package google.cloud.automl.v1beta1
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service AutoMl
service.proto:46AutoML 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}`.
rpc CreateDataset (, )
service.proto:48Creates a dataset.
message
service.proto:176Request message for [AutoMl.CreateDataset][google.cloud.automl.v1beta1.AutoMl.CreateDataset].
string parent = 1
The resource name of the project to create the dataset for.
optional dataset = 2
The dataset to create.
rpc CreateModel (, )
service.proto:105Creates a model. Returns a Model in the [response][google.longrunning.Operation.response] field when it completes. When you create a model, several model evaluations are created for it: a global evaluation, and one evaluation for each annotation spec.
message
service.proto:252Request message for [AutoMl.CreateModel][google.cloud.automl.v1beta1.AutoMl.CreateModel].
string parent = 1
Resource name of the parent project where the model is being created.
optional model = 4
The model to create.
rpc DeleteDataset (, )
service.proto:74Deletes a dataset and all of its contents. Returns empty response in the [response][google.longrunning.Operation.response] field when it completes, and `delete_details` in the [metadata][google.longrunning.Operation.metadata] field.
message
service.proto:227Request message for [AutoMl.DeleteDataset][google.cloud.automl.v1beta1.AutoMl.DeleteDataset].
string name = 1
The resource name of the dataset to delete.
rpc DeleteModel (, )
service.proto:134Deletes a model. If a model is already deployed, this only deletes the model in AutoML BE, and does not change the status of the deployed model in the production environment. Returns `google.protobuf.Empty` in the [response][google.longrunning.Operation.response] field when it completes, and `delete_details` in the [metadata][google.longrunning.Operation.metadata] field.
message
service.proto:305Request message for [AutoMl.DeleteModel][google.cloud.automl.v1beta1.AutoMl.DeleteModel].
string name = 1
Resource name of the model being deleted.
rpc DeployModel (, )
service.proto:143Deploys model. Returns a [DeployModelResponse][] in the [response][google.longrunning.Operation.response] field when it completes.
message
service.proto:311Request message for [AutoMl.DeployModel][google.cloud.automl.v1beta1.AutoMl.DeployModel].
string name = 1
Resource name of the model to deploy.
rpc ExportData (, )
service.proto:93Exports dataset's data to a Google Cloud Storage bucket. Returns an empty response in the [response][google.longrunning.Operation.response] field when it completes.
message
service.proto:243Request message for [AutoMl.ExportData][google.cloud.automl.v1beta1.AutoMl.ExportData].
string name = 1
Required. The resource name of the dataset.
optional output_config = 3
Required. The desired output location.
rpc GetDataset (, )
service.proto:56Gets a dataset.
message
service.proto:185Request message for [AutoMl.GetDataset][google.cloud.automl.v1beta1.AutoMl.GetDataset].
string name = 1
The resource name of the dataset to retrieve.
rpc GetModel (, )
service.proto:113Gets a model.
message
service.proto:261Request message for [AutoMl.GetModel][google.cloud.automl.v1beta1.AutoMl.GetModel].
string name = 1
Resource name of the model.
rpc GetModelEvaluation (, )
service.proto:161Gets a model evaluation.
message
service.proto:323Request message for [AutoMl.GetModelEvaluation][google.cloud.automl.v1beta1.AutoMl.GetModelEvaluation].
string name = 1
Resource name for the model evaluation.
rpc ImportData (, )
service.proto:83Imports data into a dataset. Returns an empty response in the [response][google.longrunning.Operation.response] field when it completes.
message
service.proto:233Request message for [AutoMl.ImportData][google.cloud.automl.v1beta1.AutoMl.ImportData].
string name = 1
Required. Dataset name. Dataset must already exist. All imported annotations and examples will be added.
optional input_config = 3
Required. The desired input location.
rpc ListDatasets (, )
service.proto:63Lists datasets in a project.
message
service.proto:191Request message for [AutoMl.ListDatasets][google.cloud.automl.v1beta1.AutoMl.ListDatasets].
string parent = 1
The resource name of the project from which to list datasets.
string filter = 3
An expression for filtering the results of the request. * `dataset_metadata` - for existence of the case. An example of using the filter is: * `translation_dataset_metadata:*` --> The dataset has translation_dataset_metadata.
int32 page_size = 4
Requested page size. Server may return fewer results than requested. If unspecified, server will pick a default size.
string page_token = 6
A token identifying a page of results for the server to return Typically obtained via [ListDatasetsResponse.next_page_token][google.cloud.automl.v1beta1.ListDatasetsResponse.next_page_token] of the previous [AutoMl.ListDatasets][google.cloud.automl.v1beta1.AutoMl.ListDatasets] call.
message
service.proto:217Response message for [AutoMl.ListDatasets][google.cloud.automl.v1beta1.AutoMl.ListDatasets].
repeated datasets = 1
The datasets read.
string next_page_token = 2
A token to retrieve next page of results. Pass to [ListDatasetsRequest.page_token][google.cloud.automl.v1beta1.ListDatasetsRequest.page_token] to obtain that page.
Lists model evaluations.
message
service.proto:329Request message for [AutoMl.ListModelEvaluations][google.cloud.automl.v1beta1.AutoMl.ListModelEvaluations].
string parent = 1
Resource name of the model to list the model evaluations for. If modelId is set as "-", this will list model evaluations from across all models of the parent location.
string filter = 3
An expression for filtering the results of the request. * `annotation_spec_id` - for =, != or existence. See example below for the last. Some examples of using the filter are: * `annotation_spec_id!=4` --> The model evaluation was done for annotation spec with ID different than 4. * `NOT annotation_spec_id:*` --> The model evaluation was done for aggregate of all annotation specs.
int32 page_size = 4
Requested page size.
string page_token = 6
A token identifying a page of results for the server to return. Typically obtained via `ListModelEvaluationsResponse.next_page_token` of the previous [AutoMl.ListModelEvaluations][google.cloud.automl.v1beta1.AutoMl.ListModelEvaluations] call.
message
service.proto:359Response message for [AutoMl.ListModelEvaluations][google.cloud.automl.v1beta1.AutoMl.ListModelEvaluations].
repeated model_evaluation = 1
List of model evaluations in the requested page.
string next_page_token = 2
A token to retrieve next page of results. Pass to [ListModelEvaluations.page_token][] to obtain that page.
rpc ListModels (, )
service.proto:120Lists models.
message
service.proto:267Request message for [AutoMl.ListModels][google.cloud.automl.v1beta1.AutoMl.ListModels].
string parent = 1
Resource name of the project, from which to list the models.
string filter = 3
An expression for filtering the results of the request. * `model_metadata` - for existence of the case. * `dataset_id` - for = or !=. Some examples of using the filter are: * `image_classification_model_metadata:*` --> The model has image_classification_model_metadata. * `dataset_id=5` --> The model was created from a sibling dataset with ID 5.
int32 page_size = 4
Requested page size.
string page_token = 6
A token identifying a page of results for the server to return Typically obtained via [ListModelsResponse.next_page_token][google.cloud.automl.v1beta1.ListModelsResponse.next_page_token] of the previous [AutoMl.ListModels][google.cloud.automl.v1beta1.AutoMl.ListModels] call.
message
service.proto:295Response message for [AutoMl.ListModels][google.cloud.automl.v1beta1.AutoMl.ListModels].
repeated model = 1
List of models in the requested page.
string next_page_token = 2
A token to retrieve next page of results. Pass to [ListModels.page_token][] to obtain that page.
rpc UndeployModel (, )
service.proto:153Undeploys model. Returns an `UndeployModelResponse` in the [response][google.longrunning.Operation.response] field when it completes.
message
service.proto:317Request message for [AutoMl.UndeployModel][google.cloud.automl.v1beta1.AutoMl.UndeployModel].
string name = 1
Resource name of the model to undeploy.
AutoML Prediction API.
rpc Predict (, )
prediction_service.proto:33Perform a prediction.
message
prediction_service.proto:42Request message for [PredictionService.Predict][google.cloud.automl.v1beta1.PredictionService.Predict].
string name = 1
Name of the model requested to serve the prediction.
optional payload = 2
Required. Payload to perform a prediction on. The payload must match the problem type that the model was trained to solve.
map<string, string> params = 3
Additional domain-specific parameters, any string must be up to 25000 characters long. * For Image Classification: `score_threshold` - (float) A value from 0.0 to 1.0. When the model makes predictions for an image, it will only produce results that have at least this confidence score threshold. The default is 0.5.
message
prediction_service.proto:68Response message for [PredictionService.Predict][google.cloud.automl.v1beta1.PredictionService.Predict]. Currently, this is only used to return an image recognition prediction result. More prediction output metadata might be introduced in the future.
repeated payload = 1
Prediction result.
map<string, string> metadata = 2
Additional domain-specific prediction response metadata.
message
annotation_payload.proto:30Contains annotation information that is relevant to AutoML.
Used in:
oneof detail
Output only . Additional information about the annotation specific to the AutoML solution.
translation = 2
Annotation details for translation.
classification = 3
Annotation details for content or image classification.
string annotation_spec_id = 1
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.
string display_name = 5
Output only. The value of [AnnotationSpec.display_name][google.cloud.automl.v1beta1.AnnotationSpec.display_name] when the model was trained. Because this field returns a value at model training time, for different models trained using the same dataset, the returned value could be different as model owner could update the display_name between any two model training.
message
classification.proto:28Contains annotation details specific to classification.
Used in:
float score = 1
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.
message
classification.proto:39Model evaluation metrics for classification problems. Visible only to v1beta1
Used in:
float au_prc = 1
Output only. The Area under precision recall curve metric.
float base_au_prc = 2
Output only. The Area under precision recall curve metric based on priors.
repeated confidence_metrics_entry = 3
Output only. Metrics that have confidence thresholds. Precision-recall curve can be derived from it.
optional confusion_matrix = 4
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.
repeated string annotation_spec_id = 5
Output only. The annotation spec ids used for this evaluation.
message
classification.proto:41Metrics for a single confidence threshold.
Used in:
float confidence_threshold = 1
Output only. The confidence threshold value used to compute the metrics.
float recall = 2
Output only. Recall under the given confidence threshold.
float precision = 3
Output only. Precision under the given confidence threshold.
float f1_score = 4
Output only. The harmonic mean of recall and precision.
float recall_at1 = 5
Output only. The recall when only considering the label that has the highest prediction score and not below the confidence threshold for each example.
float precision_at1 = 6
Output only. The precision when only considering the label that has the highest predictionscore and not below the confidence threshold for each example.
float f1_score_at1 = 7
Output only. The harmonic mean of [recall_at1][google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.recall_at1] and [precision_at1][google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.precision_at1].
message
classification.proto:69Confusion matrix of the model running the classification.
Used in:
repeated string annotation_spec_id = 1
Output only. IDs of the annotation specs used in the confusion matrix.
repeated row = 2
Output only. Rows in the confusion matrix. The number of rows is equal to the size of `annotation_spec_id`. `row[i].value[j]` is the number of examples that have ground truth of the `annotation_spec_id[i]` and are predicted as `annotation_spec_id[j]` by the model being evaluated.
message
classification.proto:71Output only. A row in the confusion matrix.
Used in:
repeated int32 example_count = 1
Output only. Value of the specific cell in the confusion matrix. The number of values each row is equal to the size of annotatin_spec_id.
Type of the classification problem.
Used in: ,
CLASSIFICATION_TYPE_UNSPECIFIED = 0
Should not be used, an un-set enum has this value by default.
MULTICLASS = 1
At most one label is allowed per example.
MULTILABEL = 2
Multiple labels are allowed for one example.
message
operations.proto:58Details of CreateModel operation.
Used in:
(message has no fields)
message
dataset.proto:35A workspace for solving a single, particular machine learning (ML) problem. A workspace contains examples that may be annotated.
Used as response type in: AutoMl.CreateDataset, AutoMl.GetDataset
Used as field type in: ,
oneof dataset_metadata
Required. The dataset metadata that is specific to the problem type.
translation_dataset_metadata = 23
Metadata for a dataset used for translation.
image_classification_dataset_metadata = 24
Metadata for a dataset used for image classification.
text_classification_dataset_metadata = 25
Metadata for a dataset used for text classification.
string name = 1
Output only. The resource name of the dataset. Form: `projects/{project_id}/locations/{location_id}/datasets/{dataset_id}`
string display_name = 2
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.
int32 example_count = 21
Output only. The number of examples in the dataset.
optional create_time = 14
Output only. Timestamp when this dataset was created.
message
data_items.proto:65Example data used for training or prediction.
Used in:
oneof payload
Required. Input only. The example data.
image = 1
An example image.
text_snippet = 2
Example text.
message
io.proto:54The GCS location where the output must be written to
Used in:
string output_uri_prefix = 1
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.
message
io.proto:46The GCS location for the input content.
Used in:
repeated string input_uris = 1
Required. Google Cloud Storage URIs to input files, up to 2000 characters long. Accepted forms: * Full object path: gs://bucket/directory/object.csv
message
data_items.proto:29A representation of an image.
Used in:
oneof 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.
bytes image_bytes = 1
Image content represented as a stream of bytes. Note: As with all `bytes` fields, protobuffers use a pure binary representation, whereas JSON representations use base64.
input_config = 6
An input config specifying the content of the image.
string thumbnail_uri = 4
Output only. HTTP URI to the thumbnail image.
message
image.proto:31Dataset metadata that is specific to image classification.
Used in:
ClassificationType classification_type = 1
Required. Type of the classification problem.
message
image.proto:38Model metadata for image classification.
Used in:
string base_model_id = 1
Optional. The ID of the `base` model. If it is specified, the new model will be created based on the `base` model. Otherwise, the new model will be created from scratch. The `base` model is expected to be in the same `project` and `location` as the new model to create.
int64 train_budget = 2
Required. The train budget of creating this model. The actual `train_cost` will be equal or less than this value.
int64 train_cost = 3
Output only. The actual train cost of creating this model. If this model is created from a `base` model, the train cost used to create the `base` model are not included.
string stop_reason = 5
Output only. The reason that this create model operation stopped, e.g. BUDGET_REACHED, CONVERGED.
message
io.proto:28Input configuration.
Used in: ,
oneof source
Required. The source of the input.
gcs_source = 1
The GCS location for the input content.
message
model.proto:32API proto representing a trained machine learning model.
Used as response type in: AutoMl.GetModel
Used as field type in: ,
oneof model_metadata
Required. The model metadata that is specific to the problem type. Must match the metadata type of the dataset used to train the model.
image_classification_model_metadata = 13
Metadata for image classification models.
text_classification_model_metadata = 14
Metadata for text classification models.
translation_model_metadata = 15
Metadata for translation models.
string name = 1
Output only. Resource name of the model. Format: `projects/{project_id}/locations/{location_id}/models/{model_id}`
string display_name = 2
Required. The name of the model 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.
string dataset_id = 3
Required. The resource ID of the dataset used to create the model. The dataset must come from the same ancestor project and location.
optional create_time = 7
Output only. Timestamp when this model was created.
optional update_time = 11
Output only. Timestamp when this model was last updated.
Model.DeploymentState deployment_state = 8
Output only. Deployment state of the model.
Deployment state of the model.
Used in:
DEPLOYMENT_STATE_UNSPECIFIED = 0
Should not be used, an un-set enum has this value by default.
DEPLOYED = 1
Model is deployed.
UNDEPLOYED = 2
Model is not deployed.
message
model_evaluation.proto:31Evaluation results of a model.
Used as response type in: AutoMl.GetModelEvaluation
Used as field type in:
oneof metrics
Output only. Problem type specific evaluation metrics.
classification_evaluation_metrics = 8
Evaluation metrics for models on classification problems models.
translation_evaluation_metrics = 9
Evaluation metrics for models on translation models.
string name = 1
Output only. Resource name of the model evaluation. Format: `projects/{project_id}/locations/{location_id}/models/{model_id}/modelEvaluations/{model_evaluation_id}`
string annotation_spec_id = 2
Output only. The ID of the annotation spec that the model evaluation applies to. The ID is empty for overall model evaluation. NOTE: Currently there is no way to obtain the display_name of the annotation spec from its ID. To see the display_names, review the model evaluations in the UI.
optional create_time = 5
Output only. Timestamp when this model evaluation was created.
int32 evaluated_example_count = 6
Output only. The number of examples used for model evaluation.
message
operations.proto:33Metadata used across all long running operations returned by AutoML API.
oneof details
Ouptut only. Details of specific operation. Even if this field is empty, the presence allows to distinguish different types of operations.
create_model_details = 10
Details of CreateModel operation.
int32 progress_percent = 13
Output only. Progress of operation. Range: [0, 100].
repeated partial_failures = 2
Output only. Partial failures encountered. E.g. single files that couldn't be read. This field should never exceed 20 entries. Status details field will contain standard GCP error details.
optional create_time = 3
Output only. Time when the operation was created.
optional update_time = 4
Output only. Time when the operation was updated for the last time.
message
io.proto:37Output configuration.
Used in:
oneof destination
Required. The destination of the output.
gcs_destination = 1
The GCS location where the output must be written to.
message
text.proto:30Dataset metadata for classification.
Used in:
ClassificationType classification_type = 1
Required. Type of the classification problem.
message
text.proto:37Model metadata that is specific to text classification.
Used in:
(message has no fields)
message
data_items.proto:49A representation of a text snippet.
Used in: ,
string content = 1
Required. The content of the text snippet as a string. Up to 250000 characters long.
string mime_type = 2
The format of the source text. For example, "text/html" or "text/plain". If left blank the format is automatically determined from the type of the uploaded content. The default is "text/html". Up to 25000 characters long.
string content_uri = 4
Output only. HTTP URI where you can download the content.
message
translation.proto:65Annotation details specific to translation.
Used in:
optional translated_content = 1
Output only . The translated content.
message
translation.proto:30Dataset metadata that is specific to translation.
Used in:
string source_language_code = 1
Required. The BCP-47 language code of the source language.
string target_language_code = 2
Required. The BCP-47 language code of the target language.
message
translation.proto:39Evaluation metrics for the dataset.
Used in:
double bleu_score = 1
Output only. BLEU score.
double base_bleu_score = 2
Output only. BLEU score for base model.
message
translation.proto:48Model metadata that is specific to translation.
Used in:
string base_model = 1
The resource name of the model to use as a baseline to train the custom model. If unset, we use the default base model provided by Google Translate. Format: `projects/{project_id}/locations/{location_id}/models/{model_id}`
string source_language_code = 2
Output only. Inferred from the dataset. The source languge (The BCP-47 language code) that is used for training.
string target_language_code = 3
Output only. The target languge (The BCP-47 language code) that is used for training.