Package com.google.cloud.automl.v1 (2.1.23)

The interfaces provided are listed below, along with usage samples.

PredictionServiceClient

Service Description: AutoML Prediction API.

On any input that is documented to expect a string parameter in snake_case or dash-case, either of those cases is accepted.

Sample for PredictionServiceClient:


 try (PredictionServiceClient predictionServiceClient = PredictionServiceClient.create()) {
   ModelName name = ModelName.of("[PROJECT]", "[LOCATION]", "[MODEL]");
   ExamplePayload payload = ExamplePayload.newBuilder().build();
   Map<String, String> params = new HashMap<>();
   PredictResponse response = predictionServiceClient.predict(name, payload, params);
 }

AutoMlClient

Service Description: AutoML Server API.

The resource names are assigned by the server. The server never reuses names that it has created after the resources with those names are deleted.

An ID of a resource is the last element of the item's resource name. For projects/{project_id}/locations/{location_id}/datasets/{dataset_id}, then the id for the item is {dataset_id}.

Currently the only supported location_id is "us-central1".

On any input that is documented to expect a string parameter in snake_case or dash-case, either of those cases is accepted.

Sample for AutoMlClient:


 try (AutoMlClient autoMlClient = AutoMlClient.create()) {
   DatasetName name = DatasetName.of("[PROJECT]", "[LOCATION]", "[DATASET]");
   Dataset response = autoMlClient.getDataset(name);
 }

Classes

AnnotationPayload

Contains annotation information that is relevant to AutoML.

Protobuf type google.cloud.automl.v1.AnnotationPayload

AnnotationPayload.Builder

Contains annotation information that is relevant to AutoML.

Protobuf type google.cloud.automl.v1.AnnotationPayload

AnnotationSpec

A definition of an annotation spec.

Protobuf type google.cloud.automl.v1.AnnotationSpec

AnnotationSpec.Builder

A definition of an annotation spec.

Protobuf type google.cloud.automl.v1.AnnotationSpec

AnnotationSpecName.Builder

Builder for projects/{project}/locations/{location}/datasets/{dataset}/annotationSpecs/{annotation_spec}.

AutoMlClient

Service Description: AutoML Server API.

The resource names are assigned by the server. The server never reuses names that it has created after the resources with those names are deleted.

An ID of a resource is the last element of the item's resource name. For projects/{project_id}/locations/{location_id}/datasets/{dataset_id}, then the id for the item is {dataset_id}.

Currently the only supported location_id is "us-central1".

On any input that is documented to expect a string parameter in snake_case or dash-case, either of those cases is accepted.

This class provides the ability to make remote calls to the backing service through method calls that map to API methods. Sample code to get started:


 try (AutoMlClient autoMlClient = AutoMlClient.create()) {
   DatasetName name = DatasetName.of("[PROJECT]", "[LOCATION]", "[DATASET]");
   Dataset response = autoMlClient.getDataset(name);
 }

Note: close() needs to be called on the AutoMlClient object to clean up resources such as threads. In the example above, try-with-resources is used, which automatically calls close().

The surface of this class includes several types of Java methods for each of the API's methods:

A "flattened" method. With this type of method, the fields of the request type have been converted into function parameters. It may be the case that not all fields are available as parameters, and not every API method will have a flattened method entry point.
A "request object" method. This type of method only takes one parameter, a request object, which must be constructed before the call. Not every API method will have a request object method.
A "callable" method. This type of method takes no parameters and returns an immutable API callable object, which can be used to initiate calls to the service.

See the individual methods for example code.

Many parameters require resource names to be formatted in a particular way. To assist with these names, this class includes a format method for each type of name, and additionally a parse method to extract the individual identifiers contained within names that are returned.

This class can be customized by passing in a custom instance of AutoMlSettings to create(). For example:

To customize credentials:


 AutoMlSettings autoMlSettings =
     AutoMlSettings.newBuilder()
         .setCredentialsProvider(FixedCredentialsProvider.create(myCredentials))
         .build();
 AutoMlClient autoMlClient = AutoMlClient.create(autoMlSettings);

To customize the endpoint:


 AutoMlSettings autoMlSettings = AutoMlSettings.newBuilder().setEndpoint(myEndpoint).build();
 AutoMlClient autoMlClient = AutoMlClient.create(autoMlSettings);

Please refer to the GitHub repository's samples for more quickstart code snippets.

AutoMlClient.ListDatasetsFixedSizeCollection

AutoMlClient.ListDatasetsPage

AutoMlClient.ListDatasetsPagedResponse

AutoMlClient.ListModelEvaluationsFixedSizeCollection

AutoMlClient.ListModelEvaluationsPage

AutoMlClient.ListModelEvaluationsPagedResponse

AutoMlClient.ListModelsFixedSizeCollection

AutoMlClient.ListModelsPage

AutoMlClient.ListModelsPagedResponse

AutoMlGrpc

AutoML Server API. The resource names are assigned by the server. The server never reuses names that it has created after the resources with those names are deleted. An ID of a resource is the last element of the item's resource name. For projects/{project_id}/locations/{location_id}/datasets/{dataset_id}, then the id for the item is {dataset_id}. Currently the only supported location_id is "us-central1". On any input that is documented to expect a string parameter in snake_case or dash-case, either of those cases is accepted.

AutoMlGrpc.AutoMlBlockingStub

AutoMlGrpc.AutoMlFutureStub

AutoMlGrpc.AutoMlImplBase

AutoMlGrpc.AutoMlStub

AutoMlProto

AutoMlSettings

Settings class to configure an instance of AutoMlClient.

The default instance has everything set to sensible defaults:

The default service address (automl.googleapis.com) and default port (443) are used.
Credentials are acquired automatically through Application Default Credentials.
Retries are configured for idempotent methods but not for non-idempotent methods.

The builder of this class is recursive, so contained classes are themselves builders. When build() is called, the tree of builders is called to create the complete settings object.

For example, to set the total timeout of getDataset to 30 seconds:


 AutoMlSettings.Builder autoMlSettingsBuilder = AutoMlSettings.newBuilder();
 autoMlSettingsBuilder
     .getDatasetSettings()
     .setRetrySettings(
         autoMlSettingsBuilder
             .getDatasetSettings()
             .getRetrySettings()
             .toBuilder()
             .setTotalTimeout(Duration.ofSeconds(30))
             .build());
 AutoMlSettings autoMlSettings = autoMlSettingsBuilder.build();

AutoMlSettings.Builder

Builder for AutoMlSettings.

BatchPredictInputConfig

Input configuration for BatchPredict Action. The format of input depends on the ML problem of the model used for prediction. As input source the gcs_source is expected, unless specified otherwise. The formats are represented in EBNF with commas being literal and with non-terminal symbols defined near the end of this comment. The formats are: <h4>AutoML Vision</h4> <div class="ds-selector-tabs"><section><h5>Classification</h5> One or more CSV files where each line is a single column: GCS_FILE_PATH The Google Cloud Storage location of an image of up to 30MB in size. Supported extensions: .JPEG, .GIF, .PNG. This path is treated as the ID in the batch predict output. Sample rows: gs://folder/image1.jpeg gs://folder/image2.gif gs://folder/image3.png </section><section><h5>Object Detection</h5> One or more CSV files where each line is a single column: GCS_FILE_PATH The Google Cloud Storage location of an image of up to 30MB in size. Supported extensions: .JPEG, .GIF, .PNG. This path is treated as the ID in the batch predict output. Sample rows: gs://folder/image1.jpeg gs://folder/image2.gif gs://folder/image3.png </section> </div> <h4>AutoML Video Intelligence</h4> <div class="ds-selector-tabs"><section><h5>Classification</h5> One or more CSV files where each line is a single column: GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END GCS_FILE_PATH is the Google Cloud Storage location of video up to 50GB in size and up to 3h in duration duration. Supported extensions: .MOV, .MPEG4, .MP4, .AVI. TIME_SEGMENT_START and TIME_SEGMENT_END must be within the length of the video, and the end time must be after the start time. Sample rows: gs://folder/video1.mp4,10,40 gs://folder/video1.mp4,20,60 gs://folder/vid2.mov,0,inf </section><section><h5>Object Tracking</h5> One or more CSV files where each line is a single column: GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END GCS_FILE_PATH is the Google Cloud Storage location of video up to 50GB in size and up to 3h in duration duration. Supported extensions: .MOV, .MPEG4, .MP4, .AVI. TIME_SEGMENT_START and TIME_SEGMENT_END must be within the length of the video, and the end time must be after the start time. Sample rows: gs://folder/video1.mp4,10,40 gs://folder/video1.mp4,20,60 gs://folder/vid2.mov,0,inf </section> </div> <h4>AutoML Natural Language</h4> <div class="ds-selector-tabs"><section><h5>Classification</h5> One or more CSV files where each line is a single column: GCS_FILE_PATH GCS_FILE_PATH is the Google Cloud Storage location of a text file. Supported file extensions: .TXT, .PDF, .TIF, .TIFF Text files can be no larger than 10MB in size. Sample rows: gs://folder/text1.txt gs://folder/text2.pdf gs://folder/text3.tif </section><section><h5>Sentiment Analysis</h5> One or more CSV files where each line is a single column: GCS_FILE_PATH GCS_FILE_PATH is the Google Cloud Storage location of a text file. Supported file extensions: .TXT, .PDF, .TIF, .TIFF Text files can be no larger than 128kB in size. Sample rows: gs://folder/text1.txt gs://folder/text2.pdf gs://folder/text3.tif </section><section><h5>Entity Extraction</h5> One or more JSONL (JSON Lines) files that either provide inline text or documents. You can only use one format, either inline text or documents, for a single call to [AutoMl.BatchPredict]. Each JSONL file contains a per line a proto that wraps a temporary user-assigned TextSnippet ID (string up to 2000 characters long) called "id", a TextSnippet proto (in JSON representation) and zero or more TextFeature protos. Any given text snippet content must have 30,000 characters or less, and also be UTF-8 NFC encoded (ASCII already is). The IDs provided should be unique. Each document JSONL file contains, per line, a proto that wraps a Document proto with input_config set. Each document cannot exceed 2MB in size. Supported document extensions: .PDF, .TIF, .TIFF Each JSONL file must not exceed 100MB in size, and no more than 20 JSONL files may be passed. Sample inline JSONL file (Shown with artificial line breaks. Actual line breaks are denoted by "\n".): { "id": "my_first_id", "text_snippet": { "content": "dog car cat"}, "text_features": [ { "text_segment": {"start_offset": 4, "end_offset": 6}, "structural_type": PARAGRAPH, "bounding_poly": { "normalized_vertices": [ {"x": 0.1, "y": 0.1}, {"x": 0.1, "y": 0.3}, {"x": 0.3, "y": 0.3}, {"x": 0.3, "y": 0.1}, ] }, } ], }\n { "id": "2", "text_snippet": { "content": "Extended sample content", "mime_type": "text/plain" } } Sample document JSONL file (Shown with artificial line breaks. Actual line breaks are denoted by "\n".): { "document": { "input_config": { "gcs_source": { "input_uris": [ "gs://folder/document1.pdf" ] } } } }\n { "document": { "input_config": { "gcs_source": { "input_uris": [ "gs://folder/document2.tif" ] } } } } </section> </div> <h4>AutoML Tables</h4><div class="ui-datasection-main"><section class="selected"> See Preparing your training data for more information. You can use either gcs_source or bigquery_source. For gcs_source: CSV file(s), each by itself 10GB or smaller and total size must be 100GB or smaller, where first file must have a header containing column names. If the first row of a subsequent file is the same as the header, then it is also treated as a header. All other rows contain values for the corresponding columns. The column names must contain the model's input_feature_column_specs' display_name-s (order doesn't matter). The columns corresponding to the model's input feature column specs must contain values compatible with the column spec's data types. Prediction on all the rows, i.e. the CSV lines, will be attempted. Sample rows from a CSV file: <pre> "First Name","Last Name","Dob","Addresses" "John","Doe","1968-01-22","[{"status":"current","address":"123_First_Avenue","city":"Seattle","state":"WA","zip":"11111","numberOfYears":"1"},{"status":"previous","address":"456_Main_Street","city":"Portland","state":"OR","zip":"22222","numberOfYears":"5"}]" "Jane","Doe","1980-10-16","[{"status":"current","address":"789_Any_Avenue","city":"Albany","state":"NY","zip":"33333","numberOfYears":"2"},{"status":"previous","address":"321_Main_Street","city":"Hoboken","state":"NJ","zip":"44444","numberOfYears":"3"}]} </pre> For bigquery_source: The URI of a BigQuery table. The user data size of the BigQuery table must be 100GB or smaller. The column names must contain the model's input_feature_column_specs' display_name-s (order doesn't matter). The columns corresponding to the model's input feature column specs must contain values compatible with the column spec's data types. Prediction on all the rows of the table will be attempted. </section> </div> Input field definitions: GCS_FILE_PATH : The path to a file on Google Cloud Storage. For example, "gs://folder/video.avi". TIME_SEGMENT_START : (TIME_OFFSET) Expresses a beginning, inclusive, of a time segment within an example that has a time dimension (e.g. video). TIME_SEGMENT_END : (TIME_OFFSET) Expresses an end, exclusive, of a time segment within n example that has a time dimension (e.g. video). TIME_OFFSET : A number of seconds as measured from the start of an example (e.g. video). Fractions are allowed, up to a microsecond precision. "inf" is allowed, and it means the end of the example. Errors: If any of the provided CSV files can't be parsed or if more than certain percent of CSV rows cannot be processed then the operation fails and prediction does not happen. Regardless of overall success or failure the per-row failures, up to a certain count cap, will be listed in Operation.metadata.partial_failures.

Protobuf type google.cloud.automl.v1.BatchPredictInputConfig

BatchPredictInputConfig.Builder

Protobuf type google.cloud.automl.v1.BatchPredictInputConfig

BatchPredictOperationMetadata

Details of BatchPredict operation.

Protobuf type google.cloud.automl.v1.BatchPredictOperationMetadata

BatchPredictOperationMetadata.BatchPredictOutputInfo

Further describes this batch predict's output. Supplements BatchPredictOutputConfig.

Protobuf type google.cloud.automl.v1.BatchPredictOperationMetadata.BatchPredictOutputInfo

BatchPredictOperationMetadata.BatchPredictOutputInfo.Builder

Further describes this batch predict's output. Supplements BatchPredictOutputConfig.

Protobuf type google.cloud.automl.v1.BatchPredictOperationMetadata.BatchPredictOutputInfo

BatchPredictOperationMetadata.Builder

Details of BatchPredict operation.

Protobuf type google.cloud.automl.v1.BatchPredictOperationMetadata

BatchPredictOutputConfig

Output configuration for BatchPredict Action. As destination the gcs_destination must be set unless specified otherwise for a domain. If gcs_destination is set then in the given directory a new directory is created. Its name will be "prediction-<model-display-name>-<timestamp-of-prediction-call>", where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. The contents of it depends on the ML problem the predictions are made for.

For Image Classification: In the created directory files image_classification_1.jsonl, image_classification_2.jsonl,...,image_classification_N.jsonl will be created, where N may be 1, and depends on the total number of the successfully predicted images and annotations. A single image will be listed only once with all its annotations, and its annotations will never be split across files. Each .JSONL file will contain, per line, a JSON representation of a proto that wraps image's "ID" : "<id_value>" followed by a list of zero or more AnnotationPayload protos (called annotations), which have classification detail populated. If prediction for any image failed (partially or completely), then an additional errors_1.jsonl, errors_2.jsonl,..., errors_N.jsonl files will be created (N depends on total number of failed predictions). These files will have a JSON representation of a proto that wraps the same "ID" : "<id_value>" but here followed by exactly one google.rpc.Status containing only code and messagefields.
For Image Object Detection: In the created directory files image_object_detection_1.jsonl, image_object_detection_2.jsonl,...,image_object_detection_N.jsonl will be created, where N may be 1, and depends on the total number of the successfully predicted images and annotations. Each .JSONL file will contain, per line, a JSON representation of a proto that wraps image's "ID" : "<id_value>" followed by a list of zero or more AnnotationPayload protos (called annotations), which have image_object_detection detail populated. A single image will be listed only once with all its annotations, and its annotations will never be split across files. If prediction for any image failed (partially or completely), then additional errors_1.jsonl, errors_2.jsonl,..., errors_N.jsonl files will be created (N depends on total number of failed predictions). These files will have a JSON representation of a proto that wraps the same "ID" : "<id_value>" but here followed by exactly one google.rpc.Status containing only code and messagefields.
For Video Classification: In the created directory a video_classification.csv file, and a .JSON file per each video classification requested in the input (i.e. each line in given CSV(s)), will be created. The format of video_classification.csv is: GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END,JSON_FILE_NAME,STATUS where: GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END = matches 1 to 1 the prediction input lines (i.e. video_classification.csv has precisely the same number of lines as the prediction input had.) JSON_FILE_NAME = Name of .JSON file in the output directory, which contains prediction responses for the video time segment. STATUS = "OK" if prediction completed successfully, or an error code with message otherwise. If STATUS is not "OK" then the .JSON file for that line may not exist or be empty. Each .JSON file, assuming STATUS is "OK", will contain a list of AnnotationPayload protos in JSON format, which are the predictions for the video time segment the file is assigned to in the video_classification.csv. All AnnotationPayload protos will have video_classification field set, and will be sorted by video_classification.type field (note that the returned types are governed by classifaction_types parameter in [PredictService.BatchPredictRequest.params][]).
For Video Object Tracking: In the created directory a video_object_tracking.csv file will be created, and multiple files video_object_trackinng_1.json, video_object_trackinng_2.json,..., video_object_trackinng_N.json, where N is the number of requests in the input (i.e. the number of lines in given CSV(s)). The format of video_object_tracking.csv is: GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END,JSON_FILE_NAME,STATUS where: GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END = matches 1 to 1 the prediction input lines (i.e. video_object_tracking.csv has precisely the same number of lines as the prediction input had.) JSON_FILE_NAME = Name of .JSON file in the output directory, which contains prediction responses for the video time segment. STATUS = "OK" if prediction completed successfully, or an error code with message otherwise. If STATUS is not "OK" then the .JSON file for that line may not exist or be empty. Each .JSON file, assuming STATUS is "OK", will contain a list of AnnotationPayload protos in JSON format, which are the predictions for each frame of the video time segment the file is assigned to in video_object_tracking.csv. All AnnotationPayload protos will have video_object_tracking field set.
For Text Classification: In the created directory files text_classification_1.jsonl, text_classification_2.jsonl,...,text_classification_N.jsonl will be created, where N may be 1, and depends on the total number of inputs and annotations found. Each .JSONL file will contain, per line, a JSON representation of a proto that wraps input text file (or document) in the text snippet (or document) proto and a list of zero or more AnnotationPayload protos (called annotations), which have classification detail populated. A single text file (or document) will be listed only once with all its annotations, and its annotations will never be split across files. If prediction for any input file (or document) failed (partially or completely), then additional errors_1.jsonl, errors_2.jsonl,..., errors_N.jsonl files will be created (N depends on total number of failed predictions). These files will have a JSON representation of a proto that wraps input file followed by exactly one google.rpc.Status containing only code and message.
For Text Sentiment: In the created directory files text_sentiment_1.jsonl, text_sentiment_2.jsonl,...,text_sentiment_N.jsonl will be created, where N may be 1, and depends on the total number of inputs and annotations found. Each .JSONL file will contain, per line, a JSON representation of a proto that wraps input text file (or document) in the text snippet (or document) proto and a list of zero or more AnnotationPayload protos (called annotations), which have text_sentiment detail populated. A single text file (or document) will be listed only once with all its annotations, and its annotations will never be split across files. If prediction for any input file (or document) failed (partially or completely), then additional errors_1.jsonl, errors_2.jsonl,..., errors_N.jsonl files will be created (N depends on total number of failed predictions). These files will have a JSON representation of a proto that wraps input file followed by exactly one google.rpc.Status containing only code and message.
- For Text Extraction: In the created directory files text_extraction_1.jsonl, text_extraction_2.jsonl,...,text_extraction_N.jsonl will be created, where N may be 1, and depends on the total number of inputs and annotations found. The contents of these .JSONL file(s) depend on whether the input used inline text, or documents. If input was inline, then each .JSONL file will contain, per line, a JSON representation of a proto that wraps given in request text snippet's "id" (if specified), followed by input text snippet, and a list of zero or more AnnotationPayload protos (called annotations), which have text_extraction detail populated. A single text snippet will be listed only once with all its annotations, and its annotations will never be split across files. If input used documents, then each .JSONL file will contain, per line, a JSON representation of a proto that wraps given in request document proto, followed by its OCR-ed representation in the form of a text snippet, finally followed by a list of zero or more AnnotationPayload protos (called annotations), which have text_extraction detail populated and refer, via their indices, to the OCR-ed text snippet. A single document (and its text snippet) will be listed only once with all its annotations, and its annotations will never be split across files. If prediction for any text snippet failed (partially or completely), then additional errors_1.jsonl, errors_2.jsonl,..., errors_N.jsonl files will be created (N depends on total number of failed predictions). These files will have a JSON representation of a proto that wraps either the "id" : "<id_value>" (in case of inline) or the document proto (in case of document) but here followed by exactly one google.rpc.Status containing only code and message.
For Tables: Output depends on whether gcs_destination or bigquery_destination is set (either is allowed). Google Cloud Storage case: In the created directory files tables_1.csv, tables_2.csv,..., tables_N.csv will be created, where N may be 1, and depends on the total number of the successfully predicted rows. For all CLASSIFICATION prediction_type-s: Each .csv file will contain a header, listing all columns' display_name-s given on input followed by M target column names in the format of "<target_column_specs display_name><target value>_score" where M is the number of distinct target values, i.e. number of distinct values in the target column of the table used to train the model. Subsequent lines will contain the respective values of successfully predicted rows, with the last, i.e. the target, columns having the corresponding prediction scores. For REGRESSION and FORECASTING prediction_type-s: Each .csv file will contain a header, listing all columns' display_name-s given on input followed by the predicted target column with name in the format of "predicted<target_column_specs display_name>" Subsequent lines will contain the respective values of successfully predicted rows, with the last, i.e. the target, column having the predicted target value. If prediction for any rows failed, then an additional errors_1.csv, errors_2.csv,..., errors_N.csv will be created (N depends on total number of failed rows). These files will have analogous format as tables_*.csv, but always with a single target column having google.rpc.Status represented as a JSON string, and containing only code and message. BigQuery case: bigquery_destination pointing to a BigQuery project must be set. In the given project a new dataset will be created with name prediction_<model-display-name><timestamp-of-prediction-call> where <model-display-name> 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 the dataset two tables will be created, predictions, and errors. The predictions table's column names will be the input columns' display_name-s followed by the target column with name in the format of "predicted<target_column_specs display_name>" The input feature columns will contain the respective values of successfully predicted rows, with the target column having an ARRAY of AnnotationPayloads, represented as STRUCT-s, containing TablesAnnotation. The errors table contains rows for which the prediction has failed, it has analogous input columns while the target column name is in the format of "errors_<target_column_specs display_name>", and as a value has google.rpc.Status represented as a STRUCT, and containing only code and message.

Protobuf type google.cloud.automl.v1.BatchPredictOutputConfig

BatchPredictOutputConfig.Builder

For Image Classification: In the created directory files image_classification_1.jsonl, image_classification_2.jsonl,...,image_classification_N.jsonl will be created, where N may be 1, and depends on the total number of the successfully predicted images and annotations. A single image will be listed only once with all its annotations, and its annotations will never be split across files. Each .JSONL file will contain, per line, a JSON representation of a proto that wraps image's "ID" : "<id_value>" followed by a list of zero or more AnnotationPayload protos (called annotations), which have classification detail populated. If prediction for any image failed (partially or completely), then an additional errors_1.jsonl, errors_2.jsonl,..., errors_N.jsonl files will be created (N depends on total number of failed predictions). These files will have a JSON representation of a proto that wraps the same "ID" : "<id_value>" but here followed by exactly one google.rpc.Status containing only code and messagefields.
For Image Object Detection: In the created directory files image_object_detection_1.jsonl, image_object_detection_2.jsonl,...,image_object_detection_N.jsonl will be created, where N may be 1, and depends on the total number of the successfully predicted images and annotations. Each .JSONL file will contain, per line, a JSON representation of a proto that wraps image's "ID" : "<id_value>" followed by a list of zero or more AnnotationPayload protos (called annotations), which have image_object_detection detail populated. A single image will be listed only once with all its annotations, and its annotations will never be split across files. If prediction for any image failed (partially or completely), then additional errors_1.jsonl, errors_2.jsonl,..., errors_N.jsonl files will be created (N depends on total number of failed predictions). These files will have a JSON representation of a proto that wraps the same "ID" : "<id_value>" but here followed by exactly one google.rpc.Status containing only code and messagefields.
For Video Classification: In the created directory a video_classification.csv file, and a .JSON file per each video classification requested in the input (i.e. each line in given CSV(s)), will be created. The format of video_classification.csv is: GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END,JSON_FILE_NAME,STATUS where: GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END = matches 1 to 1 the prediction input lines (i.e. video_classification.csv has precisely the same number of lines as the prediction input had.) JSON_FILE_NAME = Name of .JSON file in the output directory, which contains prediction responses for the video time segment. STATUS = "OK" if prediction completed successfully, or an error code with message otherwise. If STATUS is not "OK" then the .JSON file for that line may not exist or be empty. Each .JSON file, assuming STATUS is "OK", will contain a list of AnnotationPayload protos in JSON format, which are the predictions for the video time segment the file is assigned to in the video_classification.csv. All AnnotationPayload protos will have video_classification field set, and will be sorted by video_classification.type field (note that the returned types are governed by classifaction_types parameter in [PredictService.BatchPredictRequest.params][]).
For Video Object Tracking: In the created directory a video_object_tracking.csv file will be created, and multiple files video_object_trackinng_1.json, video_object_trackinng_2.json,..., video_object_trackinng_N.json, where N is the number of requests in the input (i.e. the number of lines in given CSV(s)). The format of video_object_tracking.csv is: GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END,JSON_FILE_NAME,STATUS where: GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END = matches 1 to 1 the prediction input lines (i.e. video_object_tracking.csv has precisely the same number of lines as the prediction input had.) JSON_FILE_NAME = Name of .JSON file in the output directory, which contains prediction responses for the video time segment. STATUS = "OK" if prediction completed successfully, or an error code with message otherwise. If STATUS is not "OK" then the .JSON file for that line may not exist or be empty. Each .JSON file, assuming STATUS is "OK", will contain a list of AnnotationPayload protos in JSON format, which are the predictions for each frame of the video time segment the file is assigned to in video_object_tracking.csv. All AnnotationPayload protos will have video_object_tracking field set.
For Text Classification: In the created directory files text_classification_1.jsonl, text_classification_2.jsonl,...,text_classification_N.jsonl will be created, where N may be 1, and depends on the total number of inputs and annotations found. Each .JSONL file will contain, per line, a JSON representation of a proto that wraps input text file (or document) in the text snippet (or document) proto and a list of zero or more AnnotationPayload protos (called annotations), which have classification detail populated. A single text file (or document) will be listed only once with all its annotations, and its annotations will never be split across files. If prediction for any input file (or document) failed (partially or completely), then additional errors_1.jsonl, errors_2.jsonl,..., errors_N.jsonl files will be created (N depends on total number of failed predictions). These files will have a JSON representation of a proto that wraps input file followed by exactly one google.rpc.Status containing only code and message.
For Text Sentiment: In the created directory files text_sentiment_1.jsonl, text_sentiment_2.jsonl,...,text_sentiment_N.jsonl will be created, where N may be 1, and depends on the total number of inputs and annotations found. Each .JSONL file will contain, per line, a JSON representation of a proto that wraps input text file (or document) in the text snippet (or document) proto and a list of zero or more AnnotationPayload protos (called annotations), which have text_sentiment detail populated. A single text file (or document) will be listed only once with all its annotations, and its annotations will never be split across files. If prediction for any input file (or document) failed (partially or completely), then additional errors_1.jsonl, errors_2.jsonl,..., errors_N.jsonl files will be created (N depends on total number of failed predictions). These files will have a JSON representation of a proto that wraps input file followed by exactly one google.rpc.Status containing only code and message.
- For Text Extraction: In the created directory files text_extraction_1.jsonl, text_extraction_2.jsonl,...,text_extraction_N.jsonl will be created, where N may be 1, and depends on the total number of inputs and annotations found. The contents of these .JSONL file(s) depend on whether the input used inline text, or documents. If input was inline, then each .JSONL file will contain, per line, a JSON representation of a proto that wraps given in request text snippet's "id" (if specified), followed by input text snippet, and a list of zero or more AnnotationPayload protos (called annotations), which have text_extraction detail populated. A single text snippet will be listed only once with all its annotations, and its annotations will never be split across files. If input used documents, then each .JSONL file will contain, per line, a JSON representation of a proto that wraps given in request document proto, followed by its OCR-ed representation in the form of a text snippet, finally followed by a list of zero or more AnnotationPayload protos (called annotations), which have text_extraction detail populated and refer, via their indices, to the OCR-ed text snippet. A single document (and its text snippet) will be listed only once with all its annotations, and its annotations will never be split across files. If prediction for any text snippet failed (partially or completely), then additional errors_1.jsonl, errors_2.jsonl,..., errors_N.jsonl files will be created (N depends on total number of failed predictions). These files will have a JSON representation of a proto that wraps either the "id" : "<id_value>" (in case of inline) or the document proto (in case of document) but here followed by exactly one google.rpc.Status containing only code and message.
For Tables: Output depends on whether gcs_destination or bigquery_destination is set (either is allowed). Google Cloud Storage case: In the created directory files tables_1.csv, tables_2.csv,..., tables_N.csv will be created, where N may be 1, and depends on the total number of the successfully predicted rows. For all CLASSIFICATION prediction_type-s: Each .csv file will contain a header, listing all columns' display_name-s given on input followed by M target column names in the format of "<target_column_specs display_name><target value>_score" where M is the number of distinct target values, i.e. number of distinct values in the target column of the table used to train the model. Subsequent lines will contain the respective values of successfully predicted rows, with the last, i.e. the target, columns having the corresponding prediction scores. For REGRESSION and FORECASTING prediction_type-s: Each .csv file will contain a header, listing all columns' display_name-s given on input followed by the predicted target column with name in the format of "predicted<target_column_specs display_name>" Subsequent lines will contain the respective values of successfully predicted rows, with the last, i.e. the target, column having the predicted target value. If prediction for any rows failed, then an additional errors_1.csv, errors_2.csv,..., errors_N.csv will be created (N depends on total number of failed rows). These files will have analogous format as tables_*.csv, but always with a single target column having google.rpc.Status represented as a JSON string, and containing only code and message. BigQuery case: bigquery_destination pointing to a BigQuery project must be set. In the given project a new dataset will be created with name prediction_<model-display-name><timestamp-of-prediction-call> where <model-display-name> 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 the dataset two tables will be created, predictions, and errors. The predictions table's column names will be the input columns' display_name-s followed by the target column with name in the format of "predicted<target_column_specs display_name>" The input feature columns will contain the respective values of successfully predicted rows, with the target column having an ARRAY of AnnotationPayloads, represented as STRUCT-s, containing TablesAnnotation. The errors table contains rows for which the prediction has failed, it has analogous input columns while the target column name is in the format of "errors_<target_column_specs display_name>", and as a value has google.rpc.Status represented as a STRUCT, and containing only code and message.

Protobuf type google.cloud.automl.v1.BatchPredictOutputConfig

BatchPredictRequest

Request message for PredictionService.BatchPredict.

Protobuf type google.cloud.automl.v1.BatchPredictRequest

BatchPredictRequest.Builder

Request message for PredictionService.BatchPredict.

Protobuf type google.cloud.automl.v1.BatchPredictRequest

BatchPredictResult

Result of the Batch Predict. This message is returned in response of the operation returned by the PredictionService.BatchPredict.

Protobuf type google.cloud.automl.v1.BatchPredictResult

BatchPredictResult.Builder

Result of the Batch Predict. This message is returned in response of the operation returned by the PredictionService.BatchPredict.

Protobuf type google.cloud.automl.v1.BatchPredictResult

BoundingBoxMetricsEntry

Bounding box matching model metrics for a single intersection-over-union threshold and multiple label match confidence thresholds.

Protobuf type google.cloud.automl.v1.BoundingBoxMetricsEntry

BoundingBoxMetricsEntry.Builder

Bounding box matching model metrics for a single intersection-over-union threshold and multiple label match confidence thresholds.

Protobuf type google.cloud.automl.v1.BoundingBoxMetricsEntry

BoundingBoxMetricsEntry.ConfidenceMetricsEntry

Metrics for a single confidence threshold.

Protobuf type google.cloud.automl.v1.BoundingBoxMetricsEntry.ConfidenceMetricsEntry

BoundingBoxMetricsEntry.ConfidenceMetricsEntry.Builder

Metrics for a single confidence threshold.

Protobuf type google.cloud.automl.v1.BoundingBoxMetricsEntry.ConfidenceMetricsEntry

BoundingPoly

A bounding polygon of a detected object on a plane. On output both vertices and normalized_vertices are provided. The polygon is formed by connecting vertices in the order they are listed.

Protobuf type google.cloud.automl.v1.BoundingPoly

BoundingPoly.Builder

A bounding polygon of a detected object on a plane. On output both vertices and normalized_vertices are provided. The polygon is formed by connecting vertices in the order they are listed.

Protobuf type google.cloud.automl.v1.BoundingPoly

ClassificationAnnotation

Contains annotation details specific to classification.

Protobuf type google.cloud.automl.v1.ClassificationAnnotation

ClassificationAnnotation.Builder

Contains annotation details specific to classification.

Protobuf type google.cloud.automl.v1.ClassificationAnnotation

ClassificationEvaluationMetrics

Model evaluation metrics for classification problems. Note: For Video Classification this metrics only describe quality of the Video Classification predictions of "segment_classification" type.

Protobuf type google.cloud.automl.v1.ClassificationEvaluationMetrics

ClassificationEvaluationMetrics.Builder

Model evaluation metrics for classification problems. Note: For Video Classification this metrics only describe quality of the Video Classification predictions of "segment_classification" type.

Protobuf type google.cloud.automl.v1.ClassificationEvaluationMetrics

ClassificationEvaluationMetrics.ConfidenceMetricsEntry

Metrics for a single confidence threshold.

Protobuf type google.cloud.automl.v1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry

ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder

Metrics for a single confidence threshold.

Protobuf type google.cloud.automl.v1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry

ClassificationEvaluationMetrics.ConfusionMatrix

Confusion matrix of the model running the classification.

Protobuf type google.cloud.automl.v1.ClassificationEvaluationMetrics.ConfusionMatrix

ClassificationEvaluationMetrics.ConfusionMatrix.Builder

Confusion matrix of the model running the classification.

Protobuf type google.cloud.automl.v1.ClassificationEvaluationMetrics.ConfusionMatrix

ClassificationEvaluationMetrics.ConfusionMatrix.Row

Output only. A row in the confusion matrix.

Protobuf type google.cloud.automl.v1.ClassificationEvaluationMetrics.ConfusionMatrix.Row

ClassificationEvaluationMetrics.ConfusionMatrix.Row.Builder

Output only. A row in the confusion matrix.

Protobuf type google.cloud.automl.v1.ClassificationEvaluationMetrics.ConfusionMatrix.Row

ClassificationProto

CreateDatasetOperationMetadata

Details of CreateDataset operation.

Protobuf type google.cloud.automl.v1.CreateDatasetOperationMetadata

CreateDatasetOperationMetadata.Builder

Details of CreateDataset operation.

Protobuf type google.cloud.automl.v1.CreateDatasetOperationMetadata

CreateDatasetRequest

Request message for AutoMl.CreateDataset.

Protobuf type google.cloud.automl.v1.CreateDatasetRequest

CreateDatasetRequest.Builder

Request message for AutoMl.CreateDataset.

Protobuf type google.cloud.automl.v1.CreateDatasetRequest

CreateModelOperationMetadata

Details of CreateModel operation.

Protobuf type google.cloud.automl.v1.CreateModelOperationMetadata

CreateModelOperationMetadata.Builder

Details of CreateModel operation.

Protobuf type google.cloud.automl.v1.CreateModelOperationMetadata

CreateModelRequest

Request message for AutoMl.CreateModel.

Protobuf type google.cloud.automl.v1.CreateModelRequest

CreateModelRequest.Builder

Request message for AutoMl.CreateModel.

Protobuf type google.cloud.automl.v1.CreateModelRequest

DataItems

Dataset

A workspace for solving a single, particular machine learning (ML) problem. A workspace contains examples that may be annotated.

Protobuf type google.cloud.automl.v1.Dataset

Dataset.Builder

A workspace for solving a single, particular machine learning (ML) problem. A workspace contains examples that may be annotated.

Protobuf type google.cloud.automl.v1.Dataset

DatasetName

DatasetName.Builder

Builder for projects/{project}/locations/{location}/datasets/{dataset}.

DatasetOuterClass

DeleteDatasetRequest

Request message for AutoMl.DeleteDataset.

Protobuf type google.cloud.automl.v1.DeleteDatasetRequest

DeleteDatasetRequest.Builder

Request message for AutoMl.DeleteDataset.

Protobuf type google.cloud.automl.v1.DeleteDatasetRequest

DeleteModelRequest

Request message for AutoMl.DeleteModel.

Protobuf type google.cloud.automl.v1.DeleteModelRequest

DeleteModelRequest.Builder

Request message for AutoMl.DeleteModel.

Protobuf type google.cloud.automl.v1.DeleteModelRequest

DeleteOperationMetadata

Details of operations that perform deletes of any entities.

Protobuf type google.cloud.automl.v1.DeleteOperationMetadata

DeleteOperationMetadata.Builder

Details of operations that perform deletes of any entities.

Protobuf type google.cloud.automl.v1.DeleteOperationMetadata

DeployModelOperationMetadata

Details of DeployModel operation.

Protobuf type google.cloud.automl.v1.DeployModelOperationMetadata

DeployModelOperationMetadata.Builder

Details of DeployModel operation.

Protobuf type google.cloud.automl.v1.DeployModelOperationMetadata

DeployModelRequest

Request message for AutoMl.DeployModel.

Protobuf type google.cloud.automl.v1.DeployModelRequest

DeployModelRequest.Builder

Request message for AutoMl.DeployModel.

Protobuf type google.cloud.automl.v1.DeployModelRequest

Detection

Document

A structured text document e.g. a PDF.

Protobuf type google.cloud.automl.v1.Document

Document.Builder

A structured text document e.g. a PDF.

Protobuf type google.cloud.automl.v1.Document

Document.Layout

Describes the layout information of a text_segment in the document.

Protobuf type google.cloud.automl.v1.Document.Layout

Document.Layout.Builder

Describes the layout information of a text_segment in the document.

Protobuf type google.cloud.automl.v1.Document.Layout

DocumentDimensions

Message that describes dimension of a document.

Protobuf type google.cloud.automl.v1.DocumentDimensions

DocumentDimensions.Builder

Message that describes dimension of a document.

Protobuf type google.cloud.automl.v1.DocumentDimensions

DocumentInputConfig

Input configuration of a Document.

Protobuf type google.cloud.automl.v1.DocumentInputConfig

DocumentInputConfig.Builder

Input configuration of a Document.

Protobuf type google.cloud.automl.v1.DocumentInputConfig

ExamplePayload

Example data used for training or prediction.

Protobuf type google.cloud.automl.v1.ExamplePayload

ExamplePayload.Builder

Example data used for training or prediction.

Protobuf type google.cloud.automl.v1.ExamplePayload

ExportDataOperationMetadata

Details of ExportData operation.

Protobuf type google.cloud.automl.v1.ExportDataOperationMetadata

ExportDataOperationMetadata.Builder

Details of ExportData operation.

Protobuf type google.cloud.automl.v1.ExportDataOperationMetadata

ExportDataOperationMetadata.ExportDataOutputInfo

Further describes this export data's output. Supplements OutputConfig.

Protobuf type google.cloud.automl.v1.ExportDataOperationMetadata.ExportDataOutputInfo

ExportDataOperationMetadata.ExportDataOutputInfo.Builder

Further describes this export data's output. Supplements OutputConfig.

Protobuf type google.cloud.automl.v1.ExportDataOperationMetadata.ExportDataOutputInfo

ExportDataRequest

Request message for AutoMl.ExportData.

Protobuf type google.cloud.automl.v1.ExportDataRequest

ExportDataRequest.Builder

Request message for AutoMl.ExportData.

Protobuf type google.cloud.automl.v1.ExportDataRequest

ExportModelOperationMetadata

Details of ExportModel operation.

Protobuf type google.cloud.automl.v1.ExportModelOperationMetadata

ExportModelOperationMetadata.Builder

Details of ExportModel operation.

Protobuf type google.cloud.automl.v1.ExportModelOperationMetadata

ExportModelOperationMetadata.ExportModelOutputInfo

Further describes the output of model export. Supplements ModelExportOutputConfig.

Protobuf type google.cloud.automl.v1.ExportModelOperationMetadata.ExportModelOutputInfo

ExportModelOperationMetadata.ExportModelOutputInfo.Builder

Further describes the output of model export. Supplements ModelExportOutputConfig.

Protobuf type google.cloud.automl.v1.ExportModelOperationMetadata.ExportModelOutputInfo

ExportModelRequest

Request message for AutoMl.ExportModel. Models need to be enabled for exporting, otherwise an error code will be returned.

Protobuf type google.cloud.automl.v1.ExportModelRequest

ExportModelRequest.Builder

Request message for AutoMl.ExportModel. Models need to be enabled for exporting, otherwise an error code will be returned.

Protobuf type google.cloud.automl.v1.ExportModelRequest

GcsDestination

The Google Cloud Storage location where the output is to be written to.

Protobuf type google.cloud.automl.v1.GcsDestination

GcsDestination.Builder

The Google Cloud Storage location where the output is to be written to.

Protobuf type google.cloud.automl.v1.GcsDestination

GcsSource

The Google Cloud Storage location for the input content.

Protobuf type google.cloud.automl.v1.GcsSource

GcsSource.Builder

The Google Cloud Storage location for the input content.

Protobuf type google.cloud.automl.v1.GcsSource

Geometry

GetAnnotationSpecRequest

Request message for AutoMl.GetAnnotationSpec.

Protobuf type google.cloud.automl.v1.GetAnnotationSpecRequest

GetAnnotationSpecRequest.Builder

Request message for AutoMl.GetAnnotationSpec.

Protobuf type google.cloud.automl.v1.GetAnnotationSpecRequest

GetDatasetRequest

Request message for AutoMl.GetDataset.

Protobuf type google.cloud.automl.v1.GetDatasetRequest

GetDatasetRequest.Builder

Request message for AutoMl.GetDataset.

Protobuf type google.cloud.automl.v1.GetDatasetRequest

GetModelEvaluationRequest

Request message for AutoMl.GetModelEvaluation.

Protobuf type google.cloud.automl.v1.GetModelEvaluationRequest

GetModelEvaluationRequest.Builder

Request message for AutoMl.GetModelEvaluation.

Protobuf type google.cloud.automl.v1.GetModelEvaluationRequest

GetModelRequest

Request message for AutoMl.GetModel.

Protobuf type google.cloud.automl.v1.GetModelRequest

GetModelRequest.Builder

Request message for AutoMl.GetModel.

Protobuf type google.cloud.automl.v1.GetModelRequest

Image

A representation of an image. Only images up to 30MB in size are supported.

Protobuf type google.cloud.automl.v1.Image

Image.Builder

A representation of an image. Only images up to 30MB in size are supported.

Protobuf type google.cloud.automl.v1.Image

ImageClassificationDatasetMetadata

Dataset metadata that is specific to image classification.

Protobuf type google.cloud.automl.v1.ImageClassificationDatasetMetadata

ImageClassificationDatasetMetadata.Builder

Dataset metadata that is specific to image classification.

Protobuf type google.cloud.automl.v1.ImageClassificationDatasetMetadata

ImageClassificationModelDeploymentMetadata

Model deployment metadata specific to Image Classification.

Protobuf type google.cloud.automl.v1.ImageClassificationModelDeploymentMetadata

ImageClassificationModelDeploymentMetadata.Builder

Model deployment metadata specific to Image Classification.

Protobuf type google.cloud.automl.v1.ImageClassificationModelDeploymentMetadata

ImageClassificationModelMetadata

Model metadata for image classification.

Protobuf type google.cloud.automl.v1.ImageClassificationModelMetadata

ImageClassificationModelMetadata.Builder

Model metadata for image classification.

Protobuf type google.cloud.automl.v1.ImageClassificationModelMetadata

ImageObjectDetectionAnnotation

Annotation details for image object detection.

Protobuf type google.cloud.automl.v1.ImageObjectDetectionAnnotation

ImageObjectDetectionAnnotation.Builder

Annotation details for image object detection.

Protobuf type google.cloud.automl.v1.ImageObjectDetectionAnnotation

ImageObjectDetectionDatasetMetadata

Dataset metadata specific to image object detection.

Protobuf type google.cloud.automl.v1.ImageObjectDetectionDatasetMetadata

ImageObjectDetectionDatasetMetadata.Builder

Dataset metadata specific to image object detection.

Protobuf type google.cloud.automl.v1.ImageObjectDetectionDatasetMetadata

ImageObjectDetectionEvaluationMetrics

Model evaluation metrics for image object detection problems. Evaluates prediction quality of labeled bounding boxes.

Protobuf type google.cloud.automl.v1.ImageObjectDetectionEvaluationMetrics

ImageObjectDetectionEvaluationMetrics.Builder

Model evaluation metrics for image object detection problems. Evaluates prediction quality of labeled bounding boxes.

Protobuf type google.cloud.automl.v1.ImageObjectDetectionEvaluationMetrics

ImageObjectDetectionModelDeploymentMetadata

Model deployment metadata specific to Image Object Detection.

Protobuf type google.cloud.automl.v1.ImageObjectDetectionModelDeploymentMetadata

ImageObjectDetectionModelDeploymentMetadata.Builder

Model deployment metadata specific to Image Object Detection.

Protobuf type google.cloud.automl.v1.ImageObjectDetectionModelDeploymentMetadata

ImageObjectDetectionModelMetadata

Model metadata specific to image object detection.

Protobuf type google.cloud.automl.v1.ImageObjectDetectionModelMetadata

ImageObjectDetectionModelMetadata.Builder

Model metadata specific to image object detection.

Protobuf type google.cloud.automl.v1.ImageObjectDetectionModelMetadata

ImageProto

ImportDataOperationMetadata

Details of ImportData operation.

Protobuf type google.cloud.automl.v1.ImportDataOperationMetadata

ImportDataOperationMetadata.Builder

Details of ImportData operation.

Protobuf type google.cloud.automl.v1.ImportDataOperationMetadata

ImportDataRequest

Request message for AutoMl.ImportData.

Protobuf type google.cloud.automl.v1.ImportDataRequest

ImportDataRequest.Builder

Request message for AutoMl.ImportData.

Protobuf type google.cloud.automl.v1.ImportDataRequest

InputConfig

Input configuration for AutoMl.ImportData action. The format of input depends on dataset_metadata the Dataset into which the import is happening has. As input source the gcs_source is expected, unless specified otherwise. Additionally any input .CSV file by itself must be 100MB or smaller, unless specified otherwise. If an "example" file (that is, image, video etc.) with identical content (even if it had different GCS_FILE_PATH) is mentioned multiple times, then its label, bounding boxes etc. are appended. The same file should be always provided with the same ML_USE and GCS_FILE_PATH, if it is not, then these values are nondeterministically selected from the given ones. The formats are represented in EBNF with commas being literal and with non-terminal symbols defined near the end of this comment. The formats are: <h4>AutoML Vision</h4> <div class="ds-selector-tabs"><section><h5>Classification</h5> See Preparing your training data for more information. CSV file(s) with each line in format: ML_USE,GCS_FILE_PATH,LABEL,LABEL,...

ML_USE - Identifies the data set that the current row (file) applies to. This value can be one of the following:
- TRAIN - Rows in this file are used to train the model.
- TEST - Rows in this file are used to test the model during training.
- UNASSIGNED - Rows in this file are not categorized. They are Automatically divided into train and test data. 80% for training and 20% for testing.
GCS_FILE_PATH - The Google Cloud Storage location of an image of up to 30MB in size. Supported extensions: .JPEG, .GIF, .PNG, .WEBP, .BMP, .TIFF, .ICO.
LABEL - A label that identifies the object in the image. For the MULTICLASS classification type, at most one LABEL is allowed per image. If an image has not yet been labeled, then it should be mentioned just once with no LABEL. Some sample rows: TRAIN,gs://folder/image1.jpg,daisy TEST,gs://folder/image2.jpg,dandelion,tulip,rose UNASSIGNED,gs://folder/image3.jpg,daisy UNASSIGNED,gs://folder/image4.jpg </section><section><h5>Object Detection</h5> See Preparing your training data for more information. A CSV file(s) with each line in format: ML_USE,GCS_FILE_PATH,[LABEL],(BOUNDING_BOX | ,,,,,,,)
ML_USE - Identifies the data set that the current row (file) applies to. This value can be one of the following:
- TRAIN - Rows in this file are used to train the model.
- TEST - Rows in this file are used to test the model during training.
- UNASSIGNED - Rows in this file are not categorized. They are Automatically divided into train and test data. 80% for training and 20% for testing.
GCS_FILE_PATH - The Google Cloud Storage location of an image of up to 30MB in size. Supported extensions: .JPEG, .GIF, .PNG. Each image is assumed to be exhaustively labeled.
LABEL - A label that identifies the object in the image specified by the BOUNDING_BOX.
BOUNDING BOX - The vertices of an object in the example image. The minimum allowed BOUNDING_BOX edge length is 0.01, and no more than 500 BOUNDING_BOX instances per image are allowed (one BOUNDING_BOX per line). If an image has no looked for objects then it should be mentioned just once with no LABEL and the ",,,,,,," in place of the BOUNDING_BOX. Four sample rows: TRAIN,gs://folder/image1.png,car,0.1,0.1,,,0.3,0.3,, TRAIN,gs://folder/image1.png,bike,.7,.6,,,.8,.9,, UNASSIGNED,gs://folder/im2.png,car,0.1,0.1,0.2,0.1,0.2,0.3,0.1,0.3 TEST,gs://folder/im3.png,,,,,,,,, </section> </div> <h4>AutoML Video Intelligence</h4> <div class="ds-selector-tabs"><section><h5>Classification</h5> See Preparing your training data for more information. CSV file(s) with each line in format: ML_USE,GCS_FILE_PATH For ML_USE, do not use VALIDATE. GCS_FILE_PATH is the path to another .csv file that describes training example for a given ML_USE, using the following row format: GCS_FILE_PATH,(LABEL,TIME_SEGMENT_START,TIME_SEGMENT_END | ,,) Here GCS_FILE_PATH leads to a video of up to 50GB in size and up to 3h duration. Supported extensions: .MOV, .MPEG4, .MP4, .AVI. TIME_SEGMENT_START and TIME_SEGMENT_END must be within the length of the video, and the end time must be after the start time. Any segment of a video which has one or more labels on it, is considered a hard negative for all other labels. Any segment with no labels on it is considered to be unknown. If a whole video is unknown, then it should be mentioned just once with ",," in place of LABEL, TIME_SEGMENT_START,TIME_SEGMENT_END. Sample top level CSV file: TRAIN,gs://folder/train_videos.csv TEST,gs://folder/test_videos.csv UNASSIGNED,gs://folder/other_videos.csv Sample rows of a CSV file for a particular ML_USE: gs://folder/video1.avi,car,120,180.000021 gs://folder/video1.avi,bike,150,180.000021 gs://folder/vid2.avi,car,0,60.5 gs://folder/vid3.avi,,, </section><section><h5>Object Tracking</h5> See Preparing your training data for more information. CSV file(s) with each line in format: ML_USE,GCS_FILE_PATH For ML_USE, do not use VALIDATE. GCS_FILE_PATH is the path to another .csv file that describes training example for a given ML_USE, using the following row format: GCS_FILE_PATH,LABEL,[INSTANCE_ID],TIMESTAMP,BOUNDING_BOX or GCS_FILE_PATH,,,,,,,,,, Here GCS_FILE_PATH leads to a video of up to 50GB in size and up to 3h duration. Supported extensions: .MOV, .MPEG4, .MP4, .AVI. Providing INSTANCE_IDs can help to obtain a better model. When a specific labeled entity leaves the video frame, and shows up afterwards it is not required, albeit preferable, that the same INSTANCE_ID is given to it. TIMESTAMP must be within the length of the video, the BOUNDING_BOX is assumed to be drawn on the closest video's frame to the TIMESTAMP. Any mentioned by the TIMESTAMP frame is expected to be exhaustively labeled and no more than 500 BOUNDING_BOX-es per frame are allowed. If a whole video is unknown, then it should be mentioned just once with ",,,,,,,,,," in place of LABEL, [INSTANCE_ID],TIMESTAMP,BOUNDING_BOX. Sample top level CSV file: TRAIN,gs://folder/train_videos.csv TEST,gs://folder/test_videos.csv UNASSIGNED,gs://folder/other_videos.csv Seven sample rows of a CSV file for a particular ML_USE: gs://folder/video1.avi,car,1,12.10,0.8,0.8,0.9,0.8,0.9,0.9,0.8,0.9 gs://folder/video1.avi,car,1,12.90,0.4,0.8,0.5,0.8,0.5,0.9,0.4,0.9 gs://folder/video1.avi,car,2,12.10,.4,.2,.5,.2,.5,.3,.4,.3 gs://folder/video1.avi,car,2,12.90,.8,.2,,,.9,.3,, gs://folder/video1.avi,bike,,12.50,.45,.45,,,.55,.55,, gs://folder/video2.avi,car,1,0,.1,.9,,,.9,.1,, gs://folder/video2.avi,,,,,,,,,,, </section> </div> <h4>AutoML Natural Language</h4> <div class="ds-selector-tabs"><section><h5>Entity Extraction</h5> See Preparing your training data for more information. One or more CSV file(s) with each line in the following format: ML_USE,GCS_FILE_PATH
ML_USE - Identifies the data set that the current row (file) applies to. This value can be one of the following:
- TRAIN - Rows in this file are used to train the model.
- TEST - Rows in this file are used to test the model during training.
- UNASSIGNED - Rows in this file are not categorized. They are Automatically divided into train and test data. 80% for training and 20% for testing..
GCS_FILE_PATH - a Identifies JSON Lines (.JSONL) file stored in Google Cloud Storage that contains in-line text in-line as documents for model training. After the training data set has been determined from the TRAIN and UNASSIGNED CSV files, the training data is divided into train and validation data sets. 70% for training and 30% for validation. For example: TRAIN,gs://folder/file1.jsonl VALIDATE,gs://folder/file2.jsonl TEST,gs://folder/file3.jsonl In-line JSONL files In-line .JSONL files contain, per line, a JSON document that wraps a text_snippet field followed by one or more annotations fields, which have display_name and text_extraction fields to describe the entity from the text snippet. Multiple JSON documents can be separated using line breaks (\n). The supplied text must be annotated exhaustively. For example, if you include the text "horse", but do not label it as "animal", then "horse" is assumed to not be an "animal". Any given text snippet content must have 30,000 characters or less, and also be UTF-8 NFC encoded. ASCII is accepted as it is UTF-8 NFC encoded. For example: { "text_snippet": { "content": "dog car cat" }, "annotations": [ { "display_name": "animal", "text_extraction": { "text_segment": {"start_offset": 0, "end_offset": 2} } }, { "display_name": "vehicle", "text_extraction": { "text_segment": {"start_offset": 4, "end_offset": 6} } }, { "display_name": "animal", "text_extraction": { "text_segment": {"start_offset": 8, "end_offset": 10} } } ] }\n { "text_snippet": { "content": "This dog is good." }, "annotations": [ { "display_name": "animal", "text_extraction": { "text_segment": {"start_offset": 5, "end_offset": 7} } } ] } JSONL files that reference documents .JSONL files contain, per line, a JSON document that wraps a input_config that contains the path to a source document. Multiple JSON documents can be separated using line breaks (\n). Supported document extensions: .PDF, .TIF, .TIFF For example: { "document": { "input_config": { "gcs_source": { "input_uris": [ "gs://folder/document1.pdf" ] } } } }\n { "document": { "input_config": { "gcs_source": { "input_uris": [ "gs://folder/document2.tif" ] } } } } In-line JSONL files with document layout information Note: You can only annotate documents using the UI. The format described below applies to annotated documents exported using the UI or exportData. In-line .JSONL files for documents contain, per line, a JSON document that wraps a document field that provides the textual content of the document and the layout information. For example: { "document": { "document_text": { "content": "dog car cat" } "layout": [ { "text_segment": { "start_offset": 0, "end_offset": 11, }, "page_number": 1, "bounding_poly": { "normalized_vertices": [ {"x": 0.1, "y": 0.1}, {"x": 0.1, "y": 0.3}, {"x": 0.3, "y": 0.3}, {"x": 0.3, "y": 0.1}, ], }, "text_segment_type": TOKEN, } ], "document_dimensions": { "width": 8.27, "height": 11.69, "unit": INCH, } "page_count": 3, }, "annotations": [ { "display_name": "animal", "text_extraction": { "text_segment": {"start_offset": 0, "end_offset": 3} } }, { "display_name": "vehicle", "text_extraction": { "text_segment": {"start_offset": 4, "end_offset": 7} } }, { "display_name": "animal", "text_extraction": { "text_segment": {"start_offset": 8, "end_offset": 11} } }, ], </section><section><h5>Classification</h5> See Preparing your training data for more information. One or more CSV file(s) with each line in the following format: ML_USE,(TEXT_SNIPPET | GCS_FILE_PATH),LABEL,LABEL,...
ML_USE - Identifies the data set that the current row (file) applies to. This value can be one of the following:
- TRAIN - Rows in this file are used to train the model.
- TEST - Rows in this file are used to test the model during training.
- UNASSIGNED - Rows in this file are not categorized. They are Automatically divided into train and test data. 80% for training and 20% for testing.
TEXT_SNIPPET and GCS_FILE_PATH are distinguished by a pattern. If the column content is a valid Google Cloud Storage file path, that is, prefixed by "gs://", it is treated as a GCS_FILE_PATH. Otherwise, if the content is enclosed in double quotes (""), it is treated as a TEXT_SNIPPET. For GCS_FILE_PATH, the path must lead to a file with supported extension and UTF-8 encoding, for example, "gs://folder/content.txt" AutoML imports the file content as a text snippet. For TEXT_SNIPPET, AutoML imports the column content excluding quotes. In both cases, size of the content must be 10MB or less in size. For zip files, the size of each file inside the zip must be 10MB or less in size. For the MULTICLASS classification type, at most one LABEL is allowed. The ML_USE and LABEL columns are optional. Supported file extensions: .TXT, .PDF, .TIF, .TIFF, .ZIP A maximum of 100 unique labels are allowed per CSV row. Sample rows: TRAIN,"They have bad food and very rude",RudeService,BadFood gs://folder/content.txt,SlowService TEST,gs://folder/document.pdf VALIDATE,gs://folder/text_files.zip,BadFood </section><section><h5>Sentiment Analysis</h5> See Preparing your training data for more information. CSV file(s) with each line in format: ML_USE,(TEXT_SNIPPET | GCS_FILE_PATH),SENTIMENT
ML_USE - Identifies the data set that the current row (file) applies to. This value can be one of the following:
- TRAIN - Rows in this file are used to train the model.
- TEST - Rows in this file are used to test the model during training.
- UNASSIGNED - Rows in this file are not categorized. They are Automatically divided into train and test data. 80% for training and 20% for testing.
TEXT_SNIPPET and GCS_FILE_PATH are distinguished by a pattern. If the column content is a valid Google Cloud Storage file path, that is, prefixed by "gs://", it is treated as a GCS_FILE_PATH. Otherwise, if the content is enclosed in double quotes (""), it is treated as a TEXT_SNIPPET. For GCS_FILE_PATH, the path must lead to a file with supported extension and UTF-8 encoding, for example, "gs://folder/content.txt" AutoML imports the file content as a text snippet. For TEXT_SNIPPET, AutoML imports the column content excluding quotes. In both cases, size of the content must be 128kB or less in size. For zip files, the size of each file inside the zip must be 128kB or less in size. The ML_USE and SENTIMENT columns are optional. Supported file extensions: .TXT, .PDF, .TIF, .TIFF, .ZIP
SENTIMENT - An integer between 0 and Dataset.text_sentiment_dataset_metadata.sentiment_max (inclusive). Describes the ordinal of the sentiment - higher value means a more positive sentiment. All the values are completely relative, i.e. neither 0 needs to mean a negative or neutral sentiment nor sentiment_max needs to mean a positive one - it is just required that 0 is the least positive sentiment in the data, and sentiment_max is the most positive one. The SENTIMENT shouldn't be confused with "score" or "magnitude" from the previous Natural Language Sentiment Analysis API. All SENTIMENT values between 0 and sentiment_max must be represented in the imported data. On prediction the same 0 to sentiment_max range will be used. The difference between neighboring sentiment values needs not to be uniform, e.g. 1 and 2 may be similar whereas the difference between 2 and 3 may be large. Sample rows: TRAIN,"@freewrytin this is way too good for your product",2 gs://folder/content.txt,3 TEST,gs://folder/document.pdf VALIDATE,gs://folder/text_files.zip,2 </section> </div> <h4>AutoML Tables</h4><div class="ui-datasection-main"><section class="selected"> See Preparing your training data for more information. You can use either gcs_source or bigquery_source. All input is concatenated into a single primary_table_spec_id For gcs_source: CSV file(s), where the first row of the first file is the header, containing unique column names. If the first row of a subsequent file is the same as the header, then it is also treated as a header. All other rows contain values for the corresponding columns. Each .CSV file by itself must be 10GB or smaller, and their total size must be 100GB or smaller. First three sample rows of a CSV file: <pre> "Id","First Name","Last Name","Dob","Addresses" "1","John","Doe","1968-01-22","[{"status":"current","address":"123_First_Avenue","city":"Seattle","state":"WA","zip":"11111","numberOfYears":"1"},{"status":"previous","address":"456_Main_Street","city":"Portland","state":"OR","zip":"22222","numberOfYears":"5"}]" "2","Jane","Doe","1980-10-16","[{"status":"current","address":"789_Any_Avenue","city":"Albany","state":"NY","zip":"33333","numberOfYears":"2"},{"status":"previous","address":"321_Main_Street","city":"Hoboken","state":"NJ","zip":"44444","numberOfYears":"3"}]} </pre> For bigquery_source: An URI of a BigQuery table. The user data size of the BigQuery table must be 100GB or smaller. An imported table must have between 2 and 1,000 columns, inclusive, and between 1000 and 100,000,000 rows, inclusive. There are at most 5 import data running in parallel. </section> </div> Input field definitions: ML_USE : ("TRAIN" | "VALIDATE" | "TEST" | "UNASSIGNED") Describes how the given example (file) should be used for model training. "UNASSIGNED" can be used when user has no preference. GCS_FILE_PATH : The path to a file on Google Cloud Storage. For example, "gs://folder/image1.png". LABEL : A display name of an object on an image, video etc., e.g. "dog". Must be up to 32 characters long and can consist only of ASCII Latin letters A-Z and a-z, underscores(_), and ASCII digits 0-9. For each label an AnnotationSpec is created which display_name becomes the label; AnnotationSpecs are given back in predictions. INSTANCE_ID : A positive integer that identifies a specific instance of a labeled entity on an example. Used e.g. to track two cars on a video while being able to tell apart which one is which. BOUNDING_BOX : (VERTEX,VERTEX,VERTEX,VERTEX | VERTEX,,,VERTEX,,) A rectangle parallel to the frame of the example (image, video). If 4 vertices are given they are connected by edges in the order provided, if 2 are given they are recognized as diagonally opposite vertices of the rectangle. VERTEX : (COORDINATE,COORDINATE) First coordinate is horizontal (x), the second is vertical (y). COORDINATE : A float in 0 to 1 range, relative to total length of image or video in given dimension. For fractions the leading non-decimal 0 can be omitted (i.e. 0.3 = .3). Point 0,0 is in top left. TIME_SEGMENT_START : (TIME_OFFSET) Expresses a beginning, inclusive, of a time segment within an example that has a time dimension (e.g. video). TIME_SEGMENT_END : (TIME_OFFSET) Expresses an end, exclusive, of a time segment within n example that has a time dimension (e.g. video). TIME_OFFSET : A number of seconds as measured from the start of an example (e.g. video). Fractions are allowed, up to a microsecond precision. "inf" is allowed, and it means the end of the example. TEXT_SNIPPET : The content of a text snippet, UTF-8 encoded, enclosed within double quotes (""). DOCUMENT : A field that provides the textual content with document and the layout information. Errors: If any of the provided CSV files can't be parsed or if more than certain percent of CSV rows cannot be processed then the operation fails and nothing is imported. Regardless of overall success or failure the per-row failures, up to a certain count cap, is listed in Operation.metadata.partial_failures.

Protobuf type google.cloud.automl.v1.InputConfig

InputConfig.Builder

ML_USE - Identifies the data set that the current row (file) applies to. This value can be one of the following:
- TRAIN - Rows in this file are used to train the model.
- TEST - Rows in this file are used to test the model during training.
- UNASSIGNED - Rows in this file are not categorized. They are Automatically divided into train and test data. 80% for training and 20% for testing.
GCS_FILE_PATH - The Google Cloud Storage location of an image of up to 30MB in size. Supported extensions: .JPEG, .GIF, .PNG, .WEBP, .BMP, .TIFF, .ICO.
LABEL - A label that identifies the object in the image. For the MULTICLASS classification type, at most one LABEL is allowed per image. If an image has not yet been labeled, then it should be mentioned just once with no LABEL. Some sample rows: TRAIN,gs://folder/image1.jpg,daisy TEST,gs://folder/image2.jpg,dandelion,tulip,rose UNASSIGNED,gs://folder/image3.jpg,daisy UNASSIGNED,gs://folder/image4.jpg </section><section><h5>Object Detection</h5> See Preparing your training data for more information. A CSV file(s) with each line in format: ML_USE,GCS_FILE_PATH,[LABEL],(BOUNDING_BOX | ,,,,,,,)
ML_USE - Identifies the data set that the current row (file) applies to. This value can be one of the following:
- TRAIN - Rows in this file are used to train the model.
- TEST - Rows in this file are used to test the model during training.
- UNASSIGNED - Rows in this file are not categorized. They are Automatically divided into train and test data. 80% for training and 20% for testing.
GCS_FILE_PATH - The Google Cloud Storage location of an image of up to 30MB in size. Supported extensions: .JPEG, .GIF, .PNG. Each image is assumed to be exhaustively labeled.
LABEL - A label that identifies the object in the image specified by the BOUNDING_BOX.
BOUNDING BOX - The vertices of an object in the example image. The minimum allowed BOUNDING_BOX edge length is 0.01, and no more than 500 BOUNDING_BOX instances per image are allowed (one BOUNDING_BOX per line). If an image has no looked for objects then it should be mentioned just once with no LABEL and the ",,,,,,," in place of the BOUNDING_BOX. Four sample rows: TRAIN,gs://folder/image1.png,car,0.1,0.1,,,0.3,0.3,, TRAIN,gs://folder/image1.png,bike,.7,.6,,,.8,.9,, UNASSIGNED,gs://folder/im2.png,car,0.1,0.1,0.2,0.1,0.2,0.3,0.1,0.3 TEST,gs://folder/im3.png,,,,,,,,, </section> </div> <h4>AutoML Video Intelligence</h4> <div class="ds-selector-tabs"><section><h5>Classification</h5> See Preparing your training data for more information. CSV file(s) with each line in format: ML_USE,GCS_FILE_PATH For ML_USE, do not use VALIDATE. GCS_FILE_PATH is the path to another .csv file that describes training example for a given ML_USE, using the following row format: GCS_FILE_PATH,(LABEL,TIME_SEGMENT_START,TIME_SEGMENT_END | ,,) Here GCS_FILE_PATH leads to a video of up to 50GB in size and up to 3h duration. Supported extensions: .MOV, .MPEG4, .MP4, .AVI. TIME_SEGMENT_START and TIME_SEGMENT_END must be within the length of the video, and the end time must be after the start time. Any segment of a video which has one or more labels on it, is considered a hard negative for all other labels. Any segment with no labels on it is considered to be unknown. If a whole video is unknown, then it should be mentioned just once with ",," in place of LABEL, TIME_SEGMENT_START,TIME_SEGMENT_END. Sample top level CSV file: TRAIN,gs://folder/train_videos.csv TEST,gs://folder/test_videos.csv UNASSIGNED,gs://folder/other_videos.csv Sample rows of a CSV file for a particular ML_USE: gs://folder/video1.avi,car,120,180.000021 gs://folder/video1.avi,bike,150,180.000021 gs://folder/vid2.avi,car,0,60.5 gs://folder/vid3.avi,,, </section><section><h5>Object Tracking</h5> See Preparing your training data for more information. CSV file(s) with each line in format: ML_USE,GCS_FILE_PATH For ML_USE, do not use VALIDATE. GCS_FILE_PATH is the path to another .csv file that describes training example for a given ML_USE, using the following row format: GCS_FILE_PATH,LABEL,[INSTANCE_ID],TIMESTAMP,BOUNDING_BOX or GCS_FILE_PATH,,,,,,,,,, Here GCS_FILE_PATH leads to a video of up to 50GB in size and up to 3h duration. Supported extensions: .MOV, .MPEG4, .MP4, .AVI. Providing INSTANCE_IDs can help to obtain a better model. When a specific labeled entity leaves the video frame, and shows up afterwards it is not required, albeit preferable, that the same INSTANCE_ID is given to it. TIMESTAMP must be within the length of the video, the BOUNDING_BOX is assumed to be drawn on the closest video's frame to the TIMESTAMP. Any mentioned by the TIMESTAMP frame is expected to be exhaustively labeled and no more than 500 BOUNDING_BOX-es per frame are allowed. If a whole video is unknown, then it should be mentioned just once with ",,,,,,,,,," in place of LABEL, [INSTANCE_ID],TIMESTAMP,BOUNDING_BOX. Sample top level CSV file: TRAIN,gs://folder/train_videos.csv TEST,gs://folder/test_videos.csv UNASSIGNED,gs://folder/other_videos.csv Seven sample rows of a CSV file for a particular ML_USE: gs://folder/video1.avi,car,1,12.10,0.8,0.8,0.9,0.8,0.9,0.9,0.8,0.9 gs://folder/video1.avi,car,1,12.90,0.4,0.8,0.5,0.8,0.5,0.9,0.4,0.9 gs://folder/video1.avi,car,2,12.10,.4,.2,.5,.2,.5,.3,.4,.3 gs://folder/video1.avi,car,2,12.90,.8,.2,,,.9,.3,, gs://folder/video1.avi,bike,,12.50,.45,.45,,,.55,.55,, gs://folder/video2.avi,car,1,0,.1,.9,,,.9,.1,, gs://folder/video2.avi,,,,,,,,,,, </section> </div> <h4>AutoML Natural Language</h4> <div class="ds-selector-tabs"><section><h5>Entity Extraction</h5> See Preparing your training data for more information. One or more CSV file(s) with each line in the following format: ML_USE,GCS_FILE_PATH
ML_USE - Identifies the data set that the current row (file) applies to. This value can be one of the following:
- TRAIN - Rows in this file are used to train the model.
- TEST - Rows in this file are used to test the model during training.
- UNASSIGNED - Rows in this file are not categorized. They are Automatically divided into train and test data. 80% for training and 20% for testing..
GCS_FILE_PATH - a Identifies JSON Lines (.JSONL) file stored in Google Cloud Storage that contains in-line text in-line as documents for model training. After the training data set has been determined from the TRAIN and UNASSIGNED CSV files, the training data is divided into train and validation data sets. 70% for training and 30% for validation. For example: TRAIN,gs://folder/file1.jsonl VALIDATE,gs://folder/file2.jsonl TEST,gs://folder/file3.jsonl In-line JSONL files In-line .JSONL files contain, per line, a JSON document that wraps a text_snippet field followed by one or more annotations fields, which have display_name and text_extraction fields to describe the entity from the text snippet. Multiple JSON documents can be separated using line breaks (\n). The supplied text must be annotated exhaustively. For example, if you include the text "horse", but do not label it as "animal", then "horse" is assumed to not be an "animal". Any given text snippet content must have 30,000 characters or less, and also be UTF-8 NFC encoded. ASCII is accepted as it is UTF-8 NFC encoded. For example: { "text_snippet": { "content": "dog car cat" }, "annotations": [ { "display_name": "animal", "text_extraction": { "text_segment": {"start_offset": 0, "end_offset": 2} } }, { "display_name": "vehicle", "text_extraction": { "text_segment": {"start_offset": 4, "end_offset": 6} } }, { "display_name": "animal", "text_extraction": { "text_segment": {"start_offset": 8, "end_offset": 10} } } ] }\n { "text_snippet": { "content": "This dog is good." }, "annotations": [ { "display_name": "animal", "text_extraction": { "text_segment": {"start_offset": 5, "end_offset": 7} } } ] } JSONL files that reference documents .JSONL files contain, per line, a JSON document that wraps a input_config that contains the path to a source document. Multiple JSON documents can be separated using line breaks (\n). Supported document extensions: .PDF, .TIF, .TIFF For example: { "document": { "input_config": { "gcs_source": { "input_uris": [ "gs://folder/document1.pdf" ] } } } }\n { "document": { "input_config": { "gcs_source": { "input_uris": [ "gs://folder/document2.tif" ] } } } } In-line JSONL files with document layout information Note: You can only annotate documents using the UI. The format described below applies to annotated documents exported using the UI or exportData. In-line .JSONL files for documents contain, per line, a JSON document that wraps a document field that provides the textual content of the document and the layout information. For example: { "document": { "document_text": { "content": "dog car cat" } "layout": [ { "text_segment": { "start_offset": 0, "end_offset": 11, }, "page_number": 1, "bounding_poly": { "normalized_vertices": [ {"x": 0.1, "y": 0.1}, {"x": 0.1, "y": 0.3}, {"x": 0.3, "y": 0.3}, {"x": 0.3, "y": 0.1}, ], }, "text_segment_type": TOKEN, } ], "document_dimensions": { "width": 8.27, "height": 11.69, "unit": INCH, } "page_count": 3, }, "annotations": [ { "display_name": "animal", "text_extraction": { "text_segment": {"start_offset": 0, "end_offset": 3} } }, { "display_name": "vehicle", "text_extraction": { "text_segment": {"start_offset": 4, "end_offset": 7} } }, { "display_name": "animal", "text_extraction": { "text_segment": {"start_offset": 8, "end_offset": 11} } }, ], </section><section><h5>Classification</h5> See Preparing your training data for more information. One or more CSV file(s) with each line in the following format: ML_USE,(TEXT_SNIPPET | GCS_FILE_PATH),LABEL,LABEL,...
ML_USE - Identifies the data set that the current row (file) applies to. This value can be one of the following:
- TRAIN - Rows in this file are used to train the model.
- TEST - Rows in this file are used to test the model during training.
- UNASSIGNED - Rows in this file are not categorized. They are Automatically divided into train and test data. 80% for training and 20% for testing.
TEXT_SNIPPET and GCS_FILE_PATH are distinguished by a pattern. If the column content is a valid Google Cloud Storage file path, that is, prefixed by "gs://", it is treated as a GCS_FILE_PATH. Otherwise, if the content is enclosed in double quotes (""), it is treated as a TEXT_SNIPPET. For GCS_FILE_PATH, the path must lead to a file with supported extension and UTF-8 encoding, for example, "gs://folder/content.txt" AutoML imports the file content as a text snippet. For TEXT_SNIPPET, AutoML imports the column content excluding quotes. In both cases, size of the content must be 10MB or less in size. For zip files, the size of each file inside the zip must be 10MB or less in size. For the MULTICLASS classification type, at most one LABEL is allowed. The ML_USE and LABEL columns are optional. Supported file extensions: .TXT, .PDF, .TIF, .TIFF, .ZIP A maximum of 100 unique labels are allowed per CSV row. Sample rows: TRAIN,"They have bad food and very rude",RudeService,BadFood gs://folder/content.txt,SlowService TEST,gs://folder/document.pdf VALIDATE,gs://folder/text_files.zip,BadFood </section><section><h5>Sentiment Analysis</h5> See Preparing your training data for more information. CSV file(s) with each line in format: ML_USE,(TEXT_SNIPPET | GCS_FILE_PATH),SENTIMENT
ML_USE - Identifies the data set that the current row (file) applies to. This value can be one of the following:
- TRAIN - Rows in this file are used to train the model.
- TEST - Rows in this file are used to test the model during training.
- UNASSIGNED - Rows in this file are not categorized. They are Automatically divided into train and test data. 80% for training and 20% for testing.
TEXT_SNIPPET and GCS_FILE_PATH are distinguished by a pattern. If the column content is a valid Google Cloud Storage file path, that is, prefixed by "gs://", it is treated as a GCS_FILE_PATH. Otherwise, if the content is enclosed in double quotes (""), it is treated as a TEXT_SNIPPET. For GCS_FILE_PATH, the path must lead to a file with supported extension and UTF-8 encoding, for example, "gs://folder/content.txt" AutoML imports the file content as a text snippet. For TEXT_SNIPPET, AutoML imports the column content excluding quotes. In both cases, size of the content must be 128kB or less in size. For zip files, the size of each file inside the zip must be 128kB or less in size. The ML_USE and SENTIMENT columns are optional. Supported file extensions: .TXT, .PDF, .TIF, .TIFF, .ZIP
SENTIMENT - An integer between 0 and Dataset.text_sentiment_dataset_metadata.sentiment_max (inclusive). Describes the ordinal of the sentiment - higher value means a more positive sentiment. All the values are completely relative, i.e. neither 0 needs to mean a negative or neutral sentiment nor sentiment_max needs to mean a positive one - it is just required that 0 is the least positive sentiment in the data, and sentiment_max is the most positive one. The SENTIMENT shouldn't be confused with "score" or "magnitude" from the previous Natural Language Sentiment Analysis API. All SENTIMENT values between 0 and sentiment_max must be represented in the imported data. On prediction the same 0 to sentiment_max range will be used. The difference between neighboring sentiment values needs not to be uniform, e.g. 1 and 2 may be similar whereas the difference between 2 and 3 may be large. Sample rows: TRAIN,"@freewrytin this is way too good for your product",2 gs://folder/content.txt,3 TEST,gs://folder/document.pdf VALIDATE,gs://folder/text_files.zip,2 </section> </div> <h4>AutoML Tables</h4><div class="ui-datasection-main"><section class="selected"> See Preparing your training data for more information. You can use either gcs_source or bigquery_source. All input is concatenated into a single primary_table_spec_id For gcs_source: CSV file(s), where the first row of the first file is the header, containing unique column names. If the first row of a subsequent file is the same as the header, then it is also treated as a header. All other rows contain values for the corresponding columns. Each .CSV file by itself must be 10GB or smaller, and their total size must be 100GB or smaller. First three sample rows of a CSV file: <pre> "Id","First Name","Last Name","Dob","Addresses" "1","John","Doe","1968-01-22","[{"status":"current","address":"123_First_Avenue","city":"Seattle","state":"WA","zip":"11111","numberOfYears":"1"},{"status":"previous","address":"456_Main_Street","city":"Portland","state":"OR","zip":"22222","numberOfYears":"5"}]" "2","Jane","Doe","1980-10-16","[{"status":"current","address":"789_Any_Avenue","city":"Albany","state":"NY","zip":"33333","numberOfYears":"2"},{"status":"previous","address":"321_Main_Street","city":"Hoboken","state":"NJ","zip":"44444","numberOfYears":"3"}]} </pre> For bigquery_source: An URI of a BigQuery table. The user data size of the BigQuery table must be 100GB or smaller. An imported table must have between 2 and 1,000 columns, inclusive, and between 1000 and 100,000,000 rows, inclusive. There are at most 5 import data running in parallel. </section> </div> Input field definitions: ML_USE : ("TRAIN" | "VALIDATE" | "TEST" | "UNASSIGNED") Describes how the given example (file) should be used for model training. "UNASSIGNED" can be used when user has no preference. GCS_FILE_PATH : The path to a file on Google Cloud Storage. For example, "gs://folder/image1.png". LABEL : A display name of an object on an image, video etc., e.g. "dog". Must be up to 32 characters long and can consist only of ASCII Latin letters A-Z and a-z, underscores(_), and ASCII digits 0-9. For each label an AnnotationSpec is created which display_name becomes the label; AnnotationSpecs are given back in predictions. INSTANCE_ID : A positive integer that identifies a specific instance of a labeled entity on an example. Used e.g. to track two cars on a video while being able to tell apart which one is which. BOUNDING_BOX : (VERTEX,VERTEX,VERTEX,VERTEX | VERTEX,,,VERTEX,,) A rectangle parallel to the frame of the example (image, video). If 4 vertices are given they are connected by edges in the order provided, if 2 are given they are recognized as diagonally opposite vertices of the rectangle. VERTEX : (COORDINATE,COORDINATE) First coordinate is horizontal (x), the second is vertical (y). COORDINATE : A float in 0 to 1 range, relative to total length of image or video in given dimension. For fractions the leading non-decimal 0 can be omitted (i.e. 0.3 = .3). Point 0,0 is in top left. TIME_SEGMENT_START : (TIME_OFFSET) Expresses a beginning, inclusive, of a time segment within an example that has a time dimension (e.g. video). TIME_SEGMENT_END : (TIME_OFFSET) Expresses an end, exclusive, of a time segment within n example that has a time dimension (e.g. video). TIME_OFFSET : A number of seconds as measured from the start of an example (e.g. video). Fractions are allowed, up to a microsecond precision. "inf" is allowed, and it means the end of the example. TEXT_SNIPPET : The content of a text snippet, UTF-8 encoded, enclosed within double quotes (""). DOCUMENT : A field that provides the textual content with document and the layout information. Errors: If any of the provided CSV files can't be parsed or if more than certain percent of CSV rows cannot be processed then the operation fails and nothing is imported. Regardless of overall success or failure the per-row failures, up to a certain count cap, is listed in Operation.metadata.partial_failures.

Protobuf type google.cloud.automl.v1.InputConfig

Io

ListDatasetsRequest

Request message for AutoMl.ListDatasets.

Protobuf type google.cloud.automl.v1.ListDatasetsRequest

ListDatasetsRequest.Builder

Request message for AutoMl.ListDatasets.

Protobuf type google.cloud.automl.v1.ListDatasetsRequest

ListDatasetsResponse

Response message for AutoMl.ListDatasets.

Protobuf type google.cloud.automl.v1.ListDatasetsResponse

ListDatasetsResponse.Builder

Response message for AutoMl.ListDatasets.

Protobuf type google.cloud.automl.v1.ListDatasetsResponse

ListModelEvaluationsRequest

Request message for AutoMl.ListModelEvaluations.

Protobuf type google.cloud.automl.v1.ListModelEvaluationsRequest

ListModelEvaluationsRequest.Builder

Request message for AutoMl.ListModelEvaluations.

Protobuf type google.cloud.automl.v1.ListModelEvaluationsRequest

ListModelEvaluationsResponse

Response message for AutoMl.ListModelEvaluations.

Protobuf type google.cloud.automl.v1.ListModelEvaluationsResponse

ListModelEvaluationsResponse.Builder

Response message for AutoMl.ListModelEvaluations.

Protobuf type google.cloud.automl.v1.ListModelEvaluationsResponse

ListModelsRequest

Request message for AutoMl.ListModels.

Protobuf type google.cloud.automl.v1.ListModelsRequest

ListModelsRequest.Builder

Request message for AutoMl.ListModels.

Protobuf type google.cloud.automl.v1.ListModelsRequest

ListModelsResponse

Response message for AutoMl.ListModels.

Protobuf type google.cloud.automl.v1.ListModelsResponse

ListModelsResponse.Builder

Response message for AutoMl.ListModels.

Protobuf type google.cloud.automl.v1.ListModelsResponse

LocationName

LocationName.Builder

Builder for projects/{project}/locations/{location}.

Model

API proto representing a trained machine learning model.

Protobuf type google.cloud.automl.v1.Model

Model.Builder

API proto representing a trained machine learning model.

Protobuf type google.cloud.automl.v1.Model

ModelEvaluation

Evaluation results of a model.

Protobuf type google.cloud.automl.v1.ModelEvaluation

ModelEvaluation.Builder

Evaluation results of a model.

Protobuf type google.cloud.automl.v1.ModelEvaluation

ModelEvaluationName

ModelEvaluationName.Builder

Builder for projects/{project}/locations/{location}/models/{model}/modelEvaluations/{model_evaluation}.

ModelEvaluationOuterClass

ModelExportOutputConfig

Output configuration for ModelExport Action.

Protobuf type google.cloud.automl.v1.ModelExportOutputConfig

ModelExportOutputConfig.Builder

Output configuration for ModelExport Action.

Protobuf type google.cloud.automl.v1.ModelExportOutputConfig

ModelName

ModelName.Builder

Builder for projects/{project}/locations/{location}/models/{model}.

ModelOuterClass

NormalizedVertex

A vertex represents a 2D point in the image. The normalized vertex coordinates are between 0 to 1 fractions relative to the original plane (image, video). E.g. if the plane (e.g. whole image) would have size 10 x 20 then a point with normalized coordinates (0.1, 0.3) would be at the position (1, 6) on that plane.

Protobuf type google.cloud.automl.v1.NormalizedVertex

NormalizedVertex.Builder

Protobuf type google.cloud.automl.v1.NormalizedVertex

OperationMetadata

Metadata used across all long running operations returned by AutoML API.

Protobuf type google.cloud.automl.v1.OperationMetadata

OperationMetadata.Builder

Metadata used across all long running operations returned by AutoML API.

Protobuf type google.cloud.automl.v1.OperationMetadata

Operations

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: gcs_destination 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: bigquery_destination 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 <automl-dataset-display-name> 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.

Protobuf type google.cloud.automl.v1.OutputConfig

OutputConfig.Builder

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: gcs_destination 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: bigquery_destination 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 <automl-dataset-display-name> 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.

Protobuf type google.cloud.automl.v1.OutputConfig

PredictRequest

Request message for PredictionService.Predict.

Protobuf type google.cloud.automl.v1.PredictRequest

PredictRequest.Builder

Request message for PredictionService.Predict.

Protobuf type google.cloud.automl.v1.PredictRequest

PredictResponse

Response message for PredictionService.Predict.

Protobuf type google.cloud.automl.v1.PredictResponse

PredictResponse.Builder

Response message for PredictionService.Predict.

Protobuf type google.cloud.automl.v1.PredictResponse

PredictionServiceClient

Service Description: AutoML Prediction API.

On any input that is documented to expect a string parameter in snake_case or dash-case, either of those cases is accepted.

This class provides the ability to make remote calls to the backing service through method calls that map to API methods. Sample code to get started:


 try (PredictionServiceClient predictionServiceClient = PredictionServiceClient.create()) {
   ModelName name = ModelName.of("[PROJECT]", "[LOCATION]", "[MODEL]");
   ExamplePayload payload = ExamplePayload.newBuilder().build();
   Map<String, String> params = new HashMap<>();
   PredictResponse response = predictionServiceClient.predict(name, payload, params);
 }

Note: close() needs to be called on the PredictionServiceClient object to clean up resources such as threads. In the example above, try-with-resources is used, which automatically calls close().

The surface of this class includes several types of Java methods for each of the API's methods:

A "flattened" method. With this type of method, the fields of the request type have been converted into function parameters. It may be the case that not all fields are available as parameters, and not every API method will have a flattened method entry point.
A "request object" method. This type of method only takes one parameter, a request object, which must be constructed before the call. Not every API method will have a request object method.
A "callable" method. This type of method takes no parameters and returns an immutable API callable object, which can be used to initiate calls to the service.

See the individual methods for example code.

This class can be customized by passing in a custom instance of PredictionServiceSettings to create(). For example:

To customize credentials:


 PredictionServiceSettings predictionServiceSettings =
     PredictionServiceSettings.newBuilder()
         .setCredentialsProvider(FixedCredentialsProvider.create(myCredentials))
         .build();
 PredictionServiceClient predictionServiceClient =
     PredictionServiceClient.create(predictionServiceSettings);

To customize the endpoint:


 PredictionServiceSettings predictionServiceSettings =
     PredictionServiceSettings.newBuilder().setEndpoint(myEndpoint).build();
 PredictionServiceClient predictionServiceClient =
     PredictionServiceClient.create(predictionServiceSettings);

Please refer to the GitHub repository's samples for more quickstart code snippets.

The default service address (automl.googleapis.com) and default port (443) are used.
Credentials are acquired automatically through Application Default Credentials.
Retries are configured for idempotent methods but not for non-idempotent methods.

The builder of this class is recursive, so contained classes are themselves builders. When build() is called, the tree of builders is called to create the complete settings object.

For example, to set the total timeout of predict to 30 seconds:


 PredictionServiceSettings.Builder predictionServiceSettingsBuilder =
     PredictionServiceSettings.newBuilder();
 predictionServiceSettingsBuilder
     .predictSettings()
     .setRetrySettings(
         predictionServiceSettingsBuilder
             .predictSettings()
             .getRetrySettings()
             .toBuilder()
             .setTotalTimeout(Duration.ofSeconds(30))
             .build());
 PredictionServiceSettings predictionServiceSettings = predictionServiceSettingsBuilder.build();