Package Classes (1.48.0)

Summary of entries of Classes for aiplatform.

Classes

Artifact

Metadata Artifact resource for Vertex AI

AutoMLForecastingTrainingJob

Class to train AutoML forecasting models.

AutoMLTabularTrainingJob

Constructs a AutoML Tabular Training Job.

Example usage:

job = training_jobs.AutoMLTabularTrainingJob( display_name="my_display_name", optimization_prediction_type="classification", optimization_objective="minimize-log-loss", column_specs={"column_1": "auto", "column_2": "numeric"}, labels={'key': 'value'}, )

AutoMLTextTrainingJob

Constructs a AutoML Text Training Job.

AutoMLVideoTrainingJob

Constructs a AutoML Video Training Job.

BatchPredictionJob

Retrieves a BatchPredictionJob resource and instantiates its representation.

CustomContainerTrainingJob

Class to launch a Custom Training Job in Vertex AI using a Container.

CustomJob

Vertex AI Custom Job.

CustomPythonPackageTrainingJob

Class to launch a Custom Training Job in Vertex AI using a Python Package.

Takes a training implementation as a python package and executes that package in Cloud Vertex AI Training.

CustomTrainingJob

Class to launch a Custom Training Job in Vertex AI using a script.

Takes a training implementation as a python script and executes that script in Cloud Vertex AI Training.

Endpoint

Retrieves an endpoint resource.

EntityType

Public managed EntityType resource for Vertex AI.

Execution

Metadata Execution resource for Vertex AI

Experiment

Represents a Vertex AI Experiment resource.

ExperimentRun

A Vertex AI Experiment run.

Feature

Managed feature resource for Vertex AI.

Featurestore

Managed featurestore resource for Vertex AI.

HyperparameterTuningJob

Vertex AI Hyperparameter Tuning Job.

ImageDataset

A managed image dataset resource for Vertex AI.

Use this class to work with a managed image dataset. To create a managed image dataset, you need a datasource file in CSV format and a schema file in YAML format. A schema is optional for a custom model. You put the CSV file and the schema into Cloud Storage buckets.

Use image data for the following objectives:

Single-label classification. For more information, see Prepare image training data for single-label classification.
Multi-label classification. For more information, see Prepare image training data for multi-label classification.
Object detection. For more information, see Prepare image training data for object detection.

The following code shows you how to create an image dataset by importing data from a CSV datasource file and a YAML schema file. The schema file you use depends on whether your image dataset is used for single-label classification, multi-label classification, or object detection.

my_dataset = aiplatform.ImageDataset.create(
    display_name="my-image-dataset",
    gcs_source=['gs://path/to/my/image-dataset.csv'],
    import_schema_uri=['gs://path/to/my/schema.yaml']
)

MatchingEngineIndex

Matching Engine index resource for Vertex AI.

MatchingEngineIndexEndpoint

Matching Engine index endpoint resource for Vertex AI.

Model

Retrieves the model resource and instantiates its representation.

ModelDeploymentMonitoringJob

Vertex AI Model Deployment Monitoring Job.

This class should be used in conjunction with the Endpoint class in order to configure model monitoring for deployed models.

ModelEvaluation

Retrieves the ModelEvaluation resource and instantiates its representation.

PipelineJob

Retrieves a PipelineJob resource and instantiates its representation.

PipelineJobSchedule

Retrieves a PipelineJobSchedule resource and instantiates its representation.

PrivateEndpoint

Represents a Vertex AI PrivateEndpoint resource.

SequenceToSequencePlusForecastingTrainingJob

Class to train Sequence to Sequence (Seq2Seq) forecasting models.

TabularDataset

A managed tabular dataset resource for Vertex AI.

Use this class to work with tabular datasets. You can use a CSV file, BigQuery, or a pandas DataFrame to create a tabular dataset. For more information about paging through BigQuery data, see Read data with BigQuery API using pagination. For more information about tabular data, see Tabular data.

The following code shows you how to create and import a tabular dataset with a CSV file.

my_dataset = aiplatform.TabularDataset.create(
    display_name="my-dataset", gcs_source=['gs://path/to/my/dataset.csv'])

The following code shows you how to create and import a tabular dataset in two distinct steps.

my_dataset = aiplatform.TextDataset.create(
    display_name="my-dataset")

my_dataset.import(
    gcs_source=['gs://path/to/my/dataset.csv']
    import_schema_uri=aiplatform.schema.dataset.ioformat.text.multi_label_classification
)

If you create a tabular dataset with a pandas DataFrame, you need to use a BigQuery table to stage the data for Vertex AI:

my_dataset = aiplatform.TabularDataset.create_from_dataframe(
    df_source=my_pandas_dataframe,
    staging_path=f"bq://{bq_dataset_id}.table-unique"
)

TemporalFusionTransformerForecastingTrainingJob

Class to train Temporal Fusion Transformer (TFT) forecasting models.

Tensorboard

Managed tensorboard resource for Vertex AI.

TensorboardExperiment

Managed tensorboard resource for Vertex AI.

TensorboardRun

Managed tensorboard resource for Vertex AI.

TensorboardTimeSeries

Managed tensorboard resource for Vertex AI.

TextDataset

Managed text dataset resource for Vertex AI.

TimeSeriesDataset

Managed time series dataset resource for Vertex AI

TimeSeriesDenseEncoderForecastingTrainingJob

Class to train Time series Dense Encoder (TiDE) forecasting models.

VideoDataset

Managed video dataset resource for Vertex AI.

ExperimentModel

An artifact representing a Vertex Experiment Model.

DefaultSerializer

Default serializer for serialization and deserialization for prediction.

Handler

Interface for Handler class to handle prediction requests.

LocalEndpoint

Class that represents a local endpoint.

LocalModel

Class that represents a local model.

PredictionHandler

Default prediction handler for the prediction requests sent to the application.

Predictor

Interface of the Predictor class for Custom Prediction Routines.

The Predictor is responsible for the ML logic for processing a prediction request. Specifically, the Predictor must define: (1) How to load all model artifacts used during prediction into memory. (2) The logic that should be executed at predict time.

When using the default PredictionHandler, the Predictor will be invoked as follows:

predictor.postprocess(predictor.predict(predictor.preprocess(prediction_input)))

Serializer

Interface to implement serialization and deserialization for prediction.

ImageClassificationPredictionInstance

Prediction input format for Image Classification.

ImageObjectDetectionPredictionInstance

Prediction input format for Image Object Detection.

ImageSegmentationPredictionInstance

Prediction input format for Image Segmentation.

TextClassificationPredictionInstance

Prediction input format for Text Classification.

TextExtractionPredictionInstance

Prediction input format for Text Extraction.

TextSentimentPredictionInstance

Prediction input format for Text Sentiment.

VideoActionRecognitionPredictionInstance

Prediction input format for Video Action Recognition.

VideoClassificationPredictionInstance

Prediction input format for Video Classification.

VideoObjectTrackingPredictionInstance

Prediction input format for Video Object Tracking.

ImageClassificationPredictionParams

Prediction model parameters for Image Classification.

ImageObjectDetectionPredictionParams

Prediction model parameters for Image Object Detection.

ImageSegmentationPredictionParams

Prediction model parameters for Image Segmentation.

VideoActionRecognitionPredictionParams

Prediction model parameters for Video Action Recognition.

VideoClassificationPredictionParams

Prediction model parameters for Video Classification.

VideoObjectTrackingPredictionParams

Prediction model parameters for Video Object Tracking.

ClassificationPredictionResult

Prediction output format for Image and Text Classification.

ImageObjectDetectionPredictionResult

Prediction output format for Image Object Detection.

ImageSegmentationPredictionResult

Prediction output format for Image Segmentation.

TabularClassificationPredictionResult

Prediction output format for Tabular Classification.

TabularRegressionPredictionResult

Prediction output format for Tabular Regression.

TextExtractionPredictionResult

Prediction output format for Text Extraction.

TextSentimentPredictionResult

Prediction output format for Text Sentiment

VideoActionRecognitionPredictionResult

Prediction output format for Video Action Recognition.

VideoClassificationPredictionResult

Prediction output format for Video Classification.

VideoObjectTrackingPredictionResult

Prediction output format for Video Object Tracking.

Frame

The fields xMin, xMax, yMin, and yMax refer to a bounding box, i.e. the rectangle over the video frame pinpointing the found AnnotationSpec. The coordinates are relative to the frame size, and the point 0,0 is in the top left of the frame.

AutoMlImageClassification

A TrainingJob that trains and uploads an AutoML Image Classification Model.

AutoMlImageClassificationInputs

ModelType

Values: MODEL_TYPE_UNSPECIFIED (0): Should not be set. CLOUD (1): A Model best tailored to be used within Google Cloud, and which cannot be exported. Default. MOBILE_TF_LOW_LATENCY_1 (2): A model that, in addition to being available within Google Cloud, can also be exported (see ModelService.ExportModel) as TensorFlow or Core ML model and used on a mobile or edge device afterwards. Expected to have low latency, but may have lower prediction quality than other mobile models. MOBILE_TF_VERSATILE_1 (3): A model that, in addition to being available within Google Cloud, can also be exported (see ModelService.ExportModel) as TensorFlow or Core ML model and used on a mobile or edge device with afterwards. MOBILE_TF_HIGH_ACCURACY_1 (4): A model that, in addition to being available within Google Cloud, can also be exported (see ModelService.ExportModel) as TensorFlow or Core ML model and used on a mobile or edge device afterwards. Expected to have a higher latency, but should also have a higher prediction quality than other mobile models.

AutoMlImageClassificationMetadata

SuccessfulStopReason

Values: SUCCESSFUL_STOP_REASON_UNSPECIFIED (0): Should not be set. BUDGET_REACHED (1): The inputs.budgetMilliNodeHours had been reached. MODEL_CONVERGED (2): Further training of the Model ceased to increase its quality, since it already has converged.

AutoMlImageObjectDetection

A TrainingJob that trains and uploads an AutoML Image Object Detection Model.

AutoMlImageObjectDetectionInputs

ModelType

Values: MODEL_TYPE_UNSPECIFIED (0): Should not be set. CLOUD_HIGH_ACCURACY_1 (1): A model best tailored to be used within Google Cloud, and which cannot be exported. Expected to have a higher latency, but should also have a higher prediction quality than other cloud models. CLOUD_LOW_LATENCY_1 (2): A model best tailored to be used within Google Cloud, and which cannot be exported. Expected to have a low latency, but may have lower prediction quality than other cloud models. MOBILE_TF_LOW_LATENCY_1 (3): A model that, in addition to being available within Google Cloud can also be exported (see ModelService.ExportModel) and used on a mobile or edge device with TensorFlow afterwards. Expected to have low latency, but may have lower prediction quality than other mobile models. MOBILE_TF_VERSATILE_1 (4): A model that, in addition to being available within Google Cloud can also be exported (see ModelService.ExportModel) and used on a mobile or edge device with TensorFlow afterwards. MOBILE_TF_HIGH_ACCURACY_1 (5): A model that, in addition to being available within Google Cloud, can also be exported (see ModelService.ExportModel) and used on a mobile or edge device with TensorFlow afterwards. Expected to have a higher latency, but should also have a higher prediction quality than other mobile models.

AutoMlImageObjectDetectionMetadata

SuccessfulStopReason

AutoMlImageSegmentation

A TrainingJob that trains and uploads an AutoML Image Segmentation Model.

AutoMlImageSegmentationInputs

ModelType

Values: MODEL_TYPE_UNSPECIFIED (0): Should not be set. CLOUD_HIGH_ACCURACY_1 (1): A model to be used via prediction calls to uCAIP API. Expected to have a higher latency, but should also have a higher prediction quality than other models. CLOUD_LOW_ACCURACY_1 (2): A model to be used via prediction calls to uCAIP API. Expected to have a lower latency but relatively lower prediction quality. MOBILE_TF_LOW_LATENCY_1 (3): A model that, in addition to being available within Google Cloud, can also be exported (see ModelService.ExportModel) as TensorFlow model and used on a mobile or edge device afterwards. Expected to have low latency, but may have lower prediction quality than other mobile models.

AutoMlImageSegmentationMetadata

SuccessfulStopReason

AutoMlTables

A TrainingJob that trains and uploads an AutoML Tables Model.

AutoMlTablesInputs

This message has oneof_ fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

Transformation

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

AutoTransformation

Training pipeline will infer the proper transformation based on the statistic of dataset.

CategoricalArrayTransformation

Treats the column as categorical array and performs following transformation functions.

For each element in the array, convert the category name to a dictionary lookup index and generate an embedding for each index. Combine the embedding of all elements into a single embedding using the mean.
Empty arrays treated as an embedding of zeroes.

CategoricalTransformation

Training pipeline will perform following transformation functions.

The categorical string as is--no change to case, punctuation, spelling, tense, and so on.
Convert the category name to a dictionary lookup index and generate an embedding for each index.
Categories that appear less than 5 times in the training dataset are treated as the "unknown" category. The "unknown" category gets its own special lookup index and resulting embedding.

NumericArrayTransformation

Treats the column as numerical array and performs following transformation functions.

All transformations for Numerical types applied to the average of the all elements.
The average of empty arrays is treated as zero.

NumericTransformation

Training pipeline will perform following transformation functions.

The value converted to float32.
The z_score of the value.
log(value+1) when the value is greater than or equal to 0. Otherwise, this transformation is not applied and the value is considered a missing value.
z_score of log(value+1) when the value is greater than or equal to 0. Otherwise, this transformation is not applied and the value is considered a missing value.
A boolean value that indicates whether the value is valid.

TextArrayTransformation

Treats the column as text array and performs following transformation functions.

Concatenate all text values in the array into a single text value using a space (" ") as a delimiter, and then treat the result as a single text value. Apply the transformations for Text columns.
Empty arrays treated as an empty text.

TextTransformation

Training pipeline will perform following transformation functions.

The text as is--no change to case, punctuation, spelling, tense, and so on.
Tokenize text to words. Convert each words to a dictionary lookup index and generate an embedding for each index. Combine the embedding of all elements into a single embedding using the mean.
Tokenization is based on unicode script boundaries.
Missing values get their own lookup index and resulting embedding.
Stop-words receive no special treatment and are not removed.

TimestampTransformation

Training pipeline will perform following transformation functions.

Apply the transformation functions for Numerical columns.
Determine the year, month, day,and weekday. Treat each value from the
timestamp as a Categorical column.
Invalid numerical values (for example, values that fall outside of a typical timestamp range, or are extreme values) receive no special treatment and are not removed.

AutoMlTablesMetadata

Model metadata specific to AutoML Tables.

AutoMlTextClassification

A TrainingJob that trains and uploads an AutoML Text Classification Model.

AutoMlTextClassificationInputs

AutoMlTextExtraction

A TrainingJob that trains and uploads an AutoML Text Extraction Model.

AutoMlTextExtractionInputs

API documentation for aiplatform.v1.schema.trainingjob.definition_v1.types.AutoMlTextExtractionInputs class.

AutoMlTextSentiment

A TrainingJob that trains and uploads an AutoML Text Sentiment Model.

AutoMlTextSentimentInputs

AutoMlVideoActionRecognition

A TrainingJob that trains and uploads an AutoML Video Action Recognition Model.

AutoMlVideoActionRecognitionInputs

ModelType

Values: MODEL_TYPE_UNSPECIFIED (0): Should not be set. CLOUD (1): A model best tailored to be used within Google Cloud, and which c annot be exported. Default. MOBILE_VERSATILE_1 (2): A model that, in addition to being available within Google Cloud, can also be exported (see ModelService.ExportModel) as a TensorFlow or TensorFlow Lite model and used on a mobile or edge device afterwards. MOBILE_JETSON_VERSATILE_1 (3): A model that, in addition to being available within Google Cloud, can also be exported (see ModelService.ExportModel) to a Jetson device afterwards. MOBILE_CORAL_VERSATILE_1 (4): A model that, in addition to being available within Google Cloud, can also be exported (see ModelService.ExportModel) as a TensorFlow or TensorFlow Lite model and used on a Coral device afterwards.

AutoMlVideoClassification

A TrainingJob that trains and uploads an AutoML Video Classification Model.

AutoMlVideoClassificationInputs

ModelType

AutoMlVideoObjectTracking

A TrainingJob that trains and uploads an AutoML Video ObjectTracking Model.

AutoMlVideoObjectTrackingInputs

ModelType

Values: MODEL_TYPE_UNSPECIFIED (0): Should not be set. CLOUD (1): A model best tailored to be used within Google Cloud, and which c annot be exported. Default. MOBILE_VERSATILE_1 (2): A model that, in addition to being available within Google Cloud, can also be exported (see ModelService.ExportModel) as a TensorFlow or TensorFlow Lite model and used on a mobile or edge device afterwards. MOBILE_CORAL_VERSATILE_1 (3): A versatile model that is meant to be exported (see ModelService.ExportModel) and used on a Google Coral device. MOBILE_CORAL_LOW_LATENCY_1 (4): A model that trades off quality for low latency, to be exported (see ModelService.ExportModel) and used on a Google Coral device. MOBILE_JETSON_VERSATILE_1 (5): A versatile model that is meant to be exported (see ModelService.ExportModel) and used on an NVIDIA Jetson device. MOBILE_JETSON_LOW_LATENCY_1 (6): A model that trades off quality for low latency, to be exported (see ModelService.ExportModel) and used on an NVIDIA Jetson device.

ExportEvaluatedDataItemsConfig

Configuration for exporting test set predictions to a BigQuery table.

ImageClassificationPredictionInstance

Prediction input format for Image Classification.

ImageObjectDetectionPredictionInstance

Prediction input format for Image Object Detection.

ImageSegmentationPredictionInstance

Prediction input format for Image Segmentation.

TextClassificationPredictionInstance

Prediction input format for Text Classification.

TextExtractionPredictionInstance

Prediction input format for Text Extraction.

TextSentimentPredictionInstance

Prediction input format for Text Sentiment.

VideoActionRecognitionPredictionInstance

Prediction input format for Video Action Recognition.

VideoClassificationPredictionInstance

Prediction input format for Video Classification.

VideoObjectTrackingPredictionInstance

Prediction input format for Video Object Tracking.

ImageClassificationPredictionParams

Prediction model parameters for Image Classification.

ImageObjectDetectionPredictionParams

Prediction model parameters for Image Object Detection.

ImageSegmentationPredictionParams

Prediction model parameters for Image Segmentation.

VideoActionRecognitionPredictionParams

Prediction model parameters for Video Action Recognition.

VideoClassificationPredictionParams

Prediction model parameters for Video Classification.

VideoObjectTrackingPredictionParams

Prediction model parameters for Video Object Tracking.

ClassificationPredictionResult

Prediction output format for Image and Text Classification.

ImageObjectDetectionPredictionResult

Prediction output format for Image Object Detection.

ImageSegmentationPredictionResult

Prediction output format for Image Segmentation.

TabularClassificationPredictionResult

Prediction output format for Tabular Classification.

TabularRegressionPredictionResult

Prediction output format for Tabular Regression.

TextExtractionPredictionResult

Prediction output format for Text Extraction.

TextSentimentPredictionResult

Prediction output format for Text Sentiment

TimeSeriesForecastingPredictionResult

Prediction output format for Time Series Forecasting.

VideoActionRecognitionPredictionResult

Prediction output format for Video Action Recognition.

VideoClassificationPredictionResult

Prediction output format for Video Classification.

VideoObjectTrackingPredictionResult

Prediction output format for Video Object Tracking.

Frame

AutoMlForecasting

A TrainingJob that trains and uploads an AutoML Forecasting Model.

AutoMlForecastingInputs

Granularity

A duration of time expressed in time granularity units.

Transformation

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

AutoTransformation

Training pipeline will infer the proper transformation based on the statistic of dataset.

CategoricalTransformation

Training pipeline will perform following transformation functions.

The categorical string as is--no change to case, punctuation, spelling, tense, and so on.
Convert the category name to a dictionary lookup index and generate an embedding for each index.
Categories that appear less than 5 times in the training dataset are treated as the "unknown" category. The "unknown" category gets its own special lookup index and resulting embedding.

NumericTransformation

Training pipeline will perform following transformation functions.

The value converted to float32.
The z_score of the value.
log(value+1) when the value is greater than or equal to 0. Otherwise, this transformation is not applied and the value is considered a missing value.
z_score of log(value+1) when the value is greater than or equal to 0. Otherwise, this transformation is not applied and the value is considered a missing value.
A boolean value that indicates whether the value is valid.

TextTransformation

Training pipeline will perform following transformation functions.

The text as is--no change to case, punctuation, spelling, tense, and so on.
Convert the category name to a dictionary lookup index and generate an embedding for each index.

TimestampTransformation

Training pipeline will perform following transformation functions.

Apply the transformation functions for Numerical columns.
Determine the year, month, day,and weekday. Treat each value from the timestamp as a Categorical column.
Invalid numerical values (for example, values that fall outside of a typical timestamp range, or are extreme values) receive no special treatment and are not removed.

AutoMlForecastingMetadata

Model metadata specific to AutoML Forecasting.

AutoMlImageClassification

A TrainingJob that trains and uploads an AutoML Image Classification Model.

AutoMlImageClassificationInputs

ModelType

AutoMlImageClassificationMetadata

SuccessfulStopReason

AutoMlImageObjectDetection

A TrainingJob that trains and uploads an AutoML Image Object Detection Model.

AutoMlImageObjectDetectionInputs

ModelType

AutoMlImageObjectDetectionMetadata

SuccessfulStopReason

AutoMlImageSegmentation

A TrainingJob that trains and uploads an AutoML Image Segmentation Model.

AutoMlImageSegmentationInputs

ModelType

AutoMlImageSegmentationMetadata

SuccessfulStopReason

AutoMlTables

A TrainingJob that trains and uploads an AutoML Tables Model.

AutoMlTablesInputs

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

Transformation

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

AutoTransformation

Training pipeline will infer the proper transformation based on the statistic of dataset.

CategoricalArrayTransformation

Treats the column as categorical array and performs following transformation functions.

For each element in the array, convert the category name to a dictionary lookup index and generate an embedding for each index. Combine the embedding of all elements into a single embedding using the mean.
Empty arrays treated as an embedding of zeroes.

CategoricalTransformation

Training pipeline will perform following transformation functions.

The categorical string as is--no change to case, punctuation, spelling, tense, and so on.
Convert the category name to a dictionary lookup index and generate an embedding for each index.
Categories that appear less than 5 times in the training dataset are treated as the "unknown" category. The "unknown" category gets its own special lookup index and resulting embedding.

NumericArrayTransformation

Treats the column as numerical array and performs following transformation functions.

All transformations for Numerical types applied to the average of the all elements.
The average of empty arrays is treated as zero.

NumericTransformation

Training pipeline will perform following transformation functions.

The value converted to float32.
The z_score of the value.
log(value+1) when the value is greater than or equal to 0. Otherwise, this transformation is not applied and the value is considered a missing value.
z_score of log(value+1) when the value is greater than or equal to 0. Otherwise, this transformation is not applied and the value is considered a missing value.
A boolean value that indicates whether the value is valid.

TextArrayTransformation

Treats the column as text array and performs following transformation functions.

Concatenate all text values in the array into a single text value using a space (" ") as a delimiter, and then treat the result as a single text value. Apply the transformations for Text columns.
Empty arrays treated as an empty text.

TextTransformation

Training pipeline will perform following transformation functions.

The text as is--no change to case, punctuation, spelling, tense, and so on.
Tokenize text to words. Convert each words to a dictionary lookup index and generate an embedding for each index. Combine the embedding of all elements into a single embedding using the mean.
Tokenization is based on unicode script boundaries.
Missing values get their own lookup index and resulting embedding.
Stop-words receive no special treatment and are not removed.

TimestampTransformation

Training pipeline will perform following transformation functions.

Apply the transformation functions for Numerical columns.
Determine the year, month, day,and weekday. Treat each value from the
timestamp as a Categorical column.
Invalid numerical values (for example, values that fall outside of a typical timestamp range, or are extreme values) receive no special treatment and are not removed.

AutoMlTablesMetadata

Model metadata specific to AutoML Tables.

AutoMlTextClassification

A TrainingJob that trains and uploads an AutoML Text Classification Model.

AutoMlTextClassificationInputs

AutoMlTextExtraction

A TrainingJob that trains and uploads an AutoML Text Extraction Model.

AutoMlTextExtractionInputs

API documentation for aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlTextExtractionInputs class.

AutoMlTextSentiment

A TrainingJob that trains and uploads an AutoML Text Sentiment Model.

AutoMlTextSentimentInputs

AutoMlVideoActionRecognition

A TrainingJob that trains and uploads an AutoML Video Action Recognition Model.

AutoMlVideoActionRecognitionInputs

ModelType

AutoMlVideoClassification

A TrainingJob that trains and uploads an AutoML Video Classification Model.

AutoMlVideoClassificationInputs

ModelType

AutoMlVideoObjectTracking

A TrainingJob that trains and uploads an AutoML Video ObjectTracking Model.

AutoMlVideoObjectTrackingInputs

ModelType

Values: MODEL_TYPE_UNSPECIFIED (0): Should not be set. CLOUD (1): A model best tailored to be used within Google Cloud, and which c annot be exported. Default. MOBILE_VERSATILE_1 (2): A model that, in addition to being available within Google Cloud, can also be exported (see ModelService.ExportModel) as a TensorFlow or TensorFlow Lite model and used on a mobile or edge device afterwards. MOBILE_CORAL_VERSATILE_1 (3): A versatile model that is meant to be exported (see ModelService.ExportModel) and used on a Google Coral device. MOBILE_CORAL_LOW_LATENCY_1 (4): A model that trades off quality for low latency, to be exported (see ModelService.ExportModel) and used on a Google Coral device. MOBILE_JETSON_VERSATILE_1 (5): A versatile model that is meant to be exported (see ModelService.ExportModel) and used on an NVIDIA Jetson device. MOBILE_JETSON_LOW_LATENCY_1 (6): A model that trades off quality for low latency, to be exported (see ModelService.ExportModel) and used on an NVIDIA Jetson device.

ExportEvaluatedDataItemsConfig

Configuration for exporting test set predictions to a BigQuery table.

DatasetServiceAsyncClient

The service that manages Vertex AI Dataset and its child resources.

DatasetServiceClient

The service that manages Vertex AI Dataset and its child resources.

ListAnnotationsAsyncPager

A pager for iterating through list_annotations requests.

This class thinly wraps an initial ListAnnotationsResponse object, and provides an __aiter__ method to iterate through its annotations field.

If there are more pages, the __aiter__ method will make additional ListAnnotations requests and continue to iterate through the annotations field on the corresponding responses.

All the usual ListAnnotationsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListAnnotationsPager

A pager for iterating through list_annotations requests.

This class thinly wraps an initial ListAnnotationsResponse object, and provides an __iter__ method to iterate through its annotations field.

If there are more pages, the __iter__ method will make additional ListAnnotations requests and continue to iterate through the annotations field on the corresponding responses.

All the usual ListAnnotationsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListDataItemsAsyncPager

A pager for iterating through list_data_items requests.

This class thinly wraps an initial ListDataItemsResponse object, and provides an __aiter__ method to iterate through its data_items field.

If there are more pages, the __aiter__ method will make additional ListDataItems requests and continue to iterate through the data_items field on the corresponding responses.

All the usual ListDataItemsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListDataItemsPager

A pager for iterating through list_data_items requests.

This class thinly wraps an initial ListDataItemsResponse object, and provides an __iter__ method to iterate through its data_items field.

If there are more pages, the __iter__ method will make additional ListDataItems requests and continue to iterate through the data_items field on the corresponding responses.

All the usual ListDataItemsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListDatasetVersionsAsyncPager

A pager for iterating through list_dataset_versions requests.

This class thinly wraps an initial ListDatasetVersionsResponse object, and provides an __aiter__ method to iterate through its dataset_versions field.

If there are more pages, the __aiter__ method will make additional ListDatasetVersions requests and continue to iterate through the dataset_versions field on the corresponding responses.

All the usual ListDatasetVersionsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListDatasetVersionsPager

A pager for iterating through list_dataset_versions requests.

This class thinly wraps an initial ListDatasetVersionsResponse object, and provides an __iter__ method to iterate through its dataset_versions field.

If there are more pages, the __iter__ method will make additional ListDatasetVersions requests and continue to iterate through the dataset_versions field on the corresponding responses.

All the usual ListDatasetVersionsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListDatasetsAsyncPager

A pager for iterating through list_datasets requests.

This class thinly wraps an initial ListDatasetsResponse object, and provides an __aiter__ method to iterate through its datasets field.

If there are more pages, the __aiter__ method will make additional ListDatasets requests and continue to iterate through the datasets field on the corresponding responses.

All the usual ListDatasetsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListDatasetsPager

A pager for iterating through list_datasets requests.

This class thinly wraps an initial ListDatasetsResponse object, and provides an __iter__ method to iterate through its datasets field.

If there are more pages, the __iter__ method will make additional ListDatasets requests and continue to iterate through the datasets field on the corresponding responses.

All the usual ListDatasetsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListSavedQueriesAsyncPager

A pager for iterating through list_saved_queries requests.

This class thinly wraps an initial ListSavedQueriesResponse object, and provides an __aiter__ method to iterate through its saved_queries field.

If there are more pages, the __aiter__ method will make additional ListSavedQueries requests and continue to iterate through the saved_queries field on the corresponding responses.

All the usual ListSavedQueriesResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListSavedQueriesPager

A pager for iterating through list_saved_queries requests.

This class thinly wraps an initial ListSavedQueriesResponse object, and provides an __iter__ method to iterate through its saved_queries field.

If there are more pages, the __iter__ method will make additional ListSavedQueries requests and continue to iterate through the saved_queries field on the corresponding responses.

All the usual ListSavedQueriesResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

SearchDataItemsAsyncPager

A pager for iterating through search_data_items requests.

This class thinly wraps an initial SearchDataItemsResponse object, and provides an __aiter__ method to iterate through its data_item_views field.

If there are more pages, the __aiter__ method will make additional SearchDataItems requests and continue to iterate through the data_item_views field on the corresponding responses.

All the usual SearchDataItemsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

SearchDataItemsPager

A pager for iterating through search_data_items requests.

This class thinly wraps an initial SearchDataItemsResponse object, and provides an __iter__ method to iterate through its data_item_views field.

If there are more pages, the __iter__ method will make additional SearchDataItems requests and continue to iterate through the data_item_views field on the corresponding responses.

All the usual SearchDataItemsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

DeploymentResourcePoolServiceAsyncClient

A service that manages the DeploymentResourcePool resource.

DeploymentResourcePoolServiceClient

A service that manages the DeploymentResourcePool resource.

ListDeploymentResourcePoolsAsyncPager

A pager for iterating through list_deployment_resource_pools requests.

This class thinly wraps an initial ListDeploymentResourcePoolsResponse object, and provides an __aiter__ method to iterate through its deployment_resource_pools field.

If there are more pages, the __aiter__ method will make additional ListDeploymentResourcePools requests and continue to iterate through the deployment_resource_pools field on the corresponding responses.

All the usual ListDeploymentResourcePoolsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListDeploymentResourcePoolsPager

A pager for iterating through list_deployment_resource_pools requests.

This class thinly wraps an initial ListDeploymentResourcePoolsResponse object, and provides an __iter__ method to iterate through its deployment_resource_pools field.

If there are more pages, the __iter__ method will make additional ListDeploymentResourcePools requests and continue to iterate through the deployment_resource_pools field on the corresponding responses.

All the usual ListDeploymentResourcePoolsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

QueryDeployedModelsAsyncPager

A pager for iterating through query_deployed_models requests.

This class thinly wraps an initial QueryDeployedModelsResponse object, and provides an __aiter__ method to iterate through its deployed_models field.

If there are more pages, the __aiter__ method will make additional QueryDeployedModels requests and continue to iterate through the deployed_models field on the corresponding responses.

All the usual QueryDeployedModelsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

QueryDeployedModelsPager

A pager for iterating through query_deployed_models requests.

This class thinly wraps an initial QueryDeployedModelsResponse object, and provides an __iter__ method to iterate through its deployed_models field.

If there are more pages, the __iter__ method will make additional QueryDeployedModels requests and continue to iterate through the deployed_models field on the corresponding responses.

All the usual QueryDeployedModelsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

EndpointServiceAsyncClient

A service for managing Vertex AI's Endpoints.

EndpointServiceClient

A service for managing Vertex AI's Endpoints.

ListEndpointsAsyncPager

A pager for iterating through list_endpoints requests.

This class thinly wraps an initial ListEndpointsResponse object, and provides an __aiter__ method to iterate through its endpoints field.

If there are more pages, the __aiter__ method will make additional ListEndpoints requests and continue to iterate through the endpoints field on the corresponding responses.

All the usual ListEndpointsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListEndpointsPager

A pager for iterating through list_endpoints requests.

This class thinly wraps an initial ListEndpointsResponse object, and provides an __iter__ method to iterate through its endpoints field.

If there are more pages, the __iter__ method will make additional ListEndpoints requests and continue to iterate through the endpoints field on the corresponding responses.

All the usual ListEndpointsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

FeatureOnlineStoreAdminServiceAsyncClient

The service that handles CRUD and List for resources for FeatureOnlineStore.

FeatureOnlineStoreAdminServiceClient

The service that handles CRUD and List for resources for FeatureOnlineStore.

ListFeatureOnlineStoresAsyncPager

A pager for iterating through list_feature_online_stores requests.

This class thinly wraps an initial ListFeatureOnlineStoresResponse object, and provides an __aiter__ method to iterate through its feature_online_stores field.

If there are more pages, the __aiter__ method will make additional ListFeatureOnlineStores requests and continue to iterate through the feature_online_stores field on the corresponding responses.

All the usual ListFeatureOnlineStoresResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListFeatureOnlineStoresPager

A pager for iterating through list_feature_online_stores requests.

This class thinly wraps an initial ListFeatureOnlineStoresResponse object, and provides an __iter__ method to iterate through its feature_online_stores field.

If there are more pages, the __iter__ method will make additional ListFeatureOnlineStores requests and continue to iterate through the feature_online_stores field on the corresponding responses.

All the usual ListFeatureOnlineStoresResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListFeatureViewSyncsAsyncPager

A pager for iterating through list_feature_view_syncs requests.

This class thinly wraps an initial ListFeatureViewSyncsResponse object, and provides an __aiter__ method to iterate through its feature_view_syncs field.

If there are more pages, the __aiter__ method will make additional ListFeatureViewSyncs requests and continue to iterate through the feature_view_syncs field on the corresponding responses.

All the usual ListFeatureViewSyncsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListFeatureViewSyncsPager

A pager for iterating through list_feature_view_syncs requests.

This class thinly wraps an initial ListFeatureViewSyncsResponse object, and provides an __iter__ method to iterate through its feature_view_syncs field.

If there are more pages, the __iter__ method will make additional ListFeatureViewSyncs requests and continue to iterate through the feature_view_syncs field on the corresponding responses.

All the usual ListFeatureViewSyncsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListFeatureViewsAsyncPager

A pager for iterating through list_feature_views requests.

This class thinly wraps an initial ListFeatureViewsResponse object, and provides an __aiter__ method to iterate through its feature_views field.

If there are more pages, the __aiter__ method will make additional ListFeatureViews requests and continue to iterate through the feature_views field on the corresponding responses.

All the usual ListFeatureViewsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListFeatureViewsPager

A pager for iterating through list_feature_views requests.

This class thinly wraps an initial ListFeatureViewsResponse object, and provides an __iter__ method to iterate through its feature_views field.

If there are more pages, the __iter__ method will make additional ListFeatureViews requests and continue to iterate through the feature_views field on the corresponding responses.

All the usual ListFeatureViewsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

FeatureOnlineStoreServiceAsyncClient

A service for fetching feature values from the online store.

FeatureOnlineStoreServiceClient

A service for fetching feature values from the online store.

FeatureRegistryServiceAsyncClient

The service that handles CRUD and List for resources for FeatureRegistry.

FeatureRegistryServiceClient

The service that handles CRUD and List for resources for FeatureRegistry.

ListFeatureGroupsAsyncPager

A pager for iterating through list_feature_groups requests.

This class thinly wraps an initial ListFeatureGroupsResponse object, and provides an __aiter__ method to iterate through its feature_groups field.

If there are more pages, the __aiter__ method will make additional ListFeatureGroups requests and continue to iterate through the feature_groups field on the corresponding responses.

All the usual ListFeatureGroupsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListFeatureGroupsPager

A pager for iterating through list_feature_groups requests.

This class thinly wraps an initial ListFeatureGroupsResponse object, and provides an __iter__ method to iterate through its feature_groups field.

If there are more pages, the __iter__ method will make additional ListFeatureGroups requests and continue to iterate through the feature_groups field on the corresponding responses.

All the usual ListFeatureGroupsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListFeaturesAsyncPager

A pager for iterating through list_features requests.

This class thinly wraps an initial ListFeaturesResponse object, and provides an __aiter__ method to iterate through its features field.

If there are more pages, the __aiter__ method will make additional ListFeatures requests and continue to iterate through the features field on the corresponding responses.

All the usual ListFeaturesResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListFeaturesPager

A pager for iterating through list_features requests.

This class thinly wraps an initial ListFeaturesResponse object, and provides an __iter__ method to iterate through its features field.

If there are more pages, the __iter__ method will make additional ListFeatures requests and continue to iterate through the features field on the corresponding responses.

All the usual ListFeaturesResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

FeaturestoreOnlineServingServiceAsyncClient

A service for serving online feature values.

FeaturestoreOnlineServingServiceClient

A service for serving online feature values.

FeaturestoreServiceAsyncClient

The service that handles CRUD and List for resources for Featurestore.

FeaturestoreServiceClient

The service that handles CRUD and List for resources for Featurestore.

ListEntityTypesAsyncPager

A pager for iterating through list_entity_types requests.

This class thinly wraps an initial ListEntityTypesResponse object, and provides an __aiter__ method to iterate through its entity_types field.

If there are more pages, the __aiter__ method will make additional ListEntityTypes requests and continue to iterate through the entity_types field on the corresponding responses.

All the usual ListEntityTypesResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListEntityTypesPager

A pager for iterating through list_entity_types requests.

This class thinly wraps an initial ListEntityTypesResponse object, and provides an __iter__ method to iterate through its entity_types field.

If there are more pages, the __iter__ method will make additional ListEntityTypes requests and continue to iterate through the entity_types field on the corresponding responses.

All the usual ListEntityTypesResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListFeaturesAsyncPager

A pager for iterating through list_features requests.

This class thinly wraps an initial ListFeaturesResponse object, and provides an __aiter__ method to iterate through its features field.

If there are more pages, the __aiter__ method will make additional ListFeatures requests and continue to iterate through the features field on the corresponding responses.

All the usual ListFeaturesResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListFeaturesPager

A pager for iterating through list_features requests.

This class thinly wraps an initial ListFeaturesResponse object, and provides an __iter__ method to iterate through its features field.

If there are more pages, the __iter__ method will make additional ListFeatures requests and continue to iterate through the features field on the corresponding responses.

All the usual ListFeaturesResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListFeaturestoresAsyncPager

A pager for iterating through list_featurestores requests.

This class thinly wraps an initial ListFeaturestoresResponse object, and provides an __aiter__ method to iterate through its featurestores field.

If there are more pages, the __aiter__ method will make additional ListFeaturestores requests and continue to iterate through the featurestores field on the corresponding responses.

All the usual ListFeaturestoresResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListFeaturestoresPager

A pager for iterating through list_featurestores requests.

This class thinly wraps an initial ListFeaturestoresResponse object, and provides an __iter__ method to iterate through its featurestores field.

If there are more pages, the __iter__ method will make additional ListFeaturestores requests and continue to iterate through the featurestores field on the corresponding responses.

All the usual ListFeaturestoresResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

SearchFeaturesAsyncPager

A pager for iterating through search_features requests.

This class thinly wraps an initial SearchFeaturesResponse object, and provides an __aiter__ method to iterate through its features field.

If there are more pages, the __aiter__ method will make additional SearchFeatures requests and continue to iterate through the features field on the corresponding responses.

All the usual SearchFeaturesResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

SearchFeaturesPager

A pager for iterating through search_features requests.

This class thinly wraps an initial SearchFeaturesResponse object, and provides an __iter__ method to iterate through its features field.

If there are more pages, the __iter__ method will make additional SearchFeatures requests and continue to iterate through the features field on the corresponding responses.

All the usual SearchFeaturesResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

GenAiTuningServiceAsyncClient

A service for creating and managing GenAI Tuning Jobs.

GenAiTuningServiceClient

A service for creating and managing GenAI Tuning Jobs.

ListTuningJobsAsyncPager

A pager for iterating through list_tuning_jobs requests.

This class thinly wraps an initial ListTuningJobsResponse object, and provides an __aiter__ method to iterate through its tuning_jobs field.

If there are more pages, the __aiter__ method will make additional ListTuningJobs requests and continue to iterate through the tuning_jobs field on the corresponding responses.

All the usual ListTuningJobsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListTuningJobsPager

A pager for iterating through list_tuning_jobs requests.

This class thinly wraps an initial ListTuningJobsResponse object, and provides an __iter__ method to iterate through its tuning_jobs field.

If there are more pages, the __iter__ method will make additional ListTuningJobs requests and continue to iterate through the tuning_jobs field on the corresponding responses.

All the usual ListTuningJobsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

IndexEndpointServiceAsyncClient

A service for managing Vertex AI's IndexEndpoints.

IndexEndpointServiceClient

A service for managing Vertex AI's IndexEndpoints.

ListIndexEndpointsAsyncPager

A pager for iterating through list_index_endpoints requests.

This class thinly wraps an initial ListIndexEndpointsResponse object, and provides an __aiter__ method to iterate through its index_endpoints field.

If there are more pages, the __aiter__ method will make additional ListIndexEndpoints requests and continue to iterate through the index_endpoints field on the corresponding responses.

All the usual ListIndexEndpointsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListIndexEndpointsPager

A pager for iterating through list_index_endpoints requests.

This class thinly wraps an initial ListIndexEndpointsResponse object, and provides an __iter__ method to iterate through its index_endpoints field.

If there are more pages, the __iter__ method will make additional ListIndexEndpoints requests and continue to iterate through the index_endpoints field on the corresponding responses.

All the usual ListIndexEndpointsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

IndexServiceAsyncClient

A service for creating and managing Vertex AI's Index resources.

IndexServiceClient

A service for creating and managing Vertex AI's Index resources.

ListIndexesAsyncPager

A pager for iterating through list_indexes requests.

This class thinly wraps an initial ListIndexesResponse object, and provides an __aiter__ method to iterate through its indexes field.

If there are more pages, the __aiter__ method will make additional ListIndexes requests and continue to iterate through the indexes field on the corresponding responses.

All the usual ListIndexesResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListIndexesPager

A pager for iterating through list_indexes requests.

This class thinly wraps an initial ListIndexesResponse object, and provides an __iter__ method to iterate through its indexes field.

If there are more pages, the __iter__ method will make additional ListIndexes requests and continue to iterate through the indexes field on the corresponding responses.

All the usual ListIndexesResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

JobServiceAsyncClient

A service for creating and managing Vertex AI's jobs.

JobServiceClient

A service for creating and managing Vertex AI's jobs.

ListBatchPredictionJobsAsyncPager

A pager for iterating through list_batch_prediction_jobs requests.

This class thinly wraps an initial ListBatchPredictionJobsResponse object, and provides an __aiter__ method to iterate through its batch_prediction_jobs field.

If there are more pages, the __aiter__ method will make additional ListBatchPredictionJobs requests and continue to iterate through the batch_prediction_jobs field on the corresponding responses.

All the usual ListBatchPredictionJobsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListBatchPredictionJobsPager

A pager for iterating through list_batch_prediction_jobs requests.

This class thinly wraps an initial ListBatchPredictionJobsResponse object, and provides an __iter__ method to iterate through its batch_prediction_jobs field.

If there are more pages, the __iter__ method will make additional ListBatchPredictionJobs requests and continue to iterate through the batch_prediction_jobs field on the corresponding responses.

All the usual ListBatchPredictionJobsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListCustomJobsAsyncPager

A pager for iterating through list_custom_jobs requests.

This class thinly wraps an initial ListCustomJobsResponse object, and provides an __aiter__ method to iterate through its custom_jobs field.

If there are more pages, the __aiter__ method will make additional ListCustomJobs requests and continue to iterate through the custom_jobs field on the corresponding responses.

All the usual ListCustomJobsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListCustomJobsPager

A pager for iterating through list_custom_jobs requests.

This class thinly wraps an initial ListCustomJobsResponse object, and provides an __iter__ method to iterate through its custom_jobs field.

If there are more pages, the __iter__ method will make additional ListCustomJobs requests and continue to iterate through the custom_jobs field on the corresponding responses.

All the usual ListCustomJobsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListDataLabelingJobsAsyncPager

A pager for iterating through list_data_labeling_jobs requests.

This class thinly wraps an initial ListDataLabelingJobsResponse object, and provides an __aiter__ method to iterate through its data_labeling_jobs field.

If there are more pages, the __aiter__ method will make additional ListDataLabelingJobs requests and continue to iterate through the data_labeling_jobs field on the corresponding responses.

All the usual ListDataLabelingJobsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListDataLabelingJobsPager

A pager for iterating through list_data_labeling_jobs requests.

This class thinly wraps an initial ListDataLabelingJobsResponse object, and provides an __iter__ method to iterate through its data_labeling_jobs field.

If there are more pages, the __iter__ method will make additional ListDataLabelingJobs requests and continue to iterate through the data_labeling_jobs field on the corresponding responses.

All the usual ListDataLabelingJobsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListHyperparameterTuningJobsAsyncPager

A pager for iterating through list_hyperparameter_tuning_jobs requests.

This class thinly wraps an initial ListHyperparameterTuningJobsResponse object, and provides an __aiter__ method to iterate through its hyperparameter_tuning_jobs field.

If there are more pages, the __aiter__ method will make additional ListHyperparameterTuningJobs requests and continue to iterate through the hyperparameter_tuning_jobs field on the corresponding responses.

All the usual ListHyperparameterTuningJobsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListHyperparameterTuningJobsPager

A pager for iterating through list_hyperparameter_tuning_jobs requests.

This class thinly wraps an initial ListHyperparameterTuningJobsResponse object, and provides an __iter__ method to iterate through its hyperparameter_tuning_jobs field.

If there are more pages, the __iter__ method will make additional ListHyperparameterTuningJobs requests and continue to iterate through the hyperparameter_tuning_jobs field on the corresponding responses.

All the usual ListHyperparameterTuningJobsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListModelDeploymentMonitoringJobsAsyncPager

A pager for iterating through list_model_deployment_monitoring_jobs requests.

This class thinly wraps an initial ListModelDeploymentMonitoringJobsResponse object, and provides an __aiter__ method to iterate through its model_deployment_monitoring_jobs field.

If there are more pages, the __aiter__ method will make additional ListModelDeploymentMonitoringJobs requests and continue to iterate through the model_deployment_monitoring_jobs field on the corresponding responses.

All the usual ListModelDeploymentMonitoringJobsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListModelDeploymentMonitoringJobsPager

A pager for iterating through list_model_deployment_monitoring_jobs requests.

This class thinly wraps an initial ListModelDeploymentMonitoringJobsResponse object, and provides an __iter__ method to iterate through its model_deployment_monitoring_jobs field.

If there are more pages, the __iter__ method will make additional ListModelDeploymentMonitoringJobs requests and continue to iterate through the model_deployment_monitoring_jobs field on the corresponding responses.

ListNasJobsAsyncPager

A pager for iterating through list_nas_jobs requests.

This class thinly wraps an initial ListNasJobsResponse object, and provides an __aiter__ method to iterate through its nas_jobs field.

If there are more pages, the __aiter__ method will make additional ListNasJobs requests and continue to iterate through the nas_jobs field on the corresponding responses.

All the usual ListNasJobsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListNasJobsPager

A pager for iterating through list_nas_jobs requests.

This class thinly wraps an initial ListNasJobsResponse object, and provides an __iter__ method to iterate through its nas_jobs field.

If there are more pages, the __iter__ method will make additional ListNasJobs requests and continue to iterate through the nas_jobs field on the corresponding responses.

All the usual ListNasJobsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListNasTrialDetailsAsyncPager

A pager for iterating through list_nas_trial_details requests.

This class thinly wraps an initial ListNasTrialDetailsResponse object, and provides an __aiter__ method to iterate through its nas_trial_details field.

If there are more pages, the __aiter__ method will make additional ListNasTrialDetails requests and continue to iterate through the nas_trial_details field on the corresponding responses.

All the usual ListNasTrialDetailsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListNasTrialDetailsPager

A pager for iterating through list_nas_trial_details requests.

This class thinly wraps an initial ListNasTrialDetailsResponse object, and provides an __iter__ method to iterate through its nas_trial_details field.

If there are more pages, the __iter__ method will make additional ListNasTrialDetails requests and continue to iterate through the nas_trial_details field on the corresponding responses.

All the usual ListNasTrialDetailsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

SearchModelDeploymentMonitoringStatsAnomaliesAsyncPager

A pager for iterating through search_model_deployment_monitoring_stats_anomalies requests.

This class thinly wraps an initial SearchModelDeploymentMonitoringStatsAnomaliesResponse object, and provides an __aiter__ method to iterate through its monitoring_stats field.

If there are more pages, the __aiter__ method will make additional SearchModelDeploymentMonitoringStatsAnomalies requests and continue to iterate through the monitoring_stats field on the corresponding responses.

All the usual SearchModelDeploymentMonitoringStatsAnomaliesResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

SearchModelDeploymentMonitoringStatsAnomaliesPager

A pager for iterating through search_model_deployment_monitoring_stats_anomalies requests.

This class thinly wraps an initial SearchModelDeploymentMonitoringStatsAnomaliesResponse object, and provides an __iter__ method to iterate through its monitoring_stats field.

If there are more pages, the __iter__ method will make additional SearchModelDeploymentMonitoringStatsAnomalies requests and continue to iterate through the monitoring_stats field on the corresponding responses.

LlmUtilityServiceAsyncClient

Service for LLM related utility functions.

LlmUtilityServiceClient

Service for LLM related utility functions.

MatchServiceAsyncClient

MatchService is a Google managed service for efficient vector similarity search at scale.

MatchServiceClient

MatchService is a Google managed service for efficient vector similarity search at scale.

MetadataServiceAsyncClient

Service for reading and writing metadata entries.

MetadataServiceClient

Service for reading and writing metadata entries.

ListArtifactsAsyncPager

A pager for iterating through list_artifacts requests.

This class thinly wraps an initial ListArtifactsResponse object, and provides an __aiter__ method to iterate through its artifacts field.

If there are more pages, the __aiter__ method will make additional ListArtifacts requests and continue to iterate through the artifacts field on the corresponding responses.

All the usual ListArtifactsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListArtifactsPager

A pager for iterating through list_artifacts requests.

This class thinly wraps an initial ListArtifactsResponse object, and provides an __iter__ method to iterate through its artifacts field.

If there are more pages, the __iter__ method will make additional ListArtifacts requests and continue to iterate through the artifacts field on the corresponding responses.

All the usual ListArtifactsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListContextsAsyncPager

A pager for iterating through list_contexts requests.

This class thinly wraps an initial ListContextsResponse object, and provides an __aiter__ method to iterate through its contexts field.

If there are more pages, the __aiter__ method will make additional ListContexts requests and continue to iterate through the contexts field on the corresponding responses.

All the usual ListContextsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListContextsPager

A pager for iterating through list_contexts requests.

This class thinly wraps an initial ListContextsResponse object, and provides an __iter__ method to iterate through its contexts field.

If there are more pages, the __iter__ method will make additional ListContexts requests and continue to iterate through the contexts field on the corresponding responses.

All the usual ListContextsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListExecutionsAsyncPager

A pager for iterating through list_executions requests.

This class thinly wraps an initial ListExecutionsResponse object, and provides an __aiter__ method to iterate through its executions field.

If there are more pages, the __aiter__ method will make additional ListExecutions requests and continue to iterate through the executions field on the corresponding responses.

All the usual ListExecutionsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListExecutionsPager

A pager for iterating through list_executions requests.

This class thinly wraps an initial ListExecutionsResponse object, and provides an __iter__ method to iterate through its executions field.

If there are more pages, the __iter__ method will make additional ListExecutions requests and continue to iterate through the executions field on the corresponding responses.

All the usual ListExecutionsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListMetadataSchemasAsyncPager

A pager for iterating through list_metadata_schemas requests.

This class thinly wraps an initial ListMetadataSchemasResponse object, and provides an __aiter__ method to iterate through its metadata_schemas field.

If there are more pages, the __aiter__ method will make additional ListMetadataSchemas requests and continue to iterate through the metadata_schemas field on the corresponding responses.

All the usual ListMetadataSchemasResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListMetadataSchemasPager

A pager for iterating through list_metadata_schemas requests.

This class thinly wraps an initial ListMetadataSchemasResponse object, and provides an __iter__ method to iterate through its metadata_schemas field.

If there are more pages, the __iter__ method will make additional ListMetadataSchemas requests and continue to iterate through the metadata_schemas field on the corresponding responses.

All the usual ListMetadataSchemasResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListMetadataStoresAsyncPager

A pager for iterating through list_metadata_stores requests.

This class thinly wraps an initial ListMetadataStoresResponse object, and provides an __aiter__ method to iterate through its metadata_stores field.

If there are more pages, the __aiter__ method will make additional ListMetadataStores requests and continue to iterate through the metadata_stores field on the corresponding responses.

All the usual ListMetadataStoresResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListMetadataStoresPager

A pager for iterating through list_metadata_stores requests.

This class thinly wraps an initial ListMetadataStoresResponse object, and provides an __iter__ method to iterate through its metadata_stores field.

If there are more pages, the __iter__ method will make additional ListMetadataStores requests and continue to iterate through the metadata_stores field on the corresponding responses.

All the usual ListMetadataStoresResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

MigrationServiceAsyncClient

A service that migrates resources from automl.googleapis.com, datalabeling.googleapis.com and ml.googleapis.com to Vertex AI.

MigrationServiceClient

A service that migrates resources from automl.googleapis.com, datalabeling.googleapis.com and ml.googleapis.com to Vertex AI.

SearchMigratableResourcesAsyncPager

A pager for iterating through search_migratable_resources requests.

This class thinly wraps an initial SearchMigratableResourcesResponse object, and provides an __aiter__ method to iterate through its migratable_resources field.

If there are more pages, the __aiter__ method will make additional SearchMigratableResources requests and continue to iterate through the migratable_resources field on the corresponding responses.

All the usual SearchMigratableResourcesResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

SearchMigratableResourcesPager

A pager for iterating through search_migratable_resources requests.

This class thinly wraps an initial SearchMigratableResourcesResponse object, and provides an __iter__ method to iterate through its migratable_resources field.

If there are more pages, the __iter__ method will make additional SearchMigratableResources requests and continue to iterate through the migratable_resources field on the corresponding responses.

All the usual SearchMigratableResourcesResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ModelGardenServiceAsyncClient

The interface of Model Garden Service.

ModelGardenServiceClient

The interface of Model Garden Service.

ModelServiceAsyncClient

A service for managing Vertex AI's machine learning Models.

ModelServiceClient

A service for managing Vertex AI's machine learning Models.

ListModelEvaluationSlicesAsyncPager

A pager for iterating through list_model_evaluation_slices requests.

This class thinly wraps an initial ListModelEvaluationSlicesResponse object, and provides an __aiter__ method to iterate through its model_evaluation_slices field.

If there are more pages, the __aiter__ method will make additional ListModelEvaluationSlices requests and continue to iterate through the model_evaluation_slices field on the corresponding responses.

All the usual ListModelEvaluationSlicesResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListModelEvaluationSlicesPager

A pager for iterating through list_model_evaluation_slices requests.

This class thinly wraps an initial ListModelEvaluationSlicesResponse object, and provides an __iter__ method to iterate through its model_evaluation_slices field.

If there are more pages, the __iter__ method will make additional ListModelEvaluationSlices requests and continue to iterate through the model_evaluation_slices field on the corresponding responses.

All the usual ListModelEvaluationSlicesResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListModelEvaluationsAsyncPager

A pager for iterating through list_model_evaluations requests.

This class thinly wraps an initial ListModelEvaluationsResponse object, and provides an __aiter__ method to iterate through its model_evaluations field.

If there are more pages, the __aiter__ method will make additional ListModelEvaluations requests and continue to iterate through the model_evaluations field on the corresponding responses.

All the usual ListModelEvaluationsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListModelEvaluationsPager

A pager for iterating through list_model_evaluations requests.

This class thinly wraps an initial ListModelEvaluationsResponse object, and provides an __iter__ method to iterate through its model_evaluations field.

If there are more pages, the __iter__ method will make additional ListModelEvaluations requests and continue to iterate through the model_evaluations field on the corresponding responses.

All the usual ListModelEvaluationsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListModelVersionsAsyncPager

A pager for iterating through list_model_versions requests.

This class thinly wraps an initial ListModelVersionsResponse object, and provides an __aiter__ method to iterate through its models field.

If there are more pages, the __aiter__ method will make additional ListModelVersions requests and continue to iterate through the models field on the corresponding responses.

All the usual ListModelVersionsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListModelVersionsPager

A pager for iterating through list_model_versions requests.

This class thinly wraps an initial ListModelVersionsResponse object, and provides an __iter__ method to iterate through its models field.

If there are more pages, the __iter__ method will make additional ListModelVersions requests and continue to iterate through the models field on the corresponding responses.

All the usual ListModelVersionsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListModelsAsyncPager

A pager for iterating through list_models requests.

This class thinly wraps an initial ListModelsResponse object, and provides an __aiter__ method to iterate through its models field.

If there are more pages, the __aiter__ method will make additional ListModels requests and continue to iterate through the models field on the corresponding responses.

All the usual ListModelsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListModelsPager

A pager for iterating through list_models requests.

This class thinly wraps an initial ListModelsResponse object, and provides an __iter__ method to iterate through its models field.

If there are more pages, the __iter__ method will make additional ListModels requests and continue to iterate through the models field on the corresponding responses.

All the usual ListModelsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

NotebookServiceAsyncClient

The interface for Vertex Notebook service (a.k.a. Colab on Workbench).

NotebookServiceClient

The interface for Vertex Notebook service (a.k.a. Colab on Workbench).

ListNotebookRuntimeTemplatesAsyncPager

A pager for iterating through list_notebook_runtime_templates requests.

This class thinly wraps an initial ListNotebookRuntimeTemplatesResponse object, and provides an __aiter__ method to iterate through its notebook_runtime_templates field.

If there are more pages, the __aiter__ method will make additional ListNotebookRuntimeTemplates requests and continue to iterate through the notebook_runtime_templates field on the corresponding responses.

All the usual ListNotebookRuntimeTemplatesResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListNotebookRuntimeTemplatesPager

A pager for iterating through list_notebook_runtime_templates requests.

This class thinly wraps an initial ListNotebookRuntimeTemplatesResponse object, and provides an __iter__ method to iterate through its notebook_runtime_templates field.

If there are more pages, the __iter__ method will make additional ListNotebookRuntimeTemplates requests and continue to iterate through the notebook_runtime_templates field on the corresponding responses.

All the usual ListNotebookRuntimeTemplatesResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListNotebookRuntimesAsyncPager

A pager for iterating through list_notebook_runtimes requests.

This class thinly wraps an initial ListNotebookRuntimesResponse object, and provides an __aiter__ method to iterate through its notebook_runtimes field.

If there are more pages, the __aiter__ method will make additional ListNotebookRuntimes requests and continue to iterate through the notebook_runtimes field on the corresponding responses.

All the usual ListNotebookRuntimesResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListNotebookRuntimesPager

A pager for iterating through list_notebook_runtimes requests.

This class thinly wraps an initial ListNotebookRuntimesResponse object, and provides an __iter__ method to iterate through its notebook_runtimes field.

If there are more pages, the __iter__ method will make additional ListNotebookRuntimes requests and continue to iterate through the notebook_runtimes field on the corresponding responses.

All the usual ListNotebookRuntimesResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

PersistentResourceServiceAsyncClient

A service for managing Vertex AI's machine learning PersistentResource.

PersistentResourceServiceClient

A service for managing Vertex AI's machine learning PersistentResource.

ListPersistentResourcesAsyncPager

A pager for iterating through list_persistent_resources requests.

This class thinly wraps an initial ListPersistentResourcesResponse object, and provides an __aiter__ method to iterate through its persistent_resources field.

If there are more pages, the __aiter__ method will make additional ListPersistentResources requests and continue to iterate through the persistent_resources field on the corresponding responses.

All the usual ListPersistentResourcesResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListPersistentResourcesPager

A pager for iterating through list_persistent_resources requests.

This class thinly wraps an initial ListPersistentResourcesResponse object, and provides an __iter__ method to iterate through its persistent_resources field.

If there are more pages, the __iter__ method will make additional ListPersistentResources requests and continue to iterate through the persistent_resources field on the corresponding responses.

All the usual ListPersistentResourcesResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

PipelineServiceAsyncClient

A service for creating and managing Vertex AI's pipelines. This includes both TrainingPipeline resources (used for AutoML and custom training) and PipelineJob resources (used for Vertex AI Pipelines).

PipelineServiceClient

ListPipelineJobsAsyncPager

A pager for iterating through list_pipeline_jobs requests.

This class thinly wraps an initial ListPipelineJobsResponse object, and provides an __aiter__ method to iterate through its pipeline_jobs field.

If there are more pages, the __aiter__ method will make additional ListPipelineJobs requests and continue to iterate through the pipeline_jobs field on the corresponding responses.

All the usual ListPipelineJobsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListPipelineJobsPager

A pager for iterating through list_pipeline_jobs requests.

This class thinly wraps an initial ListPipelineJobsResponse object, and provides an __iter__ method to iterate through its pipeline_jobs field.

If there are more pages, the __iter__ method will make additional ListPipelineJobs requests and continue to iterate through the pipeline_jobs field on the corresponding responses.

All the usual ListPipelineJobsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListTrainingPipelinesAsyncPager

A pager for iterating through list_training_pipelines requests.

This class thinly wraps an initial ListTrainingPipelinesResponse object, and provides an __aiter__ method to iterate through its training_pipelines field.

If there are more pages, the __aiter__ method will make additional ListTrainingPipelines requests and continue to iterate through the training_pipelines field on the corresponding responses.

All the usual ListTrainingPipelinesResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListTrainingPipelinesPager

A pager for iterating through list_training_pipelines requests.

This class thinly wraps an initial ListTrainingPipelinesResponse object, and provides an __iter__ method to iterate through its training_pipelines field.

If there are more pages, the __iter__ method will make additional ListTrainingPipelines requests and continue to iterate through the training_pipelines field on the corresponding responses.

All the usual ListTrainingPipelinesResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

PredictionServiceAsyncClient

A service for online predictions and explanations.

PredictionServiceClient

A service for online predictions and explanations.

ScheduleServiceAsyncClient

A service for creating and managing Vertex AI's Schedule resources to periodically launch shceudled runs to make API calls.

ScheduleServiceClient

A service for creating and managing Vertex AI's Schedule resources to periodically launch shceudled runs to make API calls.

ListSchedulesAsyncPager

A pager for iterating through list_schedules requests.

This class thinly wraps an initial ListSchedulesResponse object, and provides an __aiter__ method to iterate through its schedules field.

If there are more pages, the __aiter__ method will make additional ListSchedules requests and continue to iterate through the schedules field on the corresponding responses.

All the usual ListSchedulesResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListSchedulesPager

A pager for iterating through list_schedules requests.

This class thinly wraps an initial ListSchedulesResponse object, and provides an __iter__ method to iterate through its schedules field.

If there are more pages, the __iter__ method will make additional ListSchedules requests and continue to iterate through the schedules field on the corresponding responses.

All the usual ListSchedulesResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

SpecialistPoolServiceAsyncClient

A service for creating and managing Customer SpecialistPools. When customers start Data Labeling jobs, they can reuse/create Specialist Pools to bring their own Specialists to label the data. Customers can add/remove Managers for the Specialist Pool on Cloud console, then Managers will get email notifications to manage Specialists and tasks on CrowdCompute console.

SpecialistPoolServiceClient

ListSpecialistPoolsAsyncPager

A pager for iterating through list_specialist_pools requests.

This class thinly wraps an initial ListSpecialistPoolsResponse object, and provides an __aiter__ method to iterate through its specialist_pools field.

If there are more pages, the __aiter__ method will make additional ListSpecialistPools requests and continue to iterate through the specialist_pools field on the corresponding responses.

All the usual ListSpecialistPoolsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListSpecialistPoolsPager

A pager for iterating through list_specialist_pools requests.

This class thinly wraps an initial ListSpecialistPoolsResponse object, and provides an __iter__ method to iterate through its specialist_pools field.

If there are more pages, the __iter__ method will make additional ListSpecialistPools requests and continue to iterate through the specialist_pools field on the corresponding responses.

All the usual ListSpecialistPoolsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

TensorboardServiceAsyncClient

TensorboardService

TensorboardServiceClient

TensorboardService

ExportTensorboardTimeSeriesDataAsyncPager

A pager for iterating through export_tensorboard_time_series_data requests.

This class thinly wraps an initial ExportTensorboardTimeSeriesDataResponse object, and provides an __aiter__ method to iterate through its time_series_data_points field.

If there are more pages, the __aiter__ method will make additional ExportTensorboardTimeSeriesData requests and continue to iterate through the time_series_data_points field on the corresponding responses.

All the usual ExportTensorboardTimeSeriesDataResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ExportTensorboardTimeSeriesDataPager

A pager for iterating through export_tensorboard_time_series_data requests.

This class thinly wraps an initial ExportTensorboardTimeSeriesDataResponse object, and provides an __iter__ method to iterate through its time_series_data_points field.

If there are more pages, the __iter__ method will make additional ExportTensorboardTimeSeriesData requests and continue to iterate through the time_series_data_points field on the corresponding responses.

ListTensorboardExperimentsAsyncPager

A pager for iterating through list_tensorboard_experiments requests.

This class thinly wraps an initial ListTensorboardExperimentsResponse object, and provides an __aiter__ method to iterate through its tensorboard_experiments field.

If there are more pages, the __aiter__ method will make additional ListTensorboardExperiments requests and continue to iterate through the tensorboard_experiments field on the corresponding responses.

All the usual ListTensorboardExperimentsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListTensorboardExperimentsPager

A pager for iterating through list_tensorboard_experiments requests.

This class thinly wraps an initial ListTensorboardExperimentsResponse object, and provides an __iter__ method to iterate through its tensorboard_experiments field.

If there are more pages, the __iter__ method will make additional ListTensorboardExperiments requests and continue to iterate through the tensorboard_experiments field on the corresponding responses.

All the usual ListTensorboardExperimentsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListTensorboardRunsAsyncPager

A pager for iterating through list_tensorboard_runs requests.

This class thinly wraps an initial ListTensorboardRunsResponse object, and provides an __aiter__ method to iterate through its tensorboard_runs field.

If there are more pages, the __aiter__ method will make additional ListTensorboardRuns requests and continue to iterate through the tensorboard_runs field on the corresponding responses.

All the usual ListTensorboardRunsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListTensorboardRunsPager

A pager for iterating through list_tensorboard_runs requests.

This class thinly wraps an initial ListTensorboardRunsResponse object, and provides an __iter__ method to iterate through its tensorboard_runs field.

If there are more pages, the __iter__ method will make additional ListTensorboardRuns requests and continue to iterate through the tensorboard_runs field on the corresponding responses.

All the usual ListTensorboardRunsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListTensorboardTimeSeriesAsyncPager

A pager for iterating through list_tensorboard_time_series requests.

This class thinly wraps an initial ListTensorboardTimeSeriesResponse object, and provides an __aiter__ method to iterate through its tensorboard_time_series field.

If there are more pages, the __aiter__ method will make additional ListTensorboardTimeSeries requests and continue to iterate through the tensorboard_time_series field on the corresponding responses.

All the usual ListTensorboardTimeSeriesResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListTensorboardTimeSeriesPager

A pager for iterating through list_tensorboard_time_series requests.

This class thinly wraps an initial ListTensorboardTimeSeriesResponse object, and provides an __iter__ method to iterate through its tensorboard_time_series field.

If there are more pages, the __iter__ method will make additional ListTensorboardTimeSeries requests and continue to iterate through the tensorboard_time_series field on the corresponding responses.

All the usual ListTensorboardTimeSeriesResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListTensorboardsAsyncPager

A pager for iterating through list_tensorboards requests.

This class thinly wraps an initial ListTensorboardsResponse object, and provides an __aiter__ method to iterate through its tensorboards field.

If there are more pages, the __aiter__ method will make additional ListTensorboards requests and continue to iterate through the tensorboards field on the corresponding responses.

All the usual ListTensorboardsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListTensorboardsPager

A pager for iterating through list_tensorboards requests.

This class thinly wraps an initial ListTensorboardsResponse object, and provides an __iter__ method to iterate through its tensorboards field.

If there are more pages, the __iter__ method will make additional ListTensorboards requests and continue to iterate through the tensorboards field on the corresponding responses.

All the usual ListTensorboardsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

VizierServiceAsyncClient

Vertex AI Vizier API.

Vertex AI Vizier is a service to solve blackbox optimization problems, such as tuning machine learning hyperparameters and searching over deep learning architectures.

VizierServiceClient

Vertex AI Vizier API.

Vertex AI Vizier is a service to solve blackbox optimization problems, such as tuning machine learning hyperparameters and searching over deep learning architectures.

ListStudiesAsyncPager

A pager for iterating through list_studies requests.

This class thinly wraps an initial ListStudiesResponse object, and provides an __aiter__ method to iterate through its studies field.

If there are more pages, the __aiter__ method will make additional ListStudies requests and continue to iterate through the studies field on the corresponding responses.

All the usual ListStudiesResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListStudiesPager

A pager for iterating through list_studies requests.

This class thinly wraps an initial ListStudiesResponse object, and provides an __iter__ method to iterate through its studies field.

If there are more pages, the __iter__ method will make additional ListStudies requests and continue to iterate through the studies field on the corresponding responses.

All the usual ListStudiesResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListTrialsAsyncPager

A pager for iterating through list_trials requests.

This class thinly wraps an initial ListTrialsResponse object, and provides an __aiter__ method to iterate through its trials field.

If there are more pages, the __aiter__ method will make additional ListTrials requests and continue to iterate through the trials field on the corresponding responses.

All the usual ListTrialsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListTrialsPager

A pager for iterating through list_trials requests.

This class thinly wraps an initial ListTrialsResponse object, and provides an __iter__ method to iterate through its trials field.

If there are more pages, the __iter__ method will make additional ListTrials requests and continue to iterate through the trials field on the corresponding responses.

All the usual ListTrialsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

AcceleratorType

Represents a hardware accelerator type.

Values: ACCELERATOR_TYPE_UNSPECIFIED (0): Unspecified accelerator type, which means no accelerator. NVIDIA_TESLA_K80 (1): Nvidia Tesla K80 GPU. NVIDIA_TESLA_P100 (2): Nvidia Tesla P100 GPU. NVIDIA_TESLA_V100 (3): Nvidia Tesla V100 GPU. NVIDIA_TESLA_P4 (4): Nvidia Tesla P4 GPU. NVIDIA_TESLA_T4 (5): Nvidia Tesla T4 GPU. NVIDIA_TESLA_A100 (8): Nvidia Tesla A100 GPU. NVIDIA_A100_80GB (9): Nvidia A100 80GB GPU. NVIDIA_L4 (11): Nvidia L4 GPU. NVIDIA_H100_80GB (13): Nvidia H100 80Gb GPU. TPU_V2 (6): TPU v2. TPU_V3 (7): TPU v3. TPU_V4_POD (10): TPU v4.

ActiveLearningConfig

Parameters that configure the active learning pipeline. Active learning will label the data incrementally by several iterations. For every iteration, it will select a batch of data based on the sampling strategy.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

AddContextArtifactsAndExecutionsRequest

Request message for MetadataService.AddContextArtifactsAndExecutions.

AddContextArtifactsAndExecutionsResponse

Response message for MetadataService.AddContextArtifactsAndExecutions.

AddContextChildrenRequest

Request message for MetadataService.AddContextChildren.

AddContextChildrenResponse

Response message for MetadataService.AddContextChildren.

AddExecutionEventsRequest

Request message for MetadataService.AddExecutionEvents.

AddExecutionEventsResponse

Response message for MetadataService.AddExecutionEvents.

AddTrialMeasurementRequest

Request message for VizierService.AddTrialMeasurement.

Annotation

Used to assign specific AnnotationSpec to a particular area of a DataItem or the whole part of the DataItem.

LabelsEntry

The abstract base class for a message.

AnnotationSpec

Identifies a concept with which DataItems may be annotated with.

Artifact

Instance of a general artifact.

LabelsEntry

The abstract base class for a message.

State

Describes the state of the Artifact.

Values: STATE_UNSPECIFIED (0): Unspecified state for the Artifact. PENDING (1): A state used by systems like Vertex AI Pipelines to indicate that the underlying data item represented by this Artifact is being created. LIVE (2): A state indicating that the Artifact should exist, unless something external to the system deletes it.

AssignNotebookRuntimeOperationMetadata

Metadata information for NotebookService.AssignNotebookRuntime.

AssignNotebookRuntimeRequest

Request message for NotebookService.AssignNotebookRuntime.

Attribution

Attribution that explains a particular prediction output.

AutomaticResources

A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines.

AutoscalingMetricSpec

The metric specification that defines the target resource utilization (CPU utilization, accelerator's duty cycle, and so on) for calculating the desired replica count.

AvroSource

The storage details for Avro input content.

BatchCancelPipelineJobsOperationMetadata

Runtime operation information for PipelineService.BatchCancelPipelineJobs.

BatchCancelPipelineJobsRequest

Request message for PipelineService.BatchCancelPipelineJobs.

BatchCancelPipelineJobsResponse

Response message for PipelineService.BatchCancelPipelineJobs.

BatchCreateFeaturesOperationMetadata

Details of operations that perform batch create Features.

BatchCreateFeaturesRequest

Request message for FeaturestoreService.BatchCreateFeatures.

BatchCreateFeaturesResponse

Response message for FeaturestoreService.BatchCreateFeatures.

BatchCreateTensorboardRunsRequest

Request message for TensorboardService.BatchCreateTensorboardRuns.

BatchCreateTensorboardRunsResponse

Response message for TensorboardService.BatchCreateTensorboardRuns.

BatchCreateTensorboardTimeSeriesRequest

Request message for TensorboardService.BatchCreateTensorboardTimeSeries.

BatchCreateTensorboardTimeSeriesResponse

Response message for TensorboardService.BatchCreateTensorboardTimeSeries.

BatchDedicatedResources

A description of resources that are used for performing batch operations, are dedicated to a Model, and need manual configuration.

BatchDeletePipelineJobsRequest

Request message for PipelineService.BatchDeletePipelineJobs.

BatchDeletePipelineJobsResponse

Response message for PipelineService.BatchDeletePipelineJobs.

BatchImportEvaluatedAnnotationsRequest

Request message for ModelService.BatchImportEvaluatedAnnotations

BatchImportEvaluatedAnnotationsResponse

Response message for ModelService.BatchImportEvaluatedAnnotations

BatchImportModelEvaluationSlicesRequest

Request message for ModelService.BatchImportModelEvaluationSlices

BatchImportModelEvaluationSlicesResponse

Response message for ModelService.BatchImportModelEvaluationSlices

BatchMigrateResourcesOperationMetadata

Runtime operation information for MigrationService.BatchMigrateResources.

PartialResult

Represents a partial result in batch migration operation for one MigrateResourceRequest.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

BatchMigrateResourcesRequest

Request message for MigrationService.BatchMigrateResources.

BatchMigrateResourcesResponse

Response message for MigrationService.BatchMigrateResources.

BatchPredictionJob

A job that uses a Model to produce predictions on multiple [input instances][google.cloud.aiplatform.v1.BatchPredictionJob.input_config]. If predictions for significant portion of the instances fail, the job may finish without attempting predictions for all remaining instances.

InputConfig

Configures the input to BatchPredictionJob. See Model.supported_input_storage_formats for Model's supported input formats, and how instances should be expressed via any of them.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

InstanceConfig

Configuration defining how to transform batch prediction input instances to the instances that the Model accepts.

LabelsEntry

The abstract base class for a message.

OutputConfig

Configures the output of BatchPredictionJob. See Model.supported_output_storage_formats for supported output formats, and how predictions are expressed via any of them.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

OutputInfo

Further describes this job's output. Supplements output_config.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

BatchReadFeatureValuesOperationMetadata

Details of operations that batch reads Feature values.

BatchReadFeatureValuesRequest

Request message for FeaturestoreService.BatchReadFeatureValues.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

EntityTypeSpec

Selects Features of an EntityType to read values of and specifies read settings.

PassThroughField

Describe pass-through fields in read_instance source.

BatchReadFeatureValuesResponse

Response message for FeaturestoreService.BatchReadFeatureValues.

BatchReadTensorboardTimeSeriesDataRequest

Request message for TensorboardService.BatchReadTensorboardTimeSeriesData.

BatchReadTensorboardTimeSeriesDataResponse

Response message for TensorboardService.BatchReadTensorboardTimeSeriesData.

BigQueryDestination

The BigQuery location for the output content.

BigQuerySource

The BigQuery location for the input content.

Blob

Content blob.

It's preferred to send as text directly rather than raw bytes.

BlurBaselineConfig

Config for blur baseline.

When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here:

https://arxiv.org/abs/2004.03383

BoolArray

A list of boolean values.

CancelBatchPredictionJobRequest

Request message for JobService.CancelBatchPredictionJob.

CancelCustomJobRequest

Request message for JobService.CancelCustomJob.

CancelDataLabelingJobRequest

Request message for JobService.CancelDataLabelingJob.

CancelHyperparameterTuningJobRequest

Request message for JobService.CancelHyperparameterTuningJob.

CancelNasJobRequest

Request message for JobService.CancelNasJob.

CancelPipelineJobRequest

Request message for PipelineService.CancelPipelineJob.

CancelTrainingPipelineRequest

Request message for PipelineService.CancelTrainingPipeline.

CancelTuningJobRequest

Request message for GenAiTuningService.CancelTuningJob.

Candidate

A response candidate generated from the model.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

FinishReason

The reason why the model stopped generating tokens. If empty, the model has not stopped generating the tokens.

Values: FINISH_REASON_UNSPECIFIED (0): The finish reason is unspecified. STOP (1): Natural stop point of the model or provided stop sequence. MAX_TOKENS (2): The maximum number of tokens as specified in the request was reached. SAFETY (3): The token generation was stopped as the response was flagged for safety reasons. NOTE: When streaming the Candidate.content will be empty if content filters blocked the output. RECITATION (4): The token generation was stopped as the response was flagged for unauthorized citations. OTHER (5): All other reasons that stopped the token generation BLOCKLIST (6): The token generation was stopped as the response was flagged for the terms which are included from the terminology blocklist. PROHIBITED_CONTENT (7): The token generation was stopped as the response was flagged for the prohibited contents. SPII (8): The token generation was stopped as the response was flagged for Sensitive Personally Identifiable Information (SPII) contents.

CheckTrialEarlyStoppingStateMetatdata

This message will be placed in the metadata field of a google.longrunning.Operation associated with a CheckTrialEarlyStoppingState request.

CheckTrialEarlyStoppingStateRequest

Request message for VizierService.CheckTrialEarlyStoppingState.

CheckTrialEarlyStoppingStateResponse

Response message for VizierService.CheckTrialEarlyStoppingState.

Citation

Source attributions for content.

CitationMetadata

A collection of source attributions for a piece of content.

CompleteTrialRequest

Request message for VizierService.CompleteTrial.

CompletionStats

Success and error statistics of processing multiple entities (for example, DataItems or structured data rows) in batch.

ComputeTokensRequest

Request message for ComputeTokens RPC call.

ComputeTokensResponse

Response message for ComputeTokens RPC call.

ContainerRegistryDestination

The Container Registry location for the container image.

ContainerSpec

The spec of a Container.

Content

The base structured datatype containing multi-part content of a message.

A Content includes a role field designating the producer of the Content and a parts field containing multi-part data that contains the content of the message turn.

Context

Instance of a general context.

LabelsEntry

The abstract base class for a message.

CopyModelOperationMetadata

Details of ModelService.CopyModel operation.

CopyModelRequest

Request message for ModelService.CopyModel.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

CopyModelResponse

Response message of ModelService.CopyModel operation.

CountTokensRequest

Request message for [PredictionService.CountTokens][].

CountTokensResponse

Response message for [PredictionService.CountTokens][].

CreateArtifactRequest

Request message for MetadataService.CreateArtifact.

CreateBatchPredictionJobRequest

Request message for JobService.CreateBatchPredictionJob.

CreateContextRequest

Request message for MetadataService.CreateContext.

CreateCustomJobRequest

Request message for JobService.CreateCustomJob.

CreateDataLabelingJobRequest

Request message for JobService.CreateDataLabelingJob.

CreateDatasetOperationMetadata

Runtime operation information for DatasetService.CreateDataset.

CreateDatasetRequest

Request message for DatasetService.CreateDataset.

CreateDatasetVersionOperationMetadata

Runtime operation information for DatasetService.CreateDatasetVersion.

CreateDatasetVersionRequest

Request message for DatasetService.CreateDatasetVersion.

CreateDeploymentResourcePoolOperationMetadata

Runtime operation information for CreateDeploymentResourcePool method.

CreateDeploymentResourcePoolRequest

Request message for CreateDeploymentResourcePool method.

CreateEndpointOperationMetadata

Runtime operation information for EndpointService.CreateEndpoint.

CreateEndpointRequest

Request message for EndpointService.CreateEndpoint.

CreateEntityTypeOperationMetadata

Details of operations that perform create EntityType.

CreateEntityTypeRequest

Request message for FeaturestoreService.CreateEntityType.

CreateExecutionRequest

Request message for MetadataService.CreateExecution.

CreateFeatureGroupOperationMetadata

Details of operations that perform create FeatureGroup.

CreateFeatureGroupRequest

Request message for FeatureRegistryService.CreateFeatureGroup.

CreateFeatureOnlineStoreOperationMetadata

Details of operations that perform create FeatureOnlineStore.

CreateFeatureOnlineStoreRequest

Request message for FeatureOnlineStoreAdminService.CreateFeatureOnlineStore.

CreateFeatureOperationMetadata

Details of operations that perform create Feature.

CreateFeatureRequest

Request message for FeaturestoreService.CreateFeature. Request message for FeatureRegistryService.CreateFeature.

CreateFeatureViewOperationMetadata

Details of operations that perform create FeatureView.

CreateFeatureViewRequest

Request message for FeatureOnlineStoreAdminService.CreateFeatureView.

CreateFeaturestoreOperationMetadata

Details of operations that perform create Featurestore.

CreateFeaturestoreRequest

Request message for FeaturestoreService.CreateFeaturestore.

CreateHyperparameterTuningJobRequest

Request message for JobService.CreateHyperparameterTuningJob.

CreateIndexEndpointOperationMetadata

Runtime operation information for IndexEndpointService.CreateIndexEndpoint.

CreateIndexEndpointRequest

Request message for IndexEndpointService.CreateIndexEndpoint.

CreateIndexOperationMetadata

Runtime operation information for IndexService.CreateIndex.

CreateIndexRequest

Request message for IndexService.CreateIndex.

CreateMetadataSchemaRequest

Request message for MetadataService.CreateMetadataSchema.

CreateMetadataStoreOperationMetadata

Details of operations that perform MetadataService.CreateMetadataStore.

CreateMetadataStoreRequest

Request message for MetadataService.CreateMetadataStore.

CreateModelDeploymentMonitoringJobRequest

Request message for JobService.CreateModelDeploymentMonitoringJob.

CreateNasJobRequest

Request message for JobService.CreateNasJob.

CreateNotebookRuntimeTemplateOperationMetadata

Metadata information for NotebookService.CreateNotebookRuntimeTemplate.

CreateNotebookRuntimeTemplateRequest

Request message for NotebookService.CreateNotebookRuntimeTemplate.

CreatePersistentResourceOperationMetadata

Details of operations that perform create PersistentResource.

CreatePersistentResourceRequest

Request message for PersistentResourceService.CreatePersistentResource.

CreatePipelineJobRequest

Request message for PipelineService.CreatePipelineJob.

CreateRegistryFeatureOperationMetadata

Details of operations that perform create FeatureGroup.

CreateScheduleRequest

Request message for ScheduleService.CreateSchedule.

CreateSpecialistPoolOperationMetadata

Runtime operation information for SpecialistPoolService.CreateSpecialistPool.

CreateSpecialistPoolRequest

Request message for SpecialistPoolService.CreateSpecialistPool.

CreateStudyRequest

Request message for VizierService.CreateStudy.

CreateTensorboardExperimentRequest

Request message for TensorboardService.CreateTensorboardExperiment.

CreateTensorboardOperationMetadata

Details of operations that perform create Tensorboard.

CreateTensorboardRequest

Request message for TensorboardService.CreateTensorboard.

CreateTensorboardRunRequest

Request message for TensorboardService.CreateTensorboardRun.

CreateTensorboardTimeSeriesRequest

Request message for TensorboardService.CreateTensorboardTimeSeries.

CreateTrainingPipelineRequest

Request message for PipelineService.CreateTrainingPipeline.

CreateTrialRequest

Request message for VizierService.CreateTrial.

CreateTuningJobRequest

Request message for GenAiTuningService.CreateTuningJob.

CsvDestination

The storage details for CSV output content.

CsvSource

The storage details for CSV input content.

CustomJob

Represents a job that runs custom workloads such as a Docker container or a Python package. A CustomJob can have multiple worker pools and each worker pool can have its own machine and input spec. A CustomJob will be cleaned up once the job enters terminal state (failed or succeeded).

LabelsEntry

The abstract base class for a message.

WebAccessUrisEntry

The abstract base class for a message.

CustomJobSpec

Represents the spec of a CustomJob.

DataItem

A piece of data in a Dataset. Could be an image, a video, a document or plain text.

LabelsEntry

The abstract base class for a message.

DataItemView

A container for a single DataItem and Annotations on it.

DataLabelingJob

DataLabelingJob is used to trigger a human labeling job on unlabeled data from the following Dataset:

AnnotationLabelsEntry

The abstract base class for a message.

LabelsEntry

The abstract base class for a message.

Dataset

A collection of DataItems and Annotations on them.

LabelsEntry

The abstract base class for a message.

DatasetVersion

Describes the dataset version.

DedicatedResources

A description of resources that are dedicated to a DeployedModel, and that need a higher degree of manual configuration.

DeleteArtifactRequest

Request message for MetadataService.DeleteArtifact.

DeleteBatchPredictionJobRequest

Request message for JobService.DeleteBatchPredictionJob.

DeleteContextRequest

Request message for MetadataService.DeleteContext.

DeleteCustomJobRequest

Request message for JobService.DeleteCustomJob.

DeleteDataLabelingJobRequest

Request message for JobService.DeleteDataLabelingJob.

DeleteDatasetRequest

Request message for DatasetService.DeleteDataset.

DeleteDatasetVersionRequest

Request message for DatasetService.DeleteDatasetVersion.

DeleteDeploymentResourcePoolRequest

Request message for DeleteDeploymentResourcePool method.

DeleteEndpointRequest

Request message for EndpointService.DeleteEndpoint.

DeleteEntityTypeRequest

Request message for [FeaturestoreService.DeleteEntityTypes][].

DeleteExecutionRequest

Request message for MetadataService.DeleteExecution.

DeleteFeatureGroupRequest

Request message for FeatureRegistryService.DeleteFeatureGroup.

DeleteFeatureOnlineStoreRequest

Request message for FeatureOnlineStoreAdminService.DeleteFeatureOnlineStore.

DeleteFeatureRequest

Request message for FeaturestoreService.DeleteFeature. Request message for FeatureRegistryService.DeleteFeature.

DeleteFeatureValuesOperationMetadata

Details of operations that delete Feature values.

DeleteFeatureValuesRequest

Request message for FeaturestoreService.DeleteFeatureValues.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

SelectEntity

Message to select entity. If an entity id is selected, all the feature values corresponding to the entity id will be deleted, including the entityId.

SelectTimeRangeAndFeature

Message to select time range and feature. Values of the selected feature generated within an inclusive time range will be deleted. Using this option permanently deletes the feature values from the specified feature IDs within the specified time range. This might include data from the online storage. If you want to retain any deleted historical data in the online storage, you must re-ingest it.

DeleteFeatureValuesResponse

Response message for FeaturestoreService.DeleteFeatureValues.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

SelectEntity

Response message if the request uses the SelectEntity option.

SelectTimeRangeAndFeature

Response message if the request uses the SelectTimeRangeAndFeature option.

DeleteFeatureViewRequest

Request message for [FeatureOnlineStoreAdminService.DeleteFeatureViews][].

DeleteFeaturestoreRequest

Request message for FeaturestoreService.DeleteFeaturestore.

DeleteHyperparameterTuningJobRequest

Request message for JobService.DeleteHyperparameterTuningJob.

DeleteIndexEndpointRequest

Request message for IndexEndpointService.DeleteIndexEndpoint.

DeleteIndexRequest

Request message for IndexService.DeleteIndex.

DeleteMetadataStoreOperationMetadata

Details of operations that perform MetadataService.DeleteMetadataStore.

DeleteMetadataStoreRequest

Request message for MetadataService.DeleteMetadataStore.

DeleteModelDeploymentMonitoringJobRequest

Request message for JobService.DeleteModelDeploymentMonitoringJob.

DeleteModelRequest

Request message for ModelService.DeleteModel.

DeleteModelVersionRequest

Request message for ModelService.DeleteModelVersion.

DeleteNasJobRequest

Request message for JobService.DeleteNasJob.

DeleteNotebookRuntimeRequest

Request message for NotebookService.DeleteNotebookRuntime.

DeleteNotebookRuntimeTemplateRequest

Request message for NotebookService.DeleteNotebookRuntimeTemplate.

DeleteOperationMetadata

Details of operations that perform deletes of any entities.

DeletePersistentResourceRequest

Request message for PersistentResourceService.DeletePersistentResource.

DeletePipelineJobRequest

Request message for PipelineService.DeletePipelineJob.

DeleteSavedQueryRequest

Request message for DatasetService.DeleteSavedQuery.

DeleteScheduleRequest

Request message for ScheduleService.DeleteSchedule.

DeleteSpecialistPoolRequest

Request message for SpecialistPoolService.DeleteSpecialistPool.

DeleteStudyRequest

Request message for VizierService.DeleteStudy.

DeleteTensorboardExperimentRequest

Request message for TensorboardService.DeleteTensorboardExperiment.

DeleteTensorboardRequest

Request message for TensorboardService.DeleteTensorboard.

DeleteTensorboardRunRequest

Request message for TensorboardService.DeleteTensorboardRun.

DeleteTensorboardTimeSeriesRequest

Request message for TensorboardService.DeleteTensorboardTimeSeries.

DeleteTrainingPipelineRequest

Request message for PipelineService.DeleteTrainingPipeline.

DeleteTrialRequest

Request message for VizierService.DeleteTrial.

DeployIndexOperationMetadata

Runtime operation information for IndexEndpointService.DeployIndex.

DeployIndexRequest

Request message for IndexEndpointService.DeployIndex.

DeployIndexResponse

Response message for IndexEndpointService.DeployIndex.

DeployModelOperationMetadata

Runtime operation information for EndpointService.DeployModel.

DeployModelRequest

Request message for EndpointService.DeployModel.

TrafficSplitEntry

The abstract base class for a message.

DeployModelResponse

Response message for EndpointService.DeployModel.

DeployedIndex

A deployment of an Index. IndexEndpoints contain one or more DeployedIndexes.

DeployedIndexAuthConfig

Used to set up the auth on the DeployedIndex's private endpoint.

AuthProvider

Configuration for an authentication provider, including support for JSON Web Token (JWT) <https://tools.ietf.org/html/draft-ietf-oauth-json-web-token-32>__.

DeployedIndexRef

Points to a DeployedIndex.

DeployedModel

A deployment of a Model. Endpoints contain one or more DeployedModels.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

DeployedModelRef

Points to a DeployedModel.

DeploymentResourcePool

A description of resources that can be shared by multiple DeployedModels, whose underlying specification consists of a DedicatedResources.

DestinationFeatureSetting

DirectPredictRequest

Request message for PredictionService.DirectPredict.

DirectPredictResponse

Response message for PredictionService.DirectPredict.

DirectRawPredictRequest

Request message for PredictionService.DirectRawPredict.

DirectRawPredictResponse

Response message for PredictionService.DirectRawPredict.

DiskSpec

Represents the spec of disk options.

DoubleArray

A list of double values.

EncryptionSpec

Represents a customer-managed encryption key spec that can be applied to a top-level resource.

Endpoint

Models are deployed into it, and afterwards Endpoint is called to obtain predictions and explanations.

LabelsEntry

The abstract base class for a message.

TrafficSplitEntry

The abstract base class for a message.

EntityIdSelector

Selector for entityId. Getting ids from the given source.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

EntityType

An entity type is a type of object in a system that needs to be modeled and have stored information about. For example, driver is an entity type, and driver0 is an instance of an entity type driver.

LabelsEntry

The abstract base class for a message.

EnvVar

Represents an environment variable present in a Container or Python Module.

ErrorAnalysisAnnotation

Model error analysis for each annotation.

AttributedItem

Attributed items for a given annotation, typically representing neighbors from the training sets constrained by the query type.

QueryType

The query type used for finding the attributed items.

Values: QUERY_TYPE_UNSPECIFIED (0): Unspecified query type for model error analysis. ALL_SIMILAR (1): Query similar samples across all classes in the dataset. SAME_CLASS_SIMILAR (2): Query similar samples from the same class of the input sample. SAME_CLASS_DISSIMILAR (3): Query dissimilar samples from the same class of the input sample.

EvaluatedAnnotation

True positive, false positive, or false negative.

EvaluatedAnnotation is only available under ModelEvaluationSlice with slice of annotationSpec dimension.

EvaluatedAnnotationType

Describes the type of the EvaluatedAnnotation. The type is determined

Values: EVALUATED_ANNOTATION_TYPE_UNSPECIFIED (0): Invalid value. TRUE_POSITIVE (1): The EvaluatedAnnotation is a true positive. It has a prediction created by the Model and a ground truth Annotation which the prediction matches. FALSE_POSITIVE (2): The EvaluatedAnnotation is false positive. It has a prediction created by the Model which does not match any ground truth annotation. FALSE_NEGATIVE (3): The EvaluatedAnnotation is false negative. It has a ground truth annotation which is not matched by any of the model created predictions.

EvaluatedAnnotationExplanation

Explanation result of the prediction produced by the Model.

Event

An edge describing the relationship between an Artifact and an Execution in a lineage graph.

LabelsEntry

The abstract base class for a message.

Type

Describes whether an Event's Artifact is the Execution's input or output.

Values: TYPE_UNSPECIFIED (0): Unspecified whether input or output of the Execution. INPUT (1): An input of the Execution. OUTPUT (2): An output of the Execution.

Examples

Example-based explainability that returns the nearest neighbors from the provided dataset.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

ExampleGcsSource

The Cloud Storage input instances.

DataFormat

The format of the input example instances.

Values: DATA_FORMAT_UNSPECIFIED (0): Format unspecified, used when unset. JSONL (1): Examples are stored in JSONL files.

ExamplesOverride

Overrides for example-based explanations.

DataFormat

Data format enum.

Values: DATA_FORMAT_UNSPECIFIED (0): Unspecified format. Must not be used. INSTANCES (1): Provided data is a set of model inputs. EMBEDDINGS (2): Provided data is a set of embeddings.

ExamplesRestrictionsNamespace

Restrictions namespace for example-based explanations overrides.

Execution

Instance of a general execution.

LabelsEntry

The abstract base class for a message.

State

Describes the state of the Execution.

Values: STATE_UNSPECIFIED (0): Unspecified Execution state NEW (1): The Execution is new RUNNING (2): The Execution is running COMPLETE (3): The Execution has finished running FAILED (4): The Execution has failed CACHED (5): The Execution completed through Cache hit. CANCELLED (6): The Execution was cancelled.

ExplainRequest

Request message for PredictionService.Explain.

ExplainResponse

Response message for PredictionService.Explain.

Explanation

Explanation of a prediction (provided in PredictResponse.predictions) produced by the Model on a given instance.

ExplanationMetadata

Metadata describing the Model's input and output for explanation.

InputMetadata

Metadata of the input of a feature.

Fields other than InputMetadata.input_baselines are applicable only for Models that are using Vertex AI-provided images for Tensorflow.

Encoding

Defines how a feature is encoded. Defaults to IDENTITY.

Values: ENCODING_UNSPECIFIED (0): Default value. This is the same as IDENTITY. IDENTITY (1): The tensor represents one feature. BAG_OF_FEATURES (2): The tensor represents a bag of features where each index maps to a feature. InputMetadata.index_feature_mapping must be provided for this encoding. For example:

    ::

       input = [27, 6.0, 150]
       index_feature_mapping = ["age", "height", "weight"]
BAG_OF_FEATURES_SPARSE (3):
    The tensor represents a bag of features where each index
    maps to a feature. Zero values in the tensor indicates
    feature being non-existent.
    <xref uid="google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata.index_feature_mapping">InputMetadata.index_feature_mapping</xref>
    must be provided for this encoding. For example:

    ::

       input = [2, 0, 5, 0, 1]
       index_feature_mapping = ["a", "b", "c", "d", "e"]
INDICATOR (4):
    The tensor is a list of binaries representing whether a
    feature exists or not (1 indicates existence).
    <xref uid="google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata.index_feature_mapping">InputMetadata.index_feature_mapping</xref>
    must be provided for this encoding. For example:

    ::

       input = [1, 0, 1, 0, 1]
       index_feature_mapping = ["a", "b", "c", "d", "e"]
COMBINED_EMBEDDING (5):
    The tensor is encoded into a 1-dimensional array represented
    by an encoded tensor.
    <xref uid="google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata.encoded_tensor_name">InputMetadata.encoded_tensor_name</xref>
    must be provided for this encoding. For example:

    ::

       input = ["This", "is", "a", "test", "."]
       encoded = [0.1, 0.2, 0.3, 0.4, 0.5]
CONCAT_EMBEDDING (6):
    Select this encoding when the input tensor is encoded into a
    2-dimensional array represented by an encoded tensor.
    <xref uid="google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata.encoded_tensor_name">InputMetadata.encoded_tensor_name</xref>
    must be provided for this encoding. The first dimension of
    the encoded tensor's shape is the same as the input tensor's
    shape. For example:

    ::

       input = ["This", "is", "a", "test", "."]
       encoded = [[0.1, 0.2, 0.3, 0.4, 0.5],
                  [0.2, 0.1, 0.4, 0.3, 0.5],
                  [0.5, 0.1, 0.3, 0.5, 0.4],
                  [0.5, 0.3, 0.1, 0.2, 0.4],
                  [0.4, 0.3, 0.2, 0.5, 0.1]]

FeatureValueDomain

Domain details of the input feature value. Provides numeric information about the feature, such as its range (min, max). If the feature has been pre-processed, for example with z-scoring, then it provides information about how to recover the original feature. For example, if the input feature is an image and it has been pre-processed to obtain 0-mean and stddev = 1 values, then original_mean, and original_stddev refer to the mean and stddev of the original feature (e.g. image tensor) from which input feature (with mean = 0 and stddev = 1) was obtained.

Visualization

Visualization configurations for image explanation.

ColorMap

The color scheme used for highlighting areas.

Values: COLOR_MAP_UNSPECIFIED (0): Should not be used. PINK_GREEN (1): Positive: green. Negative: pink. VIRIDIS (2): Viridis color map: A perceptually uniform color mapping which is easier to see by those with colorblindness and progresses from yellow to green to blue. Positive: yellow. Negative: blue. RED (3): Positive: red. Negative: red. GREEN (4): Positive: green. Negative: green. RED_GREEN (6): Positive: green. Negative: red. PINK_WHITE_GREEN (5): PiYG palette.

OverlayType

How the original image is displayed in the visualization.

Values: OVERLAY_TYPE_UNSPECIFIED (0): Default value. This is the same as NONE. NONE (1): No overlay. ORIGINAL (2): The attributions are shown on top of the original image. GRAYSCALE (3): The attributions are shown on top of grayscaled version of the original image. MASK_BLACK (4): The attributions are used as a mask to reveal predictive parts of the image and hide the un-predictive parts.

Polarity

Whether to only highlight pixels with positive contributions, negative or both. Defaults to POSITIVE.

Values: POLARITY_UNSPECIFIED (0): Default value. This is the same as POSITIVE. POSITIVE (1): Highlights the pixels/outlines that were most influential to the model's prediction. NEGATIVE (2): Setting polarity to negative highlights areas that does not lead to the models's current prediction. BOTH (3): Shows both positive and negative attributions.

Type

Type of the image visualization. Only applicable to [Integrated Gradients attribution][google.cloud.aiplatform.v1.ExplanationParameters.integrated_gradients_attribution].

Values: TYPE_UNSPECIFIED (0): Should not be used. PIXELS (1): Shows which pixel contributed to the image prediction. OUTLINES (2): Shows which region contributed to the image prediction by outlining the region.

InputsEntry

The abstract base class for a message.

OutputMetadata

Metadata of the prediction output to be explained.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

OutputsEntry

The abstract base class for a message.

ExplanationMetadataOverride

The ExplanationMetadata entries that can be overridden at [online explanation][google.cloud.aiplatform.v1.PredictionService.Explain] time.

InputMetadataOverride

The [input metadata][google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata] entries to be overridden.

InputsEntry

The abstract base class for a message.

ExplanationParameters

Parameters to configure explaining for Model's predictions.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

ExplanationSpec

Specification of Model explanation.

ExplanationSpecOverride

The ExplanationSpec entries that can be overridden at [online explanation][google.cloud.aiplatform.v1.PredictionService.Explain] time.

ExportDataConfig

Describes what part of the Dataset is to be exported, the destination of the export and how to export.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

ExportUse

ExportUse indicates the usage of the exported files. It restricts file destination, format, annotations to be exported, whether to allow unannotated data to be exported and whether to clone files to temp Cloud Storage bucket.

Values: EXPORT_USE_UNSPECIFIED (0): Regular user export. CUSTOM_CODE_TRAINING (6): Export for custom code training.

ExportDataOperationMetadata

Runtime operation information for DatasetService.ExportData.

ExportDataRequest

Request message for DatasetService.ExportData.

ExportDataResponse

Response message for DatasetService.ExportData.

ExportFeatureValuesOperationMetadata

Details of operations that exports Features values.

ExportFeatureValuesRequest

Request message for FeaturestoreService.ExportFeatureValues.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

FullExport

Describes exporting all historical Feature values of all entities of the EntityType between [start_time, end_time].

SnapshotExport

Describes exporting the latest Feature values of all entities of the EntityType between [start_time, snapshot_time].

ExportFeatureValuesResponse

Response message for FeaturestoreService.ExportFeatureValues.

ExportFilterSplit

Assigns input data to training, validation, and test sets based on the given filters, data pieces not matched by any filter are ignored. Currently only supported for Datasets containing DataItems. If any of the filters in this message are to match nothing, then they can be set as '-' (the minus sign).

Supported only for unstructured Datasets.

ExportFractionSplit

Assigns the input data to training, validation, and test sets as per the given fractions. Any of training_fraction, validation_fraction and test_fraction may optionally be provided, they must sum to up to 1. If the provided ones sum to less than 1, the remainder is assigned to sets as decided by Vertex AI. If none of the fractions are set, by default roughly 80% of data is used for training, 10% for validation, and 10% for test.

ExportModelOperationMetadata

Details of ModelService.ExportModel operation.

OutputInfo

Further describes the output of the ExportModel. Supplements ExportModelRequest.OutputConfig.

ExportModelRequest

Request message for ModelService.ExportModel.

OutputConfig

Output configuration for the Model export.

ExportModelResponse

Response message of ModelService.ExportModel operation.

ExportTensorboardTimeSeriesDataRequest

Request message for TensorboardService.ExportTensorboardTimeSeriesData.

ExportTensorboardTimeSeriesDataResponse

Response message for TensorboardService.ExportTensorboardTimeSeriesData.

Feature

Feature Metadata information. For example, color is a feature that describes an apple.

LabelsEntry

The abstract base class for a message.

MonitoringStatsAnomaly

A list of historical SnapshotAnalysis or ImportFeaturesAnalysis stats requested by user, sorted by FeatureStatsAnomaly.start_time descending.

Objective

If the objective in the request is both Import Feature Analysis and Snapshot Analysis, this objective could be one of them. Otherwise, this objective should be the same as the objective in the request.

Values: OBJECTIVE_UNSPECIFIED (0): If it's OBJECTIVE_UNSPECIFIED, monitoring_stats will be empty. IMPORT_FEATURE_ANALYSIS (1): Stats are generated by Import Feature Analysis. SNAPSHOT_ANALYSIS (2): Stats are generated by Snapshot Analysis.

ValueType

Only applicable for Vertex AI Legacy Feature Store. An enum representing the value type of a feature.

Values: VALUE_TYPE_UNSPECIFIED (0): The value type is unspecified. BOOL (1): Used for Feature that is a boolean. BOOL_ARRAY (2): Used for Feature that is a list of boolean. DOUBLE (3): Used for Feature that is double. DOUBLE_ARRAY (4): Used for Feature that is a list of double. INT64 (9): Used for Feature that is INT64. INT64_ARRAY (10): Used for Feature that is a list of INT64. STRING (11): Used for Feature that is string. STRING_ARRAY (12): Used for Feature that is a list of String. BYTES (13): Used for Feature that is bytes.

FeatureGroup

Vertex AI Feature Group.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

BigQuery

Input source type for BigQuery Tables and Views.

LabelsEntry

The abstract base class for a message.

FeatureNoiseSigma

Noise sigma by features. Noise sigma represents the standard deviation of the gaussian kernel that will be used to add noise to interpolated inputs prior to computing gradients.

NoiseSigmaForFeature

Noise sigma for a single feature.

FeatureOnlineStore

Vertex AI Feature Online Store provides a centralized repository for serving ML features and embedding indexes at low latency. The Feature Online Store is a top-level container.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

Bigtable

AutoScaling

DedicatedServingEndpoint

The dedicated serving endpoint for this FeatureOnlineStore. Only need to set when you choose Optimized storage type. Public endpoint is provisioned by default.

LabelsEntry

The abstract base class for a message.

Optimized

Optimized storage type

State

Possible states a featureOnlineStore can have.

Values: STATE_UNSPECIFIED (0): Default value. This value is unused. STABLE (1): State when the featureOnlineStore configuration is not being updated and the fields reflect the current configuration of the featureOnlineStore. The featureOnlineStore is usable in this state. UPDATING (2): The state of the featureOnlineStore configuration when it is being updated. During an update, the fields reflect either the original configuration or the updated configuration of the featureOnlineStore. The featureOnlineStore is still usable in this state.

FeatureSelector

Selector for Features of an EntityType.

FeatureStatsAnomaly

Stats and Anomaly generated at specific timestamp for specific Feature. The start_time and end_time are used to define the time range of the dataset that current stats belongs to, e.g. prediction traffic is bucketed into prediction datasets by time window. If the Dataset is not defined by time window, start_time = end_time. Timestamp of the stats and anomalies always refers to end_time. Raw stats and anomalies are stored in stats_uri or anomaly_uri in the tensorflow defined protos. Field data_stats contains almost identical information with the raw stats in Vertex AI defined proto, for UI to display.

FeatureValue

Value for a feature.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

Metadata

Metadata of feature value.

FeatureValueDestination

A destination location for Feature values and format.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

FeatureValueList

Container for list of values.

FeatureView

FeatureView is representation of values that the FeatureOnlineStore will serve based on its syncConfig.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

BigQuerySource

FeatureRegistrySource

A Feature Registry source for features that need to be synced to Online Store.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

FeatureGroup

Features belonging to a single feature group that will be synced to Online Store.

IndexConfig

Configuration for vector indexing.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

BruteForceConfig

Configuration options for using brute force search.

DistanceMeasureType

The distance measure used in nearest neighbor search.

Values: DISTANCE_MEASURE_TYPE_UNSPECIFIED (0): Should not be set. SQUARED_L2_DISTANCE (1): Euclidean (L_2) Distance. COSINE_DISTANCE (2): Cosine Distance. Defined as 1 - cosine similarity.

    We strongly suggest using DOT_PRODUCT_DISTANCE +
    UNIT_L2_NORM instead of COSINE distance. Our algorithms have
    been more optimized for DOT_PRODUCT distance which, when
    combined with UNIT_L2_NORM, is mathematically equivalent to
    COSINE distance and results in the same ranking.
DOT_PRODUCT_DISTANCE (3):
    Dot Product Distance. Defined as a negative
    of the dot product.

TreeAHConfig

Configuration options for the tree-AH algorithm.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

LabelsEntry

The abstract base class for a message.

SyncConfig

Configuration for Sync. Only one option is set.

FeatureViewDataFormat

Format of the data in the Feature View.

Values: FEATURE_VIEW_DATA_FORMAT_UNSPECIFIED (0): Not set. Will be treated as the KeyValue format. KEY_VALUE (1): Return response data in key-value format. PROTO_STRUCT (2): Return response data in proto Struct format.

FeatureViewDataKey

Lookup key for a feature view.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

CompositeKey

ID that is comprised from several parts (columns).

FeatureViewSync

FeatureViewSync is a representation of sync operation which copies data from data source to Feature View in Online Store.

SyncSummary

Summary from the Sync job. For continuous syncs, the summary is updated periodically. For batch syncs, it gets updated on completion of the sync.

Featurestore

Vertex AI Feature Store provides a centralized repository for organizing, storing, and serving ML features. The Featurestore is a top-level container for your features and their values.

LabelsEntry

The abstract base class for a message.

OnlineServingConfig

OnlineServingConfig specifies the details for provisioning online serving resources.

Scaling

Online serving scaling configuration. If min_node_count and max_node_count are set to the same value, the cluster will be configured with the fixed number of node (no auto-scaling).

State

Possible states a featurestore can have.

Values: STATE_UNSPECIFIED (0): Default value. This value is unused. STABLE (1): State when the featurestore configuration is not being updated and the fields reflect the current configuration of the featurestore. The featurestore is usable in this state. UPDATING (2): The state of the featurestore configuration when it is being updated. During an update, the fields reflect either the original configuration or the updated configuration of the featurestore. For example, online_serving_config.fixed_node_count can take minutes to update. While the update is in progress, the featurestore is in the UPDATING state, and the value of fixed_node_count can be the original value or the updated value, depending on the progress of the operation. Until the update completes, the actual number of nodes can still be the original value of fixed_node_count. The featurestore is still usable in this state.

FeaturestoreMonitoringConfig

Configuration of how features in Featurestore are monitored.

ImportFeaturesAnalysis

Configuration of the Featurestore's ImportFeature Analysis Based Monitoring. This type of analysis generates statistics for values of each Feature imported by every ImportFeatureValues operation.

Baseline

Defines the baseline to do anomaly detection for feature values imported by each ImportFeatureValues operation.

Values: BASELINE_UNSPECIFIED (0): Should not be used. LATEST_STATS (1): Choose the later one statistics generated by either most recent snapshot analysis or previous import features analysis. If non of them exists, skip anomaly detection and only generate a statistics. MOST_RECENT_SNAPSHOT_STATS (2): Use the statistics generated by the most recent snapshot analysis if exists. PREVIOUS_IMPORT_FEATURES_STATS (3): Use the statistics generated by the previous import features analysis if exists.

State

The state defines whether to enable ImportFeature analysis.

Values: STATE_UNSPECIFIED (0): Should not be used. DEFAULT (1): The default behavior of whether to enable the monitoring. EntityType-level config: disabled. Feature-level config: inherited from the configuration of EntityType this Feature belongs to. ENABLED (2): Explicitly enables import features analysis. EntityType-level config: by default enables import features analysis for all Features under it. Feature-level config: enables import features analysis regardless of the EntityType-level config. DISABLED (3): Explicitly disables import features analysis. EntityType-level config: by default disables import features analysis for all Features under it. Feature-level config: disables import features analysis regardless of the EntityType-level config.

SnapshotAnalysis

Configuration of the Featurestore's Snapshot Analysis Based Monitoring. This type of analysis generates statistics for each Feature based on a snapshot of the latest feature value of each entities every monitoring_interval.

ThresholdConfig

The config for Featurestore Monitoring threshold.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

FetchFeatureValuesRequest

Request message for FeatureOnlineStoreService.FetchFeatureValues. All the features under the requested feature view will be returned.

FetchFeatureValuesResponse

Response message for FeatureOnlineStoreService.FetchFeatureValues

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

FeatureNameValuePairList

Response structure in the format of key (feature name) and (feature) value pair.

FeatureNameValuePair

Feature name & value pair.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

FileData

URI based data.

FilterSplit

Supported only for unstructured Datasets.

FindNeighborsRequest

The request message for MatchService.FindNeighbors.

Query

A query to find a number of the nearest neighbors (most similar vectors) of a vector.

FindNeighborsResponse

The response message for MatchService.FindNeighbors.

NearestNeighbors

Nearest neighbors for one query.

Neighbor

A neighbor of the query vector.

FractionSplit

FunctionCall

A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing the parameters and their values.

FunctionDeclaration

Structured representation of a function declaration as defined by the OpenAPI 3.0 specification <https://spec.openapis.org/oas/v3.0.3>__. Included in this declaration are the function name and parameters. This FunctionDeclaration is a representation of a block of code that can be used as a Tool by the model and executed by the client.

FunctionResponse

The result output from a [FunctionCall] that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing any output from the function is used as context to the model. This should contain the result of a [FunctionCall] made based on model prediction.

GcsDestination

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

GcsSource

The Google Cloud Storage location for the input content.

GenerateContentRequest

Request message for [PredictionService.GenerateContent].

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

GenerateContentResponse

Response message for [PredictionService.GenerateContent].

PromptFeedback

Content filter results for a prompt sent in the request.

BlockedReason

Blocked reason enumeration.

Values: BLOCKED_REASON_UNSPECIFIED (0): Unspecified blocked reason. SAFETY (1): Candidates blocked due to safety. OTHER (2): Candidates blocked due to other reason. BLOCKLIST (3): Candidates blocked due to the terms which are included from the terminology blocklist. PROHIBITED_CONTENT (4): Candidates blocked due to prohibited content.

UsageMetadata

Usage metadata about response(s).

GenerationConfig

Generation config.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

GenericOperationMetadata

Generic Metadata shared by all operations.

GenieSource

Contains information about the source of the models generated from Generative AI Studio.

GetAnnotationSpecRequest

Request message for DatasetService.GetAnnotationSpec.

GetArtifactRequest

Request message for MetadataService.GetArtifact.

GetBatchPredictionJobRequest

Request message for JobService.GetBatchPredictionJob.

GetContextRequest

Request message for MetadataService.GetContext.

GetCustomJobRequest

Request message for JobService.GetCustomJob.

GetDataLabelingJobRequest

Request message for JobService.GetDataLabelingJob.

GetDatasetRequest

Request message for DatasetService.GetDataset.

GetDatasetVersionRequest

Request message for DatasetService.GetDatasetVersion.

GetDeploymentResourcePoolRequest

Request message for GetDeploymentResourcePool method.

GetEndpointRequest

Request message for EndpointService.GetEndpoint

GetEntityTypeRequest

Request message for FeaturestoreService.GetEntityType.

GetExecutionRequest

Request message for MetadataService.GetExecution.

GetFeatureGroupRequest

Request message for FeatureRegistryService.GetFeatureGroup.

GetFeatureOnlineStoreRequest

Request message for FeatureOnlineStoreAdminService.GetFeatureOnlineStore.

GetFeatureRequest

Request message for FeaturestoreService.GetFeature. Request message for FeatureRegistryService.GetFeature.

GetFeatureViewRequest

Request message for FeatureOnlineStoreAdminService.GetFeatureView.

GetFeatureViewSyncRequest

Request message for FeatureOnlineStoreAdminService.GetFeatureViewSync.

GetFeaturestoreRequest

Request message for FeaturestoreService.GetFeaturestore.

GetHyperparameterTuningJobRequest

Request message for JobService.GetHyperparameterTuningJob.

GetIndexEndpointRequest

Request message for IndexEndpointService.GetIndexEndpoint

GetIndexRequest

Request message for IndexService.GetIndex

GetMetadataSchemaRequest

Request message for MetadataService.GetMetadataSchema.

GetMetadataStoreRequest

Request message for MetadataService.GetMetadataStore.

GetModelDeploymentMonitoringJobRequest

Request message for JobService.GetModelDeploymentMonitoringJob.

GetModelEvaluationRequest

Request message for ModelService.GetModelEvaluation.

GetModelEvaluationSliceRequest

Request message for ModelService.GetModelEvaluationSlice.

GetModelRequest

Request message for ModelService.GetModel.

GetNasJobRequest

Request message for JobService.GetNasJob.

GetNasTrialDetailRequest

Request message for JobService.GetNasTrialDetail.

GetNotebookRuntimeRequest

Request message for NotebookService.GetNotebookRuntime

GetNotebookRuntimeTemplateRequest

Request message for NotebookService.GetNotebookRuntimeTemplate

GetPersistentResourceRequest

Request message for PersistentResourceService.GetPersistentResource.

GetPipelineJobRequest

Request message for PipelineService.GetPipelineJob.

GetPublisherModelRequest

Request message for ModelGardenService.GetPublisherModel

GetScheduleRequest

Request message for ScheduleService.GetSchedule.

GetSpecialistPoolRequest

Request message for SpecialistPoolService.GetSpecialistPool.

GetStudyRequest

Request message for VizierService.GetStudy.

GetTensorboardExperimentRequest

Request message for TensorboardService.GetTensorboardExperiment.

GetTensorboardRequest

Request message for TensorboardService.GetTensorboard.

GetTensorboardRunRequest

Request message for TensorboardService.GetTensorboardRun.

GetTensorboardTimeSeriesRequest

Request message for TensorboardService.GetTensorboardTimeSeries.

GetTrainingPipelineRequest

Request message for PipelineService.GetTrainingPipeline.

GetTrialRequest

Request message for VizierService.GetTrial.

GetTuningJobRequest

Request message for GenAiTuningService.GetTuningJob.

GoogleSearchRetrieval

Tool to retrieve public web data for grounding, powered by Google.

GroundingAttribution

Grounding attribution.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

Web

Attribution from the web.

GroundingMetadata

Metadata returned to client when grounding is enabled.

HarmCategory

Harm categories that will block the content.

Values: HARM_CATEGORY_UNSPECIFIED (0): The harm category is unspecified. HARM_CATEGORY_HATE_SPEECH (1): The harm category is hate speech. HARM_CATEGORY_DANGEROUS_CONTENT (2): The harm category is dangerous content. HARM_CATEGORY_HARASSMENT (3): The harm category is harassment. HARM_CATEGORY_SEXUALLY_EXPLICIT (4): The harm category is sexually explicit content.

HyperparameterTuningJob

Represents a HyperparameterTuningJob. A HyperparameterTuningJob has a Study specification and multiple CustomJobs with identical CustomJob specification.

LabelsEntry

The abstract base class for a message.

IdMatcher

Matcher for Features of an EntityType by Feature ID.

ImportDataConfig

Describes the location from where we import data into a Dataset, together with the labels that will be applied to the DataItems and the Annotations.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

AnnotationLabelsEntry

The abstract base class for a message.

DataItemLabelsEntry

The abstract base class for a message.

ImportDataOperationMetadata

Runtime operation information for DatasetService.ImportData.

ImportDataRequest

Request message for DatasetService.ImportData.

ImportDataResponse

Response message for DatasetService.ImportData.

ImportFeatureValuesOperationMetadata

Details of operations that perform import Feature values.

ImportFeatureValuesRequest

Request message for FeaturestoreService.ImportFeatureValues.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

FeatureSpec

Defines the Feature value(s) to import.

ImportFeatureValuesResponse

Response message for FeaturestoreService.ImportFeatureValues.

ImportModelEvaluationRequest

Request message for ModelService.ImportModelEvaluation

Index

A representation of a collection of database items organized in a way that allows for approximate nearest neighbor (a.k.a ANN) algorithms search.

IndexUpdateMethod

The update method of an Index.

Values: INDEX_UPDATE_METHOD_UNSPECIFIED (0): Should not be used. BATCH_UPDATE (1): BatchUpdate: user can call UpdateIndex with files on Cloud Storage of Datapoints to update. STREAM_UPDATE (2): StreamUpdate: user can call UpsertDatapoints/DeleteDatapoints to update the Index and the updates will be applied in corresponding DeployedIndexes in nearly real-time.

LabelsEntry

The abstract base class for a message.

IndexDatapoint

A datapoint of Index.

CrowdingTag

Crowding tag is a constraint on a neighbor list produced by nearest neighbor search requiring that no more than some value k' of the k neighbors returned have the same value of crowding_attribute.

NumericRestriction

This field allows restricts to be based on numeric comparisons rather than categorical tokens.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

Operator

Which comparison operator to use. Should be specified for queries only; specifying this for a datapoint is an error.

Datapoints for which Operator is true relative to the query's Value field will be allowlisted.

Values: OPERATOR_UNSPECIFIED (0): Default value of the enum. LESS (1): Datapoints are eligible iff their value is < the query's. LESS_EQUAL (2): Datapoints are eligible iff their value is <= the query's. EQUAL (3): Datapoints are eligible iff their value is == the query's. GREATER_EQUAL (4): Datapoints are eligible iff their value is >= the query's. GREATER (5): Datapoints are eligible iff their value is > the query's. NOT_EQUAL (6): Datapoints are eligible iff their value is != the query's.

Restriction

Restriction of a datapoint which describe its attributes(tokens) from each of several attribute categories(namespaces).

IndexEndpoint

Indexes are deployed into it. An IndexEndpoint can have multiple DeployedIndexes.

LabelsEntry

The abstract base class for a message.

IndexPrivateEndpoints

IndexPrivateEndpoints proto is used to provide paths for users to send requests via private endpoints (e.g. private service access, private service connect). To send request via private service access, use match_grpc_address. To send request via private service connect, use service_attachment.

IndexStats

Stats of the Index.

InputDataConfig

Specifies Vertex AI owned input data to be used for training, and possibly evaluating, the Model.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

Int64Array

A list of int64 values.

IntegratedGradientsAttribution

An attribution method that computes the Aumann-Shapley value taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1703.01365

JobState

Describes the state of a job.

Values: JOB_STATE_UNSPECIFIED (0): The job state is unspecified. JOB_STATE_QUEUED (1): The job has been just created or resumed and processing has not yet begun. JOB_STATE_PENDING (2): The service is preparing to run the job. JOB_STATE_RUNNING (3): The job is in progress. JOB_STATE_SUCCEEDED (4): The job completed successfully. JOB_STATE_FAILED (5): The job failed. JOB_STATE_CANCELLING (6): The job is being cancelled. From this state the job may only go to either JOB_STATE_SUCCEEDED, JOB_STATE_FAILED or JOB_STATE_CANCELLED. JOB_STATE_CANCELLED (7): The job has been cancelled. JOB_STATE_PAUSED (8): The job has been stopped, and can be resumed. JOB_STATE_EXPIRED (9): The job has expired. JOB_STATE_UPDATING (10): The job is being updated. Only jobs in the RUNNING state can be updated. After updating, the job goes back to the RUNNING state. JOB_STATE_PARTIALLY_SUCCEEDED (11): The job is partially succeeded, some results may be missing due to errors.

LargeModelReference

Contains information about the Large Model.

LineageSubgraph

A subgraph of the overall lineage graph. Event edges connect Artifact and Execution nodes.

ListAnnotationsRequest

Request message for DatasetService.ListAnnotations.

ListAnnotationsResponse

Response message for DatasetService.ListAnnotations.

ListArtifactsRequest

Request message for MetadataService.ListArtifacts.

ListArtifactsResponse

Response message for MetadataService.ListArtifacts.

ListBatchPredictionJobsRequest

Request message for JobService.ListBatchPredictionJobs.

ListBatchPredictionJobsResponse

Response message for JobService.ListBatchPredictionJobs

ListContextsRequest

Request message for MetadataService.ListContexts

ListContextsResponse

Response message for MetadataService.ListContexts.

ListCustomJobsRequest

Request message for JobService.ListCustomJobs.

ListCustomJobsResponse

Response message for JobService.ListCustomJobs

ListDataItemsRequest

Request message for DatasetService.ListDataItems.

ListDataItemsResponse

Response message for DatasetService.ListDataItems.

ListDataLabelingJobsRequest

Request message for JobService.ListDataLabelingJobs.

ListDataLabelingJobsResponse

Response message for JobService.ListDataLabelingJobs.

ListDatasetVersionsRequest

Request message for DatasetService.ListDatasetVersions.

ListDatasetVersionsResponse

Response message for DatasetService.ListDatasetVersions.

ListDatasetsRequest

Request message for DatasetService.ListDatasets.

ListDatasetsResponse

Response message for DatasetService.ListDatasets.

ListDeploymentResourcePoolsRequest

Request message for ListDeploymentResourcePools method.

ListDeploymentResourcePoolsResponse

Response message for ListDeploymentResourcePools method.

ListEndpointsRequest

Request message for EndpointService.ListEndpoints.

ListEndpointsResponse

Response message for EndpointService.ListEndpoints.

ListEntityTypesRequest

Request message for FeaturestoreService.ListEntityTypes.

ListEntityTypesResponse

Response message for FeaturestoreService.ListEntityTypes.

ListExecutionsRequest

Request message for MetadataService.ListExecutions.

ListExecutionsResponse

Response message for MetadataService.ListExecutions.

ListFeatureGroupsRequest

Request message for FeatureRegistryService.ListFeatureGroups.

ListFeatureGroupsResponse

Response message for FeatureRegistryService.ListFeatureGroups.

ListFeatureOnlineStoresRequest

Request message for FeatureOnlineStoreAdminService.ListFeatureOnlineStores.

ListFeatureOnlineStoresResponse

Response message for FeatureOnlineStoreAdminService.ListFeatureOnlineStores.

ListFeatureViewSyncsRequest

Request message for FeatureOnlineStoreAdminService.ListFeatureViewSyncs.

ListFeatureViewSyncsResponse

Response message for FeatureOnlineStoreAdminService.ListFeatureViewSyncs.

ListFeatureViewsRequest

Request message for FeatureOnlineStoreAdminService.ListFeatureViews.

ListFeatureViewsResponse

Response message for FeatureOnlineStoreAdminService.ListFeatureViews.

ListFeaturesRequest

Request message for FeaturestoreService.ListFeatures. Request message for FeatureRegistryService.ListFeatures.

ListFeaturesResponse

Response message for FeaturestoreService.ListFeatures. Response message for FeatureRegistryService.ListFeatures.

ListFeaturestoresRequest

Request message for FeaturestoreService.ListFeaturestores.

ListFeaturestoresResponse

Response message for FeaturestoreService.ListFeaturestores.

ListHyperparameterTuningJobsRequest

Request message for JobService.ListHyperparameterTuningJobs.

ListHyperparameterTuningJobsResponse

Response message for JobService.ListHyperparameterTuningJobs

ListIndexEndpointsRequest

Request message for IndexEndpointService.ListIndexEndpoints.

ListIndexEndpointsResponse

Response message for IndexEndpointService.ListIndexEndpoints.

ListIndexesRequest

Request message for IndexService.ListIndexes.

ListIndexesResponse

Response message for IndexService.ListIndexes.

ListMetadataSchemasRequest

Request message for MetadataService.ListMetadataSchemas.

ListMetadataSchemasResponse

Response message for MetadataService.ListMetadataSchemas.

ListMetadataStoresRequest

Request message for MetadataService.ListMetadataStores.

ListMetadataStoresResponse

Response message for MetadataService.ListMetadataStores.

ListModelDeploymentMonitoringJobsRequest

Request message for JobService.ListModelDeploymentMonitoringJobs.

ListModelDeploymentMonitoringJobsResponse

Response message for JobService.ListModelDeploymentMonitoringJobs.

ListModelEvaluationSlicesRequest

Request message for ModelService.ListModelEvaluationSlices.

ListModelEvaluationSlicesResponse

Response message for ModelService.ListModelEvaluationSlices.

ListModelEvaluationsRequest

Request message for ModelService.ListModelEvaluations.

ListModelEvaluationsResponse

Response message for ModelService.ListModelEvaluations.

ListModelVersionsRequest

Request message for ModelService.ListModelVersions.

ListModelVersionsResponse

Response message for ModelService.ListModelVersions

ListModelsRequest

Request message for ModelService.ListModels.

ListModelsResponse

Response message for ModelService.ListModels

ListNasJobsRequest

Request message for JobService.ListNasJobs.

ListNasJobsResponse

Response message for JobService.ListNasJobs

ListNasTrialDetailsRequest

Request message for JobService.ListNasTrialDetails.

ListNasTrialDetailsResponse

Response message for JobService.ListNasTrialDetails

ListNotebookRuntimeTemplatesRequest

Request message for NotebookService.ListNotebookRuntimeTemplates.

ListNotebookRuntimeTemplatesResponse

Response message for NotebookService.ListNotebookRuntimeTemplates.

ListNotebookRuntimesRequest

Request message for NotebookService.ListNotebookRuntimes.

ListNotebookRuntimesResponse

Response message for NotebookService.ListNotebookRuntimes.

ListOptimalTrialsRequest

Request message for VizierService.ListOptimalTrials.

ListOptimalTrialsResponse

Response message for VizierService.ListOptimalTrials.

ListPersistentResourcesRequest

Request message for [PersistentResourceService.ListPersistentResource][].

ListPersistentResourcesResponse

Response message for PersistentResourceService.ListPersistentResources

ListPipelineJobsRequest

Request message for PipelineService.ListPipelineJobs.

ListPipelineJobsResponse

Response message for PipelineService.ListPipelineJobs

ListSavedQueriesRequest

Request message for DatasetService.ListSavedQueries.

ListSavedQueriesResponse

Response message for DatasetService.ListSavedQueries.

ListSchedulesRequest

Request message for ScheduleService.ListSchedules.

ListSchedulesResponse

Response message for ScheduleService.ListSchedules

ListSpecialistPoolsRequest

Request message for SpecialistPoolService.ListSpecialistPools.

ListSpecialistPoolsResponse

Response message for SpecialistPoolService.ListSpecialistPools.

ListStudiesRequest

Request message for VizierService.ListStudies.

ListStudiesResponse

Response message for VizierService.ListStudies.

ListTensorboardExperimentsRequest

Request message for TensorboardService.ListTensorboardExperiments.

ListTensorboardExperimentsResponse

Response message for TensorboardService.ListTensorboardExperiments.

ListTensorboardRunsRequest

Request message for TensorboardService.ListTensorboardRuns.

ListTensorboardRunsResponse

Response message for TensorboardService.ListTensorboardRuns.

ListTensorboardTimeSeriesRequest

Request message for TensorboardService.ListTensorboardTimeSeries.

ListTensorboardTimeSeriesResponse

Response message for TensorboardService.ListTensorboardTimeSeries.

ListTensorboardsRequest

Request message for TensorboardService.ListTensorboards.

ListTensorboardsResponse

Response message for TensorboardService.ListTensorboards.

ListTrainingPipelinesRequest

Request message for PipelineService.ListTrainingPipelines.

ListTrainingPipelinesResponse

Response message for PipelineService.ListTrainingPipelines

ListTrialsRequest

Request message for VizierService.ListTrials.

ListTrialsResponse

Response message for VizierService.ListTrials.

ListTuningJobsRequest

Request message for GenAiTuningService.ListTuningJobs.

ListTuningJobsResponse

Response message for GenAiTuningService.ListTuningJobs

LookupStudyRequest

Request message for VizierService.LookupStudy.

MachineSpec

Specification of a single machine.

ManualBatchTuningParameters

Manual batch tuning parameters.

Measurement

A message representing a Measurement of a Trial. A Measurement contains the Metrics got by executing a Trial using suggested hyperparameter values.

Metric

A message representing a metric in the measurement.

MergeVersionAliasesRequest

Request message for ModelService.MergeVersionAliases.

MetadataSchema

Instance of a general MetadataSchema.

MetadataSchemaType

Describes the type of the MetadataSchema.

Values: METADATA_SCHEMA_TYPE_UNSPECIFIED (0): Unspecified type for the MetadataSchema. ARTIFACT_TYPE (1): A type indicating that the MetadataSchema will be used by Artifacts. EXECUTION_TYPE (2): A typee indicating that the MetadataSchema will be used by Executions. CONTEXT_TYPE (3): A state indicating that the MetadataSchema will be used by Contexts.

MetadataStore

Instance of a metadata store. Contains a set of metadata that can be queried.

MetadataStoreState

Represents state information for a MetadataStore.

MigratableResource

Represents one resource that exists in automl.googleapis.com, datalabeling.googleapis.com or ml.googleapis.com.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

AutomlDataset

Represents one Dataset in automl.googleapis.com.

AutomlModel

Represents one Model in automl.googleapis.com.

DataLabelingDataset

Represents one Dataset in datalabeling.googleapis.com.

DataLabelingAnnotatedDataset

Represents one AnnotatedDataset in datalabeling.googleapis.com.

MlEngineModelVersion

Represents one model Version in ml.googleapis.com.

MigrateResourceRequest

Config of migrating one resource from automl.googleapis.com, datalabeling.googleapis.com and ml.googleapis.com to Vertex AI.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

MigrateAutomlDatasetConfig

Config for migrating Dataset in automl.googleapis.com to Vertex AI's Dataset.

MigrateAutomlModelConfig

Config for migrating Model in automl.googleapis.com to Vertex AI's Model.

MigrateDataLabelingDatasetConfig

Config for migrating Dataset in datalabeling.googleapis.com to Vertex AI's Dataset.

MigrateDataLabelingAnnotatedDatasetConfig

Config for migrating AnnotatedDataset in datalabeling.googleapis.com to Vertex AI's SavedQuery.

MigrateMlEngineModelVersionConfig

Config for migrating version in ml.googleapis.com to Vertex AI's Model.

MigrateResourceResponse

Describes a successfully migrated resource.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

Model

A trained machine learning Model.

BaseModelSource

User input field to specify the base model source. Currently it only supports specifing the Model Garden models and Genie models.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

DataStats

Stats of data used for train or evaluate the Model.

DeploymentResourcesType

Identifies a type of Model's prediction resources.

Values: DEPLOYMENT_RESOURCES_TYPE_UNSPECIFIED (0): Should not be used. DEDICATED_RESOURCES (1): Resources that are dedicated to the DeployedModel, and that need a higher degree of manual configuration. AUTOMATIC_RESOURCES (2): Resources that to large degree are decided by Vertex AI, and require only a modest additional configuration. SHARED_RESOURCES (3): Resources that can be shared by multiple DeployedModels. A pre-configured DeploymentResourcePool is required.

ExportFormat

Represents export format supported by the Model. All formats export to Google Cloud Storage.

ExportableContent

The Model content that can be exported.

Values: EXPORTABLE_CONTENT_UNSPECIFIED (0): Should not be used. ARTIFACT (1): Model artifact and any of its supported files. Will be exported to the location specified by the artifactDestination field of the ExportModelRequest.output_config object. IMAGE (2): The container image that is to be used when deploying this Model. Will be exported to the location specified by the imageDestination field of the ExportModelRequest.output_config object.

LabelsEntry

The abstract base class for a message.

OriginalModelInfo

Contains information about the original Model if this Model is a copy.

ModelContainerSpec

Specification of a container for serving predictions. Some fields in this message correspond to fields in the Kubernetes Container v1 core specification <https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.23/#container-v1-core>__.

ModelDeploymentMonitoringBigQueryTable

ModelDeploymentMonitoringBigQueryTable specifies the BigQuery table name as well as some information of the logs stored in this table.

LogSource

Indicates where does the log come from.

Values: LOG_SOURCE_UNSPECIFIED (0): Unspecified source. TRAINING (1): Logs coming from Training dataset. SERVING (2): Logs coming from Serving traffic.

LogType

Indicates what type of traffic does the log belong to.

Values: LOG_TYPE_UNSPECIFIED (0): Unspecified type. PREDICT (1): Predict logs. EXPLAIN (2): Explain logs.

ModelDeploymentMonitoringJob

Represents a job that runs periodically to monitor the deployed models in an endpoint. It will analyze the logged training & prediction data to detect any abnormal behaviors.

LabelsEntry

The abstract base class for a message.

LatestMonitoringPipelineMetadata

All metadata of most recent monitoring pipelines.

MonitoringScheduleState

The state to Specify the monitoring pipeline.

Values: MONITORING_SCHEDULE_STATE_UNSPECIFIED (0): Unspecified state. PENDING (1): The pipeline is picked up and wait to run. OFFLINE (2): The pipeline is offline and will be scheduled for next run. RUNNING (3): The pipeline is running.

ModelDeploymentMonitoringObjectiveConfig

ModelDeploymentMonitoringObjectiveConfig contains the pair of deployed_model_id to ModelMonitoringObjectiveConfig.

ModelDeploymentMonitoringObjectiveType

The Model Monitoring Objective types.

Values: MODEL_DEPLOYMENT_MONITORING_OBJECTIVE_TYPE_UNSPECIFIED (0): Default value, should not be set. RAW_FEATURE_SKEW (1): Raw feature values' stats to detect skew between Training-Prediction datasets. RAW_FEATURE_DRIFT (2): Raw feature values' stats to detect drift between Serving-Prediction datasets. FEATURE_ATTRIBUTION_SKEW (3): Feature attribution scores to detect skew between Training-Prediction datasets. FEATURE_ATTRIBUTION_DRIFT (4): Feature attribution scores to detect skew between Prediction datasets collected within different time windows.

ModelDeploymentMonitoringScheduleConfig

The config for scheduling monitoring job.

ModelEvaluation

A collection of metrics calculated by comparing Model's predictions on all of the test data against annotations from the test data.

ModelEvaluationExplanationSpec

ModelEvaluationSlice

A collection of metrics calculated by comparing Model's predictions on a slice of the test data against ground truth annotations.

Slice

Definition of a slice.

SliceSpec

Specification for how the data should be sliced.

ConfigsEntry

The abstract base class for a message.

Range

A range of values for slice(s). low is inclusive, high is exclusive.

SliceConfig

Specification message containing the config for this SliceSpec. When kind is selected as value and/or range, only a single slice will be computed. When all_values is present, a separate slice will be computed for each possible label/value for the corresponding key in config. Examples, with feature zip_code with values 12345, 23334, 88888 and feature country with values "US", "Canada", "Mexico" in the dataset:

Example 1:

{
  "zip_code": { "value": { "float_value": 12345.0 } }
}

A single slice for any data with zip_code 12345 in the dataset.

Example 2:

{
  "zip_code": { "range": { "low": 12345, "high": 20000 } }
}

A single slice containing data where the zip_codes between 12345 and 20000 For this example, data with the zip_code of 12345 will be in this slice.

Example 3:

{
  "zip_code": { "range": { "low": 10000, "high": 20000 } },
  "country": { "value": { "string_value": "US" } }
}

A single slice containing data where the zip_codes between 10000 and 20000 has the country "US". For this example, data with the zip_code of 12345 and country "US" will be in this slice.

Example 4:

{ "country": {"all_values": { "value": true } } }

Three slices are computed, one for each unique country in the dataset.

Example 5:

{
  "country": { "all_values": { "value": true } },
  "zip_code": { "value": { "float_value": 12345.0 } }
}

Three slices are computed, one for each unique country in the dataset where the zip_code is also 12345. For this example, data with zip_code 12345 and country "US" will be in one slice, zip_code 12345 and country "Canada" in another slice, and zip_code 12345 and country "Mexico" in another slice, totaling 3 slices.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

Value

Single value that supports strings and floats.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

ModelExplanation

Aggregated explanation metrics for a Model over a set of instances.

ModelGardenSource

Contains information about the source of the models generated from Model Garden.

ModelMonitoringAlertConfig

The alert config for model monitoring.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

EmailAlertConfig

The config for email alert.

ModelMonitoringObjectiveConfig

The objective configuration for model monitoring, including the information needed to detect anomalies for one particular model.

ExplanationConfig

The config for integrating with Vertex Explainable AI. Only applicable if the Model has explanation_spec populated.

ExplanationBaseline

Output from BatchPredictionJob for Model Monitoring baseline dataset, which can be used to generate baseline attribution scores.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

PredictionFormat

The storage format of the predictions generated BatchPrediction job.

Values: PREDICTION_FORMAT_UNSPECIFIED (0): Should not be set. JSONL (2): Predictions are in JSONL files. BIGQUERY (3): Predictions are in BigQuery.

PredictionDriftDetectionConfig

The config for Prediction data drift detection.

AttributionScoreDriftThresholdsEntry

The abstract base class for a message.

DriftThresholdsEntry

The abstract base class for a message.

TrainingDataset

Training Dataset information.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

TrainingPredictionSkewDetectionConfig

The config for Training & Prediction data skew detection. It specifies the training dataset sources and the skew detection parameters.

AttributionScoreSkewThresholdsEntry

The abstract base class for a message.

SkewThresholdsEntry

The abstract base class for a message.

ModelMonitoringStatsAnomalies

Statistics and anomalies generated by Model Monitoring.

FeatureHistoricStatsAnomalies

Historical Stats (and Anomalies) for a specific Feature.

ModelSourceInfo

Detail description of the source information of the model.

ModelSourceType

Source of the model. Different from objective field, this ModelSourceType enum indicates the source from which the model was accessed or obtained, whereas the objective indicates the overall aim or function of this model.

Values: MODEL_SOURCE_TYPE_UNSPECIFIED (0): Should not be used. AUTOML (1): The Model is uploaded by automl training pipeline. CUSTOM (2): The Model is uploaded by user or custom training pipeline. BQML (3): The Model is registered and sync'ed from BigQuery ML. MODEL_GARDEN (4): The Model is saved or tuned from Model Garden. GENIE (5): The Model is saved or tuned from Genie. CUSTOM_TEXT_EMBEDDING (6): The Model is uploaded by text embedding finetuning pipeline. MARKETPLACE (7): The Model is saved or tuned from Marketplace.

MutateDeployedIndexOperationMetadata

Runtime operation information for IndexEndpointService.MutateDeployedIndex.

MutateDeployedIndexRequest

Request message for IndexEndpointService.MutateDeployedIndex.

MutateDeployedIndexResponse

Response message for IndexEndpointService.MutateDeployedIndex.

MutateDeployedModelOperationMetadata

Runtime operation information for EndpointService.MutateDeployedModel.

MutateDeployedModelRequest

Request message for EndpointService.MutateDeployedModel.

MutateDeployedModelResponse

Response message for EndpointService.MutateDeployedModel.

NasJob

Represents a Neural Architecture Search (NAS) job.

LabelsEntry

The abstract base class for a message.

NasJobOutput

Represents a uCAIP NasJob output.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

MultiTrialJobOutput

The output of a multi-trial Neural Architecture Search (NAS) jobs.

NasJobSpec

Represents the spec of a NasJob.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

MultiTrialAlgorithmSpec

The spec of multi-trial Neural Architecture Search (NAS).

MetricSpec

Represents a metric to optimize.

GoalType

The available types of optimization goals.

Values: GOAL_TYPE_UNSPECIFIED (0): Goal Type will default to maximize. MAXIMIZE (1): Maximize the goal metric. MINIMIZE (2): Minimize the goal metric.

MultiTrialAlgorithm

The available types of multi-trial algorithms.

Values: MULTI_TRIAL_ALGORITHM_UNSPECIFIED (0): Defaults to REINFORCEMENT_LEARNING. REINFORCEMENT_LEARNING (1): The Reinforcement Learning Algorithm for Multi-trial Neural Architecture Search (NAS). GRID_SEARCH (2): The Grid Search Algorithm for Multi-trial Neural Architecture Search (NAS).

SearchTrialSpec

Represent spec for search trials.

TrainTrialSpec

Represent spec for train trials.

NasTrial

Represents a uCAIP NasJob trial.

State

Describes a NasTrial state.

Values: STATE_UNSPECIFIED (0): The NasTrial state is unspecified. REQUESTED (1): Indicates that a specific NasTrial has been requested, but it has not yet been suggested by the service. ACTIVE (2): Indicates that the NasTrial has been suggested. STOPPING (3): Indicates that the NasTrial should stop according to the service. SUCCEEDED (4): Indicates that the NasTrial is completed successfully. INFEASIBLE (5): Indicates that the NasTrial should not be attempted again. The service will set a NasTrial to INFEASIBLE when it's done but missing the final_measurement.

NasTrialDetail

Represents a NasTrial details along with its parameters. If there is a corresponding train NasTrial, the train NasTrial is also returned.

NearestNeighborQuery

A query to find a number of similar entities.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

Embedding

The embedding vector.

Parameters

Parameters that can be overrided in each query to tune query latency and recall.

StringFilter

String filter is used to search a subset of the entities by using boolean rules on string columns. For example: if a query specifies string filter with 'name = color, allow_tokens = {red, blue}, deny_tokens = {purple}',' then that query will match entities that are red or blue, but if those points are also purple, then they will be excluded even if they are red/blue. Only string filter is supported for now, numeric filter will be supported in the near future.

NearestNeighborSearchOperationMetadata

Runtime operation metadata with regard to Matching Engine Index.

ContentValidationStats

RecordError

RecordErrorType

Values: ERROR_TYPE_UNSPECIFIED (0): Default, shall not be used. EMPTY_LINE (1): The record is empty. INVALID_JSON_SYNTAX (2): Invalid json format. INVALID_CSV_SYNTAX (3): Invalid csv format. INVALID_AVRO_SYNTAX (4): Invalid avro format. INVALID_EMBEDDING_ID (5): The embedding id is not valid. EMBEDDING_SIZE_MISMATCH (6): The size of the embedding vectors does not match with the specified dimension. NAMESPACE_MISSING (7): The namespace field is missing. PARSING_ERROR (8): Generic catch-all error. Only used for validation failure where the root cause cannot be easily retrieved programmatically. DUPLICATE_NAMESPACE (9): There are multiple restricts with the same namespace value. OP_IN_DATAPOINT (10): Numeric restrict has operator specified in datapoint. MULTIPLE_VALUES (11): Numeric restrict has multiple values specified. INVALID_NUMERIC_VALUE (12): Numeric restrict has invalid numeric value specified. INVALID_ENCODING (13): File is not in UTF_8 format.

NearestNeighbors

Nearest neighbors for one query.

Neighbor

A neighbor of the query vector.

Neighbor

Neighbors for example-based explanations.

NetworkSpec

Network spec.

NfsMount

Represents a mount configuration for Network File System (NFS) to mount.

NotebookEucConfig

The euc configuration of NotebookRuntimeTemplate.

NotebookIdleShutdownConfig

The idle shutdown configuration of NotebookRuntimeTemplate, which contains the idle_timeout as required field.

NotebookRuntime

A runtime is a virtual machine allocated to a particular user for a particular Notebook file on temporary basis with lifetime limited to 24 hours.

HealthState

The substate of the NotebookRuntime to display health information.

Values: HEALTH_STATE_UNSPECIFIED (0): Unspecified health state. HEALTHY (1): NotebookRuntime is in healthy state. Applies to ACTIVE state. UNHEALTHY (2): NotebookRuntime is in unhealthy state. Applies to ACTIVE state.

LabelsEntry

The abstract base class for a message.

RuntimeState

The substate of the NotebookRuntime to display state of runtime. The resource of NotebookRuntime is in ACTIVE state for these sub state.

Values: RUNTIME_STATE_UNSPECIFIED (0): Unspecified runtime state. RUNNING (1): NotebookRuntime is in running state. BEING_STARTED (2): NotebookRuntime is in starting state. BEING_STOPPED (3): NotebookRuntime is in stopping state. STOPPED (4): NotebookRuntime is in stopped state. BEING_UPGRADED (5): NotebookRuntime is in upgrading state. It is in the middle of upgrading process. ERROR (100): NotebookRuntime was unable to start/stop properly. INVALID (101): NotebookRuntime is in invalid state. Cannot be recovered.

NotebookRuntimeTemplate

A template that specifies runtime configurations such as machine type, runtime version, network configurations, etc. Multiple runtimes can be created from a runtime template.

LabelsEntry

The abstract base class for a message.

NotebookRuntimeTemplateRef

Points to a NotebookRuntimeTemplateRef.

NotebookRuntimeType

Represents a notebook runtime type.

Values: NOTEBOOK_RUNTIME_TYPE_UNSPECIFIED (0): Unspecified notebook runtime type, NotebookRuntimeType will default to USER_DEFINED. USER_DEFINED (1): runtime or template with coustomized configurations from user. ONE_CLICK (2): runtime or template with system defined configurations.

Part

A datatype containing media that is part of a multi-part Content message.

A Part consists of data which has an associated datatype. A Part can only contain one of the accepted types in Part.data.

A Part must have a fixed IANA MIME type identifying the type and subtype of the media if inline_data or file_data field is filled with raw bytes.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

PauseModelDeploymentMonitoringJobRequest

Request message for JobService.PauseModelDeploymentMonitoringJob.

PauseScheduleRequest

Request message for ScheduleService.PauseSchedule.

PersistentDiskSpec

Represents the spec of [persistent disk][https://cloud.google.com/compute/docs/disks/persistent-disks] options.

PersistentResource

Represents long-lasting resources that are dedicated to users to runs custom workloads. A PersistentResource can have multiple node pools and each node pool can have its own machine spec.

LabelsEntry

The abstract base class for a message.

State

Describes the PersistentResource state.

Values: STATE_UNSPECIFIED (0): Not set. PROVISIONING (1): The PROVISIONING state indicates the persistent resources is being created. RUNNING (3): The RUNNING state indicates the persistent resource is healthy and fully usable. STOPPING (4): The STOPPING state indicates the persistent resource is being deleted. ERROR (5): The ERROR state indicates the persistent resource may be unusable. Details can be found in the error field. REBOOTING (6): The REBOOTING state indicates the persistent resource is being rebooted (PR is not available right now but is expected to be ready again later). UPDATING (7): The UPDATING state indicates the persistent resource is being updated.

PipelineFailurePolicy

Represents the failure policy of a pipeline. Currently, the default of a pipeline is that the pipeline will continue to run until no more tasks can be executed, also known as PIPELINE_FAILURE_POLICY_FAIL_SLOW. However, if a pipeline is set to PIPELINE_FAILURE_POLICY_FAIL_FAST, it will stop scheduling any new tasks when a task has failed. Any scheduled tasks will continue to completion.

Values: PIPELINE_FAILURE_POLICY_UNSPECIFIED (0): Default value, and follows fail slow behavior. PIPELINE_FAILURE_POLICY_FAIL_SLOW (1): Indicates that the pipeline should continue to run until all possible tasks have been scheduled and completed. PIPELINE_FAILURE_POLICY_FAIL_FAST (2): Indicates that the pipeline should stop scheduling new tasks after a task has failed.

PipelineJob

An instance of a machine learning PipelineJob.

LabelsEntry

The abstract base class for a message.

RuntimeConfig

The runtime config of a PipelineJob.

InputArtifact

The type of an input artifact.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

InputArtifactsEntry

The abstract base class for a message.

ParameterValuesEntry

The abstract base class for a message.

ParametersEntry

The abstract base class for a message.

PipelineJobDetail

The runtime detail of PipelineJob.

PipelineState

Describes the state of a pipeline.

Values: PIPELINE_STATE_UNSPECIFIED (0): The pipeline state is unspecified. PIPELINE_STATE_QUEUED (1): The pipeline has been created or resumed, and processing has not yet begun. PIPELINE_STATE_PENDING (2): The service is preparing to run the pipeline. PIPELINE_STATE_RUNNING (3): The pipeline is in progress. PIPELINE_STATE_SUCCEEDED (4): The pipeline completed successfully. PIPELINE_STATE_FAILED (5): The pipeline failed. PIPELINE_STATE_CANCELLING (6): The pipeline is being cancelled. From this state, the pipeline may only go to either PIPELINE_STATE_SUCCEEDED, PIPELINE_STATE_FAILED or PIPELINE_STATE_CANCELLED. PIPELINE_STATE_CANCELLED (7): The pipeline has been cancelled. PIPELINE_STATE_PAUSED (8): The pipeline has been stopped, and can be resumed.

PipelineTaskDetail

The runtime detail of a task execution.

ArtifactList

A list of artifact metadata.

InputsEntry

The abstract base class for a message.

OutputsEntry

The abstract base class for a message.

PipelineTaskStatus

A single record of the task status.

State

Specifies state of TaskExecution

Values: STATE_UNSPECIFIED (0): Unspecified. PENDING (1): Specifies pending state for the task. RUNNING (2): Specifies task is being executed. SUCCEEDED (3): Specifies task completed successfully. CANCEL_PENDING (4): Specifies Task cancel is in pending state. CANCELLING (5): Specifies task is being cancelled. CANCELLED (6): Specifies task was cancelled. FAILED (7): Specifies task failed. SKIPPED (8): Specifies task was skipped due to cache hit. NOT_TRIGGERED (9): Specifies that the task was not triggered because the task's trigger policy is not satisfied. The trigger policy is specified in the condition field of PipelineJob.pipeline_spec.

PipelineTaskExecutorDetail

The runtime detail of a pipeline executor.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

ContainerDetail

The detail of a container execution. It contains the job names of the lifecycle of a container execution.

CustomJobDetail

The detailed info for a custom job executor.

PipelineTemplateMetadata

Pipeline template metadata if PipelineJob.template_uri is from supported template registry. Currently, the only supported registry is Artifact Registry.

Port

Represents a network port in a container.

PredefinedSplit

Assigns input data to training, validation, and test sets based on the value of a provided key.

Supported only for tabular Datasets.

PredictRequest

Request message for PredictionService.Predict.

PredictRequestResponseLoggingConfig

Configuration for logging request-response to a BigQuery table.

PredictResponse

Response message for PredictionService.Predict.

PredictSchemata

Contains the schemata used in Model's predictions and explanations via PredictionService.Predict, PredictionService.Explain and BatchPredictionJob.

Presets

Preset configuration for example-based explanations

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

Modality

Preset option controlling parameters for different modalities

Values: MODALITY_UNSPECIFIED (0): Should not be set. Added as a recommended best practice for enums IMAGE (1): IMAGE modality TEXT (2): TEXT modality TABULAR (3): TABULAR modality

Query

Preset option controlling parameters for query speed-precision trade-off

Values: PRECISE (0): More precise neighbors as a trade-off against slower response. FAST (1): Faster response as a trade-off against less precise neighbors.

PrivateEndpoints

PrivateEndpoints proto is used to provide paths for users to send requests privately. To send request via private service access, use predict_http_uri, explain_http_uri or health_http_uri. To send request via private service connect, use service_attachment.

PrivateServiceConnectConfig

Represents configuration for private service connect.

Probe

Probe describes a health check to be performed against a container to determine whether it is alive or ready to receive traffic.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

ExecAction

ExecAction specifies a command to execute.

PscAutomatedEndpoints

PscAutomatedEndpoints defines the output of the forwarding rule automatically created by each PscAutomationConfig.

PublisherModel

A Model Garden Publisher Model.

CallToAction

Actions could take on this Publisher Model.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

Deploy

Model metadata that is needed for UploadModel or DeployModel/CreateEndpoint requests.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

DeployGke

Configurations for PublisherModel GKE deployment

OpenFineTuningPipelines

Open fine tuning pipelines.

OpenNotebooks

Open notebooks.

RegionalResourceReferences

The regional resource name or the URI. Key is region, e.g., us-central1, europe-west2, global, etc..

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

ReferencesEntry

The abstract base class for a message.

ViewRestApi

Rest API docs.

Documentation

A named piece of documentation.

LaunchStage

An enum representing the launch stage of a PublisherModel.

Values: LAUNCH_STAGE_UNSPECIFIED (0): The model launch stage is unspecified. EXPERIMENTAL (1): Used to indicate the PublisherModel is at Experimental launch stage, available to a small set of customers. PRIVATE_PREVIEW (2): Used to indicate the PublisherModel is at Private Preview launch stage, only available to a small set of customers, although a larger set of customers than an Experimental launch. Previews are the first launch stage used to get feedback from customers. PUBLIC_PREVIEW (3): Used to indicate the PublisherModel is at Public Preview launch stage, available to all customers, although not supported for production workloads. GA (4): Used to indicate the PublisherModel is at GA launch stage, available to all customers and ready for production workload.

OpenSourceCategory

An enum representing the open source category of a PublisherModel.

Values: OPEN_SOURCE_CATEGORY_UNSPECIFIED (0): The open source category is unspecified, which should not be used. PROPRIETARY (1): Used to indicate the PublisherModel is not open sourced. GOOGLE_OWNED_OSS_WITH_GOOGLE_CHECKPOINT (2): Used to indicate the PublisherModel is a Google-owned open source model w/ Google checkpoint. THIRD_PARTY_OWNED_OSS_WITH_GOOGLE_CHECKPOINT (3): Used to indicate the PublisherModel is a 3p-owned open source model w/ Google checkpoint. GOOGLE_OWNED_OSS (4): Used to indicate the PublisherModel is a Google-owned pure open source model. THIRD_PARTY_OWNED_OSS (5): Used to indicate the PublisherModel is a 3p-owned pure open source model.

ResourceReference

Reference to a resource.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

VersionState

An enum representing the state of the PublicModelVersion.

Values: VERSION_STATE_UNSPECIFIED (0): The version state is unspecified. VERSION_STATE_STABLE (1): Used to indicate the version is stable. VERSION_STATE_UNSTABLE (2): Used to indicate the version is unstable.

PublisherModelView

View enumeration of PublisherModel.

Values: PUBLISHER_MODEL_VIEW_UNSPECIFIED (0): The default / unset value. The API will default to the BASIC view. PUBLISHER_MODEL_VIEW_BASIC (1): Include basic metadata about the publisher model, but not the full contents. PUBLISHER_MODEL_VIEW_FULL (2): Include everything. PUBLISHER_MODEL_VERSION_VIEW_BASIC (3): Include: VersionId, ModelVersionExternalName, and SupportedActions.

PurgeArtifactsMetadata

Details of operations that perform MetadataService.PurgeArtifacts.

PurgeArtifactsRequest

Request message for MetadataService.PurgeArtifacts.

PurgeArtifactsResponse

Response message for MetadataService.PurgeArtifacts.

PurgeContextsMetadata

Details of operations that perform MetadataService.PurgeContexts.

PurgeContextsRequest

Request message for MetadataService.PurgeContexts.

PurgeContextsResponse

Response message for MetadataService.PurgeContexts.

PurgeExecutionsMetadata

Details of operations that perform MetadataService.PurgeExecutions.

PurgeExecutionsRequest

Request message for MetadataService.PurgeExecutions.

PurgeExecutionsResponse

Response message for MetadataService.PurgeExecutions.

PythonPackageSpec

The spec of a Python packaged code.

QueryArtifactLineageSubgraphRequest

Request message for MetadataService.QueryArtifactLineageSubgraph.

QueryContextLineageSubgraphRequest

Request message for MetadataService.QueryContextLineageSubgraph.

QueryDeployedModelsRequest

Request message for QueryDeployedModels method.

QueryDeployedModelsResponse

Response message for QueryDeployedModels method.

QueryExecutionInputsAndOutputsRequest

Request message for MetadataService.QueryExecutionInputsAndOutputs.

RawPredictRequest

Request message for PredictionService.RawPredict.

RaySpec

Configuration information for the Ray cluster. For experimental launch, Ray cluster creation and Persistent cluster creation are 1:1 mapping: We will provision all the nodes within the Persistent cluster as Ray nodes.

ReadFeatureValuesRequest

Request message for FeaturestoreOnlineServingService.ReadFeatureValues.

ReadFeatureValuesResponse

Response message for FeaturestoreOnlineServingService.ReadFeatureValues.

EntityView

Entity view with Feature values.

Data

Container to hold value(s), successive in time, for one Feature from the request.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

FeatureDescriptor

Metadata for requested Features.

Response header with metadata for the requested ReadFeatureValuesRequest.entity_type and Features.

ReadIndexDatapointsRequest

The request message for MatchService.ReadIndexDatapoints.

ReadIndexDatapointsResponse

The response message for MatchService.ReadIndexDatapoints.

ReadTensorboardBlobDataRequest

Request message for TensorboardService.ReadTensorboardBlobData.

ReadTensorboardBlobDataResponse

Response message for TensorboardService.ReadTensorboardBlobData.

ReadTensorboardSizeRequest

Request message for TensorboardService.ReadTensorboardSize.

ReadTensorboardSizeResponse

Response message for TensorboardService.ReadTensorboardSize.

ReadTensorboardTimeSeriesDataRequest

Request message for TensorboardService.ReadTensorboardTimeSeriesData.

ReadTensorboardTimeSeriesDataResponse

Response message for TensorboardService.ReadTensorboardTimeSeriesData.

ReadTensorboardUsageRequest

Request message for TensorboardService.ReadTensorboardUsage.

ReadTensorboardUsageResponse

Response message for TensorboardService.ReadTensorboardUsage.

MonthlyUsageDataEntry

The abstract base class for a message.

PerMonthUsageData

Per month usage data

PerUserUsageData

Per user usage data.

RebootPersistentResourceOperationMetadata

Details of operations that perform reboot PersistentResource.

RebootPersistentResourceRequest

Request message for PersistentResourceService.RebootPersistentResource.

RemoveContextChildrenRequest

Request message for [MetadataService.DeleteContextChildrenRequest][].

RemoveContextChildrenResponse

Response message for MetadataService.RemoveContextChildren.

RemoveDatapointsRequest

Request message for IndexService.RemoveDatapoints

RemoveDatapointsResponse

Response message for IndexService.RemoveDatapoints

ResourcePool

Represents the spec of a group of resources of the same type, for example machine type, disk, and accelerators, in a PersistentResource.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

AutoscalingSpec

The min/max number of replicas allowed if enabling autoscaling

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

ResourceRuntime

Persistent Cluster runtime information as output

ResourceRuntimeSpec

Configuration for the runtime on a PersistentResource instance, including but not limited to:

Service accounts used to run the workloads.
Whether to make it a dedicated Ray Cluster.

ResourcesConsumed

Statistics information about resource consumption.

RestoreDatasetVersionOperationMetadata

Runtime operation information for DatasetService.RestoreDatasetVersion.

RestoreDatasetVersionRequest

Request message for DatasetService.RestoreDatasetVersion.

ResumeModelDeploymentMonitoringJobRequest

Request message for JobService.ResumeModelDeploymentMonitoringJob.

ResumeScheduleRequest

Request message for ScheduleService.ResumeSchedule.

Retrieval

Defines a retrieval tool that model can call to access external knowledge.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

SafetyRating

Safety rating corresponding to the generated content.

HarmProbability

Harm probability levels in the content.

Values: HARM_PROBABILITY_UNSPECIFIED (0): Harm probability unspecified. NEGLIGIBLE (1): Negligible level of harm. LOW (2): Low level of harm. MEDIUM (3): Medium level of harm. HIGH (4): High level of harm.

HarmSeverity

Harm severity levels.

Values: HARM_SEVERITY_UNSPECIFIED (0): Harm severity unspecified. HARM_SEVERITY_NEGLIGIBLE (1): Negligible level of harm severity. HARM_SEVERITY_LOW (2): Low level of harm severity. HARM_SEVERITY_MEDIUM (3): Medium level of harm severity. HARM_SEVERITY_HIGH (4): High level of harm severity.

SafetySetting

Safety settings.

HarmBlockMethod

Probability vs severity.

Values: HARM_BLOCK_METHOD_UNSPECIFIED (0): The harm block method is unspecified. SEVERITY (1): The harm block method uses both probability and severity scores. PROBABILITY (2): The harm block method uses the probability score.

HarmBlockThreshold

Probability based thresholds levels for blocking.

Values: HARM_BLOCK_THRESHOLD_UNSPECIFIED (0): Unspecified harm block threshold. BLOCK_LOW_AND_ABOVE (1): Block low threshold and above (i.e. block more). BLOCK_MEDIUM_AND_ABOVE (2): Block medium threshold and above. BLOCK_ONLY_HIGH (3): Block only high threshold (i.e. block less). BLOCK_NONE (4): Block none.

SampleConfig

Active learning data sampling config. For every active learning labeling iteration, it will select a batch of data based on the sampling strategy.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

SampleStrategy

Sample strategy decides which subset of DataItems should be selected for human labeling in every batch.

Values: SAMPLE_STRATEGY_UNSPECIFIED (0): Default will be treated as UNCERTAINTY. UNCERTAINTY (1): Sample the most uncertain data to label.

SampledShapleyAttribution

An attribution method that approximates Shapley values for features that contribute to the label being predicted. A sampling strategy is used to approximate the value rather than considering all subsets of features.

SamplingStrategy

Sampling Strategy for logging, can be for both training and prediction dataset.

RandomSampleConfig

Requests are randomly selected.

SavedQuery

A SavedQuery is a view of the dataset. It references a subset of annotations by problem type and filters.

Scalar

One point viewable on a scalar metric plot.

Schedule

An instance of a Schedule periodically schedules runs to make API calls based on user specified time specification and API request type.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

RunResponse

Status of a scheduled run.

State

Possible state of the schedule.

Values: STATE_UNSPECIFIED (0): Unspecified. ACTIVE (1): The Schedule is active. Runs are being scheduled on the user-specified timespec. PAUSED (2): The schedule is paused. No new runs will be created until the schedule is resumed. Already started runs will be allowed to complete. COMPLETED (3): The Schedule is completed. No new runs will be scheduled. Already started runs will be allowed to complete. Schedules in completed state cannot be paused or resumed.

Scheduling

All parameters related to queuing and scheduling of custom jobs.

Schema

Schema is used to define the format of input/output data. Represents a select subset of an OpenAPI 3.0 schema object <https://spec.openapis.org/oas/v3.0.3#schema>__. More fields may be added in the future as needed.

PropertiesEntry

The abstract base class for a message.

SearchDataItemsRequest

Request message for DatasetService.SearchDataItems.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

OrderByAnnotation

Expression that allows ranking results based on annotation's property.

SearchDataItemsResponse

Response message for DatasetService.SearchDataItems.

SearchFeaturesRequest

Request message for FeaturestoreService.SearchFeatures.

SearchFeaturesResponse

Response message for FeaturestoreService.SearchFeatures.

SearchMigratableResourcesRequest

Request message for MigrationService.SearchMigratableResources.

SearchMigratableResourcesResponse

Response message for MigrationService.SearchMigratableResources.

SearchModelDeploymentMonitoringStatsAnomaliesRequest

Request message for JobService.SearchModelDeploymentMonitoringStatsAnomalies.

StatsAnomaliesObjective

Stats requested for specific objective.

SearchModelDeploymentMonitoringStatsAnomaliesResponse

Response message for JobService.SearchModelDeploymentMonitoringStatsAnomalies.

SearchNearestEntitiesRequest

The request message for FeatureOnlineStoreService.SearchNearestEntities.

SearchNearestEntitiesResponse

Response message for FeatureOnlineStoreService.SearchNearestEntities

Segment

Segment of the content.

ServiceAccountSpec

Configuration for the use of custom service account to run the workloads.

ShieldedVmConfig

A set of Shielded Instance options. See Images using supported Shielded VM features <https://cloud.google.com/compute/docs/instances/modifying-shielded-vm>__.

SmoothGradConfig

Config for SmoothGrad approximation of gradients.

When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details:

https://arxiv.org/pdf/1706.03825.pdf

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

SpecialistPool

SpecialistPool represents customers' own workforce to work on their data labeling jobs. It includes a group of specialist managers and workers. Managers are responsible for managing the workers in this pool as well as customers' data labeling jobs associated with this pool. Customers create specialist pool as well as start data labeling jobs on Cloud, managers and workers handle the jobs using CrowdCompute console.

StartNotebookRuntimeOperationMetadata

Metadata information for NotebookService.StartNotebookRuntime.

StartNotebookRuntimeRequest

Request message for NotebookService.StartNotebookRuntime.

StartNotebookRuntimeResponse

Response message for NotebookService.StartNotebookRuntime.

StopTrialRequest

Request message for VizierService.StopTrial.

StratifiedSplit

Assigns input data to the training, validation, and test sets so that the distribution of values found in the categorical column (as specified by the key field) is mirrored within each split. The fraction values determine the relative sizes of the splits.

For example, if the specified column has three values, with 50% of the rows having value "A", 25% value "B", and 25% value "C", and the split fractions are specified as 80/10/10, then the training set will constitute 80% of the training data, with about 50% of the training set rows having the value "A" for the specified column, about 25% having the value "B", and about 25% having the value "C".

Only the top 500 occurring values are used; any values not in the top 500 values are randomly assigned to a split. If less than three rows contain a specific value, those rows are randomly assigned.

Supported only for tabular Datasets.

StreamDirectPredictRequest

Request message for PredictionService.StreamDirectPredict.

The first message must contain endpoint field and optionally [input][]. The subsequent messages must contain [input][].

StreamDirectPredictResponse

Response message for PredictionService.StreamDirectPredict.

StreamDirectRawPredictRequest

Request message for PredictionService.StreamDirectRawPredict.

The first message must contain endpoint and method_name fields and optionally input. The subsequent messages must contain input. method_name in the subsequent messages have no effect.

StreamDirectRawPredictResponse

Response message for PredictionService.StreamDirectRawPredict.

StreamRawPredictRequest

Request message for PredictionService.StreamRawPredict.

StreamingPredictRequest

Request message for PredictionService.StreamingPredict.

The first message must contain endpoint field and optionally [input][]. The subsequent messages must contain [input][].

StreamingPredictResponse

Response message for PredictionService.StreamingPredict.

StreamingRawPredictRequest

Request message for PredictionService.StreamingRawPredict.

The first message must contain endpoint and method_name fields and optionally input. The subsequent messages must contain input. method_name in the subsequent messages have no effect.

StreamingRawPredictResponse

Response message for PredictionService.StreamingRawPredict.

StreamingReadFeatureValuesRequest

Request message for [FeaturestoreOnlineServingService.StreamingFeatureValuesRead][].

StringArray

A list of string values.

Study

A message representing a Study.

State

Describes the Study state.

Values: STATE_UNSPECIFIED (0): The study state is unspecified. ACTIVE (1): The study is active. INACTIVE (2): The study is stopped due to an internal error. COMPLETED (3): The study is done when the service exhausts the parameter search space or max_trial_count is reached.

StudySpec

Represents specification of a Study.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

Algorithm

The available search algorithms for the Study.

Values: ALGORITHM_UNSPECIFIED (0): The default algorithm used by Vertex AI for hyperparameter tuning <https://cloud.google.com/vertex-ai/docs/training/hyperparameter-tuning-overview> and Vertex AI Vizier <https://cloud.google.com/vertex-ai/docs/vizier>. GRID_SEARCH (2): Simple grid search within the feasible space. To use grid search, all parameters must be INTEGER, CATEGORICAL, or DISCRETE. RANDOM_SEARCH (3): Simple random search within the feasible space.

ConvexAutomatedStoppingSpec

Configuration for ConvexAutomatedStoppingSpec. When there are enough completed trials (configured by min_measurement_count), for pending trials with enough measurements and steps, the policy first computes an overestimate of the objective value at max_num_steps according to the slope of the incomplete objective value curve. No prediction can be made if the curve is completely flat. If the overestimation is worse than the best objective value of the completed trials, this pending trial will be early-stopped, but a last measurement will be added to the pending trial with max_num_steps and predicted objective value from the autoregression model.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

DecayCurveAutomatedStoppingSpec

The decay curve automated stopping rule builds a Gaussian Process Regressor to predict the final objective value of a Trial based on the already completed Trials and the intermediate measurements of the current Trial. Early stopping is requested for the current Trial if there is very low probability to exceed the optimal value found so far.

MeasurementSelectionType

This indicates which measurement to use if/when the service automatically selects the final measurement from previously reported intermediate measurements. Choose this based on two considerations: A) Do you expect your measurements to monotonically improve? If so, choose LAST_MEASUREMENT. On the other hand, if you're in a situation where your system can "over-train" and you expect the performance to get better for a while but then start declining, choose BEST_MEASUREMENT. B) Are your measurements significantly noisy and/or irreproducible? If so, BEST_MEASUREMENT will tend to be over-optimistic, and it may be better to choose LAST_MEASUREMENT. If both or neither of (A) and (B) apply, it doesn't matter which selection type is chosen.

Values: MEASUREMENT_SELECTION_TYPE_UNSPECIFIED (0): Will be treated as LAST_MEASUREMENT. LAST_MEASUREMENT (1): Use the last measurement reported. BEST_MEASUREMENT (2): Use the best measurement reported.

MedianAutomatedStoppingSpec

The median automated stopping rule stops a pending Trial if the Trial's best objective_value is strictly below the median 'performance' of all completed Trials reported up to the Trial's last measurement. Currently, 'performance' refers to the running average of the objective values reported by the Trial in each measurement.

MetricSpec

Represents a metric to optimize.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

GoalType

The available types of optimization goals.

Values: GOAL_TYPE_UNSPECIFIED (0): Goal Type will default to maximize. MAXIMIZE (1): Maximize the goal metric. MINIMIZE (2): Minimize the goal metric.

SafetyMetricConfig

Used in safe optimization to specify threshold levels and risk tolerance.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

ObservationNoise

Describes the noise level of the repeated observations.

"Noisy" means that the repeated observations with the same Trial parameters may lead to different metric evaluations.

Values: OBSERVATION_NOISE_UNSPECIFIED (0): The default noise level chosen by Vertex AI. LOW (1): Vertex AI assumes that the objective function is (nearly) perfectly reproducible, and will never repeat the same Trial parameters. HIGH (2): Vertex AI will estimate the amount of noise in metric evaluations, it may repeat the same Trial parameters more than once.

ParameterSpec

Represents a single parameter to optimize.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

CategoricalValueSpec

Value specification for a parameter in CATEGORICAL type.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

ConditionalParameterSpec

Represents a parameter spec with condition from its parent parameter.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

CategoricalValueCondition

Represents the spec to match categorical values from parent parameter.

DiscreteValueCondition

Represents the spec to match discrete values from parent parameter.

IntValueCondition

Represents the spec to match integer values from parent parameter.

DiscreteValueSpec

Value specification for a parameter in DISCRETE type.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

DoubleValueSpec

Value specification for a parameter in DOUBLE type.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

IntegerValueSpec

Value specification for a parameter in INTEGER type.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

ScaleType

The type of scaling that should be applied to this parameter.

Values: SCALE_TYPE_UNSPECIFIED (0): By default, no scaling is applied. UNIT_LINEAR_SCALE (1): Scales the feasible space to (0, 1) linearly. UNIT_LOG_SCALE (2): Scales the feasible space logarithmically to (0, 1). The entire feasible space must be strictly positive. UNIT_REVERSE_LOG_SCALE (3): Scales the feasible space "reverse" logarithmically to (0, 1). The result is that values close to the top of the feasible space are spread out more than points near the bottom. The entire feasible space must be strictly positive.

StudyStoppingConfig

The configuration (stopping conditions) for automated stopping of a Study. Conditions include trial budgets, time budgets, and convergence detection.

StudyTimeConstraint

Time-based Constraint for Study

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

SuggestTrialsMetadata

Details of operations that perform Trials suggestion.

SuggestTrialsRequest

Request message for VizierService.SuggestTrials.

SuggestTrialsResponse

Response message for VizierService.SuggestTrials.

SupervisedHyperParameters

Hyperparameters for SFT.

AdapterSize

Supported adapter sizes for tuning.

Values: ADAPTER_SIZE_UNSPECIFIED (0): Adapter size is unspecified. ADAPTER_SIZE_ONE (1): Adapter size 1. ADAPTER_SIZE_FOUR (2): Adapter size 4. ADAPTER_SIZE_EIGHT (3): Adapter size 8. ADAPTER_SIZE_SIXTEEN (4): Adapter size 16.

SupervisedTuningDataStats

Tuning data statistics for Supervised Tuning.

SupervisedTuningDatasetDistribution

Dataset distribution for Supervised Tuning.

DatasetBucket

Dataset bucket used to create a histogram for the distribution given a population of values.

SupervisedTuningSpec

Tuning Spec for Supervised Tuning.

SyncFeatureViewRequest

Request message for FeatureOnlineStoreAdminService.SyncFeatureView.

SyncFeatureViewResponse

Respose message for FeatureOnlineStoreAdminService.SyncFeatureView.

TFRecordDestination

The storage details for TFRecord output content.

Tensor

A tensor value type.

DataType

Data type of the tensor.

Values: DATA_TYPE_UNSPECIFIED (0): Not a legal value for DataType. Used to indicate a DataType field has not been set. BOOL (1): Data types that all computation devices are expected to be capable to support. STRING (2): No description available. FLOAT (3): No description available. DOUBLE (4): No description available. INT8 (5): No description available. INT16 (6): No description available. INT32 (7): No description available. INT64 (8): No description available. UINT8 (9): No description available. UINT16 (10): No description available. UINT32 (11): No description available. UINT64 (12): No description available.

StructValEntry

The abstract base class for a message.

Tensorboard

Tensorboard is a physical database that stores users' training metrics. A default Tensorboard is provided in each region of a Google Cloud project. If needed users can also create extra Tensorboards in their projects.

LabelsEntry

The abstract base class for a message.

TensorboardBlob

One blob (e.g, image, graph) viewable on a blob metric plot.

TensorboardBlobSequence

One point viewable on a blob metric plot, but mostly just a wrapper message to work around repeated fields can't be used directly within oneof fields.

TensorboardExperiment

A TensorboardExperiment is a group of TensorboardRuns, that are typically the results of a training job run, in a Tensorboard.

LabelsEntry

The abstract base class for a message.

TensorboardRun

TensorboardRun maps to a specific execution of a training job with a given set of hyperparameter values, model definition, dataset, etc

LabelsEntry

The abstract base class for a message.

TensorboardTensor

One point viewable on a tensor metric plot.

TensorboardTimeSeries

TensorboardTimeSeries maps to times series produced in training runs

Metadata

Describes metadata for a TensorboardTimeSeries.

ValueType

An enum representing the value type of a TensorboardTimeSeries.

Values: VALUE_TYPE_UNSPECIFIED (0): The value type is unspecified. SCALAR (1): Used for TensorboardTimeSeries that is a list of scalars. E.g. accuracy of a model over epochs/time. TENSOR (2): Used for TensorboardTimeSeries that is a list of tensors. E.g. histograms of weights of layer in a model over epoch/time. BLOB_SEQUENCE (3): Used for TensorboardTimeSeries that is a list of blob sequences. E.g. set of sample images with labels over epochs/time.

ThresholdConfig

The config for feature monitoring threshold.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

TimeSeriesData

All the data stored in a TensorboardTimeSeries.

TimeSeriesDataPoint

A TensorboardTimeSeries data point.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

TimestampSplit

Assigns input data to training, validation, and test sets based on a provided timestamps. The youngest data pieces are assigned to training set, next to validation set, and the oldest to the test set.

Supported only for tabular Datasets.

TokensInfo

Tokens info with a list of tokens and the corresponding list of token ids.

Tool

Tool details that the model may use to generate response.

A Tool is a piece of code that enables the system to interact with external systems to perform an action, or set of actions, outside of knowledge and scope of the model. A Tool object should contain exactly one type of Tool (e.g FunctionDeclaration, Retrieval or GoogleSearchRetrieval).

TrainingConfig

CMLE training config. For every active learning labeling iteration, system will train a machine learning model on CMLE. The trained model will be used by data sampling algorithm to select DataItems.

TrainingPipeline

The TrainingPipeline orchestrates tasks associated with training a Model. It always executes the training task, and optionally may also export data from Vertex AI's Dataset which becomes the training input, upload the Model to Vertex AI, and evaluate the Model.

LabelsEntry

The abstract base class for a message.

Trial

A message representing a Trial. A Trial contains a unique set of Parameters that has been or will be evaluated, along with the objective metrics got by running the Trial.

Parameter

A message representing a parameter to be tuned.

State

Describes a Trial state.

Values: STATE_UNSPECIFIED (0): The Trial state is unspecified. REQUESTED (1): Indicates that a specific Trial has been requested, but it has not yet been suggested by the service. ACTIVE (2): Indicates that the Trial has been suggested. STOPPING (3): Indicates that the Trial should stop according to the service. SUCCEEDED (4): Indicates that the Trial is completed successfully. INFEASIBLE (5): Indicates that the Trial should not be attempted again. The service will set a Trial to INFEASIBLE when it's done but missing the final_measurement.

WebAccessUrisEntry

The abstract base class for a message.

TrialContext

Next ID: 3

TunedModel

The Model Registry Model and Online Prediction Endpoint assiociated with this TuningJob.

TuningDataStats

The tuning data statistic values for TuningJob.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

TuningJob

Represents a TuningJob that runs with Google owned models.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

LabelsEntry

The abstract base class for a message.

Type

Type contains the list of OpenAPI data types as defined by https://swagger.io/docs/specification/data-models/data-types/

Values: TYPE_UNSPECIFIED (0): Not specified, should not be used. STRING (1): OpenAPI string type NUMBER (2): OpenAPI number type INTEGER (3): OpenAPI integer type BOOLEAN (4): OpenAPI boolean type ARRAY (5): OpenAPI array type OBJECT (6): OpenAPI object type

UndeployIndexOperationMetadata

Runtime operation information for IndexEndpointService.UndeployIndex.

UndeployIndexRequest

Request message for IndexEndpointService.UndeployIndex.

UndeployIndexResponse

Response message for IndexEndpointService.UndeployIndex.

UndeployModelOperationMetadata

Runtime operation information for EndpointService.UndeployModel.

UndeployModelRequest

Request message for EndpointService.UndeployModel.

TrafficSplitEntry

The abstract base class for a message.

UndeployModelResponse

Response message for EndpointService.UndeployModel.

UnmanagedContainerModel

Contains model information necessary to perform batch prediction without requiring a full model import.

UpdateArtifactRequest

Request message for MetadataService.UpdateArtifact.

UpdateContextRequest

Request message for MetadataService.UpdateContext.

UpdateDatasetRequest

Request message for DatasetService.UpdateDataset.

UpdateDeploymentResourcePoolOperationMetadata

Runtime operation information for UpdateDeploymentResourcePool method.

UpdateEndpointRequest

Request message for EndpointService.UpdateEndpoint.

UpdateEntityTypeRequest

Request message for FeaturestoreService.UpdateEntityType.

UpdateExecutionRequest

Request message for MetadataService.UpdateExecution.

UpdateExplanationDatasetOperationMetadata

Runtime operation information for ModelService.UpdateExplanationDataset.

UpdateExplanationDatasetRequest

Request message for ModelService.UpdateExplanationDataset.

UpdateExplanationDatasetResponse

Response message of ModelService.UpdateExplanationDataset operation.

UpdateFeatureGroupOperationMetadata

Details of operations that perform update FeatureGroup.

UpdateFeatureGroupRequest

Request message for FeatureRegistryService.UpdateFeatureGroup.

UpdateFeatureOnlineStoreOperationMetadata

Details of operations that perform update FeatureOnlineStore.

UpdateFeatureOnlineStoreRequest

Request message for FeatureOnlineStoreAdminService.UpdateFeatureOnlineStore.

UpdateFeatureOperationMetadata

Details of operations that perform update Feature.

UpdateFeatureRequest

Request message for FeaturestoreService.UpdateFeature. Request message for FeatureRegistryService.UpdateFeature.

UpdateFeatureViewOperationMetadata

Details of operations that perform update FeatureView.

UpdateFeatureViewRequest

Request message for FeatureOnlineStoreAdminService.UpdateFeatureView.

UpdateFeaturestoreOperationMetadata

Details of operations that perform update Featurestore.

UpdateFeaturestoreRequest

Request message for FeaturestoreService.UpdateFeaturestore.

UpdateIndexEndpointRequest

Request message for IndexEndpointService.UpdateIndexEndpoint.

UpdateIndexOperationMetadata

Runtime operation information for IndexService.UpdateIndex.

UpdateIndexRequest

Request message for IndexService.UpdateIndex.

UpdateModelDeploymentMonitoringJobOperationMetadata

Runtime operation information for JobService.UpdateModelDeploymentMonitoringJob.

UpdateModelDeploymentMonitoringJobRequest

Request message for JobService.UpdateModelDeploymentMonitoringJob.

UpdateModelRequest

Request message for ModelService.UpdateModel.

UpdatePersistentResourceOperationMetadata

Details of operations that perform update PersistentResource.

UpdatePersistentResourceRequest

Request message for UpdatePersistentResource method.

UpdateScheduleRequest

Request message for ScheduleService.UpdateSchedule.

UpdateSpecialistPoolOperationMetadata

Runtime operation metadata for SpecialistPoolService.UpdateSpecialistPool.

UpdateSpecialistPoolRequest

Request message for SpecialistPoolService.UpdateSpecialistPool.

UpdateTensorboardExperimentRequest

Request message for TensorboardService.UpdateTensorboardExperiment.

UpdateTensorboardOperationMetadata

Details of operations that perform update Tensorboard.

UpdateTensorboardRequest

Request message for TensorboardService.UpdateTensorboard.

UpdateTensorboardRunRequest

Request message for TensorboardService.UpdateTensorboardRun.

UpdateTensorboardTimeSeriesRequest

Request message for TensorboardService.UpdateTensorboardTimeSeries.

UpgradeNotebookRuntimeOperationMetadata

Metadata information for NotebookService.UpgradeNotebookRuntime.

UpgradeNotebookRuntimeRequest

Request message for NotebookService.UpgradeNotebookRuntime.

UpgradeNotebookRuntimeResponse

Response message for NotebookService.UpgradeNotebookRuntime.

UploadModelOperationMetadata

Details of ModelService.UploadModel operation.

UploadModelRequest

Request message for ModelService.UploadModel.

UploadModelResponse

Response message of ModelService.UploadModel operation.

UpsertDatapointsRequest

Request message for IndexService.UpsertDatapoints

UpsertDatapointsResponse

Response message for IndexService.UpsertDatapoints

UserActionReference

References an API call. It contains more information about long running operation and Jobs that are triggered by the API call.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

Value

Value is the value of the field.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

VertexAISearch

Retrieve from Vertex AI Search datastore for grounding. See https://cloud.google.com/vertex-ai-search-and-conversation

VideoMetadata

Metadata describes the input video content.

WorkerPoolSpec

Represents the spec of a worker pool in a job.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

WriteFeatureValuesPayload

Contains Feature values to be written for a specific entity.

FeatureValuesEntry

The abstract base class for a message.

WriteFeatureValuesRequest

Request message for FeaturestoreOnlineServingService.WriteFeatureValues.

WriteFeatureValuesResponse

Response message for FeaturestoreOnlineServingService.WriteFeatureValues.

WriteTensorboardExperimentDataRequest

Request message for TensorboardService.WriteTensorboardExperimentData.

WriteTensorboardExperimentDataResponse

Response message for TensorboardService.WriteTensorboardExperimentData.

WriteTensorboardRunDataRequest

Request message for TensorboardService.WriteTensorboardRunData.

WriteTensorboardRunDataResponse

Response message for TensorboardService.WriteTensorboardRunData.

XraiAttribution

An explanation method that redistributes Integrated Gradients attributions to segmented regions, taking advantage of the model's fully differentiable structure. Refer to this paper for more details:

https://arxiv.org/abs/1906.02825

Supported only by image Models.

DatasetServiceAsyncClient

The service that manages Vertex AI Dataset and its child resources.

DatasetServiceClient

The service that manages Vertex AI Dataset and its child resources.

ListAnnotationsAsyncPager

A pager for iterating through list_annotations requests.

This class thinly wraps an initial ListAnnotationsResponse object, and provides an __aiter__ method to iterate through its annotations field.

If there are more pages, the __aiter__ method will make additional ListAnnotations requests and continue to iterate through the annotations field on the corresponding responses.

All the usual ListAnnotationsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListAnnotationsPager

A pager for iterating through list_annotations requests.

This class thinly wraps an initial ListAnnotationsResponse object, and provides an __iter__ method to iterate through its annotations field.

If there are more pages, the __iter__ method will make additional ListAnnotations requests and continue to iterate through the annotations field on the corresponding responses.

All the usual ListAnnotationsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListDataItemsAsyncPager

A pager for iterating through list_data_items requests.

This class thinly wraps an initial ListDataItemsResponse object, and provides an __aiter__ method to iterate through its data_items field.

If there are more pages, the __aiter__ method will make additional ListDataItems requests and continue to iterate through the data_items field on the corresponding responses.

All the usual ListDataItemsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListDataItemsPager

A pager for iterating through list_data_items requests.

This class thinly wraps an initial ListDataItemsResponse object, and provides an __iter__ method to iterate through its data_items field.

If there are more pages, the __iter__ method will make additional ListDataItems requests and continue to iterate through the data_items field on the corresponding responses.

All the usual ListDataItemsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListDatasetVersionsAsyncPager

A pager for iterating through list_dataset_versions requests.

This class thinly wraps an initial ListDatasetVersionsResponse object, and provides an __aiter__ method to iterate through its dataset_versions field.

If there are more pages, the __aiter__ method will make additional ListDatasetVersions requests and continue to iterate through the dataset_versions field on the corresponding responses.

All the usual ListDatasetVersionsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListDatasetVersionsPager

A pager for iterating through list_dataset_versions requests.

This class thinly wraps an initial ListDatasetVersionsResponse object, and provides an __iter__ method to iterate through its dataset_versions field.

If there are more pages, the __iter__ method will make additional ListDatasetVersions requests and continue to iterate through the dataset_versions field on the corresponding responses.

All the usual ListDatasetVersionsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListDatasetsAsyncPager

A pager for iterating through list_datasets requests.

This class thinly wraps an initial ListDatasetsResponse object, and provides an __aiter__ method to iterate through its datasets field.

If there are more pages, the __aiter__ method will make additional ListDatasets requests and continue to iterate through the datasets field on the corresponding responses.

All the usual ListDatasetsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListDatasetsPager

A pager for iterating through list_datasets requests.

This class thinly wraps an initial ListDatasetsResponse object, and provides an __iter__ method to iterate through its datasets field.

If there are more pages, the __iter__ method will make additional ListDatasets requests and continue to iterate through the datasets field on the corresponding responses.

All the usual ListDatasetsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListSavedQueriesAsyncPager

A pager for iterating through list_saved_queries requests.

This class thinly wraps an initial ListSavedQueriesResponse object, and provides an __aiter__ method to iterate through its saved_queries field.

If there are more pages, the __aiter__ method will make additional ListSavedQueries requests and continue to iterate through the saved_queries field on the corresponding responses.

All the usual ListSavedQueriesResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListSavedQueriesPager

A pager for iterating through list_saved_queries requests.

This class thinly wraps an initial ListSavedQueriesResponse object, and provides an __iter__ method to iterate through its saved_queries field.

If there are more pages, the __iter__ method will make additional ListSavedQueries requests and continue to iterate through the saved_queries field on the corresponding responses.

All the usual ListSavedQueriesResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

SearchDataItemsAsyncPager

A pager for iterating through search_data_items requests.

This class thinly wraps an initial SearchDataItemsResponse object, and provides an __aiter__ method to iterate through its data_item_views field.

If there are more pages, the __aiter__ method will make additional SearchDataItems requests and continue to iterate through the data_item_views field on the corresponding responses.

All the usual SearchDataItemsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

SearchDataItemsPager

A pager for iterating through search_data_items requests.

This class thinly wraps an initial SearchDataItemsResponse object, and provides an __iter__ method to iterate through its data_item_views field.

If there are more pages, the __iter__ method will make additional SearchDataItems requests and continue to iterate through the data_item_views field on the corresponding responses.

All the usual SearchDataItemsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

DeploymentResourcePoolServiceAsyncClient

A service that manages the DeploymentResourcePool resource.

DeploymentResourcePoolServiceClient

A service that manages the DeploymentResourcePool resource.

ListDeploymentResourcePoolsAsyncPager

A pager for iterating through list_deployment_resource_pools requests.

This class thinly wraps an initial ListDeploymentResourcePoolsResponse object, and provides an __aiter__ method to iterate through its deployment_resource_pools field.

All the usual ListDeploymentResourcePoolsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListDeploymentResourcePoolsPager

A pager for iterating through list_deployment_resource_pools requests.

This class thinly wraps an initial ListDeploymentResourcePoolsResponse object, and provides an __iter__ method to iterate through its deployment_resource_pools field.

All the usual ListDeploymentResourcePoolsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

QueryDeployedModelsAsyncPager

A pager for iterating through query_deployed_models requests.

This class thinly wraps an initial QueryDeployedModelsResponse object, and provides an __aiter__ method to iterate through its deployed_models field.

If there are more pages, the __aiter__ method will make additional QueryDeployedModels requests and continue to iterate through the deployed_models field on the corresponding responses.

All the usual QueryDeployedModelsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

QueryDeployedModelsPager

A pager for iterating through query_deployed_models requests.

This class thinly wraps an initial QueryDeployedModelsResponse object, and provides an __iter__ method to iterate through its deployed_models field.

If there are more pages, the __iter__ method will make additional QueryDeployedModels requests and continue to iterate through the deployed_models field on the corresponding responses.

All the usual QueryDeployedModelsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

EndpointServiceAsyncClient

A service for managing Vertex AI's Endpoints.

EndpointServiceClient

A service for managing Vertex AI's Endpoints.

ListEndpointsAsyncPager

A pager for iterating through list_endpoints requests.

This class thinly wraps an initial ListEndpointsResponse object, and provides an __aiter__ method to iterate through its endpoints field.

If there are more pages, the __aiter__ method will make additional ListEndpoints requests and continue to iterate through the endpoints field on the corresponding responses.

All the usual ListEndpointsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListEndpointsPager

A pager for iterating through list_endpoints requests.

This class thinly wraps an initial ListEndpointsResponse object, and provides an __iter__ method to iterate through its endpoints field.

If there are more pages, the __iter__ method will make additional ListEndpoints requests and continue to iterate through the endpoints field on the corresponding responses.

All the usual ListEndpointsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

EvaluationServiceAsyncClient

Vertex AI Online Evaluation Service.

EvaluationServiceClient

Vertex AI Online Evaluation Service.

ExtensionExecutionServiceAsyncClient

A service for Extension execution.

ExtensionExecutionServiceClient

A service for Extension execution.

ExtensionRegistryServiceAsyncClient

A service for managing Vertex AI's Extension registry.

ExtensionRegistryServiceClient

A service for managing Vertex AI's Extension registry.

ListExtensionsAsyncPager

A pager for iterating through list_extensions requests.

This class thinly wraps an initial ListExtensionsResponse object, and provides an __aiter__ method to iterate through its extensions field.

If there are more pages, the __aiter__ method will make additional ListExtensions requests and continue to iterate through the extensions field on the corresponding responses.

All the usual ListExtensionsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListExtensionsPager

A pager for iterating through list_extensions requests.

This class thinly wraps an initial ListExtensionsResponse object, and provides an __iter__ method to iterate through its extensions field.

If there are more pages, the __iter__ method will make additional ListExtensions requests and continue to iterate through the extensions field on the corresponding responses.

All the usual ListExtensionsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

FeatureOnlineStoreAdminServiceAsyncClient

The service that handles CRUD and List for resources for FeatureOnlineStore.

FeatureOnlineStoreAdminServiceClient

The service that handles CRUD and List for resources for FeatureOnlineStore.

ListFeatureOnlineStoresAsyncPager

A pager for iterating through list_feature_online_stores requests.

This class thinly wraps an initial ListFeatureOnlineStoresResponse object, and provides an __aiter__ method to iterate through its feature_online_stores field.

All the usual ListFeatureOnlineStoresResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListFeatureOnlineStoresPager

A pager for iterating through list_feature_online_stores requests.

This class thinly wraps an initial ListFeatureOnlineStoresResponse object, and provides an __iter__ method to iterate through its feature_online_stores field.

All the usual ListFeatureOnlineStoresResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListFeatureViewSyncsAsyncPager

A pager for iterating through list_feature_view_syncs requests.

This class thinly wraps an initial ListFeatureViewSyncsResponse object, and provides an __aiter__ method to iterate through its feature_view_syncs field.

If there are more pages, the __aiter__ method will make additional ListFeatureViewSyncs requests and continue to iterate through the feature_view_syncs field on the corresponding responses.

All the usual ListFeatureViewSyncsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListFeatureViewSyncsPager

A pager for iterating through list_feature_view_syncs requests.

This class thinly wraps an initial ListFeatureViewSyncsResponse object, and provides an __iter__ method to iterate through its feature_view_syncs field.

If there are more pages, the __iter__ method will make additional ListFeatureViewSyncs requests and continue to iterate through the feature_view_syncs field on the corresponding responses.

All the usual ListFeatureViewSyncsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListFeatureViewsAsyncPager

A pager for iterating through list_feature_views requests.

This class thinly wraps an initial ListFeatureViewsResponse object, and provides an __aiter__ method to iterate through its feature_views field.

If there are more pages, the __aiter__ method will make additional ListFeatureViews requests and continue to iterate through the feature_views field on the corresponding responses.

All the usual ListFeatureViewsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListFeatureViewsPager

A pager for iterating through list_feature_views requests.

This class thinly wraps an initial ListFeatureViewsResponse object, and provides an __iter__ method to iterate through its feature_views field.

If there are more pages, the __iter__ method will make additional ListFeatureViews requests and continue to iterate through the feature_views field on the corresponding responses.

All the usual ListFeatureViewsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

FeatureOnlineStoreServiceAsyncClient

A service for fetching feature values from the online store.

FeatureOnlineStoreServiceClient

A service for fetching feature values from the online store.

FeatureRegistryServiceAsyncClient

The service that handles CRUD and List for resources for FeatureRegistry.

FeatureRegistryServiceClient

The service that handles CRUD and List for resources for FeatureRegistry.

ListFeatureGroupsAsyncPager

A pager for iterating through list_feature_groups requests.

This class thinly wraps an initial ListFeatureGroupsResponse object, and provides an __aiter__ method to iterate through its feature_groups field.

If there are more pages, the __aiter__ method will make additional ListFeatureGroups requests and continue to iterate through the feature_groups field on the corresponding responses.

All the usual ListFeatureGroupsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListFeatureGroupsPager

A pager for iterating through list_feature_groups requests.

This class thinly wraps an initial ListFeatureGroupsResponse object, and provides an __iter__ method to iterate through its feature_groups field.

If there are more pages, the __iter__ method will make additional ListFeatureGroups requests and continue to iterate through the feature_groups field on the corresponding responses.

All the usual ListFeatureGroupsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListFeaturesAsyncPager

A pager for iterating through list_features requests.

This class thinly wraps an initial ListFeaturesResponse object, and provides an __aiter__ method to iterate through its features field.

If there are more pages, the __aiter__ method will make additional ListFeatures requests and continue to iterate through the features field on the corresponding responses.

All the usual ListFeaturesResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListFeaturesPager

A pager for iterating through list_features requests.

This class thinly wraps an initial ListFeaturesResponse object, and provides an __iter__ method to iterate through its features field.

If there are more pages, the __iter__ method will make additional ListFeatures requests and continue to iterate through the features field on the corresponding responses.

All the usual ListFeaturesResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

FeaturestoreOnlineServingServiceAsyncClient

A service for serving online feature values.

FeaturestoreOnlineServingServiceClient

A service for serving online feature values.

FeaturestoreServiceAsyncClient

The service that handles CRUD and List for resources for Featurestore.

FeaturestoreServiceClient

The service that handles CRUD and List for resources for Featurestore.

ListEntityTypesAsyncPager

A pager for iterating through list_entity_types requests.

This class thinly wraps an initial ListEntityTypesResponse object, and provides an __aiter__ method to iterate through its entity_types field.

If there are more pages, the __aiter__ method will make additional ListEntityTypes requests and continue to iterate through the entity_types field on the corresponding responses.

All the usual ListEntityTypesResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListEntityTypesPager

A pager for iterating through list_entity_types requests.

This class thinly wraps an initial ListEntityTypesResponse object, and provides an __iter__ method to iterate through its entity_types field.

If there are more pages, the __iter__ method will make additional ListEntityTypes requests and continue to iterate through the entity_types field on the corresponding responses.

All the usual ListEntityTypesResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListFeaturesAsyncPager

A pager for iterating through list_features requests.

This class thinly wraps an initial ListFeaturesResponse object, and provides an __aiter__ method to iterate through its features field.

If there are more pages, the __aiter__ method will make additional ListFeatures requests and continue to iterate through the features field on the corresponding responses.

All the usual ListFeaturesResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListFeaturesPager

A pager for iterating through list_features requests.

This class thinly wraps an initial ListFeaturesResponse object, and provides an __iter__ method to iterate through its features field.

If there are more pages, the __iter__ method will make additional ListFeatures requests and continue to iterate through the features field on the corresponding responses.

All the usual ListFeaturesResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListFeaturestoresAsyncPager

A pager for iterating through list_featurestores requests.

This class thinly wraps an initial ListFeaturestoresResponse object, and provides an __aiter__ method to iterate through its featurestores field.

If there are more pages, the __aiter__ method will make additional ListFeaturestores requests and continue to iterate through the featurestores field on the corresponding responses.

All the usual ListFeaturestoresResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListFeaturestoresPager

A pager for iterating through list_featurestores requests.

This class thinly wraps an initial ListFeaturestoresResponse object, and provides an __iter__ method to iterate through its featurestores field.

If there are more pages, the __iter__ method will make additional ListFeaturestores requests and continue to iterate through the featurestores field on the corresponding responses.

All the usual ListFeaturestoresResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

SearchFeaturesAsyncPager

A pager for iterating through search_features requests.

This class thinly wraps an initial SearchFeaturesResponse object, and provides an __aiter__ method to iterate through its features field.

If there are more pages, the __aiter__ method will make additional SearchFeatures requests and continue to iterate through the features field on the corresponding responses.

All the usual SearchFeaturesResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

SearchFeaturesPager

A pager for iterating through search_features requests.

This class thinly wraps an initial SearchFeaturesResponse object, and provides an __iter__ method to iterate through its features field.

If there are more pages, the __iter__ method will make additional SearchFeatures requests and continue to iterate through the features field on the corresponding responses.

All the usual SearchFeaturesResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

IndexEndpointServiceAsyncClient

A service for managing Vertex AI's IndexEndpoints.

IndexEndpointServiceClient

A service for managing Vertex AI's IndexEndpoints.

ListIndexEndpointsAsyncPager

A pager for iterating through list_index_endpoints requests.

This class thinly wraps an initial ListIndexEndpointsResponse object, and provides an __aiter__ method to iterate through its index_endpoints field.

If there are more pages, the __aiter__ method will make additional ListIndexEndpoints requests and continue to iterate through the index_endpoints field on the corresponding responses.

All the usual ListIndexEndpointsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListIndexEndpointsPager

A pager for iterating through list_index_endpoints requests.

This class thinly wraps an initial ListIndexEndpointsResponse object, and provides an __iter__ method to iterate through its index_endpoints field.

If there are more pages, the __iter__ method will make additional ListIndexEndpoints requests and continue to iterate through the index_endpoints field on the corresponding responses.

All the usual ListIndexEndpointsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

IndexServiceAsyncClient

A service for creating and managing Vertex AI's Index resources.

IndexServiceClient

A service for creating and managing Vertex AI's Index resources.

ListIndexesAsyncPager

A pager for iterating through list_indexes requests.

This class thinly wraps an initial ListIndexesResponse object, and provides an __aiter__ method to iterate through its indexes field.

If there are more pages, the __aiter__ method will make additional ListIndexes requests and continue to iterate through the indexes field on the corresponding responses.

All the usual ListIndexesResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListIndexesPager

A pager for iterating through list_indexes requests.

This class thinly wraps an initial ListIndexesResponse object, and provides an __iter__ method to iterate through its indexes field.

If there are more pages, the __iter__ method will make additional ListIndexes requests and continue to iterate through the indexes field on the corresponding responses.

All the usual ListIndexesResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

JobServiceAsyncClient

A service for creating and managing Vertex AI's jobs.

JobServiceClient

A service for creating and managing Vertex AI's jobs.

ListBatchPredictionJobsAsyncPager

A pager for iterating through list_batch_prediction_jobs requests.

This class thinly wraps an initial ListBatchPredictionJobsResponse object, and provides an __aiter__ method to iterate through its batch_prediction_jobs field.

All the usual ListBatchPredictionJobsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListBatchPredictionJobsPager

A pager for iterating through list_batch_prediction_jobs requests.

This class thinly wraps an initial ListBatchPredictionJobsResponse object, and provides an __iter__ method to iterate through its batch_prediction_jobs field.

All the usual ListBatchPredictionJobsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListCustomJobsAsyncPager

A pager for iterating through list_custom_jobs requests.

This class thinly wraps an initial ListCustomJobsResponse object, and provides an __aiter__ method to iterate through its custom_jobs field.

If there are more pages, the __aiter__ method will make additional ListCustomJobs requests and continue to iterate through the custom_jobs field on the corresponding responses.

All the usual ListCustomJobsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListCustomJobsPager

A pager for iterating through list_custom_jobs requests.

This class thinly wraps an initial ListCustomJobsResponse object, and provides an __iter__ method to iterate through its custom_jobs field.

If there are more pages, the __iter__ method will make additional ListCustomJobs requests and continue to iterate through the custom_jobs field on the corresponding responses.

All the usual ListCustomJobsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListDataLabelingJobsAsyncPager

A pager for iterating through list_data_labeling_jobs requests.

This class thinly wraps an initial ListDataLabelingJobsResponse object, and provides an __aiter__ method to iterate through its data_labeling_jobs field.

If there are more pages, the __aiter__ method will make additional ListDataLabelingJobs requests and continue to iterate through the data_labeling_jobs field on the corresponding responses.

All the usual ListDataLabelingJobsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListDataLabelingJobsPager

A pager for iterating through list_data_labeling_jobs requests.

This class thinly wraps an initial ListDataLabelingJobsResponse object, and provides an __iter__ method to iterate through its data_labeling_jobs field.

If there are more pages, the __iter__ method will make additional ListDataLabelingJobs requests and continue to iterate through the data_labeling_jobs field on the corresponding responses.

All the usual ListDataLabelingJobsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListHyperparameterTuningJobsAsyncPager

A pager for iterating through list_hyperparameter_tuning_jobs requests.

This class thinly wraps an initial ListHyperparameterTuningJobsResponse object, and provides an __aiter__ method to iterate through its hyperparameter_tuning_jobs field.

All the usual ListHyperparameterTuningJobsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListHyperparameterTuningJobsPager

A pager for iterating through list_hyperparameter_tuning_jobs requests.

This class thinly wraps an initial ListHyperparameterTuningJobsResponse object, and provides an __iter__ method to iterate through its hyperparameter_tuning_jobs field.

All the usual ListHyperparameterTuningJobsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListModelDeploymentMonitoringJobsAsyncPager

A pager for iterating through list_model_deployment_monitoring_jobs requests.

This class thinly wraps an initial ListModelDeploymentMonitoringJobsResponse object, and provides an __aiter__ method to iterate through its model_deployment_monitoring_jobs field.

ListModelDeploymentMonitoringJobsPager

A pager for iterating through list_model_deployment_monitoring_jobs requests.

This class thinly wraps an initial ListModelDeploymentMonitoringJobsResponse object, and provides an __iter__ method to iterate through its model_deployment_monitoring_jobs field.

ListNasJobsAsyncPager

A pager for iterating through list_nas_jobs requests.

This class thinly wraps an initial ListNasJobsResponse object, and provides an __aiter__ method to iterate through its nas_jobs field.

If there are more pages, the __aiter__ method will make additional ListNasJobs requests and continue to iterate through the nas_jobs field on the corresponding responses.

All the usual ListNasJobsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListNasJobsPager

A pager for iterating through list_nas_jobs requests.

This class thinly wraps an initial ListNasJobsResponse object, and provides an __iter__ method to iterate through its nas_jobs field.

If there are more pages, the __iter__ method will make additional ListNasJobs requests and continue to iterate through the nas_jobs field on the corresponding responses.

All the usual ListNasJobsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListNasTrialDetailsAsyncPager

A pager for iterating through list_nas_trial_details requests.

This class thinly wraps an initial ListNasTrialDetailsResponse object, and provides an __aiter__ method to iterate through its nas_trial_details field.

If there are more pages, the __aiter__ method will make additional ListNasTrialDetails requests and continue to iterate through the nas_trial_details field on the corresponding responses.

All the usual ListNasTrialDetailsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListNasTrialDetailsPager

A pager for iterating through list_nas_trial_details requests.

This class thinly wraps an initial ListNasTrialDetailsResponse object, and provides an __iter__ method to iterate through its nas_trial_details field.

If there are more pages, the __iter__ method will make additional ListNasTrialDetails requests and continue to iterate through the nas_trial_details field on the corresponding responses.

All the usual ListNasTrialDetailsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

SearchModelDeploymentMonitoringStatsAnomaliesAsyncPager

A pager for iterating through search_model_deployment_monitoring_stats_anomalies requests.

This class thinly wraps an initial SearchModelDeploymentMonitoringStatsAnomaliesResponse object, and provides an __aiter__ method to iterate through its monitoring_stats field.

SearchModelDeploymentMonitoringStatsAnomaliesPager

A pager for iterating through search_model_deployment_monitoring_stats_anomalies requests.

This class thinly wraps an initial SearchModelDeploymentMonitoringStatsAnomaliesResponse object, and provides an __iter__ method to iterate through its monitoring_stats field.

LlmUtilityServiceAsyncClient

Service for LLM related utility functions.

LlmUtilityServiceClient

Service for LLM related utility functions.

MatchServiceAsyncClient

MatchService is a Google managed service for efficient vector similarity search at scale.

MatchServiceClient

MatchService is a Google managed service for efficient vector similarity search at scale.

MetadataServiceAsyncClient

Service for reading and writing metadata entries.

MetadataServiceClient

Service for reading and writing metadata entries.

ListArtifactsAsyncPager

A pager for iterating through list_artifacts requests.

This class thinly wraps an initial ListArtifactsResponse object, and provides an __aiter__ method to iterate through its artifacts field.

If there are more pages, the __aiter__ method will make additional ListArtifacts requests and continue to iterate through the artifacts field on the corresponding responses.

All the usual ListArtifactsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListArtifactsPager

A pager for iterating through list_artifacts requests.

This class thinly wraps an initial ListArtifactsResponse object, and provides an __iter__ method to iterate through its artifacts field.

If there are more pages, the __iter__ method will make additional ListArtifacts requests and continue to iterate through the artifacts field on the corresponding responses.

All the usual ListArtifactsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListContextsAsyncPager

A pager for iterating through list_contexts requests.

This class thinly wraps an initial ListContextsResponse object, and provides an __aiter__ method to iterate through its contexts field.

If there are more pages, the __aiter__ method will make additional ListContexts requests and continue to iterate through the contexts field on the corresponding responses.

All the usual ListContextsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListContextsPager

A pager for iterating through list_contexts requests.

This class thinly wraps an initial ListContextsResponse object, and provides an __iter__ method to iterate through its contexts field.

If there are more pages, the __iter__ method will make additional ListContexts requests and continue to iterate through the contexts field on the corresponding responses.

All the usual ListContextsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListExecutionsAsyncPager

A pager for iterating through list_executions requests.

This class thinly wraps an initial ListExecutionsResponse object, and provides an __aiter__ method to iterate through its executions field.

If there are more pages, the __aiter__ method will make additional ListExecutions requests and continue to iterate through the executions field on the corresponding responses.

All the usual ListExecutionsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListExecutionsPager

A pager for iterating through list_executions requests.

This class thinly wraps an initial ListExecutionsResponse object, and provides an __iter__ method to iterate through its executions field.

If there are more pages, the __iter__ method will make additional ListExecutions requests and continue to iterate through the executions field on the corresponding responses.

All the usual ListExecutionsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListMetadataSchemasAsyncPager

A pager for iterating through list_metadata_schemas requests.

This class thinly wraps an initial ListMetadataSchemasResponse object, and provides an __aiter__ method to iterate through its metadata_schemas field.

If there are more pages, the __aiter__ method will make additional ListMetadataSchemas requests and continue to iterate through the metadata_schemas field on the corresponding responses.

All the usual ListMetadataSchemasResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListMetadataSchemasPager

A pager for iterating through list_metadata_schemas requests.

This class thinly wraps an initial ListMetadataSchemasResponse object, and provides an __iter__ method to iterate through its metadata_schemas field.

If there are more pages, the __iter__ method will make additional ListMetadataSchemas requests and continue to iterate through the metadata_schemas field on the corresponding responses.

All the usual ListMetadataSchemasResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListMetadataStoresAsyncPager

A pager for iterating through list_metadata_stores requests.

This class thinly wraps an initial ListMetadataStoresResponse object, and provides an __aiter__ method to iterate through its metadata_stores field.

If there are more pages, the __aiter__ method will make additional ListMetadataStores requests and continue to iterate through the metadata_stores field on the corresponding responses.

All the usual ListMetadataStoresResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListMetadataStoresPager

A pager for iterating through list_metadata_stores requests.

This class thinly wraps an initial ListMetadataStoresResponse object, and provides an __iter__ method to iterate through its metadata_stores field.

If there are more pages, the __iter__ method will make additional ListMetadataStores requests and continue to iterate through the metadata_stores field on the corresponding responses.

All the usual ListMetadataStoresResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

MigrationServiceAsyncClient

A service that migrates resources from automl.googleapis.com, datalabeling.googleapis.com and ml.googleapis.com to Vertex AI.

MigrationServiceClient

A service that migrates resources from automl.googleapis.com, datalabeling.googleapis.com and ml.googleapis.com to Vertex AI.

SearchMigratableResourcesAsyncPager

A pager for iterating through search_migratable_resources requests.

This class thinly wraps an initial SearchMigratableResourcesResponse object, and provides an __aiter__ method to iterate through its migratable_resources field.

All the usual SearchMigratableResourcesResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

SearchMigratableResourcesPager

A pager for iterating through search_migratable_resources requests.

This class thinly wraps an initial SearchMigratableResourcesResponse object, and provides an __iter__ method to iterate through its migratable_resources field.

All the usual SearchMigratableResourcesResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ModelGardenServiceAsyncClient

The interface of Model Garden Service.

ModelGardenServiceClient

The interface of Model Garden Service.

ListPublisherModelsAsyncPager

A pager for iterating through list_publisher_models requests.

This class thinly wraps an initial ListPublisherModelsResponse object, and provides an __aiter__ method to iterate through its publisher_models field.

If there are more pages, the __aiter__ method will make additional ListPublisherModels requests and continue to iterate through the publisher_models field on the corresponding responses.

All the usual ListPublisherModelsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListPublisherModelsPager

A pager for iterating through list_publisher_models requests.

This class thinly wraps an initial ListPublisherModelsResponse object, and provides an __iter__ method to iterate through its publisher_models field.

If there are more pages, the __iter__ method will make additional ListPublisherModels requests and continue to iterate through the publisher_models field on the corresponding responses.

All the usual ListPublisherModelsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ModelServiceAsyncClient

A service for managing Vertex AI's machine learning Models.

ModelServiceClient

A service for managing Vertex AI's machine learning Models.

ListModelEvaluationSlicesAsyncPager

A pager for iterating through list_model_evaluation_slices requests.

This class thinly wraps an initial ListModelEvaluationSlicesResponse object, and provides an __aiter__ method to iterate through its model_evaluation_slices field.

All the usual ListModelEvaluationSlicesResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListModelEvaluationSlicesPager

A pager for iterating through list_model_evaluation_slices requests.

This class thinly wraps an initial ListModelEvaluationSlicesResponse object, and provides an __iter__ method to iterate through its model_evaluation_slices field.

All the usual ListModelEvaluationSlicesResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListModelEvaluationsAsyncPager

A pager for iterating through list_model_evaluations requests.

This class thinly wraps an initial ListModelEvaluationsResponse object, and provides an __aiter__ method to iterate through its model_evaluations field.

If there are more pages, the __aiter__ method will make additional ListModelEvaluations requests and continue to iterate through the model_evaluations field on the corresponding responses.

All the usual ListModelEvaluationsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListModelEvaluationsPager

A pager for iterating through list_model_evaluations requests.

This class thinly wraps an initial ListModelEvaluationsResponse object, and provides an __iter__ method to iterate through its model_evaluations field.

If there are more pages, the __iter__ method will make additional ListModelEvaluations requests and continue to iterate through the model_evaluations field on the corresponding responses.

All the usual ListModelEvaluationsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListModelVersionsAsyncPager

A pager for iterating through list_model_versions requests.

This class thinly wraps an initial ListModelVersionsResponse object, and provides an __aiter__ method to iterate through its models field.

If there are more pages, the __aiter__ method will make additional ListModelVersions requests and continue to iterate through the models field on the corresponding responses.

All the usual ListModelVersionsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListModelVersionsPager

A pager for iterating through list_model_versions requests.

This class thinly wraps an initial ListModelVersionsResponse object, and provides an __iter__ method to iterate through its models field.

If there are more pages, the __iter__ method will make additional ListModelVersions requests and continue to iterate through the models field on the corresponding responses.

All the usual ListModelVersionsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListModelsAsyncPager

A pager for iterating through list_models requests.

This class thinly wraps an initial ListModelsResponse object, and provides an __aiter__ method to iterate through its models field.

If there are more pages, the __aiter__ method will make additional ListModels requests and continue to iterate through the models field on the corresponding responses.

All the usual ListModelsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListModelsPager

A pager for iterating through list_models requests.

This class thinly wraps an initial ListModelsResponse object, and provides an __iter__ method to iterate through its models field.

If there are more pages, the __iter__ method will make additional ListModels requests and continue to iterate through the models field on the corresponding responses.

All the usual ListModelsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

NotebookServiceAsyncClient

The interface for Vertex Notebook service (a.k.a. Colab on Workbench).

NotebookServiceClient

The interface for Vertex Notebook service (a.k.a. Colab on Workbench).

ListNotebookRuntimeTemplatesAsyncPager

A pager for iterating through list_notebook_runtime_templates requests.

This class thinly wraps an initial ListNotebookRuntimeTemplatesResponse object, and provides an __aiter__ method to iterate through its notebook_runtime_templates field.

All the usual ListNotebookRuntimeTemplatesResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListNotebookRuntimeTemplatesPager

A pager for iterating through list_notebook_runtime_templates requests.

This class thinly wraps an initial ListNotebookRuntimeTemplatesResponse object, and provides an __iter__ method to iterate through its notebook_runtime_templates field.

All the usual ListNotebookRuntimeTemplatesResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListNotebookRuntimesAsyncPager

A pager for iterating through list_notebook_runtimes requests.

This class thinly wraps an initial ListNotebookRuntimesResponse object, and provides an __aiter__ method to iterate through its notebook_runtimes field.

If there are more pages, the __aiter__ method will make additional ListNotebookRuntimes requests and continue to iterate through the notebook_runtimes field on the corresponding responses.

All the usual ListNotebookRuntimesResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListNotebookRuntimesPager

A pager for iterating through list_notebook_runtimes requests.

This class thinly wraps an initial ListNotebookRuntimesResponse object, and provides an __iter__ method to iterate through its notebook_runtimes field.

If there are more pages, the __iter__ method will make additional ListNotebookRuntimes requests and continue to iterate through the notebook_runtimes field on the corresponding responses.

All the usual ListNotebookRuntimesResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

PersistentResourceServiceAsyncClient

A service for managing Vertex AI's machine learning PersistentResource.

PersistentResourceServiceClient

A service for managing Vertex AI's machine learning PersistentResource.

ListPersistentResourcesAsyncPager

A pager for iterating through list_persistent_resources requests.

This class thinly wraps an initial ListPersistentResourcesResponse object, and provides an __aiter__ method to iterate through its persistent_resources field.

All the usual ListPersistentResourcesResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListPersistentResourcesPager

A pager for iterating through list_persistent_resources requests.

This class thinly wraps an initial ListPersistentResourcesResponse object, and provides an __iter__ method to iterate through its persistent_resources field.

If there are more pages, the __iter__ method will make additional ListPersistentResources requests and continue to iterate through the persistent_resources field on the corresponding responses.

All the usual ListPersistentResourcesResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

PipelineServiceAsyncClient

PipelineServiceClient

ListPipelineJobsAsyncPager

A pager for iterating through list_pipeline_jobs requests.

This class thinly wraps an initial ListPipelineJobsResponse object, and provides an __aiter__ method to iterate through its pipeline_jobs field.

If there are more pages, the __aiter__ method will make additional ListPipelineJobs requests and continue to iterate through the pipeline_jobs field on the corresponding responses.

All the usual ListPipelineJobsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListPipelineJobsPager

A pager for iterating through list_pipeline_jobs requests.

This class thinly wraps an initial ListPipelineJobsResponse object, and provides an __iter__ method to iterate through its pipeline_jobs field.

If there are more pages, the __iter__ method will make additional ListPipelineJobs requests and continue to iterate through the pipeline_jobs field on the corresponding responses.

All the usual ListPipelineJobsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListTrainingPipelinesAsyncPager

A pager for iterating through list_training_pipelines requests.

This class thinly wraps an initial ListTrainingPipelinesResponse object, and provides an __aiter__ method to iterate through its training_pipelines field.

If there are more pages, the __aiter__ method will make additional ListTrainingPipelines requests and continue to iterate through the training_pipelines field on the corresponding responses.

All the usual ListTrainingPipelinesResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListTrainingPipelinesPager

A pager for iterating through list_training_pipelines requests.

This class thinly wraps an initial ListTrainingPipelinesResponse object, and provides an __iter__ method to iterate through its training_pipelines field.

If there are more pages, the __iter__ method will make additional ListTrainingPipelines requests and continue to iterate through the training_pipelines field on the corresponding responses.

All the usual ListTrainingPipelinesResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

PredictionServiceAsyncClient

A service for online predictions and explanations.

PredictionServiceClient

A service for online predictions and explanations.

ReasoningEngineExecutionServiceAsyncClient

A service for executing queries on Reasoning Engine.

ReasoningEngineExecutionServiceClient

A service for executing queries on Reasoning Engine.

ReasoningEngineServiceAsyncClient

A service for managing Vertex AI's Reasoning Engines.

ReasoningEngineServiceClient

A service for managing Vertex AI's Reasoning Engines.

ListReasoningEnginesAsyncPager

A pager for iterating through list_reasoning_engines requests.

This class thinly wraps an initial ListReasoningEnginesResponse object, and provides an __aiter__ method to iterate through its reasoning_engines field.

If there are more pages, the __aiter__ method will make additional ListReasoningEngines requests and continue to iterate through the reasoning_engines field on the corresponding responses.

All the usual ListReasoningEnginesResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListReasoningEnginesPager

A pager for iterating through list_reasoning_engines requests.

This class thinly wraps an initial ListReasoningEnginesResponse object, and provides an __iter__ method to iterate through its reasoning_engines field.

If there are more pages, the __iter__ method will make additional ListReasoningEngines requests and continue to iterate through the reasoning_engines field on the corresponding responses.

All the usual ListReasoningEnginesResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ScheduleServiceAsyncClient

A service for creating and managing Vertex AI's Schedule resources to periodically launch shceudled runs to make API calls.

ScheduleServiceClient

A service for creating and managing Vertex AI's Schedule resources to periodically launch shceudled runs to make API calls.

ListSchedulesAsyncPager

A pager for iterating through list_schedules requests.

This class thinly wraps an initial ListSchedulesResponse object, and provides an __aiter__ method to iterate through its schedules field.

If there are more pages, the __aiter__ method will make additional ListSchedules requests and continue to iterate through the schedules field on the corresponding responses.

All the usual ListSchedulesResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListSchedulesPager

A pager for iterating through list_schedules requests.

This class thinly wraps an initial ListSchedulesResponse object, and provides an __iter__ method to iterate through its schedules field.

If there are more pages, the __iter__ method will make additional ListSchedules requests and continue to iterate through the schedules field on the corresponding responses.

All the usual ListSchedulesResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

SpecialistPoolServiceAsyncClient

SpecialistPoolServiceClient

ListSpecialistPoolsAsyncPager

A pager for iterating through list_specialist_pools requests.

This class thinly wraps an initial ListSpecialistPoolsResponse object, and provides an __aiter__ method to iterate through its specialist_pools field.

If there are more pages, the __aiter__ method will make additional ListSpecialistPools requests and continue to iterate through the specialist_pools field on the corresponding responses.

All the usual ListSpecialistPoolsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListSpecialistPoolsPager

A pager for iterating through list_specialist_pools requests.

This class thinly wraps an initial ListSpecialistPoolsResponse object, and provides an __iter__ method to iterate through its specialist_pools field.

If there are more pages, the __iter__ method will make additional ListSpecialistPools requests and continue to iterate through the specialist_pools field on the corresponding responses.

All the usual ListSpecialistPoolsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

TensorboardServiceAsyncClient

TensorboardService

TensorboardServiceClient

TensorboardService

ExportTensorboardTimeSeriesDataAsyncPager

A pager for iterating through export_tensorboard_time_series_data requests.

This class thinly wraps an initial ExportTensorboardTimeSeriesDataResponse object, and provides an __aiter__ method to iterate through its time_series_data_points field.

ExportTensorboardTimeSeriesDataPager

A pager for iterating through export_tensorboard_time_series_data requests.

This class thinly wraps an initial ExportTensorboardTimeSeriesDataResponse object, and provides an __iter__ method to iterate through its time_series_data_points field.

ListTensorboardExperimentsAsyncPager

A pager for iterating through list_tensorboard_experiments requests.

This class thinly wraps an initial ListTensorboardExperimentsResponse object, and provides an __aiter__ method to iterate through its tensorboard_experiments field.

All the usual ListTensorboardExperimentsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListTensorboardExperimentsPager

A pager for iterating through list_tensorboard_experiments requests.

This class thinly wraps an initial ListTensorboardExperimentsResponse object, and provides an __iter__ method to iterate through its tensorboard_experiments field.

All the usual ListTensorboardExperimentsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListTensorboardRunsAsyncPager

A pager for iterating through list_tensorboard_runs requests.

This class thinly wraps an initial ListTensorboardRunsResponse object, and provides an __aiter__ method to iterate through its tensorboard_runs field.

If there are more pages, the __aiter__ method will make additional ListTensorboardRuns requests and continue to iterate through the tensorboard_runs field on the corresponding responses.

All the usual ListTensorboardRunsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListTensorboardRunsPager

A pager for iterating through list_tensorboard_runs requests.

This class thinly wraps an initial ListTensorboardRunsResponse object, and provides an __iter__ method to iterate through its tensorboard_runs field.

If there are more pages, the __iter__ method will make additional ListTensorboardRuns requests and continue to iterate through the tensorboard_runs field on the corresponding responses.

All the usual ListTensorboardRunsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListTensorboardTimeSeriesAsyncPager

A pager for iterating through list_tensorboard_time_series requests.

This class thinly wraps an initial ListTensorboardTimeSeriesResponse object, and provides an __aiter__ method to iterate through its tensorboard_time_series field.

All the usual ListTensorboardTimeSeriesResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListTensorboardTimeSeriesPager

A pager for iterating through list_tensorboard_time_series requests.

This class thinly wraps an initial ListTensorboardTimeSeriesResponse object, and provides an __iter__ method to iterate through its tensorboard_time_series field.

All the usual ListTensorboardTimeSeriesResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListTensorboardsAsyncPager

A pager for iterating through list_tensorboards requests.

This class thinly wraps an initial ListTensorboardsResponse object, and provides an __aiter__ method to iterate through its tensorboards field.

If there are more pages, the __aiter__ method will make additional ListTensorboards requests and continue to iterate through the tensorboards field on the corresponding responses.

All the usual ListTensorboardsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListTensorboardsPager

A pager for iterating through list_tensorboards requests.

This class thinly wraps an initial ListTensorboardsResponse object, and provides an __iter__ method to iterate through its tensorboards field.

If there are more pages, the __iter__ method will make additional ListTensorboards requests and continue to iterate through the tensorboards field on the corresponding responses.

All the usual ListTensorboardsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

VertexRagDataServiceAsyncClient

A service for managing user data for RAG.

VertexRagDataServiceClient

A service for managing user data for RAG.

ListRagCorporaAsyncPager

A pager for iterating through list_rag_corpora requests.

This class thinly wraps an initial ListRagCorporaResponse object, and provides an __aiter__ method to iterate through its rag_corpora field.

If there are more pages, the __aiter__ method will make additional ListRagCorpora requests and continue to iterate through the rag_corpora field on the corresponding responses.

All the usual ListRagCorporaResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListRagCorporaPager

A pager for iterating through list_rag_corpora requests.

This class thinly wraps an initial ListRagCorporaResponse object, and provides an __iter__ method to iterate through its rag_corpora field.

If there are more pages, the __iter__ method will make additional ListRagCorpora requests and continue to iterate through the rag_corpora field on the corresponding responses.

All the usual ListRagCorporaResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListRagFilesAsyncPager

A pager for iterating through list_rag_files requests.

This class thinly wraps an initial ListRagFilesResponse object, and provides an __aiter__ method to iterate through its rag_files field.

If there are more pages, the __aiter__ method will make additional ListRagFiles requests and continue to iterate through the rag_files field on the corresponding responses.

All the usual ListRagFilesResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListRagFilesPager

A pager for iterating through list_rag_files requests.

This class thinly wraps an initial ListRagFilesResponse object, and provides an __iter__ method to iterate through its rag_files field.

If there are more pages, the __iter__ method will make additional ListRagFiles requests and continue to iterate through the rag_files field on the corresponding responses.

All the usual ListRagFilesResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

VertexRagServiceAsyncClient

A service for retrieving relevant contexts.

VertexRagServiceClient

A service for retrieving relevant contexts.

VizierServiceAsyncClient

Vertex AI Vizier API.

Vertex AI Vizier is a service to solve blackbox optimization problems, such as tuning machine learning hyperparameters and searching over deep learning architectures.

VizierServiceClient

Vertex AI Vizier API.

Vertex AI Vizier is a service to solve blackbox optimization problems, such as tuning machine learning hyperparameters and searching over deep learning architectures.

ListStudiesAsyncPager

A pager for iterating through list_studies requests.

This class thinly wraps an initial ListStudiesResponse object, and provides an __aiter__ method to iterate through its studies field.

If there are more pages, the __aiter__ method will make additional ListStudies requests and continue to iterate through the studies field on the corresponding responses.

All the usual ListStudiesResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListStudiesPager

A pager for iterating through list_studies requests.

This class thinly wraps an initial ListStudiesResponse object, and provides an __iter__ method to iterate through its studies field.

If there are more pages, the __iter__ method will make additional ListStudies requests and continue to iterate through the studies field on the corresponding responses.

All the usual ListStudiesResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListTrialsAsyncPager

A pager for iterating through list_trials requests.

This class thinly wraps an initial ListTrialsResponse object, and provides an __aiter__ method to iterate through its trials field.

If there are more pages, the __aiter__ method will make additional ListTrials requests and continue to iterate through the trials field on the corresponding responses.

All the usual ListTrialsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

ListTrialsPager

A pager for iterating through list_trials requests.

This class thinly wraps an initial ListTrialsResponse object, and provides an __iter__ method to iterate through its trials field.

If there are more pages, the __iter__ method will make additional ListTrials requests and continue to iterate through the trials field on the corresponding responses.

All the usual ListTrialsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

AcceleratorType

Represents a hardware accelerator type.

ActiveLearningConfig

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

AddContextArtifactsAndExecutionsRequest

Request message for MetadataService.AddContextArtifactsAndExecutions.

AddContextArtifactsAndExecutionsResponse

Response message for MetadataService.AddContextArtifactsAndExecutions.

AddContextChildrenRequest

Request message for MetadataService.AddContextChildren.

AddContextChildrenResponse

Response message for MetadataService.AddContextChildren.

AddExecutionEventsRequest

Request message for MetadataService.AddExecutionEvents.

AddExecutionEventsResponse

Response message for MetadataService.AddExecutionEvents.

AddTrialMeasurementRequest

Request message for VizierService.AddTrialMeasurement.

Annotation

Used to assign specific AnnotationSpec to a particular area of a DataItem or the whole part of the DataItem.

LabelsEntry

The abstract base class for a message.

AnnotationSpec

Identifies a concept with which DataItems may be annotated with.

Artifact

Instance of a general artifact.

LabelsEntry

The abstract base class for a message.

State

Describes the state of the Artifact.

AssignNotebookRuntimeOperationMetadata

Metadata information for NotebookService.AssignNotebookRuntime.

AssignNotebookRuntimeRequest

Request message for NotebookService.AssignNotebookRuntime.

Attribution

Attribution that explains a particular prediction output.

AuthConfig

Auth configuration to run the extension.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

ApiKeyConfig

Config for authentication with API key.

GoogleServiceAccountConfig

Config for Google Service Account Authentication.

HttpBasicAuthConfig

Config for HTTP Basic Authentication.

OauthConfig

Config for user oauth.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

OidcConfig

Config for user OIDC auth.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

AuthType

Type of Auth.

Values: AUTH_TYPE_UNSPECIFIED (0): No description available. NO_AUTH (1): No Auth. API_KEY_AUTH (2): API Key Auth. HTTP_BASIC_AUTH (3): HTTP Basic Auth. GOOGLE_SERVICE_ACCOUNT_AUTH (4): Google Service Account Auth. OAUTH (6): OAuth auth. OIDC_AUTH (8): OpenID Connect (OIDC) Auth.

AutomaticResources

A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines.

AutoscalingMetricSpec

The metric specification that defines the target resource utilization (CPU utilization, accelerator's duty cycle, and so on) for calculating the desired replica count.

AvroSource

The storage details for Avro input content.

BatchCancelPipelineJobsOperationMetadata

Runtime operation information for PipelineService.BatchCancelPipelineJobs.

BatchCancelPipelineJobsRequest

Request message for PipelineService.BatchCancelPipelineJobs.

BatchCancelPipelineJobsResponse

Response message for PipelineService.BatchCancelPipelineJobs.

BatchCreateFeaturesOperationMetadata

Details of operations that perform batch create Features.

BatchCreateFeaturesRequest

Request message for FeaturestoreService.BatchCreateFeatures.

BatchCreateFeaturesResponse

Response message for FeaturestoreService.BatchCreateFeatures.

BatchCreateTensorboardRunsRequest

Request message for TensorboardService.BatchCreateTensorboardRuns.

BatchCreateTensorboardRunsResponse

Response message for TensorboardService.BatchCreateTensorboardRuns.

BatchCreateTensorboardTimeSeriesRequest

Request message for TensorboardService.BatchCreateTensorboardTimeSeries.

BatchCreateTensorboardTimeSeriesResponse

Response message for TensorboardService.BatchCreateTensorboardTimeSeries.

BatchDedicatedResources

A description of resources that are used for performing batch operations, are dedicated to a Model, and need manual configuration.

BatchDeletePipelineJobsRequest

Request message for PipelineService.BatchDeletePipelineJobs.

BatchDeletePipelineJobsResponse

Response message for PipelineService.BatchDeletePipelineJobs.

BatchImportEvaluatedAnnotationsRequest

Request message for ModelService.BatchImportEvaluatedAnnotations

BatchImportEvaluatedAnnotationsResponse

Response message for ModelService.BatchImportEvaluatedAnnotations

BatchImportModelEvaluationSlicesRequest

Request message for ModelService.BatchImportModelEvaluationSlices

BatchImportModelEvaluationSlicesResponse

Response message for ModelService.BatchImportModelEvaluationSlices

BatchMigrateResourcesOperationMetadata

Runtime operation information for MigrationService.BatchMigrateResources.

PartialResult

Represents a partial result in batch migration operation for one MigrateResourceRequest.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

BatchMigrateResourcesRequest

Request message for MigrationService.BatchMigrateResources.

BatchMigrateResourcesResponse

Response message for MigrationService.BatchMigrateResources.

BatchPredictionJob

A job that uses a Model to produce predictions on multiple [input instances][google.cloud.aiplatform.v1beta1.BatchPredictionJob.input_config]. If predictions for significant portion of the instances fail, the job may finish without attempting predictions for all remaining instances.

InputConfig

Configures the input to BatchPredictionJob. See Model.supported_input_storage_formats for Model's supported input formats, and how instances should be expressed via any of them.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

InstanceConfig

Configuration defining how to transform batch prediction input instances to the instances that the Model accepts.

LabelsEntry

The abstract base class for a message.

OutputConfig

Configures the output of BatchPredictionJob. See Model.supported_output_storage_formats for supported output formats, and how predictions are expressed via any of them.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

OutputInfo

Further describes this job's output. Supplements output_config.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

BatchReadFeatureValuesOperationMetadata

Details of operations that batch reads Feature values.

BatchReadFeatureValuesRequest

Request message for FeaturestoreService.BatchReadFeatureValues.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

EntityTypeSpec

Selects Features of an EntityType to read values of and specifies read settings.

PassThroughField

Describe pass-through fields in read_instance source.

BatchReadFeatureValuesResponse

Response message for FeaturestoreService.BatchReadFeatureValues.

BatchReadTensorboardTimeSeriesDataRequest

Request message for TensorboardService.BatchReadTensorboardTimeSeriesData.

BatchReadTensorboardTimeSeriesDataResponse

Response message for TensorboardService.BatchReadTensorboardTimeSeriesData.

BigQueryDestination

The BigQuery location for the output content.

BigQuerySource

The BigQuery location for the input content.

BleuInput

Input for bleu metric.

BleuInstance

Spec for bleu instance.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

BleuMetricValue

Bleu metric value for an instance.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

BleuResults

Results for bleu metric.

BleuSpec

Spec for bleu score metric - calculates the precision of n-grams in the prediction as compared to reference - returns a score ranging between 0 to 1.

Blob

Content blob.

It's preferred to send as text directly rather than raw bytes.

BlurBaselineConfig

Config for blur baseline.

When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here:

https://arxiv.org/abs/2004.03383

BoolArray

A list of boolean values.

CancelBatchPredictionJobRequest

Request message for JobService.CancelBatchPredictionJob.

CancelCustomJobRequest

Request message for JobService.CancelCustomJob.

CancelDataLabelingJobRequest

Request message for JobService.CancelDataLabelingJob.

CancelHyperparameterTuningJobRequest

Request message for JobService.CancelHyperparameterTuningJob.

CancelNasJobRequest

Request message for JobService.CancelNasJob.

CancelPipelineJobRequest

Request message for PipelineService.CancelPipelineJob.

CancelTrainingPipelineRequest

Request message for PipelineService.CancelTrainingPipeline.

Candidate

A response candidate generated from the model.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

FinishReason

The reason why the model stopped generating tokens. If empty, the model has not stopped generating the tokens.

ChatCompletionsRequest

Request message for [PredictionService.ChatCompletions]

CheckTrialEarlyStoppingStateMetatdata

This message will be placed in the metadata field of a google.longrunning.Operation associated with a CheckTrialEarlyStoppingState request.

CheckTrialEarlyStoppingStateRequest

Request message for VizierService.CheckTrialEarlyStoppingState.

CheckTrialEarlyStoppingStateResponse

Response message for VizierService.CheckTrialEarlyStoppingState.

Citation

Source attributions for content.

CitationMetadata

A collection of source attributions for a piece of content.

CoherenceInput

Input for coherence metric.

CoherenceInstance

Spec for coherence instance.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

CoherenceResult

Spec for coherence result.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

CoherenceSpec

Spec for coherence score metric.

CompleteTrialRequest

Request message for VizierService.CompleteTrial.

CompletionStats

Success and error statistics of processing multiple entities (for example, DataItems or structured data rows) in batch.

ComputeTokensRequest

Request message for ComputeTokens RPC call.

ComputeTokensResponse

Response message for ComputeTokens RPC call.

ContainerRegistryDestination

The Container Registry location for the container image.

ContainerSpec

The spec of a Container.

Content

The base structured datatype containing multi-part content of a message.

A Content includes a role field designating the producer of the Content and a parts field containing multi-part data that contains the content of the message turn.

Context

Instance of a general context.

LabelsEntry

The abstract base class for a message.

CopyModelOperationMetadata

Details of ModelService.CopyModel operation.

CopyModelRequest

Request message for ModelService.CopyModel.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

CopyModelResponse

Response message of ModelService.CopyModel operation.

CountTokensRequest

Request message for PredictionService.CountTokens.

CountTokensResponse

Response message for PredictionService.CountTokens.

CreateArtifactRequest

Request message for MetadataService.CreateArtifact.

CreateBatchPredictionJobRequest

Request message for JobService.CreateBatchPredictionJob.

CreateContextRequest

Request message for MetadataService.CreateContext.

CreateCustomJobRequest

Request message for JobService.CreateCustomJob.

CreateDataLabelingJobRequest

Request message for JobService.CreateDataLabelingJob.

CreateDatasetOperationMetadata

Runtime operation information for DatasetService.CreateDataset.

CreateDatasetRequest

Request message for DatasetService.CreateDataset.

CreateDatasetVersionOperationMetadata

Runtime operation information for DatasetService.CreateDatasetVersion.

CreateDatasetVersionRequest

Request message for DatasetService.CreateDatasetVersion.

CreateDeploymentResourcePoolOperationMetadata

Runtime operation information for CreateDeploymentResourcePool method.

CreateDeploymentResourcePoolRequest

Request message for CreateDeploymentResourcePool method.

CreateEndpointOperationMetadata

Runtime operation information for EndpointService.CreateEndpoint.

CreateEndpointRequest

Request message for EndpointService.CreateEndpoint.

CreateEntityTypeOperationMetadata

Details of operations that perform create EntityType.

CreateEntityTypeRequest

Request message for FeaturestoreService.CreateEntityType.

CreateExecutionRequest

Request message for MetadataService.CreateExecution.

CreateFeatureGroupOperationMetadata

Details of operations that perform create FeatureGroup.

CreateFeatureGroupRequest

Request message for FeatureRegistryService.CreateFeatureGroup.

CreateFeatureOnlineStoreOperationMetadata

Details of operations that perform create FeatureOnlineStore.

CreateFeatureOnlineStoreRequest

Request message for FeatureOnlineStoreAdminService.CreateFeatureOnlineStore.

CreateFeatureOperationMetadata

Details of operations that perform create Feature.

CreateFeatureRequest

Request message for FeaturestoreService.CreateFeature. Request message for FeatureRegistryService.CreateFeature.

CreateFeatureViewOperationMetadata

Details of operations that perform create FeatureView.

CreateFeatureViewRequest

Request message for FeatureOnlineStoreAdminService.CreateFeatureView.

CreateFeaturestoreOperationMetadata

Details of operations that perform create Featurestore.

CreateFeaturestoreRequest

Request message for FeaturestoreService.CreateFeaturestore.

CreateHyperparameterTuningJobRequest

Request message for JobService.CreateHyperparameterTuningJob.

CreateIndexEndpointOperationMetadata

Runtime operation information for IndexEndpointService.CreateIndexEndpoint.

CreateIndexEndpointRequest

Request message for IndexEndpointService.CreateIndexEndpoint.

CreateIndexOperationMetadata

Runtime operation information for IndexService.CreateIndex.

CreateIndexRequest

Request message for IndexService.CreateIndex.

CreateMetadataSchemaRequest

Request message for MetadataService.CreateMetadataSchema.

CreateMetadataStoreOperationMetadata

Details of operations that perform MetadataService.CreateMetadataStore.

CreateMetadataStoreRequest

Request message for MetadataService.CreateMetadataStore.

CreateModelDeploymentMonitoringJobRequest

Request message for JobService.CreateModelDeploymentMonitoringJob.

CreateNasJobRequest

Request message for JobService.CreateNasJob.

CreateNotebookRuntimeTemplateOperationMetadata

Metadata information for NotebookService.CreateNotebookRuntimeTemplate.

CreateNotebookRuntimeTemplateRequest

Request message for NotebookService.CreateNotebookRuntimeTemplate.

CreatePersistentResourceOperationMetadata

Details of operations that perform create PersistentResource.

CreatePersistentResourceRequest

Request message for PersistentResourceService.CreatePersistentResource.

CreatePipelineJobRequest

Request message for PipelineService.CreatePipelineJob.

CreateRagCorpusOperationMetadata

Runtime operation information for VertexRagDataService.CreateRagCorpus.

CreateRagCorpusRequest

Request message for VertexRagDataService.CreateRagCorpus.

CreateReasoningEngineOperationMetadata

Details of ReasoningEngineService.CreateReasoningEngine operation.

CreateReasoningEngineRequest

Request message for ReasoningEngineService.CreateReasoningEngine.

CreateRegistryFeatureOperationMetadata

Details of operations that perform create FeatureGroup.

CreateScheduleRequest

Request message for ScheduleService.CreateSchedule.

CreateSpecialistPoolOperationMetadata

Runtime operation information for SpecialistPoolService.CreateSpecialistPool.

CreateSpecialistPoolRequest

Request message for SpecialistPoolService.CreateSpecialistPool.

CreateStudyRequest

Request message for VizierService.CreateStudy.

CreateTensorboardExperimentRequest

Request message for TensorboardService.CreateTensorboardExperiment.

CreateTensorboardOperationMetadata

Details of operations that perform create Tensorboard.

CreateTensorboardRequest

Request message for TensorboardService.CreateTensorboard.

CreateTensorboardRunRequest

Request message for TensorboardService.CreateTensorboardRun.

CreateTensorboardTimeSeriesRequest

Request message for TensorboardService.CreateTensorboardTimeSeries.

CreateTrainingPipelineRequest

Request message for PipelineService.CreateTrainingPipeline.

CreateTrialRequest

Request message for VizierService.CreateTrial.

CsvDestination

The storage details for CSV output content.

CsvSource

The storage details for CSV input content.

CustomJob

LabelsEntry

The abstract base class for a message.

WebAccessUrisEntry

The abstract base class for a message.

CustomJobSpec

Represents the spec of a CustomJob.

DataItem

A piece of data in a Dataset. Could be an image, a video, a document or plain text.

LabelsEntry

The abstract base class for a message.

DataItemView

A container for a single DataItem and Annotations on it.

DataLabelingJob

DataLabelingJob is used to trigger a human labeling job on unlabeled data from the following Dataset:

AnnotationLabelsEntry

The abstract base class for a message.

LabelsEntry

The abstract base class for a message.

Dataset

A collection of DataItems and Annotations on them.

LabelsEntry

The abstract base class for a message.

DatasetVersion

Describes the dataset version.

DedicatedResources

A description of resources that are dedicated to a DeployedModel, and that need a higher degree of manual configuration.

DeleteArtifactRequest

Request message for MetadataService.DeleteArtifact.

DeleteBatchPredictionJobRequest

Request message for JobService.DeleteBatchPredictionJob.

DeleteContextRequest

Request message for MetadataService.DeleteContext.

DeleteCustomJobRequest

Request message for JobService.DeleteCustomJob.

DeleteDataLabelingJobRequest

Request message for JobService.DeleteDataLabelingJob.

DeleteDatasetRequest

Request message for DatasetService.DeleteDataset.

DeleteDatasetVersionRequest

Request message for DatasetService.DeleteDatasetVersion.

DeleteDeploymentResourcePoolRequest

Request message for DeleteDeploymentResourcePool method.

DeleteEndpointRequest

Request message for EndpointService.DeleteEndpoint.

DeleteEntityTypeRequest

Request message for [FeaturestoreService.DeleteEntityTypes][].

DeleteExecutionRequest

Request message for MetadataService.DeleteExecution.

DeleteExtensionRequest

Request message for ExtensionRegistryService.DeleteExtension.

DeleteFeatureGroupRequest

Request message for FeatureRegistryService.DeleteFeatureGroup.

DeleteFeatureOnlineStoreRequest

Request message for FeatureOnlineStoreAdminService.DeleteFeatureOnlineStore.

DeleteFeatureRequest

Request message for FeaturestoreService.DeleteFeature. Request message for FeatureRegistryService.DeleteFeature.

DeleteFeatureValuesOperationMetadata

Details of operations that delete Feature values.

DeleteFeatureValuesRequest

Request message for FeaturestoreService.DeleteFeatureValues.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

SelectEntity

Message to select entity. If an entity id is selected, all the feature values corresponding to the entity id will be deleted, including the entityId.

SelectTimeRangeAndFeature

DeleteFeatureValuesResponse

Response message for FeaturestoreService.DeleteFeatureValues.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

SelectEntity

Response message if the request uses the SelectEntity option.

SelectTimeRangeAndFeature

Response message if the request uses the SelectTimeRangeAndFeature option.

DeleteFeatureViewRequest

Request message for [FeatureOnlineStoreAdminService.DeleteFeatureViews][].

DeleteFeaturestoreRequest

Request message for FeaturestoreService.DeleteFeaturestore.

DeleteHyperparameterTuningJobRequest

Request message for JobService.DeleteHyperparameterTuningJob.

DeleteIndexEndpointRequest

Request message for IndexEndpointService.DeleteIndexEndpoint.

DeleteIndexRequest

Request message for IndexService.DeleteIndex.

DeleteMetadataStoreOperationMetadata

Details of operations that perform MetadataService.DeleteMetadataStore.

DeleteMetadataStoreRequest

Request message for MetadataService.DeleteMetadataStore.

DeleteModelDeploymentMonitoringJobRequest

Request message for JobService.DeleteModelDeploymentMonitoringJob.

DeleteModelRequest

Request message for ModelService.DeleteModel.

DeleteModelVersionRequest

Request message for ModelService.DeleteModelVersion.

DeleteNasJobRequest

Request message for JobService.DeleteNasJob.

DeleteNotebookRuntimeRequest

Request message for NotebookService.DeleteNotebookRuntime.

DeleteNotebookRuntimeTemplateRequest

Request message for NotebookService.DeleteNotebookRuntimeTemplate.

DeleteOperationMetadata

Details of operations that perform deletes of any entities.

DeletePersistentResourceRequest

Request message for PersistentResourceService.DeletePersistentResource.

DeletePipelineJobRequest

Request message for PipelineService.DeletePipelineJob.

DeleteRagCorpusRequest

Request message for VertexRagDataService.DeleteRagCorpus.

DeleteRagFileRequest

Request message for VertexRagDataService.DeleteRagFile.

DeleteReasoningEngineRequest

Request message for ReasoningEngineService.DeleteReasoningEngine.

DeleteSavedQueryRequest

Request message for DatasetService.DeleteSavedQuery.

DeleteScheduleRequest

Request message for ScheduleService.DeleteSchedule.

DeleteSpecialistPoolRequest

Request message for SpecialistPoolService.DeleteSpecialistPool.

DeleteStudyRequest

Request message for VizierService.DeleteStudy.

DeleteTensorboardExperimentRequest

Request message for TensorboardService.DeleteTensorboardExperiment.

DeleteTensorboardRequest

Request message for TensorboardService.DeleteTensorboard.

DeleteTensorboardRunRequest

Request message for TensorboardService.DeleteTensorboardRun.

DeleteTensorboardTimeSeriesRequest

Request message for TensorboardService.DeleteTensorboardTimeSeries.

DeleteTrainingPipelineRequest

Request message for PipelineService.DeleteTrainingPipeline.

DeleteTrialRequest

Request message for VizierService.DeleteTrial.

DeployIndexOperationMetadata

Runtime operation information for IndexEndpointService.DeployIndex.

DeployIndexRequest

Request message for IndexEndpointService.DeployIndex.

DeployIndexResponse

Response message for IndexEndpointService.DeployIndex.

DeployModelOperationMetadata

Runtime operation information for EndpointService.DeployModel.

DeployModelRequest

Request message for EndpointService.DeployModel.

TrafficSplitEntry

The abstract base class for a message.

DeployModelResponse

Response message for EndpointService.DeployModel.

DeployedIndex

A deployment of an Index. IndexEndpoints contain one or more DeployedIndexes.

DeployedIndexAuthConfig

Used to set up the auth on the DeployedIndex's private endpoint.

AuthProvider

Configuration for an authentication provider, including support for JSON Web Token (JWT) <https://tools.ietf.org/html/draft-ietf-oauth-json-web-token-32>__.

DeployedIndexRef

Points to a DeployedIndex.

DeployedModel

A deployment of a Model. Endpoints contain one or more DeployedModels.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

DeployedModelRef

Points to a DeployedModel.

DeploymentResourcePool

A description of resources that can be shared by multiple DeployedModels, whose underlying specification consists of a DedicatedResources.

DestinationFeatureSetting

DirectPredictRequest

Request message for PredictionService.DirectPredict.

DirectPredictResponse

Response message for PredictionService.DirectPredict.

DirectRawPredictRequest

Request message for PredictionService.DirectRawPredict.

DirectRawPredictResponse

Response message for PredictionService.DirectRawPredict.

DirectUploadSource

The input content is encapsulated and uploaded in the request.

DiskSpec

Represents the spec of disk options.

DoubleArray

A list of double values.

EncryptionSpec

Represents a customer-managed encryption key spec that can be applied to a top-level resource.

Endpoint

Models are deployed into it, and afterwards Endpoint is called to obtain predictions and explanations.

LabelsEntry

The abstract base class for a message.

TrafficSplitEntry

The abstract base class for a message.

EntityIdSelector

Selector for entityId. Getting ids from the given source.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

EntityType

An entity type is a type of object in a system that needs to be modeled and have stored information about. For example, driver is an entity type, and driver0 is an instance of an entity type driver.

LabelsEntry

The abstract base class for a message.

EnvVar

Represents an environment variable present in a Container or Python Module.

ErrorAnalysisAnnotation

Model error analysis for each annotation.

AttributedItem

Attributed items for a given annotation, typically representing neighbors from the training sets constrained by the query type.

QueryType

The query type used for finding the attributed items.

EvaluateInstancesRequest

Request message for EvaluationService.EvaluateInstances.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

EvaluateInstancesResponse

Response message for EvaluationService.EvaluateInstances.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

EvaluatedAnnotation

True positive, false positive, or false negative.

EvaluatedAnnotation is only available under ModelEvaluationSlice with slice of annotationSpec dimension.

EvaluatedAnnotationType

Describes the type of the EvaluatedAnnotation. The type is determined

EvaluatedAnnotationExplanation

Explanation result of the prediction produced by the Model.

Event

An edge describing the relationship between an Artifact and an Execution in a lineage graph.

LabelsEntry

The abstract base class for a message.

Type

Describes whether an Event's Artifact is the Execution's input or output.

Values: TYPE_UNSPECIFIED (0): Unspecified whether input or output of the Execution. INPUT (1): An input of the Execution. OUTPUT (2): An output of the Execution.

ExactMatchInput

Input for exact match metric.

ExactMatchInstance

Spec for exact match instance.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

ExactMatchMetricValue

Exact match metric value for an instance.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

ExactMatchResults

Results for exact match metric.

ExactMatchSpec

Spec for exact match metric - returns 1 if prediction and reference exactly matches, otherwise 0.

Examples

Example-based explainability that returns the nearest neighbors from the provided dataset.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

ExampleGcsSource

The Cloud Storage input instances.

DataFormat

The format of the input example instances.

Values: DATA_FORMAT_UNSPECIFIED (0): Format unspecified, used when unset. JSONL (1): Examples are stored in JSONL files.

ExamplesOverride

Overrides for example-based explanations.

DataFormat

Data format enum.

Values: DATA_FORMAT_UNSPECIFIED (0): Unspecified format. Must not be used. INSTANCES (1): Provided data is a set of model inputs. EMBEDDINGS (2): Provided data is a set of embeddings.

ExamplesRestrictionsNamespace

Restrictions namespace for example-based explanations overrides.

ExecuteExtensionRequest

Request message for ExtensionExecutionService.ExecuteExtension.

ExecuteExtensionResponse

Response message for ExtensionExecutionService.ExecuteExtension.

Execution

Instance of a general execution.

LabelsEntry

The abstract base class for a message.

State

Describes the state of the Execution.

ExplainRequest

Request message for PredictionService.Explain.

ConcurrentExplanationSpecOverrideEntry

The abstract base class for a message.

ExplainResponse

Response message for PredictionService.Explain.

ConcurrentExplanation

This message is a wrapper grouping Concurrent Explanations.

ConcurrentExplanationsEntry

The abstract base class for a message.

Explanation

Explanation of a prediction (provided in PredictResponse.predictions) produced by the Model on a given instance.

ExplanationMetadata

Metadata describing the Model's input and output for explanation.

InputMetadata

Metadata of the input of a feature.

Fields other than InputMetadata.input_baselines are applicable only for Models that are using Vertex AI-provided images for Tensorflow.

Encoding

Defines how a feature is encoded. Defaults to IDENTITY.

    ::

       input = [27, 6.0, 150]
       index_feature_mapping = ["age", "height", "weight"]
BAG_OF_FEATURES_SPARSE (3):
    The tensor represents a bag of features where each index
    maps to a feature. Zero values in the tensor indicates
    feature being non-existent.
    <xref uid="google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata.index_feature_mapping">InputMetadata.index_feature_mapping</xref>
    must be provided for this encoding. For example:

    ::

       input = [2, 0, 5, 0, 1]
       index_feature_mapping = ["a", "b", "c", "d", "e"]
INDICATOR (4):
    The tensor is a list of binaries representing whether a
    feature exists or not (1 indicates existence).
    <xref uid="google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata.index_feature_mapping">InputMetadata.index_feature_mapping</xref>
    must be provided for this encoding. For example:

    ::

       input = [1, 0, 1, 0, 1]
       index_feature_mapping = ["a", "b", "c", "d", "e"]
COMBINED_EMBEDDING (5):
    The tensor is encoded into a 1-dimensional array represented
    by an encoded tensor.
    <xref uid="google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata.encoded_tensor_name">InputMetadata.encoded_tensor_name</xref>
    must be provided for this encoding. For example:

    ::

       input = ["This", "is", "a", "test", "."]
       encoded = [0.1, 0.2, 0.3, 0.4, 0.5]
CONCAT_EMBEDDING (6):
    Select this encoding when the input tensor is encoded into a
    2-dimensional array represented by an encoded tensor.
    <xref uid="google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata.encoded_tensor_name">InputMetadata.encoded_tensor_name</xref>
    must be provided for this encoding. The first dimension of
    the encoded tensor's shape is the same as the input tensor's
    shape. For example:

    ::

       input = ["This", "is", "a", "test", "."]
       encoded = [[0.1, 0.2, 0.3, 0.4, 0.5],
                  [0.2, 0.1, 0.4, 0.3, 0.5],
                  [0.5, 0.1, 0.3, 0.5, 0.4],
                  [0.5, 0.3, 0.1, 0.2, 0.4],
                  [0.4, 0.3, 0.2, 0.5, 0.1]]

FeatureValueDomain

Visualization

Visualization configurations for image explanation.

ColorMap

The color scheme used for highlighting areas.

OverlayType

How the original image is displayed in the visualization.

Polarity

Whether to only highlight pixels with positive contributions, negative or both. Defaults to POSITIVE.

Type

Type of the image visualization. Only applicable to [Integrated Gradients attribution][google.cloud.aiplatform.v1beta1.ExplanationParameters.integrated_gradients_attribution].

InputsEntry

The abstract base class for a message.

OutputMetadata

Metadata of the prediction output to be explained.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

OutputsEntry

The abstract base class for a message.

ExplanationMetadataOverride

The ExplanationMetadata entries that can be overridden at [online explanation][google.cloud.aiplatform.v1beta1.PredictionService.Explain] time.

InputMetadataOverride

The [input metadata][google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata] entries to be overridden.

InputsEntry

The abstract base class for a message.

ExplanationParameters

Parameters to configure explaining for Model's predictions.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

ExplanationSpec

Specification of Model explanation.

ExplanationSpecOverride

The ExplanationSpec entries that can be overridden at [online explanation][google.cloud.aiplatform.v1beta1.PredictionService.Explain] time.

ExportDataConfig

Describes what part of the Dataset is to be exported, the destination of the export and how to export.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

ExportDataOperationMetadata

Runtime operation information for DatasetService.ExportData.

ExportDataRequest

Request message for DatasetService.ExportData.

ExportDataResponse

Response message for DatasetService.ExportData.

ExportFeatureValuesOperationMetadata

Details of operations that exports Features values.

ExportFeatureValuesRequest

Request message for FeaturestoreService.ExportFeatureValues.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

FullExport

Describes exporting all historical Feature values of all entities of the EntityType between [start_time, end_time].

SnapshotExport

Describes exporting the latest Feature values of all entities of the EntityType between [start_time, snapshot_time].

ExportFeatureValuesResponse

Response message for FeaturestoreService.ExportFeatureValues.

ExportFractionSplit

ExportModelOperationMetadata

Details of ModelService.ExportModel operation.

OutputInfo

Further describes the output of the ExportModel. Supplements ExportModelRequest.OutputConfig.

ExportModelRequest

Request message for ModelService.ExportModel.

OutputConfig

Output configuration for the Model export.

ExportModelResponse

Response message of ModelService.ExportModel operation.

ExportTensorboardTimeSeriesDataRequest

Request message for TensorboardService.ExportTensorboardTimeSeriesData.

ExportTensorboardTimeSeriesDataResponse

Response message for TensorboardService.ExportTensorboardTimeSeriesData.

Extension

Extensions are tools for large language models to access external data, run computations, etc.

ExtensionManifest

Manifest spec of an Extension needed for runtime execution.

ApiSpec

The API specification shown to the LLM.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

ExtensionOperation

Operation of an extension.

ExtensionPrivateServiceConnectConfig

PrivateExtensionConfig configuration for the extension.

Feature

Feature Metadata information. For example, color is a feature that describes an apple.

LabelsEntry

The abstract base class for a message.

MonitoringStatsAnomaly

A list of historical SnapshotAnalysis or ImportFeaturesAnalysis stats requested by user, sorted by FeatureStatsAnomaly.start_time descending.

Objective

ValueType

Only applicable for Vertex AI Legacy Feature Store. An enum representing the value type of a feature.

FeatureGroup

Vertex AI Feature Group.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

BigQuery

Input source type for BigQuery Tables and Views.

LabelsEntry

The abstract base class for a message.

FeatureNoiseSigma

Noise sigma by features. Noise sigma represents the standard deviation of the gaussian kernel that will be used to add noise to interpolated inputs prior to computing gradients.

NoiseSigmaForFeature

Noise sigma for a single feature.

FeatureOnlineStore

Vertex AI Feature Online Store provides a centralized repository for serving ML features and embedding indexes at low latency. The Feature Online Store is a top-level container.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

Bigtable

AutoScaling

DedicatedServingEndpoint

The dedicated serving endpoint for this FeatureOnlineStore. Only need to set when you choose Optimized storage type. Public endpoint is provisioned by default.

EmbeddingManagement

Deprecated: This sub message is no longer needed anymore and embedding management is automatically enabled when specifying Optimized storage type. Contains settings for embedding management.

LabelsEntry

The abstract base class for a message.

Optimized

Optimized storage type

State

Possible states a featureOnlineStore can have.

FeatureSelector

Selector for Features of an EntityType.

FeatureStatsAnomaly

FeatureValue

Value for a feature.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

Metadata

Metadata of feature value.

FeatureValueDestination

A destination location for Feature values and format.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

FeatureValueList

Container for list of values.

FeatureView

FeatureView is representation of values that the FeatureOnlineStore will serve based on its syncConfig.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

BigQuerySource

FeatureRegistrySource

A Feature Registry source for features that need to be synced to Online Store.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

FeatureGroup

Features belonging to a single feature group that will be synced to Online Store.

IndexConfig

Configuration for vector indexing.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

BruteForceConfig

Configuration options for using brute force search.

DistanceMeasureType

The distance measure used in nearest neighbor search.

Values: DISTANCE_MEASURE_TYPE_UNSPECIFIED (0): Should not be set. SQUARED_L2_DISTANCE (1): Euclidean (L_2) Distance. COSINE_DISTANCE (2): Cosine Distance. Defined as 1 - cosine similarity.

    We strongly suggest using DOT_PRODUCT_DISTANCE +
    UNIT_L2_NORM instead of COSINE distance. Our algorithms have
    been more optimized for DOT_PRODUCT distance which, when
    combined with UNIT_L2_NORM, is mathematically equivalent to
    COSINE distance and results in the same ranking.
DOT_PRODUCT_DISTANCE (3):
    Dot Product Distance. Defined as a negative
    of the dot product.

TreeAHConfig

Configuration options for the tree-AH algorithm.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

LabelsEntry

The abstract base class for a message.

ServiceAgentType

Service agent type used during data sync.

Values: SERVICE_AGENT_TYPE_UNSPECIFIED (0): By default, the project-level Vertex AI Service Agent is enabled. SERVICE_AGENT_TYPE_PROJECT (1): Indicates the project-level Vertex AI Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) will be used during sync jobs. SERVICE_AGENT_TYPE_FEATURE_VIEW (2): Enable a FeatureView service account to be created by Vertex AI and output in the field service_account_email. This service account will be used to read from the source BigQuery table during sync.

SyncConfig

Configuration for Sync. Only one option is set.

VectorSearchConfig

Deprecated. Use IndexConfig instead.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

DistanceMeasureType

Values: DISTANCE_MEASURE_TYPE_UNSPECIFIED (0): Should not be set. SQUARED_L2_DISTANCE (1): Euclidean (L_2) Distance. COSINE_DISTANCE (2): Cosine Distance. Defined as 1 - cosine similarity.

    We strongly suggest using DOT_PRODUCT_DISTANCE +
    UNIT_L2_NORM instead of COSINE distance. Our algorithms have
    been more optimized for DOT_PRODUCT distance which, when
    combined with UNIT_L2_NORM, is mathematically equivalent to
    COSINE distance and results in the same ranking.
DOT_PRODUCT_DISTANCE (3):
    Dot Product Distance. Defined as a negative
    of the dot product.

TreeAHConfig

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

FeatureViewDataFormat

Format of the data in the Feature View.

FeatureViewDataKey

Lookup key for a feature view.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

CompositeKey

ID that is comprised from several parts (columns).

FeatureViewSync

FeatureViewSync is a representation of sync operation which copies data from data source to Feature View in Online Store.

SyncSummary

Summary from the Sync job. For continuous syncs, the summary is updated periodically. For batch syncs, it gets updated on completion of the sync.

Featurestore

Vertex AI Feature Store provides a centralized repository for organizing, storing, and serving ML features. The Featurestore is a top-level container for your features and their values.

LabelsEntry

The abstract base class for a message.

OnlineServingConfig

OnlineServingConfig specifies the details for provisioning online serving resources.

Scaling

Online serving scaling configuration. If min_node_count and max_node_count are set to the same value, the cluster will be configured with the fixed number of node (no auto-scaling).

State

Possible states a featurestore can have.

FeaturestoreMonitoringConfig

Configuration of how features in Featurestore are monitored.

ImportFeaturesAnalysis

Configuration of the Featurestore's ImportFeature Analysis Based Monitoring. This type of analysis generates statistics for values of each Feature imported by every ImportFeatureValues operation.

Baseline

Defines the baseline to do anomaly detection for feature values imported by each ImportFeatureValues operation.

State

The state defines whether to enable ImportFeature analysis.

SnapshotAnalysis

ThresholdConfig

The config for Featurestore Monitoring threshold.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

FetchFeatureValuesRequest

Request message for FeatureOnlineStoreService.FetchFeatureValues. All the features under the requested feature view will be returned.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

Format

Format of the response data.

Values: FORMAT_UNSPECIFIED (0): Not set. Will be treated as the KeyValue format. KEY_VALUE (1): Return response data in key-value format. PROTO_STRUCT (2): Return response data in proto Struct format.

FetchFeatureValuesResponse

Response message for FeatureOnlineStoreService.FetchFeatureValues

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

FeatureNameValuePairList

Response structure in the format of key (feature name) and (feature) value pair.

FeatureNameValuePair

Feature name & value pair.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

FileData

URI based data.

FilterSplit

Supported only for unstructured Datasets.

FindNeighborsRequest

The request message for MatchService.FindNeighbors.

Query

A query to find a number of the nearest neighbors (most similar vectors) of a vector.

FindNeighborsResponse

The response message for MatchService.FindNeighbors.

NearestNeighbors

Nearest neighbors for one query.

Neighbor

A neighbor of the query vector.

FluencyInput

Input for fluency metric.

FluencyInstance

Spec for fluency instance.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

FluencyResult

Spec for fluency result.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

FluencySpec

Spec for fluency score metric.

FractionSplit

FulfillmentInput

Input for fulfillment metric.

FulfillmentInstance

Spec for fulfillment instance.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

FulfillmentResult

Spec for fulfillment result.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

FulfillmentSpec

Spec for fulfillment metric.

FunctionCall

A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing the parameters and their values.

FunctionCallingConfig

Function calling config.

Mode

Function calling mode.

Values: MODE_UNSPECIFIED (0): Unspecified function calling mode. This value should not be used. AUTO (1): Default model behavior, model decides to predict either a function call or a natural language repspose. ANY (2): Model is constrained to always predicting a function call only. If "allowed_function_names" are set, the predicted function call will be limited to any one of "allowed_function_names", else the predicted function call will be any one of the provided "function_declarations". NONE (3): Model will not predict any function call. Model behavior is same as when not passing any function declarations.

FunctionDeclaration

FunctionResponse

GcsDestination

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

GcsSource

The Google Cloud Storage location for the input content.

GenerateContentRequest

Request message for [PredictionService.GenerateContent].

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

GenerateContentResponse

Response message for [PredictionService.GenerateContent].

PromptFeedback

Content filter results for a prompt sent in the request.

BlockedReason

Blocked reason enumeration.

UsageMetadata

Usage metadata about response(s).

GenerationConfig

Generation config.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

GenericOperationMetadata

Generic Metadata shared by all operations.

GenieSource

Contains information about the source of the models generated from Generative AI Studio.

GetAnnotationSpecRequest

Request message for DatasetService.GetAnnotationSpec.

GetArtifactRequest

Request message for MetadataService.GetArtifact.

GetBatchPredictionJobRequest

Request message for JobService.GetBatchPredictionJob.

GetContextRequest

Request message for MetadataService.GetContext.

GetCustomJobRequest

Request message for JobService.GetCustomJob.

GetDataLabelingJobRequest

Request message for JobService.GetDataLabelingJob.

GetDatasetRequest

Request message for DatasetService.GetDataset.

GetDatasetVersionRequest

Request message for DatasetService.GetDatasetVersion.

GetDeploymentResourcePoolRequest

Request message for GetDeploymentResourcePool method.

GetEndpointRequest

Request message for EndpointService.GetEndpoint

GetEntityTypeRequest

Request message for FeaturestoreService.GetEntityType.

GetExecutionRequest

Request message for MetadataService.GetExecution.

GetExtensionRequest

Request message for ExtensionRegistryService.GetExtension.

GetFeatureGroupRequest

Request message for FeatureRegistryService.GetFeatureGroup.

GetFeatureOnlineStoreRequest

Request message for FeatureOnlineStoreAdminService.GetFeatureOnlineStore.

GetFeatureRequest

Request message for FeaturestoreService.GetFeature. Request message for FeatureRegistryService.GetFeature.

GetFeatureViewRequest

Request message for FeatureOnlineStoreAdminService.GetFeatureView.

GetFeatureViewSyncRequest

Request message for FeatureOnlineStoreAdminService.GetFeatureViewSync.

GetFeaturestoreRequest

Request message for FeaturestoreService.GetFeaturestore.

GetHyperparameterTuningJobRequest

Request message for JobService.GetHyperparameterTuningJob.

GetIndexEndpointRequest

Request message for IndexEndpointService.GetIndexEndpoint

GetIndexRequest

Request message for IndexService.GetIndex

GetMetadataSchemaRequest

Request message for MetadataService.GetMetadataSchema.

GetMetadataStoreRequest

Request message for MetadataService.GetMetadataStore.

GetModelDeploymentMonitoringJobRequest

Request message for JobService.GetModelDeploymentMonitoringJob.

GetModelEvaluationRequest

Request message for ModelService.GetModelEvaluation.

GetModelEvaluationSliceRequest

Request message for ModelService.GetModelEvaluationSlice.

GetModelRequest

Request message for ModelService.GetModel.

GetNasJobRequest

Request message for JobService.GetNasJob.

GetNasTrialDetailRequest

Request message for JobService.GetNasTrialDetail.

GetNotebookRuntimeRequest

Request message for NotebookService.GetNotebookRuntime

GetNotebookRuntimeTemplateRequest

Request message for NotebookService.GetNotebookRuntimeTemplate

GetPersistentResourceRequest

Request message for PersistentResourceService.GetPersistentResource.

GetPipelineJobRequest

Request message for PipelineService.GetPipelineJob.

GetPublisherModelRequest

Request message for ModelGardenService.GetPublisherModel

GetRagCorpusRequest

Request message for VertexRagDataService.GetRagCorpus

GetRagFileRequest

Request message for VertexRagDataService.GetRagFile

GetReasoningEngineRequest

Request message for ReasoningEngineService.GetReasoningEngine.

GetScheduleRequest

Request message for ScheduleService.GetSchedule.

GetSpecialistPoolRequest

Request message for SpecialistPoolService.GetSpecialistPool.

GetStudyRequest

Request message for VizierService.GetStudy.

GetTensorboardExperimentRequest

Request message for TensorboardService.GetTensorboardExperiment.

GetTensorboardRequest

Request message for TensorboardService.GetTensorboard.

GetTensorboardRunRequest

Request message for TensorboardService.GetTensorboardRun.

GetTensorboardTimeSeriesRequest

Request message for TensorboardService.GetTensorboardTimeSeries.

GetTrainingPipelineRequest

Request message for PipelineService.GetTrainingPipeline.

GetTrialRequest

Request message for VizierService.GetTrial.

GoogleDriveSource

The Google Drive location for the input content.

ResourceId

The type and ID of the Google Drive resource.

ResourceType

The type of the Google Drive resource.

Values: RESOURCE_TYPE_UNSPECIFIED (0): Unspecified resource type. RESOURCE_TYPE_FILE (1): File resource type. RESOURCE_TYPE_FOLDER (2): Folder resource type.

GoogleSearchRetrieval

Tool to retrieve public web data for grounding, powered by Google.

GroundednessInput

Input for groundedness metric.

GroundednessInstance

Spec for groundedness instance.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

GroundednessResult

Spec for groundedness result.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

GroundednessSpec

Spec for groundedness metric.

GroundingAttribution

Grounding attribution.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

RetrievedContext

Attribution from context retrieved by the retrieval tools.

Web

Attribution from the web.

GroundingMetadata

Metadata returned to client when grounding is enabled.

HarmCategory

Harm categories that will block the content.

HttpElementLocation

Enum of location an HTTP element can be.

Values: HTTP_IN_UNSPECIFIED (0): No description available. HTTP_IN_QUERY (1): Element is in the HTTP request query. HTTP_IN_HEADER (2): Element is in the HTTP request header. HTTP_IN_PATH (3): Element is in the HTTP request path. HTTP_IN_BODY (4): Element is in the HTTP request body. HTTP_IN_COOKIE (5): Element is in the HTTP request cookie.

HyperparameterTuningJob

Represents a HyperparameterTuningJob. A HyperparameterTuningJob has a Study specification and multiple CustomJobs with identical CustomJob specification.

LabelsEntry

The abstract base class for a message.

IdMatcher

Matcher for Features of an EntityType by Feature ID.

ImportDataConfig

Describes the location from where we import data into a Dataset, together with the labels that will be applied to the DataItems and the Annotations.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

AnnotationLabelsEntry

The abstract base class for a message.

DataItemLabelsEntry

The abstract base class for a message.

ImportDataOperationMetadata

Runtime operation information for DatasetService.ImportData.

ImportDataRequest

Request message for DatasetService.ImportData.

ImportDataResponse

Response message for DatasetService.ImportData.

ImportExtensionOperationMetadata

Details of ExtensionRegistryService.ImportExtension operation.

ImportExtensionRequest

Request message for ExtensionRegistryService.ImportExtension.

ImportFeatureValuesOperationMetadata

Details of operations that perform import Feature values.

ImportFeatureValuesRequest

Request message for FeaturestoreService.ImportFeatureValues.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

FeatureSpec

Defines the Feature value(s) to import.

ImportFeatureValuesResponse

Response message for FeaturestoreService.ImportFeatureValues.

ImportModelEvaluationRequest

Request message for ModelService.ImportModelEvaluation

ImportRagFilesConfig

Config for importing RagFiles.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

ImportRagFilesOperationMetadata

Runtime operation information for VertexRagDataService.ImportRagFiles.

ImportRagFilesRequest

Request message for VertexRagDataService.ImportRagFiles.

ImportRagFilesResponse

Response message for VertexRagDataService.ImportRagFiles.

Index

A representation of a collection of database items organized in a way that allows for approximate nearest neighbor (a.k.a ANN) algorithms search.

IndexUpdateMethod

The update method of an Index.

LabelsEntry

The abstract base class for a message.

IndexDatapoint

A datapoint of Index.

CrowdingTag

Crowding tag is a constraint on a neighbor list produced by nearest neighbor search requiring that no more than some value k' of the k neighbors returned have the same value of crowding_attribute.

NumericRestriction

This field allows restricts to be based on numeric comparisons rather than categorical tokens.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

Operator

Which comparison operator to use. Should be specified for queries only; specifying this for a datapoint is an error.

Datapoints for which Operator is true relative to the query's Value field will be allowlisted.

Restriction

Restriction of a datapoint which describe its attributes(tokens) from each of several attribute categories(namespaces).

IndexEndpoint

Indexes are deployed into it. An IndexEndpoint can have multiple DeployedIndexes.

LabelsEntry

The abstract base class for a message.

IndexPrivateEndpoints

IndexStats

Stats of the Index.

InputDataConfig

Specifies Vertex AI owned input data to be used for training, and possibly evaluating, the Model.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

Int64Array

A list of int64 values.

IntegratedGradientsAttribution

An attribution method that computes the Aumann-Shapley value taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1703.01365

JobState

Describes the state of a job.

LargeModelReference

Contains information about the Large Model.

LineageSubgraph

A subgraph of the overall lineage graph. Event edges connect Artifact and Execution nodes.

ListAnnotationsRequest

Request message for DatasetService.ListAnnotations.

ListAnnotationsResponse

Response message for DatasetService.ListAnnotations.

ListArtifactsRequest

Request message for MetadataService.ListArtifacts.

ListArtifactsResponse

Response message for MetadataService.ListArtifacts.

ListBatchPredictionJobsRequest

Request message for JobService.ListBatchPredictionJobs.

ListBatchPredictionJobsResponse

Response message for JobService.ListBatchPredictionJobs

ListContextsRequest

Request message for MetadataService.ListContexts

ListContextsResponse

Response message for MetadataService.ListContexts.

ListCustomJobsRequest

Request message for JobService.ListCustomJobs.

ListCustomJobsResponse

Response message for JobService.ListCustomJobs

ListDataItemsRequest

Request message for DatasetService.ListDataItems.

ListDataItemsResponse

Response message for DatasetService.ListDataItems.

ListDataLabelingJobsRequest

Request message for JobService.ListDataLabelingJobs.

ListDataLabelingJobsResponse

Response message for JobService.ListDataLabelingJobs.

ListDatasetVersionsRequest

Request message for DatasetService.ListDatasetVersions.

ListDatasetVersionsResponse

Response message for DatasetService.ListDatasetVersions.

ListDatasetsRequest

Request message for DatasetService.ListDatasets.

ListDatasetsResponse

Response message for DatasetService.ListDatasets.

ListDeploymentResourcePoolsRequest

Request message for ListDeploymentResourcePools method.

ListDeploymentResourcePoolsResponse

Response message for ListDeploymentResourcePools method.

ListEndpointsRequest

Request message for EndpointService.ListEndpoints.

ListEndpointsResponse

Response message for EndpointService.ListEndpoints.

ListEntityTypesRequest

Request message for FeaturestoreService.ListEntityTypes.

ListEntityTypesResponse

Response message for FeaturestoreService.ListEntityTypes.

ListExecutionsRequest

Request message for MetadataService.ListExecutions.

ListExecutionsResponse

Response message for MetadataService.ListExecutions.

ListExtensionsRequest

Request message for ExtensionRegistryService.ListExtensions.

ListExtensionsResponse

Response message for ExtensionRegistryService.ListExtensions

ListFeatureGroupsRequest

Request message for FeatureRegistryService.ListFeatureGroups.

ListFeatureGroupsResponse

Response message for FeatureRegistryService.ListFeatureGroups.

ListFeatureOnlineStoresRequest

Request message for FeatureOnlineStoreAdminService.ListFeatureOnlineStores.

ListFeatureOnlineStoresResponse

Response message for FeatureOnlineStoreAdminService.ListFeatureOnlineStores.

ListFeatureViewSyncsRequest

Request message for FeatureOnlineStoreAdminService.ListFeatureViewSyncs.

ListFeatureViewSyncsResponse

Response message for FeatureOnlineStoreAdminService.ListFeatureViewSyncs.

ListFeatureViewsRequest

Request message for FeatureOnlineStoreAdminService.ListFeatureViews.

ListFeatureViewsResponse

Response message for FeatureOnlineStoreAdminService.ListFeatureViews.

ListFeaturesRequest

Request message for FeaturestoreService.ListFeatures. Request message for FeatureRegistryService.ListFeatures.

ListFeaturesResponse

Response message for FeaturestoreService.ListFeatures. Response message for FeatureRegistryService.ListFeatures.

ListFeaturestoresRequest

Request message for FeaturestoreService.ListFeaturestores.

ListFeaturestoresResponse

Response message for FeaturestoreService.ListFeaturestores.

ListHyperparameterTuningJobsRequest

Request message for JobService.ListHyperparameterTuningJobs.

ListHyperparameterTuningJobsResponse

Response message for JobService.ListHyperparameterTuningJobs

ListIndexEndpointsRequest

Request message for IndexEndpointService.ListIndexEndpoints.

ListIndexEndpointsResponse

Response message for IndexEndpointService.ListIndexEndpoints.

ListIndexesRequest

Request message for IndexService.ListIndexes.

ListIndexesResponse

Response message for IndexService.ListIndexes.

ListMetadataSchemasRequest

Request message for MetadataService.ListMetadataSchemas.

ListMetadataSchemasResponse

Response message for MetadataService.ListMetadataSchemas.

ListMetadataStoresRequest

Request message for MetadataService.ListMetadataStores.

ListMetadataStoresResponse

Response message for MetadataService.ListMetadataStores.

ListModelDeploymentMonitoringJobsRequest

Request message for JobService.ListModelDeploymentMonitoringJobs.

ListModelDeploymentMonitoringJobsResponse

Response message for JobService.ListModelDeploymentMonitoringJobs.

ListModelEvaluationSlicesRequest

Request message for ModelService.ListModelEvaluationSlices.

ListModelEvaluationSlicesResponse

Response message for ModelService.ListModelEvaluationSlices.

ListModelEvaluationsRequest

Request message for ModelService.ListModelEvaluations.

ListModelEvaluationsResponse

Response message for ModelService.ListModelEvaluations.

ListModelVersionsRequest

Request message for ModelService.ListModelVersions.

ListModelVersionsResponse

Response message for ModelService.ListModelVersions

ListModelsRequest

Request message for ModelService.ListModels.

ListModelsResponse

Response message for ModelService.ListModels

ListNasJobsRequest

Request message for JobService.ListNasJobs.

ListNasJobsResponse

Response message for JobService.ListNasJobs

ListNasTrialDetailsRequest

Request message for JobService.ListNasTrialDetails.

ListNasTrialDetailsResponse

Response message for JobService.ListNasTrialDetails

ListNotebookRuntimeTemplatesRequest

Request message for NotebookService.ListNotebookRuntimeTemplates.

ListNotebookRuntimeTemplatesResponse

Response message for NotebookService.ListNotebookRuntimeTemplates.

ListNotebookRuntimesRequest

Request message for NotebookService.ListNotebookRuntimes.

ListNotebookRuntimesResponse

Response message for NotebookService.ListNotebookRuntimes.

ListOptimalTrialsRequest

Request message for VizierService.ListOptimalTrials.

ListOptimalTrialsResponse

Response message for VizierService.ListOptimalTrials.

ListPersistentResourcesRequest

Request message for [PersistentResourceService.ListPersistentResource][].

ListPersistentResourcesResponse

Response message for PersistentResourceService.ListPersistentResources

ListPipelineJobsRequest

Request message for PipelineService.ListPipelineJobs.

ListPipelineJobsResponse

Response message for PipelineService.ListPipelineJobs

ListPublisherModelsRequest

Request message for ModelGardenService.ListPublisherModels.

ListPublisherModelsResponse

Response message for ModelGardenService.ListPublisherModels.

ListRagCorporaRequest

Request message for VertexRagDataService.ListRagCorpora.

ListRagCorporaResponse

Response message for VertexRagDataService.ListRagCorpora.

ListRagFilesRequest

Request message for VertexRagDataService.ListRagFiles.

ListRagFilesResponse

Response message for VertexRagDataService.ListRagFiles.

ListReasoningEnginesRequest

Request message for ReasoningEngineService.ListReasoningEngines.

ListReasoningEnginesResponse

Response message for ReasoningEngineService.ListReasoningEngines

ListSavedQueriesRequest

Request message for DatasetService.ListSavedQueries.

ListSavedQueriesResponse

Response message for DatasetService.ListSavedQueries.

ListSchedulesRequest

Request message for ScheduleService.ListSchedules.

ListSchedulesResponse

Response message for ScheduleService.ListSchedules

ListSpecialistPoolsRequest

Request message for SpecialistPoolService.ListSpecialistPools.

ListSpecialistPoolsResponse

Response message for SpecialistPoolService.ListSpecialistPools.

ListStudiesRequest

Request message for VizierService.ListStudies.

ListStudiesResponse

Response message for VizierService.ListStudies.

ListTensorboardExperimentsRequest

Request message for TensorboardService.ListTensorboardExperiments.

ListTensorboardExperimentsResponse

Response message for TensorboardService.ListTensorboardExperiments.

ListTensorboardRunsRequest

Request message for TensorboardService.ListTensorboardRuns.

ListTensorboardRunsResponse

Response message for TensorboardService.ListTensorboardRuns.

ListTensorboardTimeSeriesRequest

Request message for TensorboardService.ListTensorboardTimeSeries.

ListTensorboardTimeSeriesResponse

Response message for TensorboardService.ListTensorboardTimeSeries.

ListTensorboardsRequest

Request message for TensorboardService.ListTensorboards.

ListTensorboardsResponse

Response message for TensorboardService.ListTensorboards.

ListTrainingPipelinesRequest

Request message for PipelineService.ListTrainingPipelines.

ListTrainingPipelinesResponse

Response message for PipelineService.ListTrainingPipelines

ListTrialsRequest

Request message for VizierService.ListTrials.

ListTrialsResponse

Response message for VizierService.ListTrials.

LookupStudyRequest

Request message for VizierService.LookupStudy.

MachineSpec

Specification of a single machine.

ManualBatchTuningParameters

Manual batch tuning parameters.

Measurement

A message representing a Measurement of a Trial. A Measurement contains the Metrics got by executing a Trial using suggested hyperparameter values.

Metric

A message representing a metric in the measurement.

MergeVersionAliasesRequest

Request message for ModelService.MergeVersionAliases.

MetadataSchema

Instance of a general MetadataSchema.

MetadataSchemaType

Describes the type of the MetadataSchema.

MetadataStore

Instance of a metadata store. Contains a set of metadata that can be queried.

MetadataStoreState

Represents state information for a MetadataStore.

MigratableResource

Represents one resource that exists in automl.googleapis.com, datalabeling.googleapis.com or ml.googleapis.com.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

AutomlDataset

Represents one Dataset in automl.googleapis.com.

AutomlModel

Represents one Model in automl.googleapis.com.

DataLabelingDataset

Represents one Dataset in datalabeling.googleapis.com.

DataLabelingAnnotatedDataset

Represents one AnnotatedDataset in datalabeling.googleapis.com.

MlEngineModelVersion

Represents one model Version in ml.googleapis.com.

MigrateResourceRequest

Config of migrating one resource from automl.googleapis.com, datalabeling.googleapis.com and ml.googleapis.com to Vertex AI.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

MigrateAutomlDatasetConfig

Config for migrating Dataset in automl.googleapis.com to Vertex AI's Dataset.

MigrateAutomlModelConfig

Config for migrating Model in automl.googleapis.com to Vertex AI's Model.

MigrateDataLabelingDatasetConfig

Config for migrating Dataset in datalabeling.googleapis.com to Vertex AI's Dataset.

MigrateDataLabelingAnnotatedDatasetConfig

Config for migrating AnnotatedDataset in datalabeling.googleapis.com to Vertex AI's SavedQuery.

MigrateMlEngineModelVersionConfig

Config for migrating version in ml.googleapis.com to Vertex AI's Model.

MigrateResourceResponse

Describes a successfully migrated resource.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

Model

A trained machine learning Model.

BaseModelSource

User input field to specify the base model source. Currently it only supports specifing the Model Garden models and Genie models.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

DeploymentResourcesType

Identifies a type of Model's prediction resources.

ExportFormat

Represents export format supported by the Model. All formats export to Google Cloud Storage.

ExportableContent

The Model content that can be exported.

LabelsEntry

The abstract base class for a message.

OriginalModelInfo

Contains information about the original Model if this Model is a copy.

ModelContainerSpec

ModelDeploymentMonitoringBigQueryTable

ModelDeploymentMonitoringBigQueryTable specifies the BigQuery table name as well as some information of the logs stored in this table.

LogSource

Indicates where does the log come from.

Values: LOG_SOURCE_UNSPECIFIED (0): Unspecified source. TRAINING (1): Logs coming from Training dataset. SERVING (2): Logs coming from Serving traffic.

LogType

Indicates what type of traffic does the log belong to.

Values: LOG_TYPE_UNSPECIFIED (0): Unspecified type. PREDICT (1): Predict logs. EXPLAIN (2): Explain logs.

ModelDeploymentMonitoringJob

Represents a job that runs periodically to monitor the deployed models in an endpoint. It will analyze the logged training & prediction data to detect any abnormal behaviors.

LabelsEntry

The abstract base class for a message.

LatestMonitoringPipelineMetadata

All metadata of most recent monitoring pipelines.

MonitoringScheduleState

The state to Specify the monitoring pipeline.

ModelDeploymentMonitoringObjectiveConfig

ModelDeploymentMonitoringObjectiveConfig contains the pair of deployed_model_id to ModelMonitoringObjectiveConfig.

ModelDeploymentMonitoringObjectiveType

The Model Monitoring Objective types.

ModelDeploymentMonitoringScheduleConfig

The config for scheduling monitoring job.

ModelEvaluation

A collection of metrics calculated by comparing Model's predictions on all of the test data against annotations from the test data.

BiasConfig

Configuration for bias detection.

ModelEvaluationExplanationSpec

ModelEvaluationSlice

A collection of metrics calculated by comparing Model's predictions on a slice of the test data against ground truth annotations.

Slice

Definition of a slice.

SliceSpec

Specification for how the data should be sliced.

ConfigsEntry

The abstract base class for a message.

Range

A range of values for slice(s). low is inclusive, high is exclusive.

SliceConfig

Example 1:

{
  "zip_code": { "value": { "float_value": 12345.0 } }
}

A single slice for any data with zip_code 12345 in the dataset.

Example 2:

{
  "zip_code": { "range": { "low": 12345, "high": 20000 } }
}

A single slice containing data where the zip_codes between 12345 and 20000 For this example, data with the zip_code of 12345 will be in this slice.

Example 3:

{
  "zip_code": { "range": { "low": 10000, "high": 20000 } },
  "country": { "value": { "string_value": "US" } }
}

A single slice containing data where the zip_codes between 10000 and 20000 has the country "US". For this example, data with the zip_code of 12345 and country "US" will be in this slice.

Example 4:

{ "country": {"all_values": { "value": true } } }

Three slices are computed, one for each unique country in the dataset.

Example 5:

{
  "country": { "all_values": { "value": true } },
  "zip_code": { "value": { "float_value": 12345.0 } }
}

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

Value

Single value that supports strings and floats.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

ModelExplanation

Aggregated explanation metrics for a Model over a set of instances.

ModelGardenSource

Contains information about the source of the models generated from Model Garden.

ModelMonitoringAlertConfig

The alert config for model monitoring.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

EmailAlertConfig

The config for email alert.

ModelMonitoringConfig

The model monitoring configuration used for Batch Prediction Job.

ModelMonitoringObjectiveConfig

The objective configuration for model monitoring, including the information needed to detect anomalies for one particular model.

ExplanationConfig

The config for integrating with Vertex Explainable AI. Only applicable if the Model has explanation_spec populated.

ExplanationBaseline

Output from BatchPredictionJob for Model Monitoring baseline dataset, which can be used to generate baseline attribution scores.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

PredictionFormat

The storage format of the predictions generated BatchPrediction job.

Values: PREDICTION_FORMAT_UNSPECIFIED (0): Should not be set. JSONL (2): Predictions are in JSONL files. BIGQUERY (3): Predictions are in BigQuery.

PredictionDriftDetectionConfig

The config for Prediction data drift detection.

AttributionScoreDriftThresholdsEntry

The abstract base class for a message.

DriftThresholdsEntry

The abstract base class for a message.

TrainingDataset

Training Dataset information.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

TrainingPredictionSkewDetectionConfig

The config for Training & Prediction data skew detection. It specifies the training dataset sources and the skew detection parameters.

AttributionScoreSkewThresholdsEntry

The abstract base class for a message.

SkewThresholdsEntry

The abstract base class for a message.

ModelMonitoringStatsAnomalies

Statistics and anomalies generated by Model Monitoring.

FeatureHistoricStatsAnomalies

Historical Stats (and Anomalies) for a specific Feature.

ModelSourceInfo

Detail description of the source information of the model.

ModelSourceType

MutateDeployedIndexOperationMetadata

Runtime operation information for IndexEndpointService.MutateDeployedIndex.

MutateDeployedIndexRequest

Request message for IndexEndpointService.MutateDeployedIndex.

MutateDeployedIndexResponse

Response message for IndexEndpointService.MutateDeployedIndex.

MutateDeployedModelOperationMetadata

Runtime operation information for EndpointService.MutateDeployedModel.

MutateDeployedModelRequest

Request message for EndpointService.MutateDeployedModel.

MutateDeployedModelResponse

Response message for EndpointService.MutateDeployedModel.

NasJob

Represents a Neural Architecture Search (NAS) job.

LabelsEntry

The abstract base class for a message.

NasJobOutput

Represents a uCAIP NasJob output.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

MultiTrialJobOutput

The output of a multi-trial Neural Architecture Search (NAS) jobs.

NasJobSpec

Represents the spec of a NasJob.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

MultiTrialAlgorithmSpec

The spec of multi-trial Neural Architecture Search (NAS).

MetricSpec

Represents a metric to optimize.

GoalType

The available types of optimization goals.

Values: GOAL_TYPE_UNSPECIFIED (0): Goal Type will default to maximize. MAXIMIZE (1): Maximize the goal metric. MINIMIZE (2): Minimize the goal metric.

MultiTrialAlgorithm

The available types of multi-trial algorithms.

SearchTrialSpec

Represent spec for search trials.

TrainTrialSpec

Represent spec for train trials.

NasTrial

Represents a uCAIP NasJob trial.

State

Describes a NasTrial state.

NasTrialDetail

Represents a NasTrial details along with its parameters. If there is a corresponding train NasTrial, the train NasTrial is also returned.

NearestNeighborQuery

A query to find a number of similar entities.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

Embedding

The embedding vector.

Parameters

Parameters that can be overrided in each query to tune query latency and recall.

StringFilter

NearestNeighborSearchOperationMetadata

Runtime operation metadata with regard to Matching Engine Index.

ContentValidationStats

RecordError

RecordErrorType

NearestNeighbors

Nearest neighbors for one query.

Neighbor

A neighbor of the query vector.

Neighbor

Neighbors for example-based explanations.

NetworkSpec

Network spec.

NfsMount

Represents a mount configuration for Network File System (NFS) to mount.

NotebookEucConfig

The euc configuration of NotebookRuntimeTemplate.

NotebookIdleShutdownConfig

The idle shutdown configuration of NotebookRuntimeTemplate, which contains the idle_timeout as required field.

NotebookRuntime

A runtime is a virtual machine allocated to a particular user for a particular Notebook file on temporary basis with lifetime limited to 24 hours.

HealthState

The substate of the NotebookRuntime to display health information.

LabelsEntry

The abstract base class for a message.

RuntimeState

The substate of the NotebookRuntime to display state of runtime. The resource of NotebookRuntime is in ACTIVE state for these sub state.

NotebookRuntimeTemplate

A template that specifies runtime configurations such as machine type, runtime version, network configurations, etc. Multiple runtimes can be created from a runtime template.

LabelsEntry

The abstract base class for a message.

NotebookRuntimeTemplateRef

Points to a NotebookRuntimeTemplateRef.

NotebookRuntimeType

Represents a notebook runtime type.

PairwiseChoice

Pairwise prediction autorater preference.

Values: PAIRWISE_CHOICE_UNSPECIFIED (0): Unspecified prediction choice. BASELINE (1): Baseline prediction wins CANDIDATE (2): Candidate prediction wins TIE (3): Winner cannot be determined

PairwiseQuestionAnsweringQualityInput

Input for pairwise question answering quality metric.

PairwiseQuestionAnsweringQualityInstance

Spec for pairwise question answering quality instance.

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PairwiseQuestionAnsweringQualityResult

Spec for pairwise question answering quality result.

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PairwiseQuestionAnsweringQualitySpec

Spec for pairwise question answering quality score metric.

PairwiseSummarizationQualityInput

Input for pairwise summarization quality metric.

PairwiseSummarizationQualityInstance

Spec for pairwise summarization quality instance.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

PairwiseSummarizationQualityResult

Spec for pairwise summarization quality result.

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PairwiseSummarizationQualitySpec

Spec for pairwise summarization quality score metric.

Part

A datatype containing media that is part of a multi-part Content message.

A Part consists of data which has an associated datatype. A Part can only contain one of the accepted types in Part.data.

A Part must have a fixed IANA MIME type identifying the type and subtype of the media if inline_data or file_data field is filled with raw bytes.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

PauseModelDeploymentMonitoringJobRequest

Request message for JobService.PauseModelDeploymentMonitoringJob.

PauseScheduleRequest

Request message for ScheduleService.PauseSchedule.

PersistentDiskSpec

Represents the spec of [persistent disk][https://cloud.google.com/compute/docs/disks/persistent-disks] options.

PersistentResource

Represents long-lasting resources that are dedicated to users to runs custom workloads. A PersistentResource can have multiple node pools and each node pool can have its own machine spec.

LabelsEntry

The abstract base class for a message.

State

Describes the PersistentResource state.

PipelineFailurePolicy

PipelineJob

An instance of a machine learning PipelineJob.

LabelsEntry

The abstract base class for a message.

RuntimeConfig

The runtime config of a PipelineJob.

InputArtifact

The type of an input artifact.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

InputArtifactsEntry

The abstract base class for a message.

ParameterValuesEntry

The abstract base class for a message.

ParametersEntry

The abstract base class for a message.

PipelineJobDetail

The runtime detail of PipelineJob.

PipelineState

Describes the state of a pipeline.

PipelineTaskDetail

The runtime detail of a task execution.

ArtifactList

A list of artifact metadata.

InputsEntry

The abstract base class for a message.

OutputsEntry

The abstract base class for a message.

PipelineTaskStatus

A single record of the task status.

State

Specifies state of TaskExecution

PipelineTaskExecutorDetail

The runtime detail of a pipeline executor.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

ContainerDetail

The detail of a container execution. It contains the job names of the lifecycle of a container execution.

CustomJobDetail

The detailed info for a custom job executor.

PipelineTemplateMetadata

Pipeline template metadata if PipelineJob.template_uri is from supported template registry. Currently, the only supported registry is Artifact Registry.

Port

Represents a network port in a container.

PredefinedSplit

Assigns input data to training, validation, and test sets based on the value of a provided key.

Supported only for tabular Datasets.

PredictRequest

Request message for PredictionService.Predict.

PredictRequestResponseLoggingConfig

Configuration for logging request-response to a BigQuery table.

PredictResponse

Response message for PredictionService.Predict.

PredictSchemata

Contains the schemata used in Model's predictions and explanations via PredictionService.Predict, PredictionService.Explain and BatchPredictionJob.

Presets

Preset configuration for example-based explanations

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

Modality

Preset option controlling parameters for different modalities

Values: MODALITY_UNSPECIFIED (0): Should not be set. Added as a recommended best practice for enums IMAGE (1): IMAGE modality TEXT (2): TEXT modality TABULAR (3): TABULAR modality

Query

Preset option controlling parameters for query speed-precision trade-off

Values: PRECISE (0): More precise neighbors as a trade-off against slower response. FAST (1): Faster response as a trade-off against less precise neighbors.

PrivateEndpoints

PrivateServiceConnectConfig

Represents configuration for private service connect.

Probe

Probe describes a health check to be performed against a container to determine whether it is alive or ready to receive traffic.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

ExecAction

ExecAction specifies a command to execute.

PscAutomatedEndpoints

PscAutomatedEndpoints defines the output of the forwarding rule automatically created by each PscAutomationConfig.

PublisherModel

A Model Garden Publisher Model.

CallToAction

Actions could take on this Publisher Model.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

Deploy

Model metadata that is needed for UploadModel or DeployModel/CreateEndpoint requests.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

DeployGke

Configurations for PublisherModel GKE deployment

OpenFineTuningPipelines

Open fine tuning pipelines.

OpenNotebooks

Open notebooks.

RegionalResourceReferences

The regional resource name or the URI. Key is region, e.g., us-central1, europe-west2, global, etc..

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

ReferencesEntry

The abstract base class for a message.

ViewRestApi

Rest API docs.

Documentation

A named piece of documentation.

LaunchStage

An enum representing the launch stage of a PublisherModel.

OpenSourceCategory

An enum representing the open source category of a PublisherModel.

Parent

The information about the parent of a model.

ResourceReference

Reference to a resource.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

VersionState

An enum representing the state of the PublicModelVersion.

PublisherModelView

View enumeration of PublisherModel.

PurgeArtifactsMetadata

Details of operations that perform MetadataService.PurgeArtifacts.

PurgeArtifactsRequest

Request message for MetadataService.PurgeArtifacts.

PurgeArtifactsResponse

Response message for MetadataService.PurgeArtifacts.

PurgeContextsMetadata

Details of operations that perform MetadataService.PurgeContexts.

PurgeContextsRequest

Request message for MetadataService.PurgeContexts.

PurgeContextsResponse

Response message for MetadataService.PurgeContexts.

PurgeExecutionsMetadata

Details of operations that perform MetadataService.PurgeExecutions.

PurgeExecutionsRequest

Request message for MetadataService.PurgeExecutions.

PurgeExecutionsResponse

Response message for MetadataService.PurgeExecutions.

PythonPackageSpec

The spec of a Python packaged code.

QueryArtifactLineageSubgraphRequest

Request message for MetadataService.QueryArtifactLineageSubgraph.

QueryContextLineageSubgraphRequest

Request message for MetadataService.QueryContextLineageSubgraph.

QueryDeployedModelsRequest

Request message for QueryDeployedModels method.

QueryDeployedModelsResponse

Response message for QueryDeployedModels method.

QueryExecutionInputsAndOutputsRequest

Request message for MetadataService.QueryExecutionInputsAndOutputs.

QueryExtensionRequest

Request message for ExtensionExecutionService.QueryExtension.

QueryExtensionResponse

Response message for ExtensionExecutionService.QueryExtension.

QueryReasoningEngineRequest

Request message for [ReasoningEngineExecutionService.Query][].

QueryReasoningEngineResponse

Response message for [ReasoningEngineExecutionService.Query][]

QuestionAnsweringCorrectnessInput

Input for question answering correctness metric.

QuestionAnsweringCorrectnessInstance

Spec for question answering correctness instance.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

QuestionAnsweringCorrectnessResult

Spec for question answering correctness result.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

QuestionAnsweringCorrectnessSpec

Spec for question answering correctness metric.

QuestionAnsweringHelpfulnessInput

Input for question answering helpfulness metric.

QuestionAnsweringHelpfulnessInstance

Spec for question answering helpfulness instance.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

QuestionAnsweringHelpfulnessResult

Spec for question answering helpfulness result.

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QuestionAnsweringHelpfulnessSpec

Spec for question answering helpfulness metric.

QuestionAnsweringQualityInput

Input for question answering quality metric.

QuestionAnsweringQualityInstance

Spec for question answering quality instance.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

QuestionAnsweringQualityResult

Spec for question answering quality result.

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QuestionAnsweringQualitySpec

Spec for question answering quality score metric.

QuestionAnsweringRelevanceInput

Input for question answering relevance metric.

QuestionAnsweringRelevanceInstance

Spec for question answering relevance instance.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

QuestionAnsweringRelevanceResult

Spec for question answering relevance result.

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QuestionAnsweringRelevanceSpec

Spec for question answering relevance metric.

RagContexts

Relevant contexts for one query.

Context

A context of the query.

RagCorpus

A RagCorpus is a RagFile container and a project can have multiple RagCorpora.

RagFile

A RagFile contains user data for chunking, embedding and indexing.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

RagFileType

The type of the RagFile.

Values: RAG_FILE_TYPE_UNSPECIFIED (0): RagFile type is unspecified. RAG_FILE_TYPE_TXT (1): RagFile type is TXT. RAG_FILE_TYPE_PDF (2): RagFile type is PDF.

RagFileChunkingConfig

Specifies the size and overlap of chunks for RagFiles.

RagQuery

A query to retrieve relevant contexts.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

RawPredictRequest

Request message for PredictionService.RawPredict.

RayMetricSpec

Configuration for the Ray metrics.

RaySpec

ResourcePoolImagesEntry

The abstract base class for a message.

ReadFeatureValuesRequest

Request message for FeaturestoreOnlineServingService.ReadFeatureValues.

ReadFeatureValuesResponse

Response message for FeaturestoreOnlineServingService.ReadFeatureValues.

EntityView

Entity view with Feature values.

Data

Container to hold value(s), successive in time, for one Feature from the request.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

FeatureDescriptor

Metadata for requested Features.

Header

Response header with metadata for the requested ReadFeatureValuesRequest.entity_type and Features.

ReadIndexDatapointsRequest

The request message for MatchService.ReadIndexDatapoints.

ReadIndexDatapointsResponse

The response message for MatchService.ReadIndexDatapoints.

ReadTensorboardBlobDataRequest

Request message for TensorboardService.ReadTensorboardBlobData.

ReadTensorboardBlobDataResponse

Response message for TensorboardService.ReadTensorboardBlobData.

ReadTensorboardSizeRequest

Request message for TensorboardService.ReadTensorboardSize.

ReadTensorboardSizeResponse

Response message for TensorboardService.ReadTensorboardSize.

ReadTensorboardTimeSeriesDataRequest

Request message for TensorboardService.ReadTensorboardTimeSeriesData.

ReadTensorboardTimeSeriesDataResponse

Response message for TensorboardService.ReadTensorboardTimeSeriesData.

ReadTensorboardUsageRequest

Request message for TensorboardService.ReadTensorboardUsage.

ReadTensorboardUsageResponse

Response message for TensorboardService.ReadTensorboardUsage.

MonthlyUsageDataEntry

The abstract base class for a message.

PerMonthUsageData

Per month usage data

PerUserUsageData

Per user usage data.

ReasoningEngine

ReasoningEngine provides a customizable runtime for models to determine which actions to take and in which order.

ReasoningEngineSpec

ReasoningEngine configurations

PackageSpec

User provided package spec like pickled object and package requirements.

RebootPersistentResourceOperationMetadata

Details of operations that perform reboot PersistentResource.

RebootPersistentResourceRequest

Request message for PersistentResourceService.RebootPersistentResource.

RemoveContextChildrenRequest

Request message for [MetadataService.DeleteContextChildrenRequest][].

RemoveContextChildrenResponse

Response message for MetadataService.RemoveContextChildren.

RemoveDatapointsRequest

Request message for IndexService.RemoveDatapoints

RemoveDatapointsResponse

Response message for IndexService.RemoveDatapoints

ResourcePool

Represents the spec of a group of resources of the same type, for example machine type, disk, and accelerators, in a PersistentResource.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

AutoscalingSpec

The min/max number of replicas allowed if enabling autoscaling

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

ResourceRuntime

Persistent Cluster runtime information as output

AccessUrisEntry

The abstract base class for a message.

ResourceRuntimeSpec

Configuration for the runtime on a PersistentResource instance, including but not limited to:

Service accounts used to run the workloads.
Whether to make it a dedicated Ray Cluster.

ResourcesConsumed

Statistics information about resource consumption.

RestoreDatasetVersionOperationMetadata

Runtime operation information for DatasetService.RestoreDatasetVersion.

RestoreDatasetVersionRequest

Request message for DatasetService.RestoreDatasetVersion.

ResumeModelDeploymentMonitoringJobRequest

Request message for JobService.ResumeModelDeploymentMonitoringJob.

ResumeScheduleRequest

Request message for ScheduleService.ResumeSchedule.

Retrieval

Defines a retrieval tool that model can call to access external knowledge.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

RetrieveContextsRequest

Request message for VertexRagService.RetrieveContexts.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

VertexRagStore

The data source for Vertex RagStore.

RetrieveContextsResponse

Response message for VertexRagService.RetrieveContexts.

RougeInput

Input for rouge metric.

RougeInstance

Spec for rouge instance.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

RougeMetricValue

Rouge metric value for an instance.

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RougeResults

Results for rouge metric.

RougeSpec

Spec for rouge score metric - calculates the recall of n-grams in prediction as compared to reference - returns a score ranging between 0 and 1.

RuntimeConfig

Runtime configuration to run the extension.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

CodeInterpreterRuntimeConfig

VertexAISearchRuntimeConfig

SafetyInput

Input for safety metric.

SafetyInstance

Spec for safety instance.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

SafetyRating

Safety rating corresponding to the generated content.

HarmProbability

Harm probability levels in the content.

HarmSeverity

Harm severity levels.

SafetyResult

Spec for safety result.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

SafetySetting

Safety settings.

HarmBlockMethod

Probability vs severity.

HarmBlockThreshold

Probability based thresholds levels for blocking.

SafetySpec

Spec for safety metric.

SampleConfig

Active learning data sampling config. For every active learning labeling iteration, it will select a batch of data based on the sampling strategy.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

SampleStrategy

Sample strategy decides which subset of DataItems should be selected for human labeling in every batch.

Values: SAMPLE_STRATEGY_UNSPECIFIED (0): Default will be treated as UNCERTAINTY. UNCERTAINTY (1): Sample the most uncertain data to label.

SampledShapleyAttribution

SamplingStrategy

Sampling Strategy for logging, can be for both training and prediction dataset.

RandomSampleConfig

Requests are randomly selected.

SavedQuery

A SavedQuery is a view of the dataset. It references a subset of annotations by problem type and filters.

Scalar

One point viewable on a scalar metric plot.

Schedule

An instance of a Schedule periodically schedules runs to make API calls based on user specified time specification and API request type.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

RunResponse

Status of a scheduled run.

State

Possible state of the schedule.

Scheduling

All parameters related to queuing and scheduling of custom jobs.

Schema

PropertiesEntry

The abstract base class for a message.

SearchDataItemsRequest

Request message for DatasetService.SearchDataItems.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

OrderByAnnotation

Expression that allows ranking results based on annotation's property.

SearchDataItemsResponse

Response message for DatasetService.SearchDataItems.

SearchFeaturesRequest

Request message for FeaturestoreService.SearchFeatures.

SearchFeaturesResponse

Response message for FeaturestoreService.SearchFeatures.

SearchMigratableResourcesRequest

Request message for MigrationService.SearchMigratableResources.

SearchMigratableResourcesResponse

Response message for MigrationService.SearchMigratableResources.

SearchModelDeploymentMonitoringStatsAnomaliesRequest

Request message for JobService.SearchModelDeploymentMonitoringStatsAnomalies.

StatsAnomaliesObjective

Stats requested for specific objective.

SearchModelDeploymentMonitoringStatsAnomaliesResponse

Response message for JobService.SearchModelDeploymentMonitoringStatsAnomalies.

SearchNearestEntitiesRequest

The request message for FeatureOnlineStoreService.SearchNearestEntities.

SearchNearestEntitiesResponse

Response message for FeatureOnlineStoreService.SearchNearestEntities

Segment

Segment of the content.

ServiceAccountSpec

Configuration for the use of custom service account to run the workloads.

ShieldedVmConfig

A set of Shielded Instance options. See Images using supported Shielded VM features <https://cloud.google.com/compute/docs/instances/modifying-shielded-vm>__.

SmoothGradConfig

Config for SmoothGrad approximation of gradients.

https://arxiv.org/pdf/1706.03825.pdf

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

SpecialistPool

StartNotebookRuntimeOperationMetadata

Metadata information for NotebookService.StartNotebookRuntime.

StartNotebookRuntimeRequest

Request message for NotebookService.StartNotebookRuntime.

StartNotebookRuntimeResponse

Response message for NotebookService.StartNotebookRuntime.

StopTrialRequest

Request message for VizierService.StopTrial.

StratifiedSplit

Only the top 500 occurring values are used; any values not in the top 500 values are randomly assigned to a split. If less than three rows contain a specific value, those rows are randomly assigned.

Supported only for tabular Datasets.

StreamDirectPredictRequest

Request message for PredictionService.StreamDirectPredict.

The first message must contain endpoint field and optionally [input][]. The subsequent messages must contain [input][].

StreamDirectPredictResponse

Response message for PredictionService.StreamDirectPredict.

StreamDirectRawPredictRequest

Request message for PredictionService.StreamDirectRawPredict.

The first message must contain endpoint and method_name fields and optionally input. The subsequent messages must contain input. method_name in the subsequent messages have no effect.

StreamDirectRawPredictResponse

Response message for PredictionService.StreamDirectRawPredict.

StreamingFetchFeatureValuesRequest

Request message for FeatureOnlineStoreService.StreamingFetchFeatureValues. For the entities requested, all features under the requested feature view will be returned.

StreamingFetchFeatureValuesResponse

Response message for FeatureOnlineStoreService.StreamingFetchFeatureValues.

StreamingPredictRequest

Request message for PredictionService.StreamingPredict.

The first message must contain endpoint field and optionally [input][]. The subsequent messages must contain [input][].

StreamingPredictResponse

Response message for PredictionService.StreamingPredict.

StreamingRawPredictRequest

Request message for PredictionService.StreamingRawPredict.

The first message must contain endpoint and method_name fields and optionally input. The subsequent messages must contain input. method_name in the subsequent messages have no effect.

StreamingRawPredictResponse

Response message for PredictionService.StreamingRawPredict.

StreamingReadFeatureValuesRequest

Request message for [FeaturestoreOnlineServingService.StreamingFeatureValuesRead][].

StringArray

A list of string values.

Study

A message representing a Study.

State

Describes the Study state.

StudySpec

Represents specification of a Study.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

Algorithm

The available search algorithms for the Study.

ConvexAutomatedStoppingSpec

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

ConvexStopConfig

Configuration for ConvexStopPolicy.

DecayCurveAutomatedStoppingSpec

MeasurementSelectionType

MedianAutomatedStoppingSpec

MetricSpec

Represents a metric to optimize.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

GoalType

The available types of optimization goals.

Values: GOAL_TYPE_UNSPECIFIED (0): Goal Type will default to maximize. MAXIMIZE (1): Maximize the goal metric. MINIMIZE (2): Minimize the goal metric.

SafetyMetricConfig

Used in safe optimization to specify threshold levels and risk tolerance.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

ObservationNoise

Describes the noise level of the repeated observations.

"Noisy" means that the repeated observations with the same Trial parameters may lead to different metric evaluations.

ParameterSpec

Represents a single parameter to optimize.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

CategoricalValueSpec

Value specification for a parameter in CATEGORICAL type.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

ConditionalParameterSpec

Represents a parameter spec with condition from its parent parameter.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

CategoricalValueCondition

Represents the spec to match categorical values from parent parameter.

DiscreteValueCondition

Represents the spec to match discrete values from parent parameter.

IntValueCondition

Represents the spec to match integer values from parent parameter.

DiscreteValueSpec

Value specification for a parameter in DISCRETE type.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

DoubleValueSpec

Value specification for a parameter in DOUBLE type.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

IntegerValueSpec

Value specification for a parameter in INTEGER type.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

ScaleType

The type of scaling that should be applied to this parameter.

StudyStoppingConfig

The configuration (stopping conditions) for automated stopping of a Study. Conditions include trial budgets, time budgets, and convergence detection.

TransferLearningConfig

This contains flag for manually disabling transfer learning for a study. The names of prior studies being used for transfer learning (if any) are also listed here.

StudyTimeConstraint

Time-based Constraint for Study

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

SuggestTrialsMetadata

Details of operations that perform Trials suggestion.

SuggestTrialsRequest

Request message for VizierService.SuggestTrials.

SuggestTrialsResponse

Response message for VizierService.SuggestTrials.

SummarizationHelpfulnessInput

Input for summarization helpfulness metric.

SummarizationHelpfulnessInstance

Spec for summarization helpfulness instance.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

SummarizationHelpfulnessResult

Spec for summarization helpfulness result.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

SummarizationHelpfulnessSpec

Spec for summarization helpfulness score metric.

SummarizationQualityInput

Input for summarization quality metric.

SummarizationQualityInstance

Spec for summarization quality instance.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

SummarizationQualityResult

Spec for summarization quality result.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

SummarizationQualitySpec

Spec for summarization quality score metric.

SummarizationVerbosityInput

Input for summarization verbosity metric.

SummarizationVerbosityInstance

Spec for summarization verbosity instance.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

SummarizationVerbosityResult

Spec for summarization verbosity result.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

SummarizationVerbositySpec

Spec for summarization verbosity score metric.

SyncFeatureViewRequest

Request message for FeatureOnlineStoreAdminService.SyncFeatureView.

SyncFeatureViewResponse

Respose message for FeatureOnlineStoreAdminService.SyncFeatureView.

TFRecordDestination

The storage details for TFRecord output content.

Tensor

A tensor value type.

DataType

Data type of the tensor.

StructValEntry

The abstract base class for a message.

Tensorboard

LabelsEntry

The abstract base class for a message.

TensorboardBlob

One blob (e.g, image, graph) viewable on a blob metric plot.

TensorboardBlobSequence

One point viewable on a blob metric plot, but mostly just a wrapper message to work around repeated fields can't be used directly within oneof fields.

TensorboardExperiment

A TensorboardExperiment is a group of TensorboardRuns, that are typically the results of a training job run, in a Tensorboard.

LabelsEntry

The abstract base class for a message.

TensorboardRun

TensorboardRun maps to a specific execution of a training job with a given set of hyperparameter values, model definition, dataset, etc

LabelsEntry

The abstract base class for a message.

TensorboardTensor

One point viewable on a tensor metric plot.

TensorboardTimeSeries

TensorboardTimeSeries maps to times series produced in training runs

Metadata

Describes metadata for a TensorboardTimeSeries.

ValueType

An enum representing the value type of a TensorboardTimeSeries.

ThresholdConfig

The config for feature monitoring threshold.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

TimeSeriesData

All the data stored in a TensorboardTimeSeries.

TimeSeriesDataPoint

A TensorboardTimeSeries data point.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

TimestampSplit

Supported only for tabular Datasets.

TokensInfo

Tokens info with a list of tokens and the corresponding list of token ids.

Tool

Tool details that the model may use to generate response.

ToolCallValidInput

Input for tool call valid metric.

ToolCallValidInstance

Spec for tool call valid instance.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

ToolCallValidMetricValue

Tool call valid metric value for an instance.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

ToolCallValidResults

Results for tool call valid metric.

ToolCallValidSpec

Spec for tool call valid metric.

ToolConfig

Tool config. This config is shared for all tools provided in the request.

ToolNameMatchInput

Input for tool name match metric.

ToolNameMatchInstance

Spec for tool name match instance.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

ToolNameMatchMetricValue

Tool name match metric value for an instance.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

ToolNameMatchResults

Results for tool name match metric.

ToolNameMatchSpec

Spec for tool name match metric.

ToolParameterKVMatchInput

Input for tool parameter key value match metric.

ToolParameterKVMatchInstance

Spec for tool parameter key value match instance.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

ToolParameterKVMatchMetricValue

Tool parameter key value match metric value for an instance.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

ToolParameterKVMatchResults

Results for tool parameter key value match metric.

ToolParameterKVMatchSpec

Spec for tool parameter key value match metric.

ToolParameterKeyMatchInput

Input for tool parameter key match metric.

ToolParameterKeyMatchInstance

Spec for tool parameter key match instance.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

ToolParameterKeyMatchMetricValue

Tool parameter key match metric value for an instance.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

ToolParameterKeyMatchResults

Results for tool parameter key match metric.

ToolParameterKeyMatchSpec

Spec for tool parameter key match metric.

ToolUseExample

A single example of the tool usage.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

ExtensionOperation

Identifies one operation of the extension.

TrainingConfig

CMLE training config. For every active learning labeling iteration, system will train a machine learning model on CMLE. The trained model will be used by data sampling algorithm to select DataItems.

TrainingPipeline

LabelsEntry

The abstract base class for a message.

Trial

A message representing a Trial. A Trial contains a unique set of Parameters that has been or will be evaluated, along with the objective metrics got by running the Trial.

Parameter

A message representing a parameter to be tuned.

State

Describes a Trial state.

WebAccessUrisEntry

The abstract base class for a message.

TrialContext

Next ID: 3

Type

Type contains the list of OpenAPI data types as defined by https://swagger.io/docs/specification/data-models/data-types/

UndeployIndexOperationMetadata

Runtime operation information for IndexEndpointService.UndeployIndex.

UndeployIndexRequest

Request message for IndexEndpointService.UndeployIndex.

UndeployIndexResponse

Response message for IndexEndpointService.UndeployIndex.

UndeployModelOperationMetadata

Runtime operation information for EndpointService.UndeployModel.

UndeployModelRequest

Request message for EndpointService.UndeployModel.

TrafficSplitEntry

The abstract base class for a message.

UndeployModelResponse

Response message for EndpointService.UndeployModel.

UnmanagedContainerModel

Contains model information necessary to perform batch prediction without requiring a full model import.

UpdateArtifactRequest

Request message for MetadataService.UpdateArtifact.

UpdateContextRequest

Request message for MetadataService.UpdateContext.

UpdateDatasetRequest

Request message for DatasetService.UpdateDataset.

UpdateDeploymentResourcePoolOperationMetadata

Runtime operation information for UpdateDeploymentResourcePool method.

UpdateEndpointRequest

Request message for EndpointService.UpdateEndpoint.

UpdateEntityTypeRequest

Request message for FeaturestoreService.UpdateEntityType.

UpdateExecutionRequest

Request message for MetadataService.UpdateExecution.

UpdateExplanationDatasetOperationMetadata

Runtime operation information for ModelService.UpdateExplanationDataset.

UpdateExplanationDatasetRequest

Request message for ModelService.UpdateExplanationDataset.

UpdateExplanationDatasetResponse

Response message of ModelService.UpdateExplanationDataset operation.

UpdateExtensionRequest

Request message for ExtensionRegistryService.UpdateExtension.

UpdateFeatureGroupOperationMetadata

Details of operations that perform update FeatureGroup.

UpdateFeatureGroupRequest

Request message for FeatureRegistryService.UpdateFeatureGroup.

UpdateFeatureOnlineStoreOperationMetadata

Details of operations that perform update FeatureOnlineStore.

UpdateFeatureOnlineStoreRequest

Request message for FeatureOnlineStoreAdminService.UpdateFeatureOnlineStore.

UpdateFeatureOperationMetadata

Details of operations that perform update Feature.

UpdateFeatureRequest

Request message for FeaturestoreService.UpdateFeature. Request message for FeatureRegistryService.UpdateFeature.

UpdateFeatureViewOperationMetadata

Details of operations that perform update FeatureView.

UpdateFeatureViewRequest

Request message for FeatureOnlineStoreAdminService.UpdateFeatureView.

UpdateFeaturestoreOperationMetadata

Details of operations that perform update Featurestore.

UpdateFeaturestoreRequest

Request message for FeaturestoreService.UpdateFeaturestore.

UpdateIndexEndpointRequest

Request message for IndexEndpointService.UpdateIndexEndpoint.

UpdateIndexOperationMetadata

Runtime operation information for IndexService.UpdateIndex.

UpdateIndexRequest

Request message for IndexService.UpdateIndex.

UpdateModelDeploymentMonitoringJobOperationMetadata

Runtime operation information for JobService.UpdateModelDeploymentMonitoringJob.

UpdateModelDeploymentMonitoringJobRequest

Request message for JobService.UpdateModelDeploymentMonitoringJob.

UpdateModelRequest

Request message for ModelService.UpdateModel.

UpdatePersistentResourceOperationMetadata

Details of operations that perform update PersistentResource.

UpdatePersistentResourceRequest

Request message for UpdatePersistentResource method.

UpdateScheduleRequest

Request message for ScheduleService.UpdateSchedule.

UpdateSpecialistPoolOperationMetadata

Runtime operation metadata for SpecialistPoolService.UpdateSpecialistPool.

UpdateSpecialistPoolRequest

Request message for SpecialistPoolService.UpdateSpecialistPool.

UpdateTensorboardExperimentRequest

Request message for TensorboardService.UpdateTensorboardExperiment.

UpdateTensorboardOperationMetadata

Details of operations that perform update Tensorboard.

UpdateTensorboardRequest

Request message for TensorboardService.UpdateTensorboard.

UpdateTensorboardRunRequest

Request message for TensorboardService.UpdateTensorboardRun.

UpdateTensorboardTimeSeriesRequest

Request message for TensorboardService.UpdateTensorboardTimeSeries.

UpgradeNotebookRuntimeOperationMetadata

Metadata information for NotebookService.UpgradeNotebookRuntime.

UpgradeNotebookRuntimeRequest

Request message for NotebookService.UpgradeNotebookRuntime.

UpgradeNotebookRuntimeResponse

Response message for NotebookService.UpgradeNotebookRuntime.

UploadModelOperationMetadata

Details of ModelService.UploadModel operation.

UploadModelRequest

Request message for ModelService.UploadModel.

UploadModelResponse

Response message of ModelService.UploadModel operation.

UploadRagFileConfig

Config for uploading RagFile.

UploadRagFileRequest

Request message for VertexRagDataService.UploadRagFile.

UploadRagFileResponse

Response message for VertexRagDataService.UploadRagFile.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

UpsertDatapointsRequest

Request message for IndexService.UpsertDatapoints

UpsertDatapointsResponse

Response message for IndexService.UpsertDatapoints

UserActionReference

References an API call. It contains more information about long running operation and Jobs that are triggered by the API call.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

Value

Value is the value of the field.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

VertexAISearch

Retrieve from Vertex AI Search datastore for grounding. See https://cloud.google.com/vertex-ai-search-and-conversation

VertexRagStore

Retrieve from Vertex RAG Store for grounding.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

VideoMetadata

Metadata describes the input video content.

WorkerPoolSpec

Represents the spec of a worker pool in a job.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

WriteFeatureValuesPayload

Contains Feature values to be written for a specific entity.

FeatureValuesEntry

The abstract base class for a message.

WriteFeatureValuesRequest

Request message for FeaturestoreOnlineServingService.WriteFeatureValues.

WriteFeatureValuesResponse

Response message for FeaturestoreOnlineServingService.WriteFeatureValues.

WriteTensorboardExperimentDataRequest

Request message for TensorboardService.WriteTensorboardExperimentData.

WriteTensorboardExperimentDataResponse

Response message for TensorboardService.WriteTensorboardExperimentData.

WriteTensorboardRunDataRequest

Request message for TensorboardService.WriteTensorboardRunData.

WriteTensorboardRunDataResponse

Response message for TensorboardService.WriteTensorboardRunData.

XraiAttribution

An explanation method that redistributes Integrated Gradients attributions to segmented regions, taking advantage of the model's fully differentiable structure. Refer to this paper for more details:

https://arxiv.org/abs/1906.02825

Supported only by image Models.

Candidate

A response candidate generated by the model.

ChatSession

Chat session holds the chat history.

Content

The multi-part content of a message.

Usage:

response = model.generate_content(contents=[
    Content(role="user", parts=[Part.from_text("Why is sky blue?")])
])
```

FinishReason

The reason why the model stopped generating tokens. If empty, the model has not stopped generating the tokens.

FunctionDeclaration

A representation of a function declaration.

Usage: Create function declaration and tool:

get_current_weather_func = generative_models.FunctionDeclaration(
    name="get_current_weather",
    description="Get the current weather in a given location",
    parameters={
        "type": "object",
        "properties": {
            "location": {
                "type": "string",
                "description": "The city and state, e.g. San Francisco, CA"
            },
            "unit": {
                "type": "string",
                "enum": [
                    "celsius",
                    "fahrenheit",
                ]
            }
        },
        "required": [
            "location"
        ]
    },
)
weather_tool = generative_models.Tool(
    function_declarations=[get_current_weather_func],
)
```
Use tool in `GenerativeModel.generate_content`:
```
model = GenerativeModel("gemini-pro")
print(model.generate_content(
    "What is the weather like in Boston?",
    # You can specify tools when creating a model to avoid having to send them with every request.
    tools=[weather_tool],
))
```
Use tool in chat:
```
model = GenerativeModel(
    "gemini-pro",
    # You can specify tools when creating a model to avoid having to send them with every request.
    tools=[weather_tool],
)
chat = model.start_chat()
print(chat.send_message("What is the weather like in Boston?"))
print(chat.send_message(
    Part.from_function_response(
        name="get_current_weather",
        response={
            "content": {"weather_there": "super nice"},
        }
    ),
))
```

GenerationConfig

Parameters for the generation.

GenerationResponse

The response from the model.

GenerativeModel

Initializes GenerativeModel.

Usage:

model = GenerativeModel("gemini-pro")
print(model.generate_content("Hello"))
```

HarmBlockThreshold

Probability based thresholds levels for blocking.

HarmCategory

Harm categories that will block the content.

Image

The image that can be sent to a generative model.

Part

A part of a multi-part Content message.

Usage:

text_part = Part.from_text("Why is sky blue?")
image_part = Part.from_image(Image.load_from_file("image.jpg"))
video_part = Part.from_uri(uri="gs://.../video.mp4", mime_type="video/mp4")
function_response_part = Part.from_function_response(
    name="get_current_weather",
    response={
        "content": {"weather_there": "super nice"},
    }
)

response1 = model.generate_content([text_part, image_part])
response2 = model.generate_content(video_part)
response3 = chat.send_message(function_response_part)
```

ResponseValidationError

Common base class for all non-exit exceptions.

SafetySetting

Parameters for the generation.

HarmBlockMethod

Probability vs severity.

HarmBlockThreshold

Probability based thresholds levels for blocking.

HarmCategory

Harm categories that will block the content.

Tool

A collection of functions that the model may use to generate response.

Usage: Create tool from function declarations:

get_current_weather_func = generative_models.FunctionDeclaration(...)
weather_tool = generative_models.Tool(
    function_declarations=[get_current_weather_func],
)
```
Use tool in `GenerativeModel.generate_content`:
```
model = GenerativeModel("gemini-pro")
print(model.generate_content(
    "What is the weather like in Boston?",
    # You can specify tools when creating a model to avoid having to send them with every request.
    tools=[weather_tool],
))
```
Use tool in chat:
```
model = GenerativeModel(
    "gemini-pro",
    # You can specify tools when creating a model to avoid having to send them with every request.
    tools=[weather_tool],
)
chat = model.start_chat()
print(chat.send_message("What is the weather like in Boston?"))
print(chat.send_message(
    Part.from_function_response(
        name="get_current_weather",
        response={
            "content": {"weather_there": "super nice"},
        }
    ),
))
```

ChatMessage

A chat message.

ChatModel

ChatModel represents a language model that is capable of chat.

Examples::

chat_model = ChatModel.from_pretrained("chat-bison@001")

chat = chat_model.start_chat(
    context="My name is Ned. You are my personal assistant. My favorite movies are Lord of the Rings and Hobbit.",
    examples=[
        InputOutputTextPair(
            input_text="Who do you work for?",
            output_text="I work for Ned.",
        ),
        InputOutputTextPair(
            input_text="What do I like?",
            output_text="Ned likes watching movies.",
        ),
    ],
    temperature=0.3,
)

chat.send_message("Do you know any cool events this weekend?")

ChatSession

ChatSession represents a chat session with a language model.

Within a chat session, the model keeps context and remembers the previous conversation.

CodeChatModel

CodeChatModel represents a model that is capable of completing code.

.. rubric:: Examples

code_chat_model = CodeChatModel.from_pretrained("codechat-bison@001")

code_chat = code_chat_model.start_chat( context="I'm writing a large-scale enterprise application.", max_output_tokens=128, temperature=0.2, )

code_chat.send_message("Please help write a function to calculate the min of two numbers")

CodeChatSession

CodeChatSession represents a chat session with code chat language model.

Within a code chat session, the model keeps context and remembers the previous converstion.

CodeGenerationModel

Creates a LanguageModel.

This constructor should not be called directly. Use LanguageModel.from_pretrained(model_name=...) instead.

GroundingSource

GroundingSource()

InlineContext

InlineContext represents a grounding source using provided inline context. .. attribute:: inline_context

The content used as inline context.

:type: str

VertexAISearch

VertexAISearchDatastore represents a grounding source using Vertex AI Search datastore .. attribute:: data_store_id

Data store ID of the Vertex AI Search datastore.

:type: str

WebSearch

WebSearch represents a grounding source using public web search. .. attribute:: disable_attribution

If set to True, skip finding claim attributions (i.e not generate grounding citation). Default: False.

:type: bool

InputOutputTextPair

InputOutputTextPair represents a pair of input and output texts.

TextEmbedding

Text embedding vector and statistics.

TextEmbeddingInput

Structural text embedding input.

TextEmbeddingModel

TextEmbeddingModel class calculates embeddings for the given texts.

Examples::

# Getting embedding:
model = TextEmbeddingModel.from_pretrained("textembedding-gecko@001")
embeddings = model.get_embeddings(["What is life?"])
for embedding in embeddings:
    vector = embedding.values
    print(len(vector))

TextGenerationModel

Creates a LanguageModel.

This constructor should not be called directly. Use LanguageModel.from_pretrained(model_name=...) instead.

TextGenerationResponse

TextGenerationResponse represents a response of a language model. .. attribute:: text

The generated text

:type: str

_TunableModelMixin

Model that can be tuned with supervised fine tuning (SFT).

VertexModel

mixin class that can be used to add Vertex AI remote execution to a custom model.

CustomMetric

The custom evaluation metric.

The evaluation function. Must use the dataset row/instance as the metric_function input. Returns per-instance metric result as a dictionary. The metric score must mapped to the CustomMetric.name as key.

EvalResult

Evaluation result.

EvalTask

A class representing an EvalTask.

An Evaluation Tasks is defined to measure the model's ability to perform a certain task in response to specific prompts or inputs. Evaluation tasks must contain an evaluation dataset, and a list of metrics to evaluate. Evaluation tasks help developers compare propmpt templates, track experiments, compare models and their settings, and assess the quality of the model's generated text.

Dataset details: Default dataset column names:

content_column_name: "content"
reference_column_name: "reference"
response_column_name: "response" Requirement for different use cases:
- Bring your own prediction: A response column is required. Response column name can be customized by providing response_column_name parameter.
- Without prompt template: A column representing the input prompt to the model is required. If content_column_name is not specified, the eval dataset requires content column by default. The response column is not used if present and new responses from the model are generated with the content column and used for evaluation.
- With prompt template: Dataset must contain column names corresponding to the placeholder names in the prompt template. For example, if prompt template is "Instruction: {instruction}, context: {context}", the dataset must contain instruction and context column.

Metrics Details: The supported metrics, metric bundle descriptions, grading rubrics, and the required input fields can be found on the Vertex AI public documentation.

Usage:

To perform bring your own prediction evaluation, provide the model responses in the response column in the dataset. The response column name is "response" by default, or specify response_column_name parameter to customize.

eval_dataset = pd.DataFrame({
       "reference": [...],
       "response" : [...],
})
eval_task = EvalTask(
 dataset=eval_dataset,
 metrics=["bleu", "rouge_l_sum", "coherence", "fluency"],
 experiment="my-experiment",
)
eval_result = eval_task.evaluate(
     experiment_run_name="eval-experiment-run"
)

To perform evaluation with built-in Gemini model inference, specify the model parameter with a GenerativeModel instance. The default query column name to the model is content.

eval_dataset = pd.DataFrame({
     "reference": [...],
     "content"  : [...],
})
result = EvalTask(
   dataset=eval_dataset,
   metrics=["exact_match", "bleu", "rouge_1", "rouge_2",
   "rouge_l_sum"],
   experiment="my-experiment",
).evaluate(
   model=GenerativeModel("gemini-pro"),
   experiment_run_name="gemini-pro-eval-run"
)

If a prompt_template is specified, the content column is not required. Prompts can be assembled from the evaluation dataset, and all placeholder names must be present in the dataset columns.

eval_dataset = pd.DataFrame({
   "context"    : [...],
   "instruction": [...],
   "reference"  : [...],
})
result = EvalTask(
   dataset=eval_dataset,
   metrics=["summarization_quality"],
).evaluate(
   model=model,
   prompt_template="{instruction}. Article: {context}. Summary:",
)

To perform evaluation with custom model inference, specify the model parameter with a custom prediction function. The content column in the dataset is used to generate predictions with the custom model function for evaluation.

def custom_model_fn(input: str) -> str:
 response = client.chat.completions.create(
   model="gpt-3.5-turbo",
   messages=[
     {"role": "user", "content": input}
   ]
 )
 return response.choices[0].message.content

eval_dataset = pd.DataFrame({
     "content"  : [...],
     "reference": [...],
})
result = EvalTask(
   dataset=eval_dataset,
   metrics=["text_generation_similarity","text_generation_quality"],
   experiment="my-experiment",
).evaluate(
   model=custom_model_fn,
   experiment_run_name="gpt-eval-run"
)

PromptTemplate

A prompt template for creating prompts with placeholders.

The PromptTemplate class allows users to define a template string with placeholders represented in curly braces {placeholder}. The placeholder names cannot contain spaces. These placeholders can be replaced with specific values using the assemble method, providing flexibility in generating dynamic prompts.

Example Usage:

```
    template_str = "Hello, {name}! Today is {day}. How are you?"
    prompt_template = PromptTemplate(template_str)
    completed_prompt = prompt_template.assemble(name="John", day="Monday")
    print(completed_prompt)
```

A set of placeholder names from the template string.

AutomaticFunctionCallingResponder

Responder that automatically responds to model's function calls.

CallableFunctionDeclaration

A function declaration plus a function.

Candidate

A response candidate generated by the model.

ChatSession

Chat session holds the chat history.

Content

The multi-part content of a message.

Usage:

response = model.generate_content(contents=[
    Content(role="user", parts=[Part.from_text("Why is sky blue?")])
])
```

FinishReason

The reason why the model stopped generating tokens. If empty, the model has not stopped generating the tokens.

FunctionDeclaration

A representation of a function declaration.

Usage: Create function declaration and tool:

get_current_weather_func = generative_models.FunctionDeclaration(
    name="get_current_weather",
    description="Get the current weather in a given location",
    parameters={
        "type": "object",
        "properties": {
            "location": {
                "type": "string",
                "description": "The city and state, e.g. San Francisco, CA"
            },
            "unit": {
                "type": "string",
                "enum": [
                    "celsius",
                    "fahrenheit",
                ]
            }
        },
        "required": [
            "location"
        ]
    },
)
weather_tool = generative_models.Tool(
    function_declarations=[get_current_weather_func],
)
```
Use tool in `GenerativeModel.generate_content`:
```
model = GenerativeModel("gemini-pro")
print(model.generate_content(
    "What is the weather like in Boston?",
    # You can specify tools when creating a model to avoid having to send them with every request.
    tools=[weather_tool],
))
```
Use tool in chat:
```
model = GenerativeModel(
    "gemini-pro",
    # You can specify tools when creating a model to avoid having to send them with every request.
    tools=[weather_tool],
)
chat = model.start_chat()
print(chat.send_message("What is the weather like in Boston?"))
print(chat.send_message(
    Part.from_function_response(
        name="get_current_weather",
        response={
            "content": {"weather_there": "super nice"},
        }
    ),
))
```

GenerationConfig

Parameters for the generation.

GenerationResponse

The response from the model.

GenerativeModel

Initializes GenerativeModel.

Usage:

model = GenerativeModel("gemini-pro")
print(model.generate_content("Hello"))
```

HarmBlockThreshold

Probability based thresholds levels for blocking.

HarmCategory

Harm categories that will block the content.

Image

The image that can be sent to a generative model.

Part

A part of a multi-part Content message.

Usage:

text_part = Part.from_text("Why is sky blue?")
image_part = Part.from_image(Image.load_from_file("image.jpg"))
video_part = Part.from_uri(uri="gs://.../video.mp4", mime_type="video/mp4")
function_response_part = Part.from_function_response(
    name="get_current_weather",
    response={
        "content": {"weather_there": "super nice"},
    }
)

response1 = model.generate_content([text_part, image_part])
response2 = model.generate_content(video_part)
response3 = chat.send_message(function_response_part)
```

ResponseBlockedError

Common base class for all non-exit exceptions.

ResponseValidationError

Common base class for all non-exit exceptions.

SafetySetting

Parameters for the generation.

HarmBlockMethod

Probability vs severity.

HarmBlockThreshold

Probability based thresholds levels for blocking.

HarmCategory

Harm categories that will block the content.

Tool

A collection of functions that the model may use to generate response.

Usage: Create tool from function declarations:

get_current_weather_func = generative_models.FunctionDeclaration(...)
weather_tool = generative_models.Tool(
    function_declarations=[get_current_weather_func],
)
```
Use tool in `GenerativeModel.generate_content`:
```
model = GenerativeModel("gemini-pro")
print(model.generate_content(
    "What is the weather like in Boston?",
    # You can specify tools when creating a model to avoid having to send them with every request.
    tools=[weather_tool],
))
```
Use tool in chat:
```
model = GenerativeModel(
    "gemini-pro",
    # You can specify tools when creating a model to avoid having to send them with every request.
    tools=[weather_tool],
)
chat = model.start_chat()
print(chat.send_message("What is the weather like in Boston?"))
print(chat.send_message(
    Part.from_function_response(
        name="get_current_weather",
        response={
            "content": {"weather_there": "super nice"},
        }
    ),
))
```

ToolConfig

Config shared for all tools provided in the request.

Usage: Create ToolConfig

tool_config = ToolConfig(
    function_calling_config=ToolConfig.FunctionCallingConfig(
        mode=ToolConfig.FunctionCallingConfig.Mode.ANY,
        allowed_function_names=["get_current_weather_func"],
))
```
Use ToolConfig in `GenerativeModel.generate_content`:
```
model = GenerativeModel("gemini-pro")
print(model.generate_content(
    "What is the weather like in Boston?",
    # You can specify tools when creating a model to avoid having to send them with every request.
    tools=[weather_tool],
    tool_config=tool_config,
))
```
Use ToolConfig in chat:
```
model = GenerativeModel(
    "gemini-pro",
    # You can specify tools when creating a model to avoid having to send them with every request.
    tools=[weather_tool],
    tool_config=tool_config,
)
chat = model.start_chat()
print(chat.send_message("What is the weather like in Boston?"))
print(chat.send_message(
    Part.from_function_response(
        name="get_current_weather",
        response={
            "content": {"weather_there": "super nice"},
        }
    ),
))
```

grounding

Grounding namespace.

GoogleSearchRetrieval

Tool to retrieve public web data for grounding, powered by Google Search.

Retrieval

Defines a retrieval tool that model can call to access external knowledge.

VertexAISearch

Retrieve from Vertex AI Search datastore for grounding. See https://cloud.google.com/vertex-ai-search-and-conversation

ChatMessage

A chat message.

CountTokensResponse

The response from a count_tokens request. .. attribute:: total_tokens

The total number of tokens counted across all instances passed to the request.

:type: int

EvaluationClassificationMetric

The evaluation metric response for classification metrics.

EvaluationMetric

The evaluation metric response.

EvaluationQuestionAnsweringSpec

Spec for question answering model evaluation tasks.

EvaluationTextClassificationSpec

Spec for text classification model evaluation tasks.

EvaluationTextGenerationSpec

Spec for text generation model evaluation tasks.

EvaluationTextSummarizationSpec

Spec for text summarization model evaluation tasks.

InputOutputTextPair

InputOutputTextPair represents a pair of input and output texts.

TextEmbedding

Text embedding vector and statistics.

TextEmbeddingInput

Structural text embedding input.

TextGenerationResponse

TextGenerationResponse represents a response of a language model. .. attribute:: text

The generated text

:type: str

TuningEvaluationSpec

Specification for model evaluation to perform during tuning.

LangchainAgent

A Langchain Agent.

Reference:

Queryable

Protocol for Reasoning Engine applications that can be queried.

ReasoningEngine

Represents a Vertex AI Reasoning Engine resource.

GeneratedImage

Generated image.

Image

Image.

ImageCaptioningModel

Generates captions from image.

Examples::

model = ImageCaptioningModel.from_pretrained("imagetext@001")
image = Image.load_from_file("image.png")
captions = model.get_captions(
    image=image,
    # Optional:
    number_of_results=1,
    language="en",
)

ImageGenerationModel

Generates images from text prompt.

Examples::

model = ImageGenerationModel.from_pretrained("imagegeneration@002")
response = model.generate_images(
    prompt="Astronaut riding a horse",
    # Optional:
    number_of_images=1,
    seed=0,
)
response[0].show()
response[0].save("image1.png")

ImageGenerationResponse

Image generation response.

ImageQnAModel

Answers questions about an image.

Examples::

model = ImageQnAModel.from_pretrained("imagetext@001")
image = Image.load_from_file("image.png")
answers = model.ask_question(
    image=image,
    question="What color is the car in this image?",
    # Optional:
    number_of_results=1,
)

ImageTextModel

Generates text from images.

Examples::

model = ImageTextModel.from_pretrained("imagetext@001")
image = Image.load_from_file("image.png")

captions = model.get_captions(
    image=image,
    # Optional:
    number_of_results=1,
    language="en",
)

answers = model.ask_question(
    image=image,
    question="What color is the car in this image?",
    # Optional:
    number_of_results=1,
)

MultiModalEmbeddingModel

Generates embedding vectors from images and videos.

Examples::

model = MultiModalEmbeddingModel.from_pretrained("multimodalembedding@001")
image = Image.load_from_file("image.png")
video = Video.load_from_file("video.mp4")

embeddings = model.get_embeddings(
    image=image,
    video=video,
    contextual_text="Hello world",
)
image_embedding = embeddings.image_embedding
video_embeddings = embeddings.video_embeddings
text_embedding = embeddings.text_embedding

model = ImageCaptioningModel.from_pretrained("imagetext@001")
image = Image.load_from_file("image.png")
captions = model.get_captions(
    image=image,
    # Optional:
    number_of_results=1,
    language="en",
)

ImageQnAModel

Answers questions about an image.

Examples::

model = ImageQnAModel.from_pretrained("imagetext@001")
image = Image.load_from_file("image.png")
answers = model.ask_question(
    image=image,
    question="What color is the car in this image?",
    # Optional:
    number_of_results=1,
)

ImageTextModel

Generates text from images.

Examples::

model = ImageTextModel.from_pretrained("imagetext@001")
image = Image.load_from_file("image.png")

captions = model.get_captions(
    image=image,
    # Optional:
    number_of_results=1,
    language="en",
)

answers = model.ask_question(
    image=image,
    question="What color is the car in this image?",
    # Optional:
    number_of_results=1,
)

MultiModalEmbeddingModel

Generates embedding vectors from images and videos.

Examples::

model = MultiModalEmbeddingModel.from_pretrained("multimodalembedding@001")
image = Image.load_from_file("image.png")
video = Video.load_from_file("video.mp4")

embeddings = model.get_embeddings(
    image=image,
    video=video,
    contextual_text="Hello world",
)
image_embedding = embeddings.image_embedding
video_embeddings = embeddings.video_embeddings
text_embedding = embeddings.text_embedding