Package Classes (1.48.0)

Metadata Artifact resource for Vertex AI

Class to train AutoML forecasting models.

Constructs a AutoML Image Training Job.

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'}, )

Constructs a AutoML Text Training Job.

Constructs a AutoML Video Training Job.

Retrieves a BatchPredictionJob resource and instantiates its representation.

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

Vertex AI Custom Job.

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.

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.

Retrieves an endpoint resource.

Public managed EntityType resource for Vertex AI.

Metadata Execution resource for Vertex AI

Represents a Vertex AI Experiment resource.

A Vertex AI Experiment run.

Managed feature resource for Vertex AI.

Managed featurestore resource for Vertex AI.

Vertex AI Hyperparameter Tuning Job.

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']
)

Matching Engine index resource for Vertex AI.

Matching Engine index endpoint resource for Vertex AI.

Retrieves the model resource and instantiates its representation.

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.

Retrieves the ModelEvaluation resource and instantiates its representation.

Retrieves a PipelineJob resource and instantiates its representation.

Retrieves a PipelineJobSchedule resource and instantiates its representation.

Represents a Vertex AI PrivateEndpoint resource.

Read more about private endpoints in the documentation.

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

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"
)

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

Managed tensorboard resource for Vertex AI.

Managed tensorboard resource for Vertex AI.

Managed tensorboard resource for Vertex AI.

Managed tensorboard resource for Vertex AI.

Managed text dataset resource for Vertex AI.

Managed time series dataset resource for Vertex AI

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

Managed video dataset resource for Vertex AI.

An artifact representing a Vertex Experiment Model.

Default serializer for serialization and deserialization for prediction.

Interface for Handler class to handle prediction requests.

Class that represents a local endpoint.

Class that represents a local model.

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

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)))

Interface to implement serialization and deserialization for prediction.

Prediction input format for Image Classification.

Prediction input format for Image Object Detection.

Prediction input format for Image Segmentation.

Prediction input format for Text Classification.

Prediction input format for Text Extraction.

Prediction input format for Text Sentiment.

Prediction input format for Video Action Recognition.

Prediction input format for Video Classification.

Prediction input format for Video Object Tracking.

Prediction model parameters for Image Classification.

Prediction model parameters for Image Object Detection.

Prediction model parameters for Image Segmentation.

Prediction model parameters for Video Action Recognition.

Prediction model parameters for Video Classification.

Prediction model parameters for Video Object Tracking.

Prediction output format for Image and Text Classification.

Prediction output format for Image Object Detection.

Prediction output format for Image Segmentation.

Prediction output format for Tabular Classification.

Prediction output format for Tabular Regression.

Prediction output format for Text Extraction.

Prediction output format for Text Sentiment

Prediction output format for Video Action Recognition.

Prediction output format for Video Classification.

Prediction output format for Video Object Tracking.

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.

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

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.

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.

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

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.

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.

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

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.

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.

A TrainingJob that trains and uploads an AutoML Tables Model.

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

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

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

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.

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.

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.

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.

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.

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.

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.

Model metadata specific to AutoML Tables.

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

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

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

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

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

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.

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

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_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.

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

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.

Configuration for exporting test set predictions to a BigQuery table.

Prediction input format for Image Classification.

Prediction input format for Image Object Detection.

Prediction input format for Image Segmentation.

Prediction input format for Text Classification.

Prediction input format for Text Extraction.

Prediction input format for Text Sentiment.

Prediction input format for Video Action Recognition.

Prediction input format for Video Classification.

Prediction input format for Video Object Tracking.

Prediction model parameters for Image Classification.

Prediction model parameters for Image Object Detection.

Prediction model parameters for Image Segmentation.

Prediction model parameters for Video Action Recognition.

Prediction model parameters for Video Classification.

Prediction model parameters for Video Object Tracking.

Prediction output format for Image and Text Classification.

Prediction output format for Image Object Detection.

Prediction output format for Image Segmentation.

Prediction output format for Tabular Classification.

Prediction output format for Tabular Regression.

Prediction output format for Text Extraction.

Prediction output format for Text Sentiment

Prediction output format for Time Series Forecasting.

Prediction output format for Video Action Recognition.

Prediction output format for Video Classification.

Prediction output format for Video Object Tracking.

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.

A TrainingJob that trains and uploads an AutoML Forecasting Model.

A duration of time expressed in time granularity units.

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

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

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.

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.

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.

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.

Model metadata specific to AutoML Forecasting.

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

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.

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.

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

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.

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.

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

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.

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.

A TrainingJob that trains and uploads an AutoML Tables Model.

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

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

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

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.

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.

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.

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.

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.

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.

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.

Model metadata specific to AutoML Tables.

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

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

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

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

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

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.

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

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_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.

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

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.

Configuration for exporting test set predictions to a BigQuery table.

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

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

A service that manages the DeploymentResourcePool resource.

A service that manages the DeploymentResourcePool resource.

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.

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.

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.

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.

A service for managing Vertex AI's Endpoints.

A service for managing Vertex AI's Endpoints.

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.

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.

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

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

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.

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.

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.

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.

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.

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.

A service for fetching feature values from the online store.

A service for fetching feature values from the online store.

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

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

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.

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.

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.

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.

A service for serving online feature values.

A service for serving online feature values.

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

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

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.

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.

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.

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.

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.

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.

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.

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.

A service for creating and managing GenAI Tuning Jobs.

A service for creating and managing GenAI Tuning Jobs.

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.

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.

A service for managing Vertex AI's IndexEndpoints.

A service for managing Vertex AI's IndexEndpoints.

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.

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.

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

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

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.

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.

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

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Service for LLM related utility functions.

Service for LLM related utility functions.

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

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

Service for reading and writing metadata entries.

Service for reading and writing metadata entries.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

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

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.

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.

The interface of Model Garden Service.

The interface of Model Garden Service.

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

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

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.

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.

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.

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.

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.

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.

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.

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.

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

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

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.

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.

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.

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.

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

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

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.

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.

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).

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).

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.

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.

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.

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.

A service for online predictions and explanations.

A service for online predictions and explanations.

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

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

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.

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.

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.

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.

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.

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.

TensorboardService

TensorboardService

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

Request message for MetadataService.AddContextArtifactsAndExecutions.

Response message for MetadataService.AddContextArtifactsAndExecutions.

Request message for MetadataService.AddContextChildren.

Response message for MetadataService.AddContextChildren.

Request message for MetadataService.AddExecutionEvents.

Response message for MetadataService.AddExecutionEvents.

Request message for VizierService.AddTrialMeasurement.

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

The abstract base class for a message.

Identifies a concept with which DataItems may be annotated with.

Instance of a general artifact.

The abstract base class for a message.

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.

Metadata information for NotebookService.AssignNotebookRuntime.

Request message for NotebookService.AssignNotebookRuntime.

Attribution that explains a particular prediction output.

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.

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

The storage details for Avro input content.

Runtime operation information for PipelineService.BatchCancelPipelineJobs.

Request message for PipelineService.BatchCancelPipelineJobs.

Response message for PipelineService.BatchCancelPipelineJobs.

Details of operations that perform batch create Features.

Request message for FeaturestoreService.BatchCreateFeatures.

Response message for FeaturestoreService.BatchCreateFeatures.

Request message for TensorboardService.BatchCreateTensorboardRuns.

Response message for TensorboardService.BatchCreateTensorboardRuns.

Request message for TensorboardService.BatchCreateTensorboardTimeSeries.

Response message for TensorboardService.BatchCreateTensorboardTimeSeries.

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

Request message for PipelineService.BatchDeletePipelineJobs.

Response message for PipelineService.BatchDeletePipelineJobs.

Request message for ModelService.BatchImportEvaluatedAnnotations

Response message for ModelService.BatchImportEvaluatedAnnotations

Request message for ModelService.BatchImportModelEvaluationSlices

Response message for ModelService.BatchImportModelEvaluationSlices

Runtime operation information for MigrationService.BatchMigrateResources.

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

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

Request message for MigrationService.BatchMigrateResources.

Response message for MigrationService.BatchMigrateResources.

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.

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.

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

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

The abstract base class for a message.

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

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

Further describes this job's output. Supplements output_config.

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

Details of operations that batch reads Feature values.

Request message for FeaturestoreService.BatchReadFeatureValues.

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

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

Describe pass-through fields in read_instance source.

Response message for FeaturestoreService.BatchReadFeatureValues.

Request message for TensorboardService.BatchReadTensorboardTimeSeriesData.

Response message for TensorboardService.BatchReadTensorboardTimeSeriesData.

The BigQuery location for the output content.

The BigQuery location for the input content.

Content blob.

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

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

A list of boolean values.

Request message for JobService.CancelBatchPredictionJob.

Request message for JobService.CancelCustomJob.

Request message for JobService.CancelDataLabelingJob.

Request message for JobService.CancelHyperparameterTuningJob.

Request message for JobService.CancelNasJob.

Request message for PipelineService.CancelPipelineJob.

Request message for PipelineService.CancelTrainingPipeline.

Request message for GenAiTuningService.CancelTuningJob.

A response candidate generated from the model.

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

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.

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

Request message for VizierService.CheckTrialEarlyStoppingState.

Response message for VizierService.CheckTrialEarlyStoppingState.

Source attributions for content.

A collection of source attributions for a piece of content.

Request message for VizierService.CompleteTrial.

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

Request message for ComputeTokens RPC call.

Response message for ComputeTokens RPC call.

The Container Registry location for the container image.

The spec of a Container.

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.

Instance of a general context.

The abstract base class for a message.

Details of ModelService.CopyModel operation.

Request message for ModelService.CopyModel.

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

Response message of ModelService.CopyModel operation.

Request message for [PredictionService.CountTokens][].

Response message for [PredictionService.CountTokens][].

Request message for MetadataService.CreateArtifact.

Request message for JobService.CreateBatchPredictionJob.

Request message for MetadataService.CreateContext.

Request message for JobService.CreateCustomJob.

Request message for JobService.CreateDataLabelingJob.

Runtime operation information for DatasetService.CreateDataset.

Request message for DatasetService.CreateDataset.

Runtime operation information for DatasetService.CreateDatasetVersion.

Request message for DatasetService.CreateDatasetVersion.

Runtime operation information for CreateDeploymentResourcePool method.

Request message for CreateDeploymentResourcePool method.

Runtime operation information for EndpointService.CreateEndpoint.

Request message for EndpointService.CreateEndpoint.

Details of operations that perform create EntityType.

Request message for FeaturestoreService.CreateEntityType.

Request message for MetadataService.CreateExecution.

Details of operations that perform create FeatureGroup.

Request message for FeatureRegistryService.CreateFeatureGroup.

Details of operations that perform create FeatureOnlineStore.

Request message for FeatureOnlineStoreAdminService.CreateFeatureOnlineStore.

Details of operations that perform create Feature.

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

Details of operations that perform create FeatureView.

Request message for FeatureOnlineStoreAdminService.CreateFeatureView.

Details of operations that perform create Featurestore.

Request message for FeaturestoreService.CreateFeaturestore.

Request message for JobService.CreateHyperparameterTuningJob.

Runtime operation information for IndexEndpointService.CreateIndexEndpoint.

Request message for IndexEndpointService.CreateIndexEndpoint.

Runtime operation information for IndexService.CreateIndex.

Request message for IndexService.CreateIndex.

Request message for MetadataService.CreateMetadataSchema.

Details of operations that perform MetadataService.CreateMetadataStore.

Request message for MetadataService.CreateMetadataStore.

Request message for JobService.CreateModelDeploymentMonitoringJob.

Request message for JobService.CreateNasJob.

Metadata information for NotebookService.CreateNotebookRuntimeTemplate.

Request message for NotebookService.CreateNotebookRuntimeTemplate.

Details of operations that perform create PersistentResource.

Request message for PersistentResourceService.CreatePersistentResource.

Request message for PipelineService.CreatePipelineJob.

Details of operations that perform create FeatureGroup.

Request message for ScheduleService.CreateSchedule.

Runtime operation information for SpecialistPoolService.CreateSpecialistPool.

Request message for SpecialistPoolService.CreateSpecialistPool.

Request message for VizierService.CreateStudy.

Request message for TensorboardService.CreateTensorboardExperiment.

Details of operations that perform create Tensorboard.

Request message for TensorboardService.CreateTensorboard.

Request message for TensorboardService.CreateTensorboardRun.

Request message for TensorboardService.CreateTensorboardTimeSeries.

Request message for PipelineService.CreateTrainingPipeline.

Request message for VizierService.CreateTrial.

Request message for GenAiTuningService.CreateTuningJob.

The storage details for CSV output content.

The storage details for CSV input content.

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).

The abstract base class for a message.

The abstract base class for a message.

Represents the spec of a CustomJob.

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

The abstract base class for a message.

A container for a single DataItem and Annotations on it.

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

The abstract base class for a message.

The abstract base class for a message.

A collection of DataItems and Annotations on them.

The abstract base class for a message.

Describes the dataset version.

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

Request message for MetadataService.DeleteArtifact.

Request message for JobService.DeleteBatchPredictionJob.

Request message for MetadataService.DeleteContext.

Request message for JobService.DeleteCustomJob.

Request message for JobService.DeleteDataLabelingJob.

Request message for DatasetService.DeleteDataset.

Request message for DatasetService.DeleteDatasetVersion.

Request message for DeleteDeploymentResourcePool method.

Request message for EndpointService.DeleteEndpoint.

Request message for [FeaturestoreService.DeleteEntityTypes][].

Request message for MetadataService.DeleteExecution.

Request message for FeatureRegistryService.DeleteFeatureGroup.

Request message for FeatureOnlineStoreAdminService.DeleteFeatureOnlineStore.

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

Details of operations that delete Feature values.

Request message for FeaturestoreService.DeleteFeatureValues.

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

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.

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.

Response message for FeaturestoreService.DeleteFeatureValues.

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

Response message if the request uses the SelectEntity option.

Response message if the request uses the SelectTimeRangeAndFeature option.

Request message for [FeatureOnlineStoreAdminService.DeleteFeatureViews][].

Request message for FeaturestoreService.DeleteFeaturestore.

Request message for JobService.DeleteHyperparameterTuningJob.

Request message for IndexEndpointService.DeleteIndexEndpoint.

Request message for IndexService.DeleteIndex.

Details of operations that perform MetadataService.DeleteMetadataStore.

Request message for MetadataService.DeleteMetadataStore.

Request message for JobService.DeleteModelDeploymentMonitoringJob.

Request message for ModelService.DeleteModel.

Request message for ModelService.DeleteModelVersion.

Request message for JobService.DeleteNasJob.

Request message for NotebookService.DeleteNotebookRuntime.

Request message for NotebookService.DeleteNotebookRuntimeTemplate.

Details of operations that perform deletes of any entities.

Request message for PersistentResourceService.DeletePersistentResource.

Request message for PipelineService.DeletePipelineJob.

Request message for DatasetService.DeleteSavedQuery.

Request message for ScheduleService.DeleteSchedule.

Request message for SpecialistPoolService.DeleteSpecialistPool.

Request message for VizierService.DeleteStudy.

Request message for TensorboardService.DeleteTensorboardExperiment.

Request message for TensorboardService.DeleteTensorboard.

Request message for TensorboardService.DeleteTensorboardRun.

Request message for TensorboardService.DeleteTensorboardTimeSeries.

Request message for PipelineService.DeleteTrainingPipeline.

Request message for VizierService.DeleteTrial.

Runtime operation information for IndexEndpointService.DeployIndex.

Request message for IndexEndpointService.DeployIndex.

Response message for IndexEndpointService.DeployIndex.

Runtime operation information for EndpointService.DeployModel.

Request message for EndpointService.DeployModel.

The abstract base class for a message.

Response message for EndpointService.DeployModel.

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

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

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

Points to a DeployedIndex.

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

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

Points to a DeployedModel.

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

Request message for PredictionService.DirectPredict.

Response message for PredictionService.DirectPredict.

Request message for PredictionService.DirectRawPredict.

Response message for PredictionService.DirectRawPredict.

Represents the spec of disk options.

A list of double values.

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

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

The abstract base class for a message.

The abstract base class for a message.

Selector for entityId. Getting ids from the given source.

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

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.

The abstract base class for a message.

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

Model error analysis for each annotation.

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

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.

True positive, false positive, or false negative.

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

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.

Explanation result of the prediction produced by the Model.

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

The abstract base class for a message.

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.

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

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

The Cloud Storage input instances.

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.

Overrides for example-based explanations.

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.

Restrictions namespace for example-based explanations overrides.

Instance of a general execution.

The abstract base class for a message.

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.

Request message for PredictionService.Explain.

Response message for PredictionService.Explain.

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

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

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.

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]]

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 configurations for image explanation.

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.

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.

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 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.

The abstract base class for a message.

Metadata of the prediction output to be explained.

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

The abstract base class for a message.

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

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

The abstract base class for a message.

Parameters to configure explaining for Model's predictions.

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

Specification of Model explanation.

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

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

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

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.

Runtime operation information for DatasetService.ExportData.

Request message for DatasetService.ExportData.

Response message for DatasetService.ExportData.

Details of operations that exports Features values.

Request message for FeaturestoreService.ExportFeatureValues.

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

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

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

Response message for FeaturestoreService.ExportFeatureValues.

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.

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.

Details of ModelService.ExportModel operation.

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

Request message for ModelService.ExportModel.

Output configuration for the Model export.

Response message of ModelService.ExportModel operation.

Request message for TensorboardService.ExportTensorboardTimeSeriesData.

Response message for TensorboardService.ExportTensorboardTimeSeriesData.

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

The abstract base class for a message.

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

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.

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.

Vertex AI Feature Group.

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

Input source type for BigQuery Tables and Views.

The abstract base class for a message.

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.

Noise sigma for a single feature.

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.

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

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

The abstract base class for a message.

Optimized storage type

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.

Selector for Features of an EntityType.

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.

Value for a feature.

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

Metadata of feature value.

A destination location for Feature values and format.

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

Container for list of values.

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

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

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

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

Configuration for vector indexing.

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

Configuration options for using brute force search.

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.

Configuration options for the tree-AH algorithm.

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

The abstract base class for a message.

Configuration for Sync. Only one option is set.

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.

Lookup key for a feature view.

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

ID that is comprised from several parts (columns).

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

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

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.

The abstract base class for a message.

OnlineServingConfig specifies the details for provisioning online serving resources.

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).

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.

Configuration of how features in Featurestore are monitored.

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.

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.

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.

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.

The config for Featurestore Monitoring threshold.

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

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

Response message for FeatureOnlineStoreService.FetchFeatureValues

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

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

Feature name & value pair.

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

URI based data.

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.

The request message for MatchService.FindNeighbors.

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

The response message for MatchService.FindNeighbors.

Nearest neighbors for one query.

A neighbor of the query vector.

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.

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.

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.

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.

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

The Google Cloud Storage location for the input content.

Request message for [PredictionService.GenerateContent].

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

Response message for [PredictionService.GenerateContent].

Content filter results for a prompt sent in the request.

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.

Usage metadata about response(s).

Generation config.

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

Generic Metadata shared by all operations.

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

Request message for DatasetService.GetAnnotationSpec.

Request message for MetadataService.GetArtifact.

Request message for JobService.GetBatchPredictionJob.

Request message for MetadataService.GetContext.

Request message for JobService.GetCustomJob.

Request message for JobService.GetDataLabelingJob.

Request message for DatasetService.GetDataset.

Request message for DatasetService.GetDatasetVersion.

Request message for GetDeploymentResourcePool method.

Request message for EndpointService.GetEndpoint

Request message for FeaturestoreService.GetEntityType.

Request message for MetadataService.GetExecution.

Request message for FeatureRegistryService.GetFeatureGroup.

Request message for FeatureOnlineStoreAdminService.GetFeatureOnlineStore.

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

Request message for FeatureOnlineStoreAdminService.GetFeatureView.

Request message for FeatureOnlineStoreAdminService.GetFeatureViewSync.

Request message for FeaturestoreService.GetFeaturestore.

Request message for JobService.GetHyperparameterTuningJob.

Request message for IndexEndpointService.GetIndexEndpoint

Request message for IndexService.GetIndex

Request message for MetadataService.GetMetadataSchema.

Request message for MetadataService.GetMetadataStore.

Request message for JobService.GetModelDeploymentMonitoringJob.

Request message for ModelService.GetModelEvaluation.

Request message for ModelService.GetModelEvaluationSlice.

Request message for ModelService.GetModel.

Request message for JobService.GetNasJob.

Request message for JobService.GetNasTrialDetail.

Request message for NotebookService.GetNotebookRuntime

Request message for NotebookService.GetNotebookRuntimeTemplate

Request message for PersistentResourceService.GetPersistentResource.

Request message for PipelineService.GetPipelineJob.

Request message for ModelGardenService.GetPublisherModel

Request message for ScheduleService.GetSchedule.

Request message for SpecialistPoolService.GetSpecialistPool.

Request message for VizierService.GetStudy.

Request message for TensorboardService.GetTensorboardExperiment.

Request message for TensorboardService.GetTensorboard.

Request message for TensorboardService.GetTensorboardRun.

Request message for TensorboardService.GetTensorboardTimeSeries.

Request message for PipelineService.GetTrainingPipeline.

Request message for VizierService.GetTrial.

Request message for GenAiTuningService.GetTuningJob.

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

Grounding attribution.

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

Attribution from the web.

Metadata returned to client when grounding is enabled.

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.

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

The abstract base class for a message.

Matcher for Features of an EntityType by Feature ID.

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

The abstract base class for a message.

The abstract base class for a message.

Runtime operation information for DatasetService.ImportData.

Request message for DatasetService.ImportData.

Response message for DatasetService.ImportData.

Details of operations that perform import Feature values.

Request message for FeaturestoreService.ImportFeatureValues.

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

Defines the Feature value(s) to import.

Response message for FeaturestoreService.ImportFeatureValues.

Request message for ModelService.ImportModelEvaluation

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

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.

The abstract base class for a message.

A datapoint of Index.

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.

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

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

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

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

The abstract base class for a message.

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.

Stats of the Index.

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

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

A list of int64 values.

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

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.

Contains information about the Large Model.

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

Request message for DatasetService.ListAnnotations.

Response message for DatasetService.ListAnnotations.

Request message for MetadataService.ListArtifacts.

Response message for MetadataService.ListArtifacts.

Request message for JobService.ListBatchPredictionJobs.

Response message for JobService.ListBatchPredictionJobs

Request message for MetadataService.ListContexts

Response message for MetadataService.ListContexts.

Request message for JobService.ListCustomJobs.

Response message for JobService.ListCustomJobs

Request message for DatasetService.ListDataItems.

Response message for DatasetService.ListDataItems.

Request message for JobService.ListDataLabelingJobs.

Response message for JobService.ListDataLabelingJobs.

Request message for DatasetService.ListDatasetVersions.

Response message for DatasetService.ListDatasetVersions.

Request message for DatasetService.ListDatasets.

Response message for DatasetService.ListDatasets.

Request message for ListDeploymentResourcePools method.

Response message for ListDeploymentResourcePools method.

Request message for EndpointService.ListEndpoints.

Response message for EndpointService.ListEndpoints.

Request message for FeaturestoreService.ListEntityTypes.

Response message for FeaturestoreService.ListEntityTypes.

Request message for MetadataService.ListExecutions.

Response message for MetadataService.ListExecutions.

Request message for FeatureRegistryService.ListFeatureGroups.

Response message for FeatureRegistryService.ListFeatureGroups.

Request message for FeatureOnlineStoreAdminService.ListFeatureOnlineStores.

Response message for FeatureOnlineStoreAdminService.ListFeatureOnlineStores.

Request message for FeatureOnlineStoreAdminService.ListFeatureViewSyncs.

Response message for FeatureOnlineStoreAdminService.ListFeatureViewSyncs.

Request message for FeatureOnlineStoreAdminService.ListFeatureViews.

Response message for FeatureOnlineStoreAdminService.ListFeatureViews.

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

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

Request message for FeaturestoreService.ListFeaturestores.

Response message for FeaturestoreService.ListFeaturestores.

Request message for JobService.ListHyperparameterTuningJobs.

Response message for JobService.ListHyperparameterTuningJobs

Request message for IndexEndpointService.ListIndexEndpoints.

Response message for IndexEndpointService.ListIndexEndpoints.

Request message for IndexService.ListIndexes.

Response message for IndexService.ListIndexes.

Request message for MetadataService.ListMetadataSchemas.

Response message for MetadataService.ListMetadataSchemas.

Request message for MetadataService.ListMetadataStores.

Response message for MetadataService.ListMetadataStores.

Request message for JobService.ListModelDeploymentMonitoringJobs.

Response message for JobService.ListModelDeploymentMonitoringJobs.

Request message for ModelService.ListModelEvaluationSlices.

Response message for ModelService.ListModelEvaluationSlices.

Request message for ModelService.ListModelEvaluations.

Response message for ModelService.ListModelEvaluations.

Request message for ModelService.ListModelVersions.

Response message for ModelService.ListModelVersions

Request message for ModelService.ListModels.

Response message for ModelService.ListModels

Request message for JobService.ListNasJobs.

Response message for JobService.ListNasJobs

Request message for JobService.ListNasTrialDetails.

Response message for JobService.ListNasTrialDetails

Request message for NotebookService.ListNotebookRuntimeTemplates.

Response message for NotebookService.ListNotebookRuntimeTemplates.

Request message for NotebookService.ListNotebookRuntimes.

Response message for NotebookService.ListNotebookRuntimes.

Request message for VizierService.ListOptimalTrials.

Response message for VizierService.ListOptimalTrials.

Request message for [PersistentResourceService.ListPersistentResource][].

Response message for PersistentResourceService.ListPersistentResources

Request message for PipelineService.ListPipelineJobs.

Response message for PipelineService.ListPipelineJobs

Request message for DatasetService.ListSavedQueries.

Response message for DatasetService.ListSavedQueries.

Request message for ScheduleService.ListSchedules.

Response message for ScheduleService.ListSchedules

Request message for SpecialistPoolService.ListSpecialistPools.

Response message for SpecialistPoolService.ListSpecialistPools.

Request message for VizierService.ListStudies.

Response message for VizierService.ListStudies.

Request message for TensorboardService.ListTensorboardExperiments.

Response message for TensorboardService.ListTensorboardExperiments.

Request message for TensorboardService.ListTensorboardRuns.

Response message for TensorboardService.ListTensorboardRuns.

Request message for TensorboardService.ListTensorboardTimeSeries.

Response message for TensorboardService.ListTensorboardTimeSeries.

Request message for TensorboardService.ListTensorboards.

Response message for TensorboardService.ListTensorboards.

Request message for PipelineService.ListTrainingPipelines.

Response message for PipelineService.ListTrainingPipelines

Request message for VizierService.ListTrials.

Response message for VizierService.ListTrials.

Request message for GenAiTuningService.ListTuningJobs.

Response message for GenAiTuningService.ListTuningJobs

Request message for VizierService.LookupStudy.

Specification of a single machine.

Manual batch tuning parameters.

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

A message representing a metric in the measurement.

Request message for ModelService.MergeVersionAliases.

Instance of a general MetadataSchema.

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.

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

Represents state information for a MetadataStore.

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

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

Represents one Dataset in automl.googleapis.com.

Represents one Model in automl.googleapis.com.

Represents one Dataset in datalabeling.googleapis.com.

Represents one AnnotatedDataset in datalabeling.googleapis.com.

Represents one model Version in ml.googleapis.com.

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

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

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

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

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

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

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

Describes a successfully migrated resource.

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

A trained machine learning Model.

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

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

Stats of data used for train or evaluate the Model.

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.

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

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.

The abstract base class for a message.

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

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

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.

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.

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.

The abstract base class for a message.

All metadata of most recent monitoring pipelines.

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 contains the pair of deployed_model_id to ModelMonitoringObjectiveConfig.

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.

The config for scheduling monitoring job.

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

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

Definition of a slice.

Specification for how the data should be sliced.

The abstract base class for a message.

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

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.

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

Single value that supports strings and floats.

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

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

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

The alert config for model monitoring.

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

The config for email alert.

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

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

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

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

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.

The config for Prediction data drift detection.

The abstract base class for a message.

The abstract base class for a message.

Training Dataset information.

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

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

The abstract base class for a message.

The abstract base class for a message.

Statistics and anomalies generated by Model Monitoring.

Historical Stats (and Anomalies) for a specific Feature.

Detail description of the source information of the model.

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.

Runtime operation information for IndexEndpointService.MutateDeployedIndex.

Request message for IndexEndpointService.MutateDeployedIndex.

Response message for IndexEndpointService.MutateDeployedIndex.

Runtime operation information for EndpointService.MutateDeployedModel.

Request message for EndpointService.MutateDeployedModel.

Response message for EndpointService.MutateDeployedModel.

Represents a Neural Architecture Search (NAS) job.

The abstract base class for a message.

Represents a uCAIP NasJob output.

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

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

Represents the spec of a NasJob.

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

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

Represents a metric to optimize.

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.

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).

Represent spec for search trials.

Represent spec for train trials.

Represents a uCAIP NasJob trial.

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.

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

A query to find a number of similar entities.

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

The embedding vector.

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

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.

Runtime operation metadata with regard to Matching Engine Index.

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.

Nearest neighbors for one query.

A neighbor of the query vector.

Neighbors for example-based explanations.

Network spec.

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

The euc configuration of NotebookRuntimeTemplate.

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

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.

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.

The abstract base class for a message.

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.

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

The abstract base class for a message.

Points to a NotebookRuntimeTemplateRef.

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.

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.

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

Request message for JobService.PauseModelDeploymentMonitoringJob.

Request message for ScheduleService.PauseSchedule.

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

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.

The abstract base class for a message.

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.

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.

An instance of a machine learning PipelineJob.

The abstract base class for a message.

The runtime config of a PipelineJob.

The type of an input artifact.

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

The abstract base class for a message.

The abstract base class for a message.

The abstract base class for a message.

The runtime detail of PipelineJob.

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.

The runtime detail of a task execution.

A list of artifact metadata.

The abstract base class for a message.

The abstract base class for a message.

A single record of the task status.

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.

The runtime detail of a pipeline executor.

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

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

The detailed info for a custom job executor.

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

Represents a network port in a container.

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

Supported only for tabular Datasets.

Request message for PredictionService.Predict.

Configuration for logging request-response to a BigQuery table.

Response message for PredictionService.Predict.

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

Preset configuration for example-based explanations

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

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

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 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.

Represents configuration for private service connect.

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 specifies a command to execute.

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

A Model Garden Publisher Model.

Actions could take on this Publisher Model.

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

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

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

Configurations for PublisherModel GKE deployment

Open fine tuning pipelines.

Open notebooks.

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

The abstract base class for a message.

Rest API docs.

A named piece of documentation.

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.

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.

Reference to a resource.

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

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.

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.

Details of operations that perform MetadataService.PurgeArtifacts.

Request message for MetadataService.PurgeArtifacts.

Response message for MetadataService.PurgeArtifacts.

Details of operations that perform MetadataService.PurgeContexts.

Request message for MetadataService.PurgeContexts.

Response message for MetadataService.PurgeContexts.

Details of operations that perform MetadataService.PurgeExecutions.

Request message for MetadataService.PurgeExecutions.

Response message for MetadataService.PurgeExecutions.

The spec of a Python packaged code.

Request message for MetadataService.QueryArtifactLineageSubgraph.

Request message for MetadataService.QueryContextLineageSubgraph.

Request message for QueryDeployedModels method.

Response message for QueryDeployedModels method.

Request message for MetadataService.QueryExecutionInputsAndOutputs.

Request message for PredictionService.RawPredict.

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.

Request message for FeaturestoreOnlineServingService.ReadFeatureValues.

Response message for FeaturestoreOnlineServingService.ReadFeatureValues.

Entity view with Feature values.

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

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

Metadata for requested Features.

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

The request message for MatchService.ReadIndexDatapoints.

The response message for MatchService.ReadIndexDatapoints.

Request message for TensorboardService.ReadTensorboardBlobData.

Response message for TensorboardService.ReadTensorboardBlobData.

Request message for TensorboardService.ReadTensorboardSize.

Response message for TensorboardService.ReadTensorboardSize.

Request message for TensorboardService.ReadTensorboardTimeSeriesData.

Response message for TensorboardService.ReadTensorboardTimeSeriesData.

Request message for TensorboardService.ReadTensorboardUsage.

Response message for TensorboardService.ReadTensorboardUsage.

The abstract base class for a message.

Per month usage data

Per user usage data.

Details of operations that perform reboot PersistentResource.

Request message for PersistentResourceService.RebootPersistentResource.

Request message for [MetadataService.DeleteContextChildrenRequest][].

Response message for MetadataService.RemoveContextChildren.

Request message for IndexService.RemoveDatapoints

Response message for IndexService.RemoveDatapoints

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

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

Persistent Cluster runtime information as output

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.

Statistics information about resource consumption.

Runtime operation information for DatasetService.RestoreDatasetVersion.

Request message for DatasetService.RestoreDatasetVersion.

Request message for JobService.ResumeModelDeploymentMonitoringJob.

Request message for ScheduleService.ResumeSchedule.

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

Safety rating corresponding to the generated content.

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.

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.

Safety settings.

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.

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.

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

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.

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.

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

Requests are randomly selected.

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

One point viewable on a scalar metric plot.

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

Status of a scheduled run.

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.

All parameters related to queuing and scheduling of custom jobs.

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.

The abstract base class for a message.

Request message for DatasetService.SearchDataItems.

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

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

Response message for DatasetService.SearchDataItems.

Request message for FeaturestoreService.SearchFeatures.

Response message for FeaturestoreService.SearchFeatures.

Request message for MigrationService.SearchMigratableResources.

Response message for MigrationService.SearchMigratableResources.

Request message for JobService.SearchModelDeploymentMonitoringStatsAnomalies.

Stats requested for specific objective.

Response message for JobService.SearchModelDeploymentMonitoringStatsAnomalies.

The request message for FeatureOnlineStoreService.SearchNearestEntities.

Response message for FeatureOnlineStoreService.SearchNearestEntities

Segment of the content.

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

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

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

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

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.

Metadata information for NotebookService.StartNotebookRuntime.

Request message for NotebookService.StartNotebookRuntime.

Response message for NotebookService.StartNotebookRuntime.

Request message for VizierService.StopTrial.

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.

Request message for PredictionService.StreamDirectPredict.

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

Response message for PredictionService.StreamDirectPredict.

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.

Response message for PredictionService.StreamDirectRawPredict.

Request message for PredictionService.StreamRawPredict.

Request message for PredictionService.StreamingPredict.

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

Response message for PredictionService.StreamingPredict.

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.

Response message for PredictionService.StreamingRawPredict.

Request message for [FeaturestoreOnlineServingService.StreamingFeatureValuesRead][].

A list of string values.

A message representing a Study.

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.

Represents specification of a Study.

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

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.

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

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.

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.

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.

Represents a metric to optimize.

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

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.

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

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.

Represents a single parameter to optimize.

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

Value specification for a parameter in CATEGORICAL type.

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

Represents a parameter spec with condition from its parent parameter.

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

Represents the spec to match categorical values from parent parameter.

Represents the spec to match discrete values from parent parameter.

Represents the spec to match integer values from parent parameter.

Value specification for a parameter in DISCRETE type.

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

Value specification for a parameter in DOUBLE type.

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

Value specification for a parameter in INTEGER type.

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

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.

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

Time-based Constraint for Study

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

Details of operations that perform Trials suggestion.

Request message for VizierService.SuggestTrials.

Response message for VizierService.SuggestTrials.

Hyperparameters for SFT.

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.

Tuning data statistics for Supervised Tuning.

Dataset distribution for Supervised Tuning.

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

Tuning Spec for Supervised Tuning.

Request message for FeatureOnlineStoreAdminService.SyncFeatureView.

Respose message for FeatureOnlineStoreAdminService.SyncFeatureView.

The storage details for TFRecord output content.

A tensor value type.

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.

The abstract base class for a message.

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.

The abstract base class for a message.

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

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.

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

The abstract base class for a message.

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

The abstract base class for a message.

One point viewable on a tensor metric plot.

TensorboardTimeSeries maps to times series produced in training runs

Describes metadata for a TensorboardTimeSeries.

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.

The config for feature monitoring threshold.

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

All the data stored in a TensorboardTimeSeries.

A TensorboardTimeSeries data point.

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

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.

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

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).

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.

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.

The abstract base class for a message.

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.

A message representing a parameter to be tuned.

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.

The abstract base class for a message.

Next ID: 3

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

The tuning data statistic values for TuningJob.

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

Represents a TuningJob that runs with Google owned models.

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

The abstract base class for a message.

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

Runtime operation information for IndexEndpointService.UndeployIndex.

Request message for IndexEndpointService.UndeployIndex.

Response message for IndexEndpointService.UndeployIndex.

Runtime operation information for EndpointService.UndeployModel.

Request message for EndpointService.UndeployModel.

The abstract base class for a message.

Response message for EndpointService.UndeployModel.

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

Request message for MetadataService.UpdateArtifact.

Request message for MetadataService.UpdateContext.

Request message for DatasetService.UpdateDataset.

Runtime operation information for UpdateDeploymentResourcePool method.

Request message for EndpointService.UpdateEndpoint.

Request message for FeaturestoreService.UpdateEntityType.

Request message for MetadataService.UpdateExecution.

Runtime operation information for ModelService.UpdateExplanationDataset.

Request message for ModelService.UpdateExplanationDataset.

Response message of ModelService.UpdateExplanationDataset operation.

Details of operations that perform update FeatureGroup.

Request message for FeatureRegistryService.UpdateFeatureGroup.

Details of operations that perform update FeatureOnlineStore.

Request message for FeatureOnlineStoreAdminService.UpdateFeatureOnlineStore.

Details of operations that perform update Feature.

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

Details of operations that perform update FeatureView.

Request message for FeatureOnlineStoreAdminService.UpdateFeatureView.

Details of operations that perform update Featurestore.

Request message for FeaturestoreService.UpdateFeaturestore.

Request message for IndexEndpointService.UpdateIndexEndpoint.

Runtime operation information for IndexService.UpdateIndex.

Request message for IndexService.UpdateIndex.

Runtime operation information for JobService.UpdateModelDeploymentMonitoringJob.

Request message for JobService.UpdateModelDeploymentMonitoringJob.

Request message for ModelService.UpdateModel.

Details of operations that perform update PersistentResource.

Request message for UpdatePersistentResource method.

Request message for ScheduleService.UpdateSchedule.

Runtime operation metadata for SpecialistPoolService.UpdateSpecialistPool.

Request message for SpecialistPoolService.UpdateSpecialistPool.

Request message for TensorboardService.UpdateTensorboardExperiment.

Details of operations that perform update Tensorboard.

Request message for TensorboardService.UpdateTensorboard.

Request message for TensorboardService.UpdateTensorboardRun.

Request message for TensorboardService.UpdateTensorboardTimeSeries.

Metadata information for NotebookService.UpgradeNotebookRuntime.

Request message for NotebookService.UpgradeNotebookRuntime.

Response message for NotebookService.UpgradeNotebookRuntime.

Details of ModelService.UploadModel operation.

Request message for ModelService.UploadModel.

Response message of ModelService.UploadModel operation.

Request message for IndexService.UpsertDatapoints

Response message for IndexService.UpsertDatapoints

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

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

Value is the value of the field.

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

Retrieve from Vertex AI Search datastore for grounding. See https://cloud.google.com/vertex-ai-search-and-conversation

Metadata describes the input video content.

Represents the spec of a worker pool in a job.

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

Contains Feature values to be written for a specific entity.

The abstract base class for a message.

Request message for FeaturestoreOnlineServingService.WriteFeatureValues.

Response message for FeaturestoreOnlineServingService.WriteFeatureValues.

Request message for TensorboardService.WriteTensorboardExperimentData.

Response message for TensorboardService.WriteTensorboardExperimentData.

Request message for TensorboardService.WriteTensorboardRunData.

Response message for TensorboardService.WriteTensorboardRunData.

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.

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

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

A service that manages the DeploymentResourcePool resource.

A service that manages the DeploymentResourcePool resource.

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.

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.

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.

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.

A service for managing Vertex AI's Endpoints.

A service for managing Vertex AI's Endpoints.

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.

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.

Vertex AI Online Evaluation Service.

Vertex AI Online Evaluation Service.

A service for Extension execution.

A service for Extension execution.

A service for managing Vertex AI's Extension registry.

A service for managing Vertex AI's Extension registry.

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.

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.

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

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

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.

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.