {your_dataset_name}/{your_table_name}" [Learn more](/ml-engine/docs/continuous-evaluation/create-job#table-schema).
},
"classificationMetadata": { # Metadata for classification annotations. # Optional. Metadata about annotations for the input. You must specify this field if you are using this InputConfig in an EvaluationJob for a model version that performs classification.
"isMultiLabel": True or False, # Whether the classification task is multi-label or not.
},
"dataType": "A String", # Required. Data type must be specifed when user tries to import data.
"gcsSource": { # Source of the Cloud Storage file to be imported. # Source located in Cloud Storage.
"inputUri": "A String", # Required. The input URI of source file. This must be a Cloud Storage path (`gs://...`).
"mimeType": "A String", # Required. The format of the source file. Only "text/csv" is supported.
},
"textMetadata": { # Metadata for the text. # Required for text import, as language code must be specified.
"languageCode": "A String", # The language of this text, as a [BCP-47](https://www.rfc-editor.org/rfc/bcp/bcp47.txt). Default value is en-US.
},
},
"userEmailAddress": "A String", # Email of the user who started the import task and should be notified by email. If empty no notification will be sent.
}

x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format

Returns:
An object of the form:

{ # This resource represents a long-running operation that is the result of a network API call.
"done": True or False, # If the value is `false`, it means the operation is still in progress. If `true`, the operation is completed, and either `error` or `response` is available.
"error": { # The `Status` type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by [gRPC](https://github.com/grpc). Each `Status` message contains three pieces of data: error code, error message, and error details. You can find out more about this error model and how to work with it in the [API Design Guide](https://cloud.google.com/apis/design/errors). # The error result of the operation in case of failure or cancellation.
"code": 42, # The status code, which should be an enum value of google.rpc.Code.
"details": [ # A list of messages that carry the error details. There is a common set of message types for APIs to use.
{
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
],
"message": "A String", # A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
},
"metadata": { # Service-specific metadata associated with the operation. It typically contains progress information and common metadata such as create time. Some services might not provide such metadata. Any method that returns a long-running operation should document the metadata type, if any.
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
"name": "A String", # The server-assigned name, which is only unique within the same service that originally returns it. If you use the default HTTP mapping, the `name` should be a resource name ending with `operations/{unique_id}`.
"response": { # The normal, successful response of the operation. If the original method returns no data on success, such as `Delete`, the response is `google.protobuf.Empty`. If the original method is standard `Get`/`Create`/`Update`, the response should be the resource. For other methods, the response should have the type `XxxResponse`, where `Xxx` is the original method name. For example, if the original method name is `TakeSnapshot()`, the inferred response type is `TakeSnapshotResponse`.
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
}

list(parent, filter=None, pageSize=None, pageToken=None, x__xgafv=None)

Lists datasets under a project. Pagination is supported.

Args:
  parent: string, Required. Dataset resource parent, format: projects/{project_id} (required)
  filter: string, Optional. Filter on dataset is not supported at this moment.
  pageSize: integer, Optional. Requested page size. Server may return fewer results than requested. Default value is 100.
  pageToken: string, Optional. A token identifying a page of results for the server to return. Typically obtained by ListDatasetsResponse.next_page_token of the previous [DataLabelingService.ListDatasets] call. Returns the first page if empty.
  x__xgafv: string, V1 error format.
    Allowed values
      1 - v1 error format
      2 - v2 error format

Returns:
  An object of the form:

    { # Results of listing datasets within a project.
  "datasets": [ # The list of datasets to return.
    { # Dataset is the resource to hold your data. You can request multiple labeling tasks for a dataset while each one will generate an AnnotatedDataset.
      "blockingResources": [ # Output only. The names of any related resources that are blocking changes to the dataset.
        "A String",
      ],
      "createTime": "A String", # Output only. Time the dataset is created.
      "dataItemCount": "A String", # Output only. The number of data items in the dataset.
      "description": "A String", # Optional. User-provided description of the annotation specification set. The description can be up to 10000 characters long.
      "displayName": "A String", # Required. The display name of the dataset. Maximum of 64 characters.
      "inputConfigs": [ # Output only. This is populated with the original input configs where ImportData is called. It is available only after the clients import data to this dataset.
        { # The configuration of input data, including data type, location, etc.
          "annotationType": "A String", # Optional. The type of annotation to be performed on this data. You must specify this field if you are using this InputConfig in an EvaluationJob.
          "bigquerySource": { # The BigQuery location for input data. If used in an EvaluationJob, this is where the service saves the prediction input and output sampled from the model version. # Source located in BigQuery. You must specify this field if you are using this InputConfig in an EvaluationJob.
            "inputUri": "A String", # Required. BigQuery URI to a table, up to 2,000 characters long. If you specify the URI of a table that does not exist, Data Labeling Service creates a table at the URI with the correct schema when you create your EvaluationJob. If you specify the URI of a table that already exists, it must have the [correct schema](/ml-engine/docs/continuous-evaluation/create-job#table-schema). Provide the table URI in the following format: "bq://{your_project_id}/ {your_dataset_name}/{your_table_name}" [Learn more](/ml-engine/docs/continuous-evaluation/create-job#table-schema).
          },
          "classificationMetadata": { # Metadata for classification annotations. # Optional. Metadata about annotations for the input. You must specify this field if you are using this InputConfig in an EvaluationJob for a model version that performs classification.
            "isMultiLabel": True or False, # Whether the classification task is multi-label or not.
          },
          "dataType": "A String", # Required. Data type must be specifed when user tries to import data.
          "gcsSource": { # Source of the Cloud Storage file to be imported. # Source located in Cloud Storage.
            "inputUri": "A String", # Required. The input URI of source file. This must be a Cloud Storage path (`gs://...`).
            "mimeType": "A String", # Required. The format of the source file. Only "text/csv" is supported.
          },
          "textMetadata": { # Metadata for the text. # Required for text import, as language code must be specified.
            "languageCode": "A String", # The language of this text, as a [BCP-47](https://www.rfc-editor.org/rfc/bcp/bcp47.txt). Default value is en-US.
          },
        },
      ],
      "lastMigrateTime": "A String", # Last time that the Dataset is migrated to AI Platform V2. If any of the AnnotatedDataset is migrated, the last_migration_time in Dataset is also updated.
      "name": "A String", # Output only. Dataset resource name, format is: projects/{project_id}/datasets/{dataset_id}
    },
  ],
  "nextPageToken": "A String", # A token to retrieve next page of results.
}

list_next()

Retrieves the next page of results.

        Args:
          previous_request: The request for the previous page. (required)
          previous_response: The response from the request for the previous page. (required)

        Returns:
          A request object that you can call 'execute()' on to request the next
          page. Returns None if there are no more items in the collection.

Data Labeling API . projects . datasets

Instance Methods

Method Details