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A client to Cloud AutoML API
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 kebab-case, either of those cases is accepted.
Sample for PredictionServiceClient:
// This snippet has been automatically generated and should be regarded as a code template only.
// It will require modifications to work:
// - It may require correct/in-range values for request initialization.
// - It may require specifying regional endpoints when creating the service client as shown in
// https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
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 kebab-case, either of those cases is accepted.
Sample for AutoMlClient:
// This snippet has been automatically generated and should be regarded as a code template only.
// It will require modifications to work:
// - It may require correct/in-range values for request initialization.
// - It may require specifying regional endpoints when creating the service client as shown in
// https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
try (AutoMlClient autoMlClient = AutoMlClient.create()) {
LocationName parent = LocationName.of("[PROJECT]", "[LOCATION]");
Dataset dataset = Dataset.newBuilder().build();
Dataset response = autoMlClient.createDataset(parent, dataset);
}
Classes
AnnotationPayload
Contains annotation information that is relevant to AutoML.
Protobuf type google.cloud.automl.v1beta1.AnnotationPayload
AnnotationPayload.Builder
Contains annotation information that is relevant to AutoML.
Protobuf type google.cloud.automl.v1beta1.AnnotationPayload
AnnotationPayloadOuterClass
AnnotationSpec
A definition of an annotation spec.
Protobuf type google.cloud.automl.v1beta1.AnnotationSpec
AnnotationSpec.Builder
A definition of an annotation spec.
Protobuf type google.cloud.automl.v1beta1.AnnotationSpec
AnnotationSpecName
AnnotationSpecName.Builder
Builder for projects/{project}/locations/{location}/datasets/{dataset}/annotationSpecs/{annotation_spec}.
AnnotationSpecOuterClass
ArrayStats
The data statistics of a series of ARRAY values.
Protobuf type google.cloud.automl.v1beta1.ArrayStats
ArrayStats.Builder
The data statistics of a series of ARRAY values.
Protobuf type google.cloud.automl.v1beta1.ArrayStats
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 kebab-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:
// This snippet has been automatically generated and should be regarded as a code template only.
// It will require modifications to work:
// - It may require correct/in-range values for request initialization.
// - It may require specifying regional endpoints when creating the service client as shown in
// https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
try (AutoMlClient autoMlClient = AutoMlClient.create()) {
LocationName parent = LocationName.of("[PROJECT]", "[LOCATION]");
Dataset dataset = Dataset.newBuilder().build();
Dataset response = autoMlClient.createDataset(parent, dataset);
}
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:
// This snippet has been automatically generated and should be regarded as a code template only.
// It will require modifications to work:
// - It may require correct/in-range values for request initialization.
// - It may require specifying regional endpoints when creating the service client as shown in
// https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
AutoMlSettings autoMlSettings =
AutoMlSettings.newBuilder()
.setCredentialsProvider(FixedCredentialsProvider.create(myCredentials))
.build();
AutoMlClient autoMlClient = AutoMlClient.create(autoMlSettings);
To customize the endpoint:
// This snippet has been automatically generated and should be regarded as a code template only.
// It will require modifications to work:
// - It may require correct/in-range values for request initialization.
// - It may require specifying regional endpoints when creating the service client as shown in
// https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
AutoMlSettings autoMlSettings = AutoMlSettings.newBuilder().setEndpoint(myEndpoint).build();
AutoMlClient autoMlClient = AutoMlClient.create(autoMlSettings);
To use REST (HTTP1.1/JSON) transport (instead of gRPC) for sending and receiving requests over the wire:
// This snippet has been automatically generated and should be regarded as a code template only.
// It will require modifications to work:
// - It may require correct/in-range values for request initialization.
// - It may require specifying regional endpoints when creating the service client as shown in
// https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
AutoMlSettings autoMlSettings = AutoMlSettings.newHttpJsonBuilder().build();
AutoMlClient autoMlClient = AutoMlClient.create(autoMlSettings);
Please refer to the GitHub repository's samples for more quickstart code snippets.
AutoMlClient.ListColumnSpecsFixedSizeCollection
AutoMlClient.ListColumnSpecsPage
AutoMlClient.ListColumnSpecsPagedResponse
AutoMlClient.ListDatasetsFixedSizeCollection
AutoMlClient.ListDatasetsPage
AutoMlClient.ListDatasetsPagedResponse
AutoMlClient.ListModelEvaluationsFixedSizeCollection
AutoMlClient.ListModelEvaluationsPage
AutoMlClient.ListModelEvaluationsPagedResponse
AutoMlClient.ListModelsFixedSizeCollection
AutoMlClient.ListModelsPage
AutoMlClient.ListModelsPagedResponse
AutoMlClient.ListTableSpecsFixedSizeCollection
AutoMlClient.ListTableSpecsPage
AutoMlClient.ListTableSpecsPagedResponse
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 kebab-case, either of those cases is accepted.
AutoMlGrpc.AutoMlBlockingStub
A stub to allow clients to do synchronous rpc calls to service AutoMl.
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 kebab-case, either of those cases is accepted.
AutoMlGrpc.AutoMlFutureStub
A stub to allow clients to do ListenableFuture-style rpc calls to service AutoMl.
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 kebab-case, either of those cases is accepted.
AutoMlGrpc.AutoMlImplBase
Base class for the server implementation of the service AutoMl.
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 kebab-case, either of those cases is accepted.
AutoMlGrpc.AutoMlStub
A stub to allow clients to do asynchronous rpc calls to service AutoMl.
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 kebab-case, either of those cases is accepted.
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 createDataset to 30 seconds:
// This snippet has been automatically generated and should be regarded as a code template only.
// It will require modifications to work:
// - It may require correct/in-range values for request initialization.
// - It may require specifying regional endpoints when creating the service client as shown in
// https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
AutoMlSettings.Builder autoMlSettingsBuilder = AutoMlSettings.newBuilder();
autoMlSettingsBuilder
.createDatasetSettings()
.setRetrySettings(
autoMlSettingsBuilder
.createDatasetSettings()
.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:
For Image Classification: CSV file(s) with each line having just a single column: GCS_FILE_PATH which leads to image of up to 30MB in size. Supported extensions: .JPEG, .GIF, .PNG. This path is treated as the ID in the Batch predict output. Three sample rows: gs://folder/image1.jpeg gs://folder/image2.gif gs://folder/image3.png
For Image Object Detection: CSV file(s) with each line having just a single column: GCS_FILE_PATH which leads to image of up to 30MB in size. Supported extensions: .JPEG, .GIF, .PNG. This path is treated as the ID in the Batch predict output. Three sample rows: gs://folder/image1.jpeg gs://folder/image2.gif gs://folder/image3.png
For Video Classification: CSV file(s) with each line in format: GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END GCS_FILE_PATH leads to 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 end has to be after the start. Three sample rows: gs://folder/video1.mp4,10,40 gs://folder/video1.mp4,20,60 gs://folder/vid2.mov,0,inf
For Video Object Tracking: CSV file(s) with each line in format: GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END GCS_FILE_PATH leads to 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 end has to be after the start. Three sample rows: gs://folder/video1.mp4,10,240 gs://folder/video1.mp4,300,360 gs://folder/vid2.mov,0,inf
For Text Classification: CSV file(s) with each line having just a single column: GCS_FILE_PATH | TEXT_SNIPPET Any given text file can have size upto 128kB. Any given text snippet content must have 60,000 characters or less. Three sample rows: gs://folder/text1.txt "Some text content to predict" gs://folder/text3.pdf Supported file extensions: .txt, .pdf
For Text Sentiment: CSV file(s) with each line having just a single column: GCS_FILE_PATH | TEXT_SNIPPET Any given text file can have size upto 128kB. Any given text snippet content must have 500 characters or less. Three sample rows: gs://folder/text1.txt "Some text content to predict" gs://folder/text3.pdf Supported file extensions: .txt, .pdf
For Text Extraction .JSONL (i.e. JSON Lines) file(s) which either provide text in-line or as documents (for a single BatchPredict call only one of the these formats may be used). The in-line .JSONL file(s) contain 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. The document .JSONL file(s) contain, per line, a proto that wraps a Document proto with input_config set. Only PDF documents are supported now, and each document must be up to 2MB large. Any given .JSONL file must be 100MB or smaller, and no more than 20 files may be given. Sample in-line JSON Lines file (presented here with artificial line breaks, but the only actual line break is 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": "An elaborate content", "mime_type": "text/plain" } } Sample document JSON Lines file (presented here with artificial line breaks, but the only actual line break is 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.pdf" ] } } } }
For Tables: Either gcs_source or
bigquery_source. GCS case: 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. For FORECASTING prediction_type: all columns having TIME_SERIES_AVAILABLE_PAST_ONLY type will be ignored. First three sample rows of a CSV file: "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"}]} BigQuery case: An 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. For FORECASTING
prediction_type: all columns having
TIME_SERIES_AVAILABLE_PAST_ONLY type will be ignored.
Definitions: GCS_FILE_PATH = A path to file on GCS, e.g. "gs://folder/video.avi". TEXT_SNIPPET = A content of a text snippet, UTF-8 encoded, enclosed within double quotes ("") 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 an 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.v1beta1.BatchPredictInputConfig
BatchPredictInputConfig.Builder
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:
For Image Classification: CSV file(s) with each line having just a single column: GCS_FILE_PATH which leads to image of up to 30MB in size. Supported extensions: .JPEG, .GIF, .PNG. This path is treated as the ID in the Batch predict output. Three sample rows: gs://folder/image1.jpeg gs://folder/image2.gif gs://folder/image3.png
For Image Object Detection: CSV file(s) with each line having just a single column: GCS_FILE_PATH which leads to image of up to 30MB in size. Supported extensions: .JPEG, .GIF, .PNG. This path is treated as the ID in the Batch predict output. Three sample rows: gs://folder/image1.jpeg gs://folder/image2.gif gs://folder/image3.png
For Video Classification: CSV file(s) with each line in format: GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END GCS_FILE_PATH leads to 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 end has to be after the start. Three sample rows: gs://folder/video1.mp4,10,40 gs://folder/video1.mp4,20,60 gs://folder/vid2.mov,0,inf
For Video Object Tracking: CSV file(s) with each line in format: GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END GCS_FILE_PATH leads to 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 end has to be after the start. Three sample rows: gs://folder/video1.mp4,10,240 gs://folder/video1.mp4,300,360 gs://folder/vid2.mov,0,inf
For Text Classification: CSV file(s) with each line having just a single column: GCS_FILE_PATH | TEXT_SNIPPET Any given text file can have size upto 128kB. Any given text snippet content must have 60,000 characters or less. Three sample rows: gs://folder/text1.txt "Some text content to predict" gs://folder/text3.pdf Supported file extensions: .txt, .pdf
For Text Sentiment: CSV file(s) with each line having just a single column: GCS_FILE_PATH | TEXT_SNIPPET Any given text file can have size upto 128kB. Any given text snippet content must have 500 characters or less. Three sample rows: gs://folder/text1.txt "Some text content to predict" gs://folder/text3.pdf Supported file extensions: .txt, .pdf
For Text Extraction .JSONL (i.e. JSON Lines) file(s) which either provide text in-line or as documents (for a single BatchPredict call only one of the these formats may be used). The in-line .JSONL file(s) contain 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. The document .JSONL file(s) contain, per line, a proto that wraps a Document proto with input_config set. Only PDF documents are supported now, and each document must be up to 2MB large. Any given .JSONL file must be 100MB or smaller, and no more than 20 files may be given. Sample in-line JSON Lines file (presented here with artificial line breaks, but the only actual line break is 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": "An elaborate content", "mime_type": "text/plain" } } Sample document JSON Lines file (presented here with artificial line breaks, but the only actual line break is 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.pdf" ] } } } }
For Tables: Either gcs_source or
bigquery_source. GCS case: 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. For FORECASTING prediction_type: all columns having TIME_SERIES_AVAILABLE_PAST_ONLY type will be ignored. First three sample rows of a CSV file: "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"}]} BigQuery case: An 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. For FORECASTING
prediction_type: all columns having
TIME_SERIES_AVAILABLE_PAST_ONLY type will be ignored.
Definitions: GCS_FILE_PATH = A path to file on GCS, e.g. "gs://folder/video.avi". TEXT_SNIPPET = A content of a text snippet, UTF-8 encoded, enclosed within double quotes ("") 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 an 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.v1beta1.BatchPredictInputConfig
BatchPredictOperationMetadata
Details of BatchPredict operation.
Protobuf type google.cloud.automl.v1beta1.BatchPredictOperationMetadata
BatchPredictOperationMetadata.BatchPredictOutputInfo
Further describes this batch predict's output. Supplements
BatchPredictOutputConfig.
Protobuf type
google.cloud.automl.v1beta1.BatchPredictOperationMetadata.BatchPredictOutputInfo
BatchPredictOperationMetadata.BatchPredictOutputInfo.Builder
Further describes this batch predict's output. Supplements
BatchPredictOutputConfig.
Protobuf type
google.cloud.automl.v1beta1.BatchPredictOperationMetadata.BatchPredictOutputInfo
BatchPredictOperationMetadata.Builder
Details of BatchPredict operation.
Protobuf type google.cloud.automl.v1beta1.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 filesimage_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 message
fields.
* 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 message
fields.
* 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 snippet or input text file and a list of
zero or more AnnotationPayload protos (called annotations), which
have classification detail populated. A single text snippet or file
will be listed only once with all its annotations, and its
annotations will never be split across files.
If prediction for any text snippet or file 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 text snippet or input text 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 snippet or input text file and a list of
zero or more AnnotationPayload protos (called annotations), which
have text_sentiment detail populated. A single text snippet or file
will be listed only once with all its annotations, and its
annotations will never be split across files.
If prediction for any text snippet or file 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 text snippet or input text 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).
GCS 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.v1beta1.BatchPredictOutputConfig
BatchPredictOutputConfig.Builder
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 filesimage_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 message
fields.
* 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 message
fields.
* 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 snippet or input text file and a list of
zero or more AnnotationPayload protos (called annotations), which
have classification detail populated. A single text snippet or file
will be listed only once with all its annotations, and its
annotations will never be split across files.
If prediction for any text snippet or file 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 text snippet or input text 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 snippet or input text file and a list of
zero or more AnnotationPayload protos (called annotations), which
have text_sentiment detail populated. A single text snippet or file
will be listed only once with all its annotations, and its
annotations will never be split across files.
If prediction for any text snippet or file 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 text snippet or input text 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).
GCS 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.v1beta1.BatchPredictOutputConfig
BatchPredictRequest
Request message for PredictionService.BatchPredict.
Protobuf type google.cloud.automl.v1beta1.BatchPredictRequest
BatchPredictRequest.Builder
Request message for PredictionService.BatchPredict.
Protobuf type google.cloud.automl.v1beta1.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.v1beta1.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.v1beta1.BatchPredictResult
BigQueryDestination
The BigQuery location for the output content.
Protobuf type google.cloud.automl.v1beta1.BigQueryDestination
BigQueryDestination.Builder
The BigQuery location for the output content.
Protobuf type google.cloud.automl.v1beta1.BigQueryDestination
BigQuerySource
The BigQuery location for the input content.
Protobuf type google.cloud.automl.v1beta1.BigQuerySource
BigQuerySource.Builder
The BigQuery location for the input content.
Protobuf type google.cloud.automl.v1beta1.BigQuerySource
BoundingBoxMetricsEntry
Bounding box matching model metrics for a single intersection-over-union threshold and multiple label match confidence thresholds.
Protobuf type google.cloud.automl.v1beta1.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.v1beta1.BoundingBoxMetricsEntry
BoundingBoxMetricsEntry.ConfidenceMetricsEntry
Metrics for a single confidence threshold.
Protobuf type
google.cloud.automl.v1beta1.BoundingBoxMetricsEntry.ConfidenceMetricsEntry
BoundingBoxMetricsEntry.ConfidenceMetricsEntry.Builder
Metrics for a single confidence threshold.
Protobuf type
google.cloud.automl.v1beta1.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.v1beta1.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.v1beta1.BoundingPoly
CategoryStats
The data statistics of a series of CATEGORY values.
Protobuf type google.cloud.automl.v1beta1.CategoryStats
CategoryStats.Builder
The data statistics of a series of CATEGORY values.
Protobuf type google.cloud.automl.v1beta1.CategoryStats
CategoryStats.SingleCategoryStats
The statistics of a single CATEGORY value.
Protobuf type google.cloud.automl.v1beta1.CategoryStats.SingleCategoryStats
CategoryStats.SingleCategoryStats.Builder
The statistics of a single CATEGORY value.
Protobuf type google.cloud.automl.v1beta1.CategoryStats.SingleCategoryStats
ClassificationProto
ClassificationProto.ClassificationAnnotation
Contains annotation details specific to classification.
Protobuf type google.cloud.automl.v1beta1.ClassificationAnnotation
ClassificationProto.ClassificationAnnotation.Builder
Contains annotation details specific to classification.
Protobuf type google.cloud.automl.v1beta1.ClassificationAnnotation
ClassificationProto.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.v1beta1.ClassificationEvaluationMetrics
ClassificationProto.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.v1beta1.ClassificationEvaluationMetrics
ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry
Metrics for a single confidence threshold.
Protobuf type
google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry
ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder
Metrics for a single confidence threshold.
Protobuf type
google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry
ClassificationProto.ClassificationEvaluationMetrics.ConfusionMatrix
Confusion matrix of the model running the classification.
Protobuf type
google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix
ClassificationProto.ClassificationEvaluationMetrics.ConfusionMatrix.Builder
Confusion matrix of the model running the classification.
Protobuf type
google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix
ClassificationProto.ClassificationEvaluationMetrics.ConfusionMatrix.Row
Output only. A row in the confusion matrix.
Protobuf type
google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix.Row
ClassificationProto.ClassificationEvaluationMetrics.ConfusionMatrix.Row.Builder
Output only. A row in the confusion matrix.
Protobuf type
google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix.Row
ClassificationProto.VideoClassificationAnnotation
Contains annotation details specific to video classification.
Protobuf type google.cloud.automl.v1beta1.VideoClassificationAnnotation
ClassificationProto.VideoClassificationAnnotation.Builder
Contains annotation details specific to video classification.
Protobuf type google.cloud.automl.v1beta1.VideoClassificationAnnotation
ColumnSpec
A representation of a column in a relational table. When listing them, column specs are returned in the same order in which they were given on import . Used by:
- Tables
Protobuf type google.cloud.automl.v1beta1.ColumnSpec
ColumnSpec.Builder
A representation of a column in a relational table. When listing them, column specs are returned in the same order in which they were given on import . Used by:
- Tables
Protobuf type google.cloud.automl.v1beta1.ColumnSpec
ColumnSpec.CorrelatedColumn
Identifies the table's column, and its correlation with the column this ColumnSpec describes.
Protobuf type google.cloud.automl.v1beta1.ColumnSpec.CorrelatedColumn
ColumnSpec.CorrelatedColumn.Builder
Identifies the table's column, and its correlation with the column this ColumnSpec describes.
Protobuf type google.cloud.automl.v1beta1.ColumnSpec.CorrelatedColumn
ColumnSpecName
ColumnSpecName.Builder
Builder for projects/{project}/locations/{location}/datasets/{dataset}/tableSpecs/{table_spec}/columnSpecs/{column_spec}.
ColumnSpecOuterClass
CorrelationStats
A correlation statistics between two series of DataType values. The series may have differing DataType-s, but within a single series the DataType must be the same.
Protobuf type google.cloud.automl.v1beta1.CorrelationStats
CorrelationStats.Builder
A correlation statistics between two series of DataType values. The series may have differing DataType-s, but within a single series the DataType must be the same.
Protobuf type google.cloud.automl.v1beta1.CorrelationStats
CreateDatasetRequest
Request message for AutoMl.CreateDataset.
Protobuf type google.cloud.automl.v1beta1.CreateDatasetRequest
CreateDatasetRequest.Builder
Request message for AutoMl.CreateDataset.
Protobuf type google.cloud.automl.v1beta1.CreateDatasetRequest
CreateModelOperationMetadata
Details of CreateModel operation.
Protobuf type google.cloud.automl.v1beta1.CreateModelOperationMetadata
CreateModelOperationMetadata.Builder
Details of CreateModel operation.
Protobuf type google.cloud.automl.v1beta1.CreateModelOperationMetadata
CreateModelRequest
Request message for AutoMl.CreateModel.
Protobuf type google.cloud.automl.v1beta1.CreateModelRequest
CreateModelRequest.Builder
Request message for AutoMl.CreateModel.
Protobuf type google.cloud.automl.v1beta1.CreateModelRequest
DataItems
DataStats
The data statistics of a series of values that share the same DataType.
Protobuf type google.cloud.automl.v1beta1.DataStats
DataStats.Builder
The data statistics of a series of values that share the same DataType.
Protobuf type google.cloud.automl.v1beta1.DataStats
DataStatsOuterClass
DataType
Indicated the type of data that can be stored in a structured data entity (e.g. a table).
Protobuf type google.cloud.automl.v1beta1.DataType
DataType.Builder
Indicated the type of data that can be stored in a structured data entity (e.g. a table).
Protobuf type google.cloud.automl.v1beta1.DataType
DataTypes
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.v1beta1.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.v1beta1.Dataset
DatasetName
DatasetName.Builder
Builder for projects/{project}/locations/{location}/datasets/{dataset}.
DatasetOuterClass
DeleteDatasetRequest
Request message for AutoMl.DeleteDataset.
Protobuf type google.cloud.automl.v1beta1.DeleteDatasetRequest
DeleteDatasetRequest.Builder
Request message for AutoMl.DeleteDataset.
Protobuf type google.cloud.automl.v1beta1.DeleteDatasetRequest
DeleteModelRequest
Request message for AutoMl.DeleteModel.
Protobuf type google.cloud.automl.v1beta1.DeleteModelRequest
DeleteModelRequest.Builder
Request message for AutoMl.DeleteModel.
Protobuf type google.cloud.automl.v1beta1.DeleteModelRequest
DeleteOperationMetadata
Details of operations that perform deletes of any entities.
Protobuf type google.cloud.automl.v1beta1.DeleteOperationMetadata
DeleteOperationMetadata.Builder
Details of operations that perform deletes of any entities.
Protobuf type google.cloud.automl.v1beta1.DeleteOperationMetadata
DeployModelOperationMetadata
Details of DeployModel operation.
Protobuf type google.cloud.automl.v1beta1.DeployModelOperationMetadata
DeployModelOperationMetadata.Builder
Details of DeployModel operation.
Protobuf type google.cloud.automl.v1beta1.DeployModelOperationMetadata
DeployModelRequest
Request message for AutoMl.DeployModel.
Protobuf type google.cloud.automl.v1beta1.DeployModelRequest
DeployModelRequest.Builder
Request message for AutoMl.DeployModel.
Protobuf type google.cloud.automl.v1beta1.DeployModelRequest
Detection
Document
A structured text document e.g. a PDF.
Protobuf type google.cloud.automl.v1beta1.Document
Document.Builder
A structured text document e.g. a PDF.
Protobuf type google.cloud.automl.v1beta1.Document
Document.Layout
Describes the layout information of a text_segment in the document.
Protobuf type google.cloud.automl.v1beta1.Document.Layout
Document.Layout.Builder
Describes the layout information of a text_segment in the document.
Protobuf type google.cloud.automl.v1beta1.Document.Layout
DocumentDimensions
Message that describes dimension of a document.
Protobuf type google.cloud.automl.v1beta1.DocumentDimensions
DocumentDimensions.Builder
Message that describes dimension of a document.
Protobuf type google.cloud.automl.v1beta1.DocumentDimensions
DocumentInputConfig
Input configuration of a Document.
Protobuf type google.cloud.automl.v1beta1.DocumentInputConfig
DocumentInputConfig.Builder
Input configuration of a Document.
Protobuf type google.cloud.automl.v1beta1.DocumentInputConfig
DoubleRange
A range between two double numbers.
Protobuf type google.cloud.automl.v1beta1.DoubleRange
DoubleRange.Builder
A range between two double numbers.
Protobuf type google.cloud.automl.v1beta1.DoubleRange
ExamplePayload
Example data used for training or prediction.
Protobuf type google.cloud.automl.v1beta1.ExamplePayload
ExamplePayload.Builder
Example data used for training or prediction.
Protobuf type google.cloud.automl.v1beta1.ExamplePayload
ExportDataOperationMetadata
Details of ExportData operation.
Protobuf type google.cloud.automl.v1beta1.ExportDataOperationMetadata
ExportDataOperationMetadata.Builder
Details of ExportData operation.
Protobuf type google.cloud.automl.v1beta1.ExportDataOperationMetadata
ExportDataOperationMetadata.ExportDataOutputInfo
Further describes this export data's output. Supplements OutputConfig.
Protobuf type
google.cloud.automl.v1beta1.ExportDataOperationMetadata.ExportDataOutputInfo
ExportDataOperationMetadata.ExportDataOutputInfo.Builder
Further describes this export data's output. Supplements OutputConfig.
Protobuf type
google.cloud.automl.v1beta1.ExportDataOperationMetadata.ExportDataOutputInfo
ExportDataRequest
Request message for AutoMl.ExportData.
Protobuf type google.cloud.automl.v1beta1.ExportDataRequest
ExportDataRequest.Builder
Request message for AutoMl.ExportData.
Protobuf type google.cloud.automl.v1beta1.ExportDataRequest
ExportEvaluatedExamplesOperationMetadata
Details of EvaluatedExamples operation.
Protobuf type google.cloud.automl.v1beta1.ExportEvaluatedExamplesOperationMetadata
ExportEvaluatedExamplesOperationMetadata.Builder
Details of EvaluatedExamples operation.
Protobuf type google.cloud.automl.v1beta1.ExportEvaluatedExamplesOperationMetadata
ExportEvaluatedExamplesOperationMetadata.ExportEvaluatedExamplesOutputInfo
Further describes the output of the evaluated examples export. Supplements
ExportEvaluatedExamplesOutputConfig.
Protobuf type
google.cloud.automl.v1beta1.ExportEvaluatedExamplesOperationMetadata.ExportEvaluatedExamplesOutputInfo
ExportEvaluatedExamplesOperationMetadata.ExportEvaluatedExamplesOutputInfo.Builder
Further describes the output of the evaluated examples export. Supplements
ExportEvaluatedExamplesOutputConfig.
Protobuf type
google.cloud.automl.v1beta1.ExportEvaluatedExamplesOperationMetadata.ExportEvaluatedExamplesOutputInfo
ExportEvaluatedExamplesOutputConfig
Output configuration for ExportEvaluatedExamples Action. Note that this call is available only for 30 days since the moment the model was evaluated. The output depends on the domain, as follows (note that only examples from the TEST set are exported):
For Tables:
bigquery_destination pointing to a BigQuery project must be set. In the given project a new dataset will be created with name
export_evaluated_examples_<model-display-name>_<timestamp-of-export-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 anevaluated_examples
table will be created. It will have all the same columns as theprimary_table of the dataset from which the model was created, as they were at the moment of model's evaluation (this includes the target column with its ground truth), followed by a column called "predicted_<target_column>". That last column will contain the model's prediction result for each respective row, given as ARRAY of AnnotationPayloads, represented as STRUCT-s, containing TablesAnnotation.
Protobuf type google.cloud.automl.v1beta1.ExportEvaluatedExamplesOutputConfig
ExportEvaluatedExamplesOutputConfig.Builder
Output configuration for ExportEvaluatedExamples Action. Note that this call is available only for 30 days since the moment the model was evaluated. The output depends on the domain, as follows (note that only examples from the TEST set are exported):
For Tables:
bigquery_destination pointing to a BigQuery project must be set. In the given project a new dataset will be created with name
export_evaluated_examples_<model-display-name>_<timestamp-of-export-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 anevaluated_examples
table will be created. It will have all the same columns as theprimary_table of the dataset from which the model was created, as they were at the moment of model's evaluation (this includes the target column with its ground truth), followed by a column called "predicted_<target_column>". That last column will contain the model's prediction result for each respective row, given as ARRAY of AnnotationPayloads, represented as STRUCT-s, containing TablesAnnotation.
Protobuf type google.cloud.automl.v1beta1.ExportEvaluatedExamplesOutputConfig
ExportEvaluatedExamplesRequest
Request message for AutoMl.ExportEvaluatedExamples.
Protobuf type google.cloud.automl.v1beta1.ExportEvaluatedExamplesRequest
ExportEvaluatedExamplesRequest.Builder
Request message for AutoMl.ExportEvaluatedExamples.
Protobuf type google.cloud.automl.v1beta1.ExportEvaluatedExamplesRequest
ExportModelOperationMetadata
Details of ExportModel operation.
Protobuf type google.cloud.automl.v1beta1.ExportModelOperationMetadata
ExportModelOperationMetadata.Builder
Details of ExportModel operation.
Protobuf type google.cloud.automl.v1beta1.ExportModelOperationMetadata
ExportModelOperationMetadata.ExportModelOutputInfo
Further describes the output of model export. Supplements
ModelExportOutputConfig.
Protobuf type
google.cloud.automl.v1beta1.ExportModelOperationMetadata.ExportModelOutputInfo
ExportModelOperationMetadata.ExportModelOutputInfo.Builder
Further describes the output of model export. Supplements
ModelExportOutputConfig.
Protobuf type
google.cloud.automl.v1beta1.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.v1beta1.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.v1beta1.ExportModelRequest
Float64Stats
The data statistics of a series of FLOAT64 values.
Protobuf type google.cloud.automl.v1beta1.Float64Stats
Float64Stats.Builder
The data statistics of a series of FLOAT64 values.
Protobuf type google.cloud.automl.v1beta1.Float64Stats
Float64Stats.HistogramBucket
A bucket of a histogram.
Protobuf type google.cloud.automl.v1beta1.Float64Stats.HistogramBucket
Float64Stats.HistogramBucket.Builder
A bucket of a histogram.
Protobuf type google.cloud.automl.v1beta1.Float64Stats.HistogramBucket
GcrDestination
The GCR location where the image must be pushed to.
Protobuf type google.cloud.automl.v1beta1.GcrDestination
GcrDestination.Builder
The GCR location where the image must be pushed to.
Protobuf type google.cloud.automl.v1beta1.GcrDestination
GcsDestination
The Google Cloud Storage location where the output is to be written to.
Protobuf type google.cloud.automl.v1beta1.GcsDestination
GcsDestination.Builder
The Google Cloud Storage location where the output is to be written to.
Protobuf type google.cloud.automl.v1beta1.GcsDestination
GcsSource
The Google Cloud Storage location for the input content.
Protobuf type google.cloud.automl.v1beta1.GcsSource
GcsSource.Builder
The Google Cloud Storage location for the input content.
Protobuf type google.cloud.automl.v1beta1.GcsSource
Geometry
GetAnnotationSpecRequest
Request message for AutoMl.GetAnnotationSpec.
Protobuf type google.cloud.automl.v1beta1.GetAnnotationSpecRequest
GetAnnotationSpecRequest.Builder
Request message for AutoMl.GetAnnotationSpec.
Protobuf type google.cloud.automl.v1beta1.GetAnnotationSpecRequest
GetColumnSpecRequest
Request message for AutoMl.GetColumnSpec.
Protobuf type google.cloud.automl.v1beta1.GetColumnSpecRequest
GetColumnSpecRequest.Builder
Request message for AutoMl.GetColumnSpec.
Protobuf type google.cloud.automl.v1beta1.GetColumnSpecRequest
GetDatasetRequest
Request message for AutoMl.GetDataset.
Protobuf type google.cloud.automl.v1beta1.GetDatasetRequest
GetDatasetRequest.Builder
Request message for AutoMl.GetDataset.
Protobuf type google.cloud.automl.v1beta1.GetDatasetRequest
GetModelEvaluationRequest
Request message for AutoMl.GetModelEvaluation.
Protobuf type google.cloud.automl.v1beta1.GetModelEvaluationRequest
GetModelEvaluationRequest.Builder
Request message for AutoMl.GetModelEvaluation.
Protobuf type google.cloud.automl.v1beta1.GetModelEvaluationRequest
GetModelRequest
Request message for AutoMl.GetModel.
Protobuf type google.cloud.automl.v1beta1.GetModelRequest
GetModelRequest.Builder
Request message for AutoMl.GetModel.
Protobuf type google.cloud.automl.v1beta1.GetModelRequest
GetTableSpecRequest
Request message for AutoMl.GetTableSpec.
Protobuf type google.cloud.automl.v1beta1.GetTableSpecRequest
GetTableSpecRequest.Builder
Request message for AutoMl.GetTableSpec.
Protobuf type google.cloud.automl.v1beta1.GetTableSpecRequest
Image
A representation of an image. Only images up to 30MB in size are supported.
Protobuf type google.cloud.automl.v1beta1.Image
Image.Builder
A representation of an image. Only images up to 30MB in size are supported.
Protobuf type google.cloud.automl.v1beta1.Image
ImageClassificationDatasetMetadata
Dataset metadata that is specific to image classification.
Protobuf type google.cloud.automl.v1beta1.ImageClassificationDatasetMetadata
ImageClassificationDatasetMetadata.Builder
Dataset metadata that is specific to image classification.
Protobuf type google.cloud.automl.v1beta1.ImageClassificationDatasetMetadata
ImageClassificationModelDeploymentMetadata
Model deployment metadata specific to Image Classification.
Protobuf type google.cloud.automl.v1beta1.ImageClassificationModelDeploymentMetadata
ImageClassificationModelDeploymentMetadata.Builder
Model deployment metadata specific to Image Classification.
Protobuf type google.cloud.automl.v1beta1.ImageClassificationModelDeploymentMetadata
ImageClassificationModelMetadata
Model metadata for image classification.
Protobuf type google.cloud.automl.v1beta1.ImageClassificationModelMetadata
ImageClassificationModelMetadata.Builder
Model metadata for image classification.
Protobuf type google.cloud.automl.v1beta1.ImageClassificationModelMetadata
ImageObjectDetectionAnnotation
Annotation details for image object detection.
Protobuf type google.cloud.automl.v1beta1.ImageObjectDetectionAnnotation
ImageObjectDetectionAnnotation.Builder
Annotation details for image object detection.
Protobuf type google.cloud.automl.v1beta1.ImageObjectDetectionAnnotation
ImageObjectDetectionDatasetMetadata
Dataset metadata specific to image object detection.
Protobuf type google.cloud.automl.v1beta1.ImageObjectDetectionDatasetMetadata
ImageObjectDetectionDatasetMetadata.Builder
Dataset metadata specific to image object detection.
Protobuf type google.cloud.automl.v1beta1.ImageObjectDetectionDatasetMetadata
ImageObjectDetectionEvaluationMetrics
Model evaluation metrics for image object detection problems. Evaluates prediction quality of labeled bounding boxes.
Protobuf type google.cloud.automl.v1beta1.ImageObjectDetectionEvaluationMetrics
ImageObjectDetectionEvaluationMetrics.Builder
Model evaluation metrics for image object detection problems. Evaluates prediction quality of labeled bounding boxes.
Protobuf type google.cloud.automl.v1beta1.ImageObjectDetectionEvaluationMetrics
ImageObjectDetectionModelDeploymentMetadata
Model deployment metadata specific to Image Object Detection.
Protobuf type google.cloud.automl.v1beta1.ImageObjectDetectionModelDeploymentMetadata
ImageObjectDetectionModelDeploymentMetadata.Builder
Model deployment metadata specific to Image Object Detection.
Protobuf type google.cloud.automl.v1beta1.ImageObjectDetectionModelDeploymentMetadata
ImageObjectDetectionModelMetadata
Model metadata specific to image object detection.
Protobuf type google.cloud.automl.v1beta1.ImageObjectDetectionModelMetadata
ImageObjectDetectionModelMetadata.Builder
Model metadata specific to image object detection.
Protobuf type google.cloud.automl.v1beta1.ImageObjectDetectionModelMetadata
ImageProto
ImportDataOperationMetadata
Details of ImportData operation.
Protobuf type google.cloud.automl.v1beta1.ImportDataOperationMetadata
ImportDataOperationMetadata.Builder
Details of ImportData operation.
Protobuf type google.cloud.automl.v1beta1.ImportDataOperationMetadata
ImportDataRequest
Request message for AutoMl.ImportData.
Protobuf type google.cloud.automl.v1beta1.ImportDataRequest
ImportDataRequest.Builder
Request message for AutoMl.ImportData.
Protobuf type google.cloud.automl.v1beta1.ImportDataRequest
InputConfig
Input configuration for 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:
For Image Classification: CSV file(s) with each line in format: ML_USE,GCS_FILE_PATH,LABEL,LABEL,... GCS_FILE_PATH leads to image of up to 30MB in size. Supported extensions: .JPEG, .GIF, .PNG, .WEBP, .BMP, .TIFF, .ICO For 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
For Image Object Detection: CSV file(s) with each line in format: ML_USE,GCS_FILE_PATH,(LABEL,BOUNDING_BOX | ,,,,,,,) GCS_FILE_PATH leads to image of up to 30MB in size. Supported extensions: .JPEG, .GIF, .PNG. Each image is assumed to be exhaustively labeled. The minimum allowed BOUNDING_BOX edge length is 0.01, and no more than 500 BOUNDING_BOX-es per image are allowed (one BOUNDING_BOX is defined per line). If an image has not yet been labeled, then it should be mentioned just once with no LABEL and the ",,,,,,," in place of the BOUNDING_BOX. For images which are known to not contain any bounding boxes, they should be labelled explictly as "NEGATIVE_IMAGE", followed by ",,,,,,," in place of the BOUNDING_BOX. 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,,,,,,,,, TRAIN,gs://folder/im4.png,NEGATIVE_IMAGE,,,,,,,,,
For Video Classification: CSV file(s) with each line in format: ML_USE,GCS_FILE_PATH where ML_USE VALIDATE value should not be used. The GCS_FILE_PATH should lead to another .csv file which describes examples that have 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 end has to be after the start. 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 shuold 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,,,
For Video Object Tracking: CSV file(s) with each line in format: ML_USE,GCS_FILE_PATH where ML_USE VALIDATE value should not be used. The GCS_FILE_PATH should lead to another .csv file which describes examples that have given ML_USE, using one of 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,,,,,,,,,,,
For Text Extraction: CSV file(s) with each line in format: ML_USE,GCS_FILE_PATH GCS_FILE_PATH leads to a .JSONL (that is, JSON Lines) file which either imports text in-line or as documents. Any given .JSONL file must be 100MB or smaller. The in-line .JSONL file contains, per line, a proto that wraps a TextSnippet proto (in json representation) followed by one or more AnnotationPayload protos (called annotations), which have display_name and text_extraction detail populated. The given text is expected to be annotated exhaustively, for example, if you look for animals and text contains "dolphin" that is not labeled, then "dolphin" is assumed to not be an animal. Any given text snippet content must be 10KB or smaller, and also be UTF-8 NFC encoded (ASCII already is). The document .JSONL file contains, per line, a proto that wraps a Document proto. The Document proto must have either document_text or input_config set. In document_text case, the Document proto may also contain the spatial information of the document, including layout, document dimension and page number. In input_config case, only PDF documents are supported now, and each document may be up to 2MB large. Currently, annotations on documents cannot be specified at import. Three sample CSV rows: TRAIN,gs://folder/file1.jsonl VALIDATE,gs://folder/file2.jsonl TEST,gs://folder/file3.jsonl Sample in-line JSON Lines file for entity extraction (presented here with artificial line breaks, but the only actual line break is denoted by \n).: { "document": { "document_text": {"content": "dog cat"} "layout": [ { "text_segment": { "start_offset": 0, "end_offset": 3, }, "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, }, { "text_segment": { "start_offset": 4, "end_offset": 7, }, "page_number": 1, "bounding_poly": { "normalized_vertices": [ {"x": 0.4, "y": 0.1}, {"x": 0.4, "y": 0.3}, {"x": 0.8, "y": 0.3}, {"x": 0.8, "y": 0.1}, ], }, "text_segment_type": TOKEN, }
], "document_dimensions": { "width": 8.27, "height": 11.69, "unit": INCH, } "page_count": 1, }, "annotations": [ { "display_name": "animal", "text_extraction": {"text_segment": {"start_offset": 0, "end_offset": 3}} }, { "display_name": "animal", "text_extraction": {"text_segment": {"start_offset": 4, "end_offset": 7}} } ], }\n { "text_snippet": { "content": "This dog is good." }, "annotations": [ { "display_name": "animal", "text_extraction": { "text_segment": {"start_offset": 5, "end_offset": 8} } } ] } Sample document JSON Lines file (presented here with artificial line breaks, but the only actual line break is 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.pdf" ] } } } }
For Text Classification: CSV file(s) with each line in format: ML_USE,(TEXT_SNIPPET | GCS_FILE_PATH),LABEL,LABEL,... TEXT_SNIPPET and GCS_FILE_PATH are distinguished by a pattern. If the column content is a valid gcs file path, i.e. prefixed by "gs://", it will be treated as a GCS_FILE_PATH, else if the content is enclosed within double quotes (""), it is treated as a TEXT_SNIPPET. In the GCS_FILE_PATH case, the path must lead to a .txt file with UTF-8 encoding, for example, "gs://folder/content.txt", and the content in it is extracted as a text snippet. In TEXT_SNIPPET case, the column content excluding quotes is treated as to be imported text snippet. In both cases, the text snippet/file size must be within 128kB. Maximum 100 unique labels are allowed per CSV row. Sample rows: TRAIN,"They have bad food and very rude",RudeService,BadFood TRAIN,gs://folder/content.txt,SlowService TEST,"Typically always bad service there.",RudeService VALIDATE,"Stomach ache to go.",BadFood
For Text Sentiment: CSV file(s) with each line in format: ML_USE,(TEXT_SNIPPET | GCS_FILE_PATH),SENTIMENT TEXT_SNIPPET and GCS_FILE_PATH are distinguished by a pattern. If the column content is a valid gcs file path, that is, prefixed by "gs://", it is treated as a GCS_FILE_PATH, otherwise it is treated as a TEXT_SNIPPET. In the GCS_FILE_PATH case, the path must lead to a .txt file with UTF-8 encoding, for example, "gs://folder/content.txt", and the content in it is extracted as a text snippet. In TEXT_SNIPPET case, the column content itself is treated as to be imported text snippet. In both cases, the text snippet must be up to 500 characters long. Sample rows: TRAIN,"@freewrytin this is way too good for your product",2 TRAIN,"I need this product so bad",3 TEST,"Thank you for this product.",4 VALIDATE,gs://folder/content.txt,2
- For Tables: Either gcs_source or
bigquery_source can be used. All inputs is concatenated into a single
primary_table 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: "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"}]} 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. 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 = A path to file on GCS, e.g. "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 an 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 = A content of a text snippet, UTF-8 encoded, enclosed within double quotes (""). 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 huge.
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.v1beta1.InputConfig
InputConfig.Builder
Input configuration for 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:
For Image Classification: CSV file(s) with each line in format: ML_USE,GCS_FILE_PATH,LABEL,LABEL,... GCS_FILE_PATH leads to image of up to 30MB in size. Supported extensions: .JPEG, .GIF, .PNG, .WEBP, .BMP, .TIFF, .ICO For 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
For Image Object Detection: CSV file(s) with each line in format: ML_USE,GCS_FILE_PATH,(LABEL,BOUNDING_BOX | ,,,,,,,) GCS_FILE_PATH leads to image of up to 30MB in size. Supported extensions: .JPEG, .GIF, .PNG. Each image is assumed to be exhaustively labeled. The minimum allowed BOUNDING_BOX edge length is 0.01, and no more than 500 BOUNDING_BOX-es per image are allowed (one BOUNDING_BOX is defined per line). If an image has not yet been labeled, then it should be mentioned just once with no LABEL and the ",,,,,,," in place of the BOUNDING_BOX. For images which are known to not contain any bounding boxes, they should be labelled explictly as "NEGATIVE_IMAGE", followed by ",,,,,,," in place of the BOUNDING_BOX. 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,,,,,,,,, TRAIN,gs://folder/im4.png,NEGATIVE_IMAGE,,,,,,,,,
For Video Classification: CSV file(s) with each line in format: ML_USE,GCS_FILE_PATH where ML_USE VALIDATE value should not be used. The GCS_FILE_PATH should lead to another .csv file which describes examples that have 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 end has to be after the start. 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 shuold 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,,,
For Video Object Tracking: CSV file(s) with each line in format: ML_USE,GCS_FILE_PATH where ML_USE VALIDATE value should not be used. The GCS_FILE_PATH should lead to another .csv file which describes examples that have given ML_USE, using one of 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,,,,,,,,,,,
For Text Extraction: CSV file(s) with each line in format: ML_USE,GCS_FILE_PATH GCS_FILE_PATH leads to a .JSONL (that is, JSON Lines) file which either imports text in-line or as documents. Any given .JSONL file must be 100MB or smaller. The in-line .JSONL file contains, per line, a proto that wraps a TextSnippet proto (in json representation) followed by one or more AnnotationPayload protos (called annotations), which have display_name and text_extraction detail populated. The given text is expected to be annotated exhaustively, for example, if you look for animals and text contains "dolphin" that is not labeled, then "dolphin" is assumed to not be an animal. Any given text snippet content must be 10KB or smaller, and also be UTF-8 NFC encoded (ASCII already is). The document .JSONL file contains, per line, a proto that wraps a Document proto. The Document proto must have either document_text or input_config set. In document_text case, the Document proto may also contain the spatial information of the document, including layout, document dimension and page number. In input_config case, only PDF documents are supported now, and each document may be up to 2MB large. Currently, annotations on documents cannot be specified at import. Three sample CSV rows: TRAIN,gs://folder/file1.jsonl VALIDATE,gs://folder/file2.jsonl TEST,gs://folder/file3.jsonl Sample in-line JSON Lines file for entity extraction (presented here with artificial line breaks, but the only actual line break is denoted by \n).: { "document": { "document_text": {"content": "dog cat"} "layout": [ { "text_segment": { "start_offset": 0, "end_offset": 3, }, "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, }, { "text_segment": { "start_offset": 4, "end_offset": 7, }, "page_number": 1, "bounding_poly": { "normalized_vertices": [ {"x": 0.4, "y": 0.1}, {"x": 0.4, "y": 0.3}, {"x": 0.8, "y": 0.3}, {"x": 0.8, "y": 0.1}, ], }, "text_segment_type": TOKEN, }
], "document_dimensions": { "width": 8.27, "height": 11.69, "unit": INCH, } "page_count": 1, }, "annotations": [ { "display_name": "animal", "text_extraction": {"text_segment": {"start_offset": 0, "end_offset": 3}} }, { "display_name": "animal", "text_extraction": {"text_segment": {"start_offset": 4, "end_offset": 7}} } ], }\n { "text_snippet": { "content": "This dog is good." }, "annotations": [ { "display_name": "animal", "text_extraction": { "text_segment": {"start_offset": 5, "end_offset": 8} } } ] } Sample document JSON Lines file (presented here with artificial line breaks, but the only actual line break is 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.pdf" ] } } } }
For Text Classification: CSV file(s) with each line in format: ML_USE,(TEXT_SNIPPET | GCS_FILE_PATH),LABEL,LABEL,... TEXT_SNIPPET and GCS_FILE_PATH are distinguished by a pattern. If the column content is a valid gcs file path, i.e. prefixed by "gs://", it will be treated as a GCS_FILE_PATH, else if the content is enclosed within double quotes (""), it is treated as a TEXT_SNIPPET. In the GCS_FILE_PATH case, the path must lead to a .txt file with UTF-8 encoding, for example, "gs://folder/content.txt", and the content in it is extracted as a text snippet. In TEXT_SNIPPET case, the column content excluding quotes is treated as to be imported text snippet. In both cases, the text snippet/file size must be within 128kB. Maximum 100 unique labels are allowed per CSV row. Sample rows: TRAIN,"They have bad food and very rude",RudeService,BadFood TRAIN,gs://folder/content.txt,SlowService TEST,"Typically always bad service there.",RudeService VALIDATE,"Stomach ache to go.",BadFood
For Text Sentiment: CSV file(s) with each line in format: ML_USE,(TEXT_SNIPPET | GCS_FILE_PATH),SENTIMENT TEXT_SNIPPET and GCS_FILE_PATH are distinguished by a pattern. If the column content is a valid gcs file path, that is, prefixed by "gs://", it is treated as a GCS_FILE_PATH, otherwise it is treated as a TEXT_SNIPPET. In the GCS_FILE_PATH case, the path must lead to a .txt file with UTF-8 encoding, for example, "gs://folder/content.txt", and the content in it is extracted as a text snippet. In TEXT_SNIPPET case, the column content itself is treated as to be imported text snippet. In both cases, the text snippet must be up to 500 characters long. Sample rows: TRAIN,"@freewrytin this is way too good for your product",2 TRAIN,"I need this product so bad",3 TEST,"Thank you for this product.",4 VALIDATE,gs://folder/content.txt,2
- For Tables: Either gcs_source or
bigquery_source can be used. All inputs is concatenated into a single
primary_table 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: "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"}]} 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. 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 = A path to file on GCS, e.g. "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 an 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 = A content of a text snippet, UTF-8 encoded, enclosed within double quotes (""). 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 huge.
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.v1beta1.InputConfig
Io
ListColumnSpecsRequest
Request message for AutoMl.ListColumnSpecs.
Protobuf type google.cloud.automl.v1beta1.ListColumnSpecsRequest
ListColumnSpecsRequest.Builder
Request message for AutoMl.ListColumnSpecs.
Protobuf type google.cloud.automl.v1beta1.ListColumnSpecsRequest
ListColumnSpecsResponse
Response message for AutoMl.ListColumnSpecs.
Protobuf type google.cloud.automl.v1beta1.ListColumnSpecsResponse
ListColumnSpecsResponse.Builder
Response message for AutoMl.ListColumnSpecs.
Protobuf type google.cloud.automl.v1beta1.ListColumnSpecsResponse
ListDatasetsRequest
Request message for AutoMl.ListDatasets.
Protobuf type google.cloud.automl.v1beta1.ListDatasetsRequest
ListDatasetsRequest.Builder
Request message for AutoMl.ListDatasets.
Protobuf type google.cloud.automl.v1beta1.ListDatasetsRequest
ListDatasetsResponse
Response message for AutoMl.ListDatasets.
Protobuf type google.cloud.automl.v1beta1.ListDatasetsResponse
ListDatasetsResponse.Builder
Response message for AutoMl.ListDatasets.
Protobuf type google.cloud.automl.v1beta1.ListDatasetsResponse
ListModelEvaluationsRequest
Request message for AutoMl.ListModelEvaluations.
Protobuf type google.cloud.automl.v1beta1.ListModelEvaluationsRequest
ListModelEvaluationsRequest.Builder
Request message for AutoMl.ListModelEvaluations.
Protobuf type google.cloud.automl.v1beta1.ListModelEvaluationsRequest
ListModelEvaluationsResponse
Response message for AutoMl.ListModelEvaluations.
Protobuf type google.cloud.automl.v1beta1.ListModelEvaluationsResponse
ListModelEvaluationsResponse.Builder
Response message for AutoMl.ListModelEvaluations.
Protobuf type google.cloud.automl.v1beta1.ListModelEvaluationsResponse
ListModelsRequest
Request message for AutoMl.ListModels.
Protobuf type google.cloud.automl.v1beta1.ListModelsRequest
ListModelsRequest.Builder
Request message for AutoMl.ListModels.
Protobuf type google.cloud.automl.v1beta1.ListModelsRequest
ListModelsResponse
Response message for AutoMl.ListModels.
Protobuf type google.cloud.automl.v1beta1.ListModelsResponse
ListModelsResponse.Builder
Response message for AutoMl.ListModels.
Protobuf type google.cloud.automl.v1beta1.ListModelsResponse
ListTableSpecsRequest
Request message for AutoMl.ListTableSpecs.
Protobuf type google.cloud.automl.v1beta1.ListTableSpecsRequest
ListTableSpecsRequest.Builder
Request message for AutoMl.ListTableSpecs.
Protobuf type google.cloud.automl.v1beta1.ListTableSpecsRequest
ListTableSpecsResponse
Response message for AutoMl.ListTableSpecs.
Protobuf type google.cloud.automl.v1beta1.ListTableSpecsResponse
ListTableSpecsResponse.Builder
Response message for AutoMl.ListTableSpecs.
Protobuf type google.cloud.automl.v1beta1.ListTableSpecsResponse
LocationName
LocationName.Builder
Builder for projects/{project}/locations/{location}.
Model
API proto representing a trained machine learning model.
Protobuf type google.cloud.automl.v1beta1.Model
Model.Builder
API proto representing a trained machine learning model.
Protobuf type google.cloud.automl.v1beta1.Model
ModelEvaluation
Evaluation results of a model.
Protobuf type google.cloud.automl.v1beta1.ModelEvaluation
ModelEvaluation.Builder
Evaluation results of a model.
Protobuf type google.cloud.automl.v1beta1.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.v1beta1.ModelExportOutputConfig
ModelExportOutputConfig.Builder
Output configuration for ModelExport Action.
Protobuf type google.cloud.automl.v1beta1.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.v1beta1.NormalizedVertex
NormalizedVertex.Builder
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.v1beta1.NormalizedVertex
OperationMetadata
Metadata used across all long running operations returned by AutoML API.
Protobuf type google.cloud.automl.v1beta1.OperationMetadata
OperationMetadata.Builder
Metadata used across all long running operations returned by AutoML API.
Protobuf type google.cloud.automl.v1beta1.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 GCS or BigQuery. GCS 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 calledprimary_table
will be created, and filled with precisely the same data as this obtained on import.
Protobuf type google.cloud.automl.v1beta1.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 GCS or BigQuery. GCS 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 calledprimary_table
will be created, and filled with precisely the same data as this obtained on import.
Protobuf type google.cloud.automl.v1beta1.OutputConfig
PredictRequest
Request message for PredictionService.Predict.
Protobuf type google.cloud.automl.v1beta1.PredictRequest
PredictRequest.Builder
Request message for PredictionService.Predict.
Protobuf type google.cloud.automl.v1beta1.PredictRequest
PredictResponse
Response message for PredictionService.Predict.
Protobuf type google.cloud.automl.v1beta1.PredictResponse
PredictResponse.Builder
Response message for PredictionService.Predict.
Protobuf type google.cloud.automl.v1beta1.PredictResponse
PredictionServiceClient
Service Description: AutoML Prediction API.
On any input that is documented to expect a string parameter in snake_case or kebab-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:
// This snippet has been automatically generated and should be regarded as a code template only.
// It will require modifications to work:
// - It may require correct/in-range values for request initialization.
// - It may require specifying regional endpoints when creating the service client as shown in
// https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
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.
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 PredictionServiceSettings to create(). For example:
To customize credentials:
// This snippet has been automatically generated and should be regarded as a code template only.
// It will require modifications to work:
// - It may require correct/in-range values for request initialization.
// - It may require specifying regional endpoints when creating the service client as shown in
// https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
PredictionServiceSettings predictionServiceSettings =
PredictionServiceSettings.newBuilder()
.setCredentialsProvider(FixedCredentialsProvider.create(myCredentials))
.build();
PredictionServiceClient predictionServiceClient =
PredictionServiceClient.create(predictionServiceSettings);
To customize the endpoint:
// This snippet has been automatically generated and should be regarded as a code template only.
// It will require modifications to work:
// - It may require correct/in-range values for request initialization.
// - It may require specifying regional endpoints when creating the service client as shown in
// https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
PredictionServiceSettings predictionServiceSettings =
PredictionServiceSettings.newBuilder().setEndpoint(myEndpoint).build();
PredictionServiceClient predictionServiceClient =
PredictionServiceClient.create(predictionServiceSettings);
To use REST (HTTP1.1/JSON) transport (instead of gRPC) for sending and receiving requests over the wire:
// This snippet has been automatically generated and should be regarded as a code template only.
// It will require modifications to work:
// - It may require correct/in-range values for request initialization.
// - It may require specifying regional endpoints when creating the service client as shown in
// https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
PredictionServiceSettings predictionServiceSettings =
PredictionServiceSettings.newHttpJsonBuilder().build();
PredictionServiceClient predictionServiceClient =
PredictionServiceClient.create(predictionServiceSettings);
Please refer to the GitHub repository's samples for more quickstart code snippets.
PredictionServiceGrpc
AutoML Prediction API. On any input that is documented to expect a string parameter in snake_case or kebab-case, either of those cases is accepted.
PredictionServiceGrpc.PredictionServiceBlockingStub
A stub to allow clients to do synchronous rpc calls to service PredictionService.
AutoML Prediction API. On any input that is documented to expect a string parameter in snake_case or kebab-case, either of those cases is accepted.
PredictionServiceGrpc.PredictionServiceFutureStub
A stub to allow clients to do ListenableFuture-style rpc calls to service PredictionService.
AutoML Prediction API. On any input that is documented to expect a string parameter in snake_case or kebab-case, either of those cases is accepted.
PredictionServiceGrpc.PredictionServiceImplBase
Base class for the server implementation of the service PredictionService.
AutoML Prediction API. On any input that is documented to expect a string parameter in snake_case or kebab-case, either of those cases is accepted.
PredictionServiceGrpc.PredictionServiceStub
A stub to allow clients to do asynchronous rpc calls to service PredictionService.
AutoML Prediction API. On any input that is documented to expect a string parameter in snake_case or kebab-case, either of those cases is accepted.
PredictionServiceProto
PredictionServiceSettings
Settings class to configure an instance of PredictionServiceClient.
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 predict to 30 seconds:
// This snippet has been automatically generated and should be regarded as a code template only.
// It will require modifications to work:
// - It may require correct/in-range values for request initialization.
// - It may require specifying regional endpoints when creating the service client as shown in
// https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
PredictionServiceSettings.Builder predictionServiceSettingsBuilder =
PredictionServiceSettings.newBuilder();
predictionServiceSettingsBuilder
.predictSettings()
.setRetrySettings(
predictionServiceSettingsBuilder
.predictSettings()
.getRetrySettings()
.toBuilder()
.setTotalTimeout(Duration.ofSeconds(30))
.build());
PredictionServiceSettings predictionServiceSettings = predictionServiceSettingsBuilder.build();
PredictionServiceSettings.Builder
Builder for PredictionServiceSettings.
RangesProto
RegressionProto
RegressionProto.RegressionEvaluationMetrics
Metrics for regression problems.
Protobuf type google.cloud.automl.v1beta1.RegressionEvaluationMetrics
RegressionProto.RegressionEvaluationMetrics.Builder
Metrics for regression problems.
Protobuf type google.cloud.automl.v1beta1.RegressionEvaluationMetrics
Row
A representation of a row in a relational table.
Protobuf type google.cloud.automl.v1beta1.Row
Row.Builder
A representation of a row in a relational table.
Protobuf type google.cloud.automl.v1beta1.Row
StringStats
The data statistics of a series of STRING values.
Protobuf type google.cloud.automl.v1beta1.StringStats
StringStats.Builder
The data statistics of a series of STRING values.
Protobuf type google.cloud.automl.v1beta1.StringStats
StringStats.UnigramStats
The statistics of a unigram.
Protobuf type google.cloud.automl.v1beta1.StringStats.UnigramStats
StringStats.UnigramStats.Builder
The statistics of a unigram.
Protobuf type google.cloud.automl.v1beta1.StringStats.UnigramStats
StructStats
The data statistics of a series of STRUCT values.
Protobuf type google.cloud.automl.v1beta1.StructStats
StructStats.Builder
The data statistics of a series of STRUCT values.
Protobuf type google.cloud.automl.v1beta1.StructStats
StructType
StructType
defines the DataType-s of a STRUCT type.
Protobuf type google.cloud.automl.v1beta1.StructType
StructType.Builder
StructType
defines the DataType-s of a STRUCT type.
Protobuf type google.cloud.automl.v1beta1.StructType
TableSpec
A specification of a relational table. The table's schema is represented via its child column specs. It is pre-populated as part of ImportData by schema inference algorithm, the version of which is a required parameter of ImportData InputConfig. Note: While working with a table, at times the schema may be inconsistent with the data in the table (e.g. string in a FLOAT64 column). The consistency validation is done upon creation of a model. Used by:
- Tables
Protobuf type google.cloud.automl.v1beta1.TableSpec
TableSpec.Builder
A specification of a relational table. The table's schema is represented via its child column specs. It is pre-populated as part of ImportData by schema inference algorithm, the version of which is a required parameter of ImportData InputConfig. Note: While working with a table, at times the schema may be inconsistent with the data in the table (e.g. string in a FLOAT64 column). The consistency validation is done upon creation of a model. Used by:
- Tables
Protobuf type google.cloud.automl.v1beta1.TableSpec
TableSpecName
TableSpecName.Builder
Builder for projects/{project}/locations/{location}/datasets/{dataset}/tableSpecs/{table_spec}.
TableSpecOuterClass
Tables
TablesAnnotation
Contains annotation details specific to Tables.
Protobuf type google.cloud.automl.v1beta1.TablesAnnotation
TablesAnnotation.Builder
Contains annotation details specific to Tables.
Protobuf type google.cloud.automl.v1beta1.TablesAnnotation
TablesDatasetMetadata
Metadata for a dataset used for AutoML Tables.
Protobuf type google.cloud.automl.v1beta1.TablesDatasetMetadata
TablesDatasetMetadata.Builder
Metadata for a dataset used for AutoML Tables.
Protobuf type google.cloud.automl.v1beta1.TablesDatasetMetadata
TablesModelColumnInfo
An information specific to given column and Tables Model, in context of the Model and the predictions created by it.
Protobuf type google.cloud.automl.v1beta1.TablesModelColumnInfo
TablesModelColumnInfo.Builder
An information specific to given column and Tables Model, in context of the Model and the predictions created by it.
Protobuf type google.cloud.automl.v1beta1.TablesModelColumnInfo
TablesModelMetadata
Model metadata specific to AutoML Tables.
Protobuf type google.cloud.automl.v1beta1.TablesModelMetadata
TablesModelMetadata.Builder
Model metadata specific to AutoML Tables.
Protobuf type google.cloud.automl.v1beta1.TablesModelMetadata
Temporal
TextClassificationDatasetMetadata
Dataset metadata for classification.
Protobuf type google.cloud.automl.v1beta1.TextClassificationDatasetMetadata
TextClassificationDatasetMetadata.Builder
Dataset metadata for classification.
Protobuf type google.cloud.automl.v1beta1.TextClassificationDatasetMetadata
TextClassificationModelMetadata
Model metadata that is specific to text classification.
Protobuf type google.cloud.automl.v1beta1.TextClassificationModelMetadata
TextClassificationModelMetadata.Builder
Model metadata that is specific to text classification.
Protobuf type google.cloud.automl.v1beta1.TextClassificationModelMetadata
TextExtraction
TextExtractionAnnotation
Annotation for identifying spans of text.
Protobuf type google.cloud.automl.v1beta1.TextExtractionAnnotation
TextExtractionAnnotation.Builder
Annotation for identifying spans of text.
Protobuf type google.cloud.automl.v1beta1.TextExtractionAnnotation
TextExtractionDatasetMetadata
Dataset metadata that is specific to text extraction
Protobuf type google.cloud.automl.v1beta1.TextExtractionDatasetMetadata
TextExtractionDatasetMetadata.Builder
Dataset metadata that is specific to text extraction
Protobuf type google.cloud.automl.v1beta1.TextExtractionDatasetMetadata
TextExtractionEvaluationMetrics
Model evaluation metrics for text extraction problems.
Protobuf type google.cloud.automl.v1beta1.TextExtractionEvaluationMetrics
TextExtractionEvaluationMetrics.Builder
Model evaluation metrics for text extraction problems.
Protobuf type google.cloud.automl.v1beta1.TextExtractionEvaluationMetrics
TextExtractionEvaluationMetrics.ConfidenceMetricsEntry
Metrics for a single confidence threshold.
Protobuf type
google.cloud.automl.v1beta1.TextExtractionEvaluationMetrics.ConfidenceMetricsEntry
TextExtractionEvaluationMetrics.ConfidenceMetricsEntry.Builder
Metrics for a single confidence threshold.
Protobuf type
google.cloud.automl.v1beta1.TextExtractionEvaluationMetrics.ConfidenceMetricsEntry
TextExtractionModelMetadata
Model metadata that is specific to text extraction.
Protobuf type google.cloud.automl.v1beta1.TextExtractionModelMetadata
TextExtractionModelMetadata.Builder
Model metadata that is specific to text extraction.
Protobuf type google.cloud.automl.v1beta1.TextExtractionModelMetadata
TextProto
TextSegment
A contiguous part of a text (string), assuming it has an UTF-8 NFC encoding.
Protobuf type google.cloud.automl.v1beta1.TextSegment
TextSegment.Builder
A contiguous part of a text (string), assuming it has an UTF-8 NFC encoding.
Protobuf type google.cloud.automl.v1beta1.TextSegment
TextSegmentProto
TextSentimentDatasetMetadata
Dataset metadata for text sentiment.
Protobuf type google.cloud.automl.v1beta1.TextSentimentDatasetMetadata
TextSentimentDatasetMetadata.Builder
Dataset metadata for text sentiment.
Protobuf type google.cloud.automl.v1beta1.TextSentimentDatasetMetadata
TextSentimentModelMetadata
Model metadata that is specific to text sentiment.
Protobuf type google.cloud.automl.v1beta1.TextSentimentModelMetadata
TextSentimentModelMetadata.Builder
Model metadata that is specific to text sentiment.
Protobuf type google.cloud.automl.v1beta1.TextSentimentModelMetadata
TextSentimentProto
TextSentimentProto.TextSentimentAnnotation
Contains annotation details specific to text sentiment.
Protobuf type google.cloud.automl.v1beta1.TextSentimentAnnotation
TextSentimentProto.TextSentimentAnnotation.Builder
Contains annotation details specific to text sentiment.
Protobuf type google.cloud.automl.v1beta1.TextSentimentAnnotation
TextSentimentProto.TextSentimentEvaluationMetrics
Model evaluation metrics for text sentiment problems.
Protobuf type google.cloud.automl.v1beta1.TextSentimentEvaluationMetrics
TextSentimentProto.TextSentimentEvaluationMetrics.Builder
Model evaluation metrics for text sentiment problems.
Protobuf type google.cloud.automl.v1beta1.TextSentimentEvaluationMetrics
TextSnippet
A representation of a text snippet.
Protobuf type google.cloud.automl.v1beta1.TextSnippet
TextSnippet.Builder
A representation of a text snippet.
Protobuf type google.cloud.automl.v1beta1.TextSnippet
TimeSegment
A time period inside of an example that has a time dimension (e.g. video).
Protobuf type google.cloud.automl.v1beta1.TimeSegment
TimeSegment.Builder
A time period inside of an example that has a time dimension (e.g. video).
Protobuf type google.cloud.automl.v1beta1.TimeSegment
TimestampStats
The data statistics of a series of TIMESTAMP values.
Protobuf type google.cloud.automl.v1beta1.TimestampStats
TimestampStats.Builder
The data statistics of a series of TIMESTAMP values.
Protobuf type google.cloud.automl.v1beta1.TimestampStats
TimestampStats.GranularStats
Stats split by a defined in context granularity.
Protobuf type google.cloud.automl.v1beta1.TimestampStats.GranularStats
TimestampStats.GranularStats.Builder
Stats split by a defined in context granularity.
Protobuf type google.cloud.automl.v1beta1.TimestampStats.GranularStats
TranslationAnnotation
Annotation details specific to translation.
Protobuf type google.cloud.automl.v1beta1.TranslationAnnotation
TranslationAnnotation.Builder
Annotation details specific to translation.
Protobuf type google.cloud.automl.v1beta1.TranslationAnnotation
TranslationDatasetMetadata
Dataset metadata that is specific to translation.
Protobuf type google.cloud.automl.v1beta1.TranslationDatasetMetadata
TranslationDatasetMetadata.Builder
Dataset metadata that is specific to translation.
Protobuf type google.cloud.automl.v1beta1.TranslationDatasetMetadata
TranslationEvaluationMetrics
Evaluation metrics for the dataset.
Protobuf type google.cloud.automl.v1beta1.TranslationEvaluationMetrics
TranslationEvaluationMetrics.Builder
Evaluation metrics for the dataset.
Protobuf type google.cloud.automl.v1beta1.TranslationEvaluationMetrics
TranslationModelMetadata
Model metadata that is specific to translation.
Protobuf type google.cloud.automl.v1beta1.TranslationModelMetadata
TranslationModelMetadata.Builder
Model metadata that is specific to translation.
Protobuf type google.cloud.automl.v1beta1.TranslationModelMetadata
TranslationProto
UndeployModelOperationMetadata
Details of UndeployModel operation.
Protobuf type google.cloud.automl.v1beta1.UndeployModelOperationMetadata
UndeployModelOperationMetadata.Builder
Details of UndeployModel operation.
Protobuf type google.cloud.automl.v1beta1.UndeployModelOperationMetadata
UndeployModelRequest
Request message for AutoMl.UndeployModel.
Protobuf type google.cloud.automl.v1beta1.UndeployModelRequest
UndeployModelRequest.Builder
Request message for AutoMl.UndeployModel.
Protobuf type google.cloud.automl.v1beta1.UndeployModelRequest
UpdateColumnSpecRequest
Request message for AutoMl.UpdateColumnSpec
Protobuf type google.cloud.automl.v1beta1.UpdateColumnSpecRequest
UpdateColumnSpecRequest.Builder
Request message for AutoMl.UpdateColumnSpec
Protobuf type google.cloud.automl.v1beta1.UpdateColumnSpecRequest
UpdateDatasetRequest
Request message for AutoMl.UpdateDataset
Protobuf type google.cloud.automl.v1beta1.UpdateDatasetRequest
UpdateDatasetRequest.Builder
Request message for AutoMl.UpdateDataset
Protobuf type google.cloud.automl.v1beta1.UpdateDatasetRequest
UpdateTableSpecRequest
Request message for AutoMl.UpdateTableSpec
Protobuf type google.cloud.automl.v1beta1.UpdateTableSpecRequest
UpdateTableSpecRequest.Builder
Request message for AutoMl.UpdateTableSpec
Protobuf type google.cloud.automl.v1beta1.UpdateTableSpecRequest
VideoClassificationDatasetMetadata
Dataset metadata specific to video classification. All Video Classification datasets are treated as multi label.
Protobuf type google.cloud.automl.v1beta1.VideoClassificationDatasetMetadata
VideoClassificationDatasetMetadata.Builder
Dataset metadata specific to video classification. All Video Classification datasets are treated as multi label.
Protobuf type google.cloud.automl.v1beta1.VideoClassificationDatasetMetadata
VideoClassificationModelMetadata
Model metadata specific to video classification.
Protobuf type google.cloud.automl.v1beta1.VideoClassificationModelMetadata
VideoClassificationModelMetadata.Builder
Model metadata specific to video classification.
Protobuf type google.cloud.automl.v1beta1.VideoClassificationModelMetadata
VideoObjectTrackingAnnotation
Annotation details for video object tracking.
Protobuf type google.cloud.automl.v1beta1.VideoObjectTrackingAnnotation
VideoObjectTrackingAnnotation.Builder
Annotation details for video object tracking.
Protobuf type google.cloud.automl.v1beta1.VideoObjectTrackingAnnotation
VideoObjectTrackingDatasetMetadata
Dataset metadata specific to video object tracking.
Protobuf type google.cloud.automl.v1beta1.VideoObjectTrackingDatasetMetadata
VideoObjectTrackingDatasetMetadata.Builder
Dataset metadata specific to video object tracking.
Protobuf type google.cloud.automl.v1beta1.VideoObjectTrackingDatasetMetadata
VideoObjectTrackingEvaluationMetrics
Model evaluation metrics for video object tracking problems. Evaluates prediction quality of both labeled bounding boxes and labeled tracks (i.e. series of bounding boxes sharing same label and instance ID).
Protobuf type google.cloud.automl.v1beta1.VideoObjectTrackingEvaluationMetrics
VideoObjectTrackingEvaluationMetrics.Builder
Model evaluation metrics for video object tracking problems. Evaluates prediction quality of both labeled bounding boxes and labeled tracks (i.e. series of bounding boxes sharing same label and instance ID).
Protobuf type google.cloud.automl.v1beta1.VideoObjectTrackingEvaluationMetrics
VideoObjectTrackingModelMetadata
Model metadata specific to video object tracking.
Protobuf type google.cloud.automl.v1beta1.VideoObjectTrackingModelMetadata
VideoObjectTrackingModelMetadata.Builder
Model metadata specific to video object tracking.
Protobuf type google.cloud.automl.v1beta1.VideoObjectTrackingModelMetadata
VideoProto
Interfaces
AnnotationPayloadOrBuilder
AnnotationSpecOrBuilder
ArrayStatsOrBuilder
AutoMlGrpc.AsyncService
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 kebab-case, either of those cases is accepted.
BatchPredictInputConfigOrBuilder
BatchPredictOperationMetadata.BatchPredictOutputInfoOrBuilder
BatchPredictOperationMetadataOrBuilder
BatchPredictOutputConfigOrBuilder
BatchPredictRequestOrBuilder
BatchPredictResultOrBuilder
BigQueryDestinationOrBuilder
BigQuerySourceOrBuilder
BoundingBoxMetricsEntry.ConfidenceMetricsEntryOrBuilder
BoundingBoxMetricsEntryOrBuilder
BoundingPolyOrBuilder
CategoryStats.SingleCategoryStatsOrBuilder
CategoryStatsOrBuilder
ClassificationProto.ClassificationAnnotationOrBuilder
ClassificationProto.ClassificationEvaluationMetrics.ConfidenceMetricsEntryOrBuilder
ClassificationProto.ClassificationEvaluationMetrics.ConfusionMatrix.RowOrBuilder
ClassificationProto.ClassificationEvaluationMetrics.ConfusionMatrixOrBuilder
ClassificationProto.ClassificationEvaluationMetricsOrBuilder
ClassificationProto.VideoClassificationAnnotationOrBuilder
ColumnSpec.CorrelatedColumnOrBuilder
ColumnSpecOrBuilder
CorrelationStatsOrBuilder
CreateDatasetRequestOrBuilder
CreateModelOperationMetadataOrBuilder
CreateModelRequestOrBuilder
DataStatsOrBuilder
DataTypeOrBuilder
DatasetOrBuilder
DeleteDatasetRequestOrBuilder
DeleteModelRequestOrBuilder
DeleteOperationMetadataOrBuilder
DeployModelOperationMetadataOrBuilder
DeployModelRequestOrBuilder
Document.LayoutOrBuilder
DocumentDimensionsOrBuilder
DocumentInputConfigOrBuilder
DocumentOrBuilder
DoubleRangeOrBuilder
ExamplePayloadOrBuilder
ExportDataOperationMetadata.ExportDataOutputInfoOrBuilder
ExportDataOperationMetadataOrBuilder
ExportDataRequestOrBuilder
ExportEvaluatedExamplesOperationMetadata.ExportEvaluatedExamplesOutputInfoOrBuilder
ExportEvaluatedExamplesOperationMetadataOrBuilder
ExportEvaluatedExamplesOutputConfigOrBuilder
ExportEvaluatedExamplesRequestOrBuilder
ExportModelOperationMetadata.ExportModelOutputInfoOrBuilder
ExportModelOperationMetadataOrBuilder
ExportModelRequestOrBuilder
Float64Stats.HistogramBucketOrBuilder
Float64StatsOrBuilder
GcrDestinationOrBuilder
GcsDestinationOrBuilder
GcsSourceOrBuilder
GetAnnotationSpecRequestOrBuilder
GetColumnSpecRequestOrBuilder
GetDatasetRequestOrBuilder
GetModelEvaluationRequestOrBuilder
GetModelRequestOrBuilder
GetTableSpecRequestOrBuilder
ImageClassificationDatasetMetadataOrBuilder
ImageClassificationModelDeploymentMetadataOrBuilder
ImageClassificationModelMetadataOrBuilder
ImageObjectDetectionAnnotationOrBuilder
ImageObjectDetectionDatasetMetadataOrBuilder
ImageObjectDetectionEvaluationMetricsOrBuilder
ImageObjectDetectionModelDeploymentMetadataOrBuilder
ImageObjectDetectionModelMetadataOrBuilder
ImageOrBuilder
ImportDataOperationMetadataOrBuilder
ImportDataRequestOrBuilder
InputConfigOrBuilder
ListColumnSpecsRequestOrBuilder
ListColumnSpecsResponseOrBuilder
ListDatasetsRequestOrBuilder
ListDatasetsResponseOrBuilder
ListModelEvaluationsRequestOrBuilder
ListModelEvaluationsResponseOrBuilder
ListModelsRequestOrBuilder
ListModelsResponseOrBuilder
ListTableSpecsRequestOrBuilder
ListTableSpecsResponseOrBuilder
ModelEvaluationOrBuilder
ModelExportOutputConfigOrBuilder
ModelOrBuilder
NormalizedVertexOrBuilder
OperationMetadataOrBuilder
OutputConfigOrBuilder
PredictRequestOrBuilder
PredictResponseOrBuilder
PredictionServiceGrpc.AsyncService
AutoML Prediction API. On any input that is documented to expect a string parameter in snake_case or kebab-case, either of those cases is accepted.
RegressionProto.RegressionEvaluationMetricsOrBuilder
RowOrBuilder
StringStats.UnigramStatsOrBuilder
StringStatsOrBuilder
StructStatsOrBuilder
StructTypeOrBuilder
TableSpecOrBuilder
TablesAnnotationOrBuilder
TablesDatasetMetadataOrBuilder
TablesModelColumnInfoOrBuilder
TablesModelMetadataOrBuilder
TextClassificationDatasetMetadataOrBuilder
TextClassificationModelMetadataOrBuilder
TextExtractionAnnotationOrBuilder
TextExtractionDatasetMetadataOrBuilder
TextExtractionEvaluationMetrics.ConfidenceMetricsEntryOrBuilder
TextExtractionEvaluationMetricsOrBuilder
TextExtractionModelMetadataOrBuilder
TextSegmentOrBuilder
TextSentimentDatasetMetadataOrBuilder
TextSentimentModelMetadataOrBuilder
TextSentimentProto.TextSentimentAnnotationOrBuilder
TextSentimentProto.TextSentimentEvaluationMetricsOrBuilder
TextSnippetOrBuilder
TimeSegmentOrBuilder
TimestampStats.GranularStatsOrBuilder
TimestampStatsOrBuilder
TranslationAnnotationOrBuilder
TranslationDatasetMetadataOrBuilder
TranslationEvaluationMetricsOrBuilder
TranslationModelMetadataOrBuilder
UndeployModelOperationMetadataOrBuilder
UndeployModelRequestOrBuilder
UpdateColumnSpecRequestOrBuilder
UpdateDatasetRequestOrBuilder
UpdateTableSpecRequestOrBuilder
VideoClassificationDatasetMetadataOrBuilder
VideoClassificationModelMetadataOrBuilder
VideoObjectTrackingAnnotationOrBuilder
VideoObjectTrackingDatasetMetadataOrBuilder
VideoObjectTrackingEvaluationMetricsOrBuilder
VideoObjectTrackingModelMetadataOrBuilder
Enums
AnnotationPayload.DetailCase
BatchPredictInputConfig.SourceCase
BatchPredictOperationMetadata.BatchPredictOutputInfo.OutputLocationCase
BatchPredictOutputConfig.DestinationCase
ClassificationProto.ClassificationType
Type of the classification problem.
Protobuf enum google.cloud.automl.v1beta1.ClassificationType
DataStats.StatsCase
DataType.DetailsCase
Dataset.DatasetMetadataCase
DeployModelRequest.ModelDeploymentMetadataCase
Document.Layout.TextSegmentType
The type of TextSegment in the context of the original document.
Protobuf enum google.cloud.automl.v1beta1.Document.Layout.TextSegmentType
DocumentDimensions.DocumentDimensionUnit
Unit of the document dimension.
Protobuf enum google.cloud.automl.v1beta1.DocumentDimensions.DocumentDimensionUnit
ExamplePayload.PayloadCase
ExportDataOperationMetadata.ExportDataOutputInfo.OutputLocationCase
ExportEvaluatedExamplesOutputConfig.DestinationCase
Image.DataCase
InputConfig.SourceCase
Model.DeploymentState
Deployment state of the model.
Protobuf enum google.cloud.automl.v1beta1.Model.DeploymentState
Model.ModelMetadataCase
ModelEvaluation.MetricsCase
ModelExportOutputConfig.DestinationCase
OperationMetadata.DetailsCase
OutputConfig.DestinationCase
TablesModelMetadata.AdditionalOptimizationObjectiveConfigCase
TextExtractionAnnotation.AnnotationCase
TypeCode
TypeCode
is used as a part of
DataType.
Protobuf enum google.cloud.automl.v1beta1.TypeCode