Index
AutoMl(interface)PredictionService(interface)StreamingPredictionService(interface)AnnotationPayload(message)AnnotationSpec(message)ArrayStats(message)BatchPredictInputConfig(message)BatchPredictOperationMetadata(message)BatchPredictOperationMetadata.BatchPredictOutputInfo(message)BatchPredictOutputConfig(message)BatchPredictRequest(message)BatchPredictResult(message)BigQueryDestination(message)BigQuerySource(message)BoundingBoxMetricsEntry(message)BoundingBoxMetricsEntry.ConfidenceMetricsEntry(message)BoundingPoly(message)CategoryStats(message)CategoryStats.SingleCategoryStats(message)ClassificationAnnotation(message)ClassificationEvaluationMetrics(message)ClassificationEvaluationMetrics.ConfidenceMetricsEntry(message)ClassificationEvaluationMetrics.ConfusionMatrix(message)ClassificationEvaluationMetrics.ConfusionMatrix.Row(message)ClassificationType(enum)ColumnSpec(message)ColumnSpec.CorrelatedColumn(message)CorrelationStats(message)CreateDatasetRequest(message)CreateModelOperationMetadata(message)CreateModelRequest(message)DataStats(message)DataType(message)Dataset(message)DeleteDatasetRequest(message)DeleteModelRequest(message)DeleteOperationMetadata(message)DeployModelOperationMetadata(message)DeployModelRequest(message)Document(message)Document.Layout(message)Document.Layout.TextSegmentType(enum)DocumentDimensions(message)DocumentDimensions.DocumentDimensionUnit(enum)DocumentInputConfig(message)DoubleRange(message)ExamplePayload(message)ExportDataOperationMetadata(message)ExportDataOperationMetadata.ExportDataOutputInfo(message)ExportDataRequest(message)ExportEvaluatedExamplesOperationMetadata(message)ExportEvaluatedExamplesOperationMetadata.ExportEvaluatedExamplesOutputInfo(message)ExportEvaluatedExamplesOutputConfig(message)ExportEvaluatedExamplesRequest(message)ExportModelOperationMetadata(message)ExportModelOperationMetadata.ExportModelOutputInfo(message)ExportModelRequest(message)Float64Stats(message)Float64Stats.HistogramBucket(message)GcrDestination(message)GcsDestination(message)GcsSource(message)GetAnnotationSpecRequest(message)GetColumnSpecRequest(message)GetDatasetRequest(message)GetModelEvaluationRequest(message)GetModelRequest(message)GetTableSpecRequest(message)Image(message)ImageClassificationDatasetMetadata(message)ImageClassificationModelDeploymentMetadata(message)ImageClassificationModelMetadata(message)ImageObjectDetectionAnnotation(message)ImageObjectDetectionDatasetMetadata(message)ImageObjectDetectionEvaluationMetrics(message)ImageObjectDetectionModelDeploymentMetadata(message)ImageObjectDetectionModelMetadata(message)ImportDataOperationMetadata(message)ImportDataRequest(message)InputConfig(message)ListColumnSpecsRequest(message)ListColumnSpecsResponse(message)ListDatasetsRequest(message)ListDatasetsResponse(message)ListModelEvaluationsRequest(message)ListModelEvaluationsResponse(message)ListModelsRequest(message)ListModelsResponse(message)ListTableSpecsRequest(message)ListTableSpecsResponse(message)Model(message)Model.DeploymentState(enum)ModelEvaluation(message)ModelExportOutputConfig(message)NormalizedVertex(message)OperationMetadata(message)OutputConfig(message)PredictRequest(message)PredictResponse(message)RegressionEvaluationMetrics(message)Row(message)StringStats(message)StringStats.UnigramStats(message)StructStats(message)StructType(message)TableSpec(message)TablesAnnotation(message)TablesDatasetMetadata(message)TablesModelColumnInfo(message)TablesModelMetadata(message)TextClassificationDatasetMetadata(message)TextClassificationModelMetadata(message)TextExtractionAnnotation(message)TextExtractionDatasetMetadata(message)TextExtractionEvaluationMetrics(message)TextExtractionEvaluationMetrics.ConfidenceMetricsEntry(message)TextExtractionModelMetadata(message)TextSegment(message)TextSentimentAnnotation(message)TextSentimentDatasetMetadata(message)TextSentimentEvaluationMetrics(message)TextSentimentModelMetadata(message)TextSnippet(message)TimeSegment(message)TimestampStats(message)TimestampStats.GranularStats(message)TranslationAnnotation(message)TranslationDatasetMetadata(message)TranslationEvaluationMetrics(message)TranslationModelMetadata(message)TypeCode(enum)UndeployModelOperationMetadata(message)UndeployModelRequest(message)UpdateColumnSpecRequest(message)UpdateDatasetRequest(message)UpdateTableSpecRequest(message)VideoClassificationAnnotation(message)VideoClassificationDatasetMetadata(message)VideoClassificationModelMetadata(message)VideoObjectTrackingAnnotation(message)VideoObjectTrackingDatasetMetadata(message)VideoObjectTrackingEvaluationMetrics(message)VideoObjectTrackingModelMetadata(message)
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.
| CreateDataset | |
|---|---|
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Creates a dataset.
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| CreateModel | |
|---|---|
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Creates a model. Returns a Model in the
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| DeleteDataset | |
|---|---|
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Deletes a dataset and all of its contents. Returns empty response in the
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| DeleteModel | |
|---|---|
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Deletes a model. Returns
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| DeployModel | |
|---|---|
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Deploys a model. If a model is already deployed, deploying it with the same parameters has no effect. Deploying with different parametrs (as e.g. changing [node_number][google.cloud.automl.v1beta1.ImageObjectDetectionModelDeploymentMetadata.node_number]) will reset the deployment state without pausing the model's availability. Only applicable for Text Classification, Image Object Detection , Tables, and Image Segmentation; all other domains manage deployment automatically. Returns an empty response in the
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| ExportData | |
|---|---|
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Exports dataset's data to the provided output location. Returns an empty response in the
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| ExportEvaluatedExamples | |
|---|---|
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Exports examples on which the model was evaluated (i.e. which were in the TEST set of the dataset the model was created from), together with their ground truth annotations and the annotations created (predicted) by the model. The examples, ground truth and predictions are exported in the state they were at the moment the model was evaluated. This export is available only for 30 days since the model evaluation is created. Currently only available for Tables. Returns an empty response in the
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| ExportModel | |
|---|---|
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Exports a trained, "export-able", model to a user specified Google Cloud Storage location. A model is considered export-able if and only if it has an export format defined for it in Returns an empty response in the
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| GetAnnotationSpec | |
|---|---|
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Gets an annotation spec.
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| GetColumnSpec | |
|---|---|
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Gets a column spec.
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| GetDataset | |
|---|---|
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Gets a dataset.
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| GetModel | |
|---|---|
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Gets a model.
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| GetModelEvaluation | |
|---|---|
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Gets a model evaluation.
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| GetTableSpec | |
|---|---|
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Gets a table spec.
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| ImportData | |
|---|---|
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Imports data into a dataset. For Tables this method can only be called on an empty Dataset. For Tables: * A
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| ListColumnSpecs | |
|---|---|
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Lists column specs in a table spec.
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| ListDatasets | |
|---|---|
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Lists datasets in a project.
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| ListModelEvaluations | |
|---|---|
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Lists model evaluations.
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| ListModels | |
|---|---|
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Lists models.
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| ListTableSpecs | |
|---|---|
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Lists table specs in a dataset.
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| UndeployModel | |
|---|---|
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Undeploys a model. If the model is not deployed this method has no effect. Only applicable for Text Classification, Image Object Detection and Tables; all other domains manage deployment automatically. Returns an empty response in the
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| UpdateColumnSpec | |
|---|---|
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Updates a column spec.
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| UpdateDataset | |
|---|---|
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Updates a dataset.
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| UpdateTableSpec | |
|---|---|
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Updates a table spec.
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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.
| BatchPredict | |
|---|---|
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Perform a batch prediction. Unlike the online
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| Predict | |
|---|---|
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Perform an online prediction. The prediction result will be directly returned in the response. Available for following ML problems, and their expected request payloads: * Image Classification - Image in .JPEG, .GIF or .PNG format, image_bytes up to 30MB. * Image Object Detection - Image in .JPEG, .GIF or .PNG format, image_bytes up to 30MB. * Text Classification - TextSnippet, content up to 60,000 characters, UTF-8 encoded. * Text Extraction - TextSnippet, content up to 30,000 characters, UTF-8 NFC encoded. * Translation - TextSnippet, content up to 25,000 characters, UTF-8 encoded. * Tables - Row, with column values matching the columns of the model, up to 5MB. Not available for FORECASTING
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StreamingPredictionService
AutoML Streaming 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.
AnnotationPayload
Contains annotation information that is relevant to AutoML.
| Fields | ||
|---|---|---|
annotation_spec_id |
Output only . The resource ID of the annotation spec that this annotation pertains to. The annotation spec comes from either an ancestor dataset, or the dataset that was used to train the model in use. |
|
display_name |
Output only. The value of |
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Union field detail. Output only . Additional information about the annotation specific to the AutoML domain. detail can be only one of the following: |
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translation |
Annotation details for translation. |
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classification |
Annotation details for content or image classification. |
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image_object_detection |
Annotation details for image object detection. |
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video_classification |
Annotation details for video classification. Returned for Video Classification predictions. |
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video_object_tracking |
Annotation details for video object tracking. |
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text_extraction |
Annotation details for text extraction. |
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text_sentiment |
Annotation details for text sentiment. |
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tables |
Annotation details for Tables. |
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AnnotationSpec
A definition of an annotation spec.
| Fields | |
|---|---|
name |
Output only. Resource name of the annotation spec. Form: 'projects/{project_id}/locations/{location_id}/datasets/{dataset_id}/annotationSpecs/{annotation_spec_id}' |
display_name |
Required. The name of the annotation spec to show in the interface. The name can be up to 32 characters long and must match the regexp |
example_count |
Output only. The number of examples in the parent dataset labeled by the annotation spec. |
ArrayStats
The data statistics of a series of ARRAY values.
| Fields | |
|---|---|
member_stats |
Stats of all the values of all arrays, as if they were a single long series of data. The type depends on the element type of the array. |
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_sourceor
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
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
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.
| Fields | ||
|---|---|---|
Union field source. Required. The source of the input. source can be only one of the following: |
||
gcs_source |
The Google Cloud Storage location for the input content. |
|
bigquery_source |
The BigQuery location for the input content. |
|
BatchPredictOperationMetadata
Details of BatchPredict operation.
| Fields | |
|---|---|
input_config |
Output only. The input config that was given upon starting this batch predict operation. |
output_info |
Output only. Information further describing this batch predict's output. |
BatchPredictOutputInfo
Further describes this batch predict's output. Supplements
| Fields | ||
|---|---|---|
Union field output_location. The output location into which prediction output is written. output_location can be only one of the following: |
||
gcs_output_directory |
The full path of the Google Cloud Storage directory created, into which the prediction output is written. |
|
bigquery_output_dataset |
The path of the BigQuery dataset created, in bq://projectId.bqDatasetId format, into which the prediction output is written. |
|
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-
- For Image Classification: In the created directory files
image_classification_1.jsonl,image_classification_2.jsonl,...,image_classification_N.jsonlwill 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" : "" 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.jsonlfiles 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" : "" but here followed by exactly one
[google.rpc.Status](https: //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto) containing only code and messagefields.
- For Image Object Detection: In the created directory files
image_object_detection_1.jsonl,image_object_detection_2.jsonl,...,image_object_detection_N.jsonlwill 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" : "" 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.jsonlfiles 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" : "" but here followed by exactly one
[google.rpc.Status](https: //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto) containing only code and messagefields. * For Video Classification: In the created directory a video_classification.csv file, and a .JSON file per each video classification requested in the input (i.e. each line in given CSV(s)), will be created.
The format of video_classification.csv is:
GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END,JSON_FILE_NAME,STATUS where: GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END = matches 1 to 1 the prediction input lines (i.e. video_classification.csv has precisely the same number of lines as the prediction input had.) JSON_FILE_NAME = Name of .JSON file in the output directory, which contains prediction responses for the video time segment. STATUS = "OK" if prediction completed successfully, or an error code with message otherwise. If STATUS is not "OK" then the .JSON file for that line may not exist or be empty.
Each .JSON file, assuming STATUS is "OK", will contain a list of
AnnotationPayload protos in JSON format, which are the predictions
for the video time segment the file is assigned to in the
video_classification.csv. All AnnotationPayload protos will have
video_classification field set, and will be sorted by
video_classification.type field (note that the returned types are
governed by `classifaction_types` parameter in
[PredictService.BatchPredictRequest.params][]).
- For Video Object Tracking: In the created directory a video_object_tracking.csv file will be created, and multiple files video_object_trackinng_1.json, video_object_trackinng_2.json,..., video_object_trackinng_N.json, where N is the number of requests in the input (i.e. the number of lines in given CSV(s)).
The format of video_object_tracking.csv is:
GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END,JSON_FILE_NAME,STATUS where: GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END = matches 1 to 1 the prediction input lines (i.e. video_object_tracking.csv has precisely the same number of lines as the prediction input had.) JSON_FILE_NAME = Name of .JSON file in the output directory, which contains prediction responses for the video time segment. STATUS = "OK" if prediction completed successfully, or an error code with message otherwise. If STATUS is not "OK" then the .JSON file for that line may not exist or be empty.
Each .JSON file, assuming STATUS is "OK", will contain a list of
AnnotationPayload protos in JSON format, which are the predictions
for each frame of the video time segment the file is assigned to in
video_object_tracking.csv. All AnnotationPayload protos will have
video_object_tracking field set.
- For Text Classification: In the created directory files
text_classification_1.jsonl,text_classification_2.jsonl,...,text_classification_N.jsonlwill 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](https: //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto) containing only code and message.
- For Text Sentiment: In the created directory files
text_sentiment_1.jsonl,text_sentiment_2.jsonl,...,text_sentiment_N.jsonlwill 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](https: //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto) containing only code and message.
- For Text Extraction: In the created directory files
text_extraction_1.jsonl,text_extraction_2.jsonl,...,text_extraction_N.jsonlwill 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 additionalerrors_1.jsonl,errors_2.jsonl,...,errors_N.jsonlfiles 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" : "" (in case of inline) or the document proto (in case of document) but here followed by exactly one
[google.rpc.Status](https: //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto) containing only code and message.
- For Tables: Output depends on whether
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
display_name>_scores. For REGRESSION and FORECASTING
prediction_type-s: Each .csv file will contain a header, listing all columns' [display_name-s][google.cloud.automl.v1beta1.display_name] 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](https: //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto) 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 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](https: //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto) represented as a STRUCT, and containing only code and message.
| Fields | ||
|---|---|---|
Union field destination. Required. The destination of the output. destination can be only one of the following: |
||
gcs_destination |
The Google Cloud Storage location of the directory where the output is to be written to. |
|
bigquery_destination |
The BigQuery location where the output is to be written to. |
|
BatchPredictRequest
Request message for PredictionService.BatchPredict.
| Fields | |
|---|---|
name |
Name of the model requested to serve the batch prediction. Authorization requires the following Google IAM permission on the specified resource
|
input_config |
Required. The input configuration for batch prediction. |
output_config |
Required. The Configuration specifying where output predictions should be written. |
params |
Additional domain-specific parameters for the predictions, any string must be up to 25000 characters long.
|
BatchPredictResult
Result of the Batch Predict. This message is returned in response of the operation returned by the PredictionService.BatchPredict.
| Fields | |
|---|---|
metadata |
Additional domain-specific prediction response metadata.
|
BigQueryDestination
The BigQuery location for the output content.
| Fields | |
|---|---|
output_uri |
Required. BigQuery URI to a project, up to 2000 characters long. Accepted forms: * BigQuery path e.g. bq://projectId |
BigQuerySource
The BigQuery location for the input content.
| Fields | |
|---|---|
input_uri |
Required. BigQuery URI to a table, up to 2000 characters long. Accepted forms: * BigQuery path e.g. bq://projectId.bqDatasetId.bqTableId |
BoundingBoxMetricsEntry
Bounding box matching model metrics for a single intersection-over-union threshold and multiple label match confidence thresholds.
| Fields | |
|---|---|
iou_threshold |
Output only. The intersection-over-union threshold value used to compute this metrics entry. |
mean_average_precision |
Output only. The mean average precision, most often close to au_prc. |
confidence_metrics_entries[] |
Output only. Metrics for each label-match confidence_threshold from 0.05,0.10,...,0.95,0.96,0.97,0.98,0.99. Precision-recall curve is derived from them. |
ConfidenceMetricsEntry
Metrics for a single confidence threshold.
| Fields | |
|---|---|
confidence_threshold |
Output only. The confidence threshold value used to compute the metrics. |
recall |
Output only. Recall under the given confidence threshold. |
precision |
Output only. Precision under the given confidence threshold. |
f1_score |
Output only. The harmonic mean of recall and precision. |
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.
| Fields | |
|---|---|
normalized_vertices[] |
Output only . The bounding polygon normalized vertices. |
CategoryStats
The data statistics of a series of CATEGORY values.
| Fields | |
|---|---|
top_category_stats[] |
The statistics of the top 20 CATEGORY values, ordered by |
SingleCategoryStats
The statistics of a single CATEGORY value.
| Fields | |
|---|---|
value |
The CATEGORY value. |
count |
The number of occurrences of this value in the series. |
ClassificationAnnotation
Contains annotation details specific to classification.
| Fields | |
|---|---|
score |
Output only. A confidence estimate between 0.0 and 1.0. A higher value means greater confidence that the annotation is positive. If a user approves an annotation as negative or positive, the score value remains unchanged. If a user creates an annotation, the score is 0 for negative or 1 for positive. |
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.
| Fields | |
|---|---|
au_prc |
Output only. The Area Under Precision-Recall Curve metric. Micro-averaged for the overall evaluation. |
base_au_prc |
Output only. The Area Under Precision-Recall Curve metric based on priors. Micro-averaged for the overall evaluation. Deprecated. |
au_roc |
Output only. The Area Under Receiver Operating Characteristic curve metric. Micro-averaged for the overall evaluation. |
log_loss |
Output only. The Log Loss metric. |
confidence_metrics_entry[] |
Output only. Metrics for each confidence_threshold in 0.00,0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 and position_threshold = INT32_MAX_VALUE. ROC and precision-recall curves, and other aggregated metrics are derived from them. The confidence metrics entries may also be supplied for additional values of position_threshold, but from these no aggregated metrics are computed. |
confusion_matrix |
Output only. Confusion matrix of the evaluation. Only set for MULTICLASS classification problems where number of labels is no more than 10. Only set for model level evaluation, not for evaluation per label. |
annotation_spec_id[] |
Output only. The annotation spec ids used for this evaluation. |
ConfidenceMetricsEntry
Metrics for a single confidence threshold.
| Fields | |
|---|---|
confidence_threshold |
Output only. Metrics are computed with an assumption that the model never returns predictions with score lower than this value. |
position_threshold |
Output only. Metrics are computed with an assumption that the model always returns at most this many predictions (ordered by their score, descendingly), but they all still need to meet the confidence_threshold. |
recall |
Output only. Recall (True Positive Rate) for the given confidence threshold. |
precision |
Output only. Precision for the given confidence threshold. |
false_positive_rate |
Output only. False Positive Rate for the given confidence threshold. |
f1_score |
Output only. The harmonic mean of recall and precision. |
recall_at1 |
Output only. The Recall (True Positive Rate) when only considering the label that has the highest prediction score and not below the confidence threshold for each example. |
precision_at1 |
Output only. The precision when only considering the label that has the highest prediction score and not below the confidence threshold for each example. |
false_positive_rate_at1 |
Output only. The False Positive Rate when only considering the label that has the highest prediction score and not below the confidence threshold for each example. |
f1_score_at1 |
Output only. The harmonic mean of |
true_positive_count |
Output only. The number of model created labels that match a ground truth label. |
false_positive_count |
Output only. The number of model created labels that do not match a ground truth label. |
false_negative_count |
Output only. The number of ground truth labels that are not matched by a model created label. |
true_negative_count |
Output only. The number of labels that were not created by the model, but if they would, they would not match a ground truth label. |
ConfusionMatrix
Confusion matrix of the model running the classification.
| Fields | |
|---|---|
annotation_spec_id[] |
Output only. IDs of the annotation specs used in the confusion matrix. For Tables CLASSIFICATION
|
display_name[] |
Output only. Display name of the annotation specs used in the confusion matrix, as they were at the moment of the evaluation. For Tables CLASSIFICATION
|
row[] |
Output only. Rows in the confusion matrix. The number of rows is equal to the size of |
Row
Output only. A row in the confusion matrix.
| Fields | |
|---|---|
example_count[] |
Output only. Value of the specific cell in the confusion matrix. The number of values each row has (i.e. the length of the row) is equal to the length of the |
ClassificationType
Type of the classification problem.
| Enums | |
|---|---|
CLASSIFICATION_TYPE_UNSPECIFIED |
An un-set value of this enum. |
MULTICLASS |
At most one label is allowed per example. |
MULTILABEL |
Multiple labels are allowed for one example. |
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
| Fields | |
|---|---|
name |
Output only. The resource name of the column specs. Form:
|
data_type |
The data type of elements stored in the column. |
display_name |
Output only. The name of the column to show in the interface. The name can be up to 100 characters long and can consist only of ASCII Latin letters A-Z and a-z, ASCII digits 0-9, underscores(_), and forward slashes(/), and must start with a letter or a digit. |
data_stats |
Output only. Stats of the series of values in the column. This field may be stale, see the ancestor's Dataset.tables_dataset_metadata.stats_update_time field for the timestamp at which these stats were last updated. |
top_correlated_columns[] |
Deprecated. |
etag |
Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens. |
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.
| Fields | |
|---|---|
cramers_v |
The correlation value using the Cramer's V measure. |
CreateDatasetRequest
Request message for AutoMl.CreateDataset.
| Fields | |
|---|---|
parent |
The resource name of the project to create the dataset for. Authorization requires the following Google IAM permission on the specified resource
|
dataset |
The dataset to create. |
CreateModelOperationMetadata
Details of CreateModel operation.
CreateModelRequest
Request message for AutoMl.CreateModel.
| Fields | |
|---|---|
parent |
Resource name of the parent project where the model is being created. Authorization requires the following Google IAM permission on the specified resource
|
model |
The model to create. |
DataStats
The data statistics of a series of values that share the same DataType.
| Fields | ||
|---|---|---|
distinct_value_count |
The number of distinct values. |
|
null_value_count |
The number of values that are null. |
|
valid_value_count |
The number of values that are valid. |
|
Union field stats. The data statistics specific to a DataType. stats can be only one of the following: |
||
float64_stats |
The statistics for FLOAT64 DataType. |
|
string_stats |
The statistics for STRING DataType. |
|
timestamp_stats |
The statistics for TIMESTAMP DataType. |
|
array_stats |
The statistics for ARRAY DataType. |
|
struct_stats |
The statistics for STRUCT DataType. |
|
category_stats |
The statistics for CATEGORY DataType. |
|
DataType
Indicated the type of data that can be stored in a structured data entity (e.g. a table).
| Fields | ||
|---|---|---|
type_code |
Required. The |
|
nullable |
If true, this DataType can also be |
|
Union field details. Details of DataType-s that need additional specification. details can be only one of the following: |
||
list_element_type |
If |
|
struct_type |
If |
|
time_format |
If |
|
Dataset
A workspace for solving a single, particular machine learning (ML) problem. A workspace contains examples that may be annotated.
| Fields | ||
|---|---|---|
name |
Output only. The resource name of the dataset. Form: |
|
display_name |
Required. The name of the dataset to show in the interface. The name can 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. |
|
description |
User-provided description of the dataset. The description can be up to 25000 characters long. |
|
example_count |
Output only. The number of examples in the dataset. |
|
create_time |
Output only. Timestamp when this dataset was created. |
|
etag |
Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens. |
|
Union field dataset_metadata. Required. The dataset metadata that is specific to the problem type. dataset_metadata can be only one of the following: |
||
translation_dataset_metadata |
Metadata for a dataset used for translation. |
|
image_classification_dataset_metadata |
Metadata for a dataset used for image classification. |
|
text_classification_dataset_metadata |
Metadata for a dataset used for text classification. |
|
image_object_detection_dataset_metadata |
Metadata for a dataset used for image object detection. |
|
video_classification_dataset_metadata |
Metadata for a dataset used for video classification. |
|
video_object_tracking_dataset_metadata |
Metadata for a dataset used for video object tracking. |
|
text_extraction_dataset_metadata |
Metadata for a dataset used for text extraction. |
|
text_sentiment_dataset_metadata |
Metadata for a dataset used for text sentiment. |
|
tables_dataset_metadata |
Metadata for a dataset used for Tables. |
|
DeleteDatasetRequest
Request message for AutoMl.DeleteDataset.
| Fields | |
|---|---|
name |
The resource name of the dataset to delete. Authorization requires the following Google IAM permission on the specified resource
|
DeleteModelRequest
Request message for AutoMl.DeleteModel.
| Fields | |
|---|---|
name |
Resource name of the model being deleted. Authorization requires the following Google IAM permission on the specified resource
|
DeleteOperationMetadata
Details of operations that perform deletes of any entities.
DeployModelOperationMetadata
Details of DeployModel operation.
DeployModelRequest
Request message for AutoMl.DeployModel.
| Fields | ||
|---|---|---|
name |
Resource name of the model to deploy. Authorization requires the following Google IAM permission on the specified resource
|
|
Union field model_deployment_metadata. The per-domain specific deployment parameters. model_deployment_metadata can be only one of the following: |
||
image_object_detection_model_deployment_metadata |
Model deployment metadata specific to Image Object Detection. |
|
image_classification_model_deployment_metadata |
Model deployment metadata specific to Image Classification. |
|
Document
A structured text document e.g. a PDF.
| Fields | |
|---|---|
input_config |
An input config specifying the content of the document. |
document_text |
The plain text version of this document. |
layout[] |
Describes the layout of the document. Sorted by [page_number][]. |
document_dimensions |
The dimensions of the page in the document. |
page_count |
Number of pages in the document. |
Layout
Describes the layout information of a text_segment in the document.
| Fields | |
|---|---|
text_segment |
Text Segment that represents a segment in |
page_number |
Page number of the |
bounding_poly |
The position of the
|
text_segment_type |
The type of the |
TextSegmentType
The type of TextSegment in the context of the original document.
| Enums | |
|---|---|
TEXT_SEGMENT_TYPE_UNSPECIFIED |
Should not be used. |
TOKEN |
The text segment is a token. e.g. word. |
PARAGRAPH |
The text segment is a paragraph. |
FORM_FIELD |
The text segment is a form field. |
FORM_FIELD_NAME |
The text segment is the name part of a form field. It will be treated as child of another FORM_FIELD TextSegment if its span is subspan of another TextSegment with type FORM_FIELD. |
FORM_FIELD_CONTENTS |
The text segment is the text content part of a form field. It will be treated as child of another FORM_FIELD TextSegment if its span is subspan of another TextSegment with type FORM_FIELD. |
TABLE |
The text segment is a whole table, including headers, and all rows. |
TABLE_HEADER |
The text segment is a table's headers. It will be treated as child of another TABLE TextSegment if its span is subspan of another TextSegment with type TABLE. |
TABLE_ROW |
The text segment is a row in table. It will be treated as child of another TABLE TextSegment if its span is subspan of another TextSegment with type TABLE. |
TABLE_CELL |
The text segment is a cell in table. It will be treated as child of another TABLE_ROW TextSegment if its span is subspan of another TextSegment with type TABLE_ROW. |
DocumentDimensions
Message that describes dimension of a document.
| Fields | |
|---|---|
unit |
Unit of the dimension. |
width |
Width value of the document, works together with the unit. |
height |
Height value of the document, works together with the unit. |
DocumentDimensionUnit
Unit of the document dimension.
| Enums | |
|---|---|
DOCUMENT_DIMENSION_UNIT_UNSPECIFIED |
Should not be used. |
INCH |
Document dimension is measured in inches. |
CENTIMETER |
Document dimension is measured in centimeters. |
POINT |
Document dimension is measured in points. 72 points = 1 inch. |
DocumentInputConfig
Input configuration of a Document.
| Fields | |
|---|---|
gcs_source |
The Google Cloud Storage location of the document file. Only a single path should be given. Max supported size: 512MB. Supported extensions: .PDF. |
DoubleRange
A range between two double numbers.
| Fields | |
|---|---|
start |
Start of the range, inclusive. |
end |
End of the range, exclusive. |
ExamplePayload
Example data used for training or prediction.
| Fields | ||
|---|---|---|
Union field payload. Required. Input only. The example data. payload can be only one of the following: |
||
image |
Example image. |
|
text_snippet |
Example text. |
|
document |
Example document. |
|
row |
Example relational table row. |
|
ExportDataOperationMetadata
Details of ExportData operation.
| Fields | |
|---|---|
output_info |
Output only. Information further describing this export data's output. |
ExportDataOutputInfo
Further describes this export data's output. Supplements OutputConfig.
| Fields | ||
|---|---|---|
Union field output_location. The output location to which the exported data is written. output_location can be only one of the following: |
||
gcs_output_directory |
The full path of the Google Cloud Storage directory created, into which the exported data is written. |
|
bigquery_output_dataset |
The path of the BigQuery dataset created, in bq://projectId.bqDatasetId format, into which the exported data is written. |
|
ExportDataRequest
Request message for AutoMl.ExportData.
| Fields | |
|---|---|
name |
Required. The resource name of the dataset. Authorization requires the following Google IAM permission on the specified resource
|
output_config |
Required. The desired output location. |
ExportEvaluatedExamplesOperationMetadata
Details of EvaluatedExamples operation.
| Fields | |
|---|---|
output_info |
Output only. Information further describing the output of this evaluated examples export. |
ExportEvaluatedExamplesOutputInfo
Further describes the output of the evaluated examples export. Supplements
| Fields | |
|---|---|
bigquery_output_dataset |
The path of the BigQuery dataset created, in bq://projectId.bqDatasetId format, into which the output of export evaluated examples is written. |
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 evaluated_examples table will be created. It will have all the same columns as the
primary_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_AnnotationPayloads, represented as STRUCT-s, containing TablesAnnotation.
| Fields | |
|---|---|
bigquery_destination |
The BigQuery location where the output is to be written to. |
ExportEvaluatedExamplesRequest
Request message for AutoMl.ExportEvaluatedExamples.
| Fields | |
|---|---|
name |
Required. The resource name of the model whose evaluated examples are to be exported. Authorization requires the following Google IAM permission on the specified resource
|
output_config |
Required. The desired output location and configuration. |
ExportModelOperationMetadata
Details of ExportModel operation.
| Fields | |
|---|---|
output_info |
Output only. Information further describing the output of this model export. |
ExportModelOutputInfo
Further describes the output of model export. Supplements
| Fields | |
|---|---|
gcs_output_directory |
The full path of the Google Cloud Storage directory created, into which the model will be exported. |
ExportModelRequest
Request message for AutoMl.ExportModel. Models need to be enabled for exporting, otherwise an error code will be returned.
| Fields | |
|---|---|
name |
Required. The resource name of the model to export. Authorization requires the following Google IAM permission on the specified resource
|
output_config |
Required. The desired output location and configuration. |
Float64Stats
The data statistics of a series of FLOAT64 values.
| Fields | |
|---|---|
mean |
The mean of the series. |
standard_deviation |
The standard deviation of the series. |
quantiles[] |
Ordered from 0 to k k-quantile values of the data series of n values. The value at index i is, approximately, the i*n/k-th smallest value in the series; for i = 0 and i = k these are, respectively, the min and max values. |
histogram_buckets[] |
Histogram buckets of the data series. Sorted by the min value of the bucket, ascendingly, and the number of the buckets is dynamically generated. The buckets are non-overlapping and completely cover whole FLOAT64 range with min of first bucket being |
HistogramBucket
A bucket of a histogram.
| Fields | |
|---|---|
min |
The minimum value of the bucket, inclusive. |
max |
The maximum value of the bucket, exclusive unless max = |
count |
The number of data values that are in the bucket, i.e. are between min and max values. |
GcrDestination
The GCR location where the image must be pushed to.
| Fields | |
|---|---|
output_uri |
Required. Google Contained Registry URI of the new image, up to 2000 characters long. See https: //cloud.google.com/container-registry/do // cs/pushing-and-pulling#pushing_an_image_to_a_registry Accepted forms: * [HOSTNAME]/[PROJECT-ID]/[IMAGE] * [HOSTNAME]/[PROJECT-ID]/[IMAGE]:[TAG] The requesting user must have permission to push images the project. |
GcsDestination
The Google Cloud Storage location where the output is to be written to.
| Fields | |
|---|---|
output_uri_prefix |
Required. Google Cloud Storage URI to output directory, up to 2000 characters long. Accepted forms: * Prefix path: gs://bucket/directory The requesting user must have write permission to the bucket. The directory is created if it doesn't exist. |
GcsSource
The Google Cloud Storage location for the input content.
| Fields | |
|---|---|
input_uris[] |
Required. Google Cloud Storage URIs to input files, up to 2000 characters long. Accepted forms: * Full object path, e.g. gs://bucket/directory/object.csv |
GetAnnotationSpecRequest
Request message for AutoMl.GetAnnotationSpec.
| Fields | |
|---|---|
name |
The resource name of the annotation spec to retrieve. Authorization requires the following Google IAM permission on the specified resource
|
GetColumnSpecRequest
Request message for AutoMl.GetColumnSpec.
| Fields | |
|---|---|
name |
The resource name of the column spec to retrieve. Authorization requires the following Google IAM permission on the specified resource
|
field_mask |
Mask specifying which fields to read. |
GetDatasetRequest
Request message for AutoMl.GetDataset.
| Fields | |
|---|---|
name |
The resource name of the dataset to retrieve. Authorization requires the following Google IAM permission on the specified resource
|
GetModelEvaluationRequest
Request message for AutoMl.GetModelEvaluation.
| Fields | |
|---|---|
name |
Resource name for the model evaluation. Authorization requires the following Google IAM permission on the specified resource
|
GetModelRequest
Request message for AutoMl.GetModel.
| Fields | |
|---|---|
name |
Resource name of the model. Authorization requires the following Google IAM permission on the specified resource
|
GetTableSpecRequest
Request message for AutoMl.GetTableSpec.
| Fields | |
|---|---|
name |
The resource name of the table spec to retrieve. Authorization requires the following Google IAM permission on the specified resource
|
field_mask |
Mask specifying which fields to read. |
Image
A representation of an image. Only images up to 30MB in size are supported.
| Fields | ||
|---|---|---|
thumbnail_uri |
Output only. HTTP URI to the thumbnail image. |
|
Union field data. Input only. The data representing the image. For Predict calls image_bytes must be set, as other options are not currently supported by prediction API. You can read the contents of an uploaded image by using the content_uri field. data can be only one of the following: |
||
image_bytes |
Image content represented as a stream of bytes. Note: As with all |
|
input_config |
An input config specifying the content of the image. |
|
ImageClassificationDatasetMetadata
Dataset metadata that is specific to image classification.
| Fields | |
|---|---|
classification_type |
Required. Type of the classification problem. |
ImageClassificationModelDeploymentMetadata
Model deployment metadata specific to Image Classification.
| Fields | |
|---|---|
node_count |
Input only. The number of nodes to deploy the model on. A node is an abstraction of a machine resource, which can handle online prediction QPS as given in the model's
|
ImageClassificationModelMetadata
Model metadata for image classification.
| Fields | |
|---|---|
base_model_id |
Optional. The ID of the |
train_budget |
Required. The train budget of creating this model, expressed in hours. The actual |
train_cost |
Output only. The actual train cost of creating this model, expressed in hours. If this model is created from a |
stop_reason |
Output only. The reason that this create model operation stopped, e.g. |
model_type |
Optional. Type of the model. The available values are: * |
node_qps |
Output only. An approximate number of online prediction QPS that can be supported by this model per each node on which it is deployed. |
node_count |
Output only. The number of nodes this model is deployed on. A node is an abstraction of a machine resource, which can handle online prediction QPS as given in the node_qps field. |
ImageObjectDetectionAnnotation
Annotation details for image object detection.
| Fields | |
|---|---|
bounding_box |
Output only. The rectangle representing the object location. |
score |
Output only. The confidence that this annotation is positive for the parent example, value in [0, 1], higher means higher positivity confidence. |
ImageObjectDetectionDatasetMetadata
Dataset metadata specific to image object detection.
ImageObjectDetectionEvaluationMetrics
Model evaluation metrics for image object detection problems. Evaluates prediction quality of labeled bounding boxes.
| Fields | |
|---|---|
evaluated_bounding_box_count |
Output only. The total number of bounding boxes (i.e. summed over all images) the ground truth used to create this evaluation had. |
bounding_box_metrics_entries[] |
Output only. The bounding boxes match metrics for each Intersection-over-union threshold 0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 and each label confidence threshold 0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 pair. |
bounding_box_mean_average_precision |
Output only. The single metric for bounding boxes evaluation: the mean_average_precision averaged over all bounding_box_metrics_entries. |
ImageObjectDetectionModelDeploymentMetadata
Model deployment metadata specific to Image Object Detection.
| Fields | |
|---|---|
node_count |
Input only. The number of nodes to deploy the model on. A node is an abstraction of a machine resource, which can handle online prediction QPS as given in the model's [qps_per_node][google.cloud.automl.v1beta1.ImageObjectDetectionModelMetadata.qps_per_node]. Must be between 1 and 100, inclusive on both ends. |
ImageObjectDetectionModelMetadata
Model metadata specific to image object detection.
| Fields | |
|---|---|
model_type |
Optional. Type of the model. The available values are: * |
node_count |
Output only. The number of nodes this model is deployed on. A node is an abstraction of a machine resource, which can handle online prediction QPS as given in the qps_per_node field. |
node_qps |
Output only. An approximate number of online prediction QPS that can be supported by this model per each node on which it is deployed. |
stop_reason |
Output only. The reason that this create model operation stopped, e.g. |
train_budget_milli_node_hours |
The train budget of creating this model, expressed in milli node hours i.e. 1,000 value in this field means 1 node hour. The actual |
train_cost_milli_node_hours |
Output only. The actual train cost of creating this model, expressed in milli node hours, i.e. 1,000 value in this field means 1 node hour. Guaranteed to not exceed the train budget. |
ImportDataOperationMetadata
Details of ImportData operation.
ImportDataRequest
Request message for AutoMl.ImportData.
| Fields | |
|---|---|
name |
Required. Dataset name. Dataset must already exist. All imported annotations and examples will be added. Authorization requires the following Google IAM permission on the specified resource
|
input_config |
Required. The desired input location and its domain specific semantics, if any. |
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_sourceor
bigquery_source can be used. All inputs is concatenated into a single
[primary_table][google.cloud.automl.v1beta1.TablesDatasetMetadata.primary_table_name] 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.
| Fields | |
|---|---|
params |
Additional domain-specific parameters describing the semantic |