Metadata Artifact resource for Vertex AI

Class to train AutoML forecasting models.

TheAutoMLForecastingTrainingJob class uses the AutoML training methodto train and run a forecasting model. TheAutoML training method is a goodchoice for most forecasting use cases. If your use case doesn't benefit fromtheSeq2seq or theTemporal fusion transformer training method offeredby theSequenceToSequencePlusForecastingTrainingJoband[TemporalFusionTransformerForecastingTrainingJob]https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform.TemporalFusionTransformerForecastingTrainingJob)classes respectively, thenAutoML is likely the best training method foryour forecasting predictions.

For sample code that shows you how to useAutoMLForecastingTrainingJob seetheCreate a training pipeline forecasting sampleon GitHub.

Creates an AutoML image training job.

Use theAutoMLImageTrainingJob class to create, train, and return animage model. For more information about working with image data modelsin Vertex AI, seeImage data.

For an example of how to use theAutoMLImageTrainingJob class, see thetutorial in theAutoML imageclassificationnotebook on GitHub.

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 itsrepresentation.

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

Vertex AI Custom Job.

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

Use theCustomPythonPackageTrainingJob class to use a Python package tolaunch a custom training pipeline in Vertex AI. For an example of how to usetheCustomPythonPackageTrainingJob class, see the tutorial in theCustomtraining using Python package, managed text dataset, and TensorFlow servingcontainernotebook.

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

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

Retrieves a DeploymentResourcePool.

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 managedimage dataset, you need a datasource file in CSV format and a schema file inYAML format. A schema is optional for a custom model. You put the CSV fileand the schema into Cloud Storage buckets.

Use image data for the following objectives:

• Single-label classification. For more information, seePrepare image training data for single-label classification.
• Multi-label classification. For more information, seePrepare image training data for multi-label classification.
• Object detection. For more information, seePrepare image training datafor object detection.

The following code shows you how to create an image dataset by importing data froma CSV datasource file and a YAML schema file. The schema file you usedepends on whether your image dataset is used for single-labelclassification, 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 classin order to configure model monitoring for deployed models.

Retrieves the ModelEvaluation resource and instantiates its representation.

Retrieves a PipelineJob resource and instantiates itsrepresentation.

Retrieves a PipelineJobSchedule resource and instantiates itsrepresentation.

Represents a Vertex AI PrivateEndpoint resource.

Read moreabout private endpoints in the documentation.

Represents a Vertex AI PrivateServiceConnectConfig resource.

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

TheSequenceToSequencePlusForecastingTrainingJob class uses theSeq2seq+training method to train and run a forecasting model. TheSeq2seq+training method is a good choice for experimentation. Its algorithm issimpler and uses a smaller search space than theAutoML option.Seq2seq+is a good option if you want fast results and your datasets are smaller than1 GB.

For sample code that shows you how to useSequenceToSequencePlusForecastingTrainingJob, see theCreate a trainingpipeline forecasting Seq2seqsampleon GitHub.

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 pandasDataFrameto create a tabular dataset. For more information about paging throughBigQuery data, seeRead data with BigQuery API usingpagination. For moreinformation about tabular data, seeTabulardata.

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

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

Contrary to unstructured datasets, creating and importing a tabular datasetcan only be done in a single step.

If you create a tabular dataset with a pandasDataFrame,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.

TheTemporalFusionTransformerForecastingTrainingJob class uses theTemporal Fusion Transformer (TFT) training method to train and run aforecasting model. The TFT training method implements an attention-baseddeep neural network (DNN) model that uses a multi-horizon forecasting taskto produce predictions.

For sample code that shows you how to use`TemporalFusionTransformerForecastingTrainingJob, see theCreate a training pipeline forecasting temporal fusion transformersampleon GitHub.

Managed tensorboard resource for Vertex AI.

Managed tensorboard resource for Vertex AI.

Managed tensorboard resource for Vertex AI.

Managed tensorboard resource for Vertex AI.

A managed text dataset resource for Vertex AI.

Use this class to work with a managed text dataset. To create a managedtext dataset, you need a datasource file in CSV format and a schema file inYAML format. A schema is optional for a custom model. The CSV file and theschema are accessed in Cloud Storage buckets.

Use text data for the following objectives:

• Classification. For more information, seePrepare text training data for classification.
• Entity extraction. For more information, seePrepare text training data for entity extraction.
• Sentiment analysis. For more information, see[Prepare text training data for sentiment analysis](Prepare text training data for sentiment analysis).

The following code shows you how to create and import a text dataset witha CSV datasource file and a YAML schema file. The schema file you usedepends on whether your text dataset is used for single-labelclassification, multi-label classification, or object detection.

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

A managed time series dataset resource for Vertex AI.

Use this class to work with time series datasets. A time series is a datasetthat contains data recorded at different time intervals. The datasetincludes time and at least one variable that's dependent on time. You use atime series dataset for forecasting predictions. For more information, seeForecasting overview.

You can create a managed time series dataset from CSV files in a CloudStorage bucket or from a BigQuery table.

The following code shows you how to create aTimeSeriesDataset with a CSVfile that has the time series dataset:

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

The following code shows you how to create with aTimeSeriesDataset with aBigQuery table file that has the time series dataset:

my_dataset = aiplatform.TimeSeriesDataset.create(    display_name="my-dataset",    bq_source=['bq://path/to/my/bigquerydataset.train'],)

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

TheTimeSeriesDenseEncoderForecastingTrainingJob class uses theTime-series Dense Encoder (TiDE) training method to train and run aforecasting model. TiDE uses amulti-layer perceptron (MLP) to providethe speed of forecasting linear models with covariates and non-lineardependencies. For more information about TiDE, seeRecent advances in deep long-horizon forecastingand thisTiDE blog post.

A managed video dataset resource for Vertex AI.

Use this class to work with a managed video dataset. To create a videodataset, you need a datasource in CSV format and a schema in YAML format.The CSV file and the schema are accessed in Cloud Storage buckets.

Use video data for the following objectives:

Classification. For more information, see Classification schema files.Action recognition. For more information, see Action recognition schemafiles. Object tracking. For more information, see Object tracking schemafiles. The following code shows you how to create and import a dataset totrain a video classification model. The schema file you use depends onwhether you use your video dataset for action classification, recognition,or object tracking.

my_dataset = aiplatform.VideoDataset.create(    gcs_source=['gs://path/to/my/dataset.csv'],    import_schema_uri=['gs://aip.schema.dataset.ioformat.video.classification.yaml'])

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 defaultPredictionHandler, thePredictor will be invoked asfollows:

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 fieldsxMin,xMax,yMin, andyMax refer to abounding box, i.e. the rectangle over the video frame pinpointingthe found AnnotationSpec. The coordinates are relative to the framesize, and the point 0,0 is in the top left of the frame.

A TrainingJob that trains and uploads an AutoML ImageClassification Model.

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

A TrainingJob that trains and uploads an AutoML ImageSegmentation Model.

A TrainingJob that trains and uploads an AutoML Tables Model.

This message hasoneof_ 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 othermembers.

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

This message hasoneof_ 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 othermembers.

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

Training pipeline will infer the proper transformation basedon the statistic of dataset.

Treats the column as categorical array and performs followingtransformation functions.

• For each element in the array, convert the category name to adictionary lookup index and generate an embedding for each index.Combine the embedding of all elements into a single embeddingusing 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 andgenerate an embedding for each index.
• Categories that appear less than 5 times in the training datasetare treated as the "unknown" category. The "unknown" categorygets its own special lookup index and resulting embedding.

Treats the column as numerical array and performs followingtransformation functions.

• All transformations for Numerical types applied to the average ofthe 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 isconsidered a missing value.
• z_score of log(value+1) when the value is greater than or equalto 0. Otherwise, this transformation is not applied and the valueis considered a missing value.
• A boolean value that indicates whether the value is valid.

Treats the column as text array and performs followingtransformation functions.

• Concatenate all text values in the array into a single text valueusing a space (" ") as a delimiter, and then treat the result asa 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 lookupindex and generate an embedding for each index. Combine theembedding 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 resultingembedding.
• 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 fromthe
• timestamp as a Categorical column.
• Invalid numerical values (for example, values that fall outsideof a typical timestamp range, or are extreme values) receive nospecial treatment and are not removed.

Model metadata specific to AutoML Tables.

A TrainingJob that trains and uploads an AutoML TextClassification Model.

A TrainingJob that trains and uploads an AutoML TextExtraction Model.

API documentation foraiplatform.v1.schema.trainingjob.definition_v1.types.AutoMlTextExtractionInputs class.

A TrainingJob that trains and uploads an AutoML TextSentiment Model.

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

A TrainingJob that trains and uploads an AutoML VideoClassification Model.

A TrainingJob that trains and uploads an AutoML VideoObjectTracking Model.

Configuration for exporting test set predictions to aBigQuery 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 fieldsxMin,xMax,yMin, andyMax refer to abounding box, i.e. the rectangle over the video frame pinpointingthe found AnnotationSpec. The coordinates are relative to the framesize, and the point 0,0 is in the top left of the frame.

A TrainingJob that trains and uploads an AutoML ForecastingModel.

A duration of time expressed in time granularity units.

This message hasoneof_ 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 othermembers.

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

Training pipeline will infer the proper transformation basedon 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 andgenerate an embedding for each index.

• Categories that appear less than 5 times in the training datasetare treated as the "unknown" category. The "unknown" categorygets 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 isconsidered a missing value.

• z_score of log(value+1) when the value is greater than or equalto 0. Otherwise, this transformation is not applied and the valueis 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 andgenerate 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 fromthe timestamp as a Categorical column.

• Invalid numerical values (for example, values that fall outsideof a typical timestamp range, or are extreme values) receive nospecial treatment and are not removed.

Model metadata specific to AutoML Forecasting.

A TrainingJob that trains and uploads an AutoML ImageClassification Model.

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

A TrainingJob that trains and uploads an AutoML ImageSegmentation Model.

A TrainingJob that trains and uploads an AutoML Tables Model.

This message hasoneof_ 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 othermembers.

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

This message hasoneof_ 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 othermembers.

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

Training pipeline will infer the proper transformation basedon the statistic of dataset.

Treats the column as categorical array and performs followingtransformation functions.

• For each element in the array, convert the category name to adictionary lookup index and generate an embedding for each index.Combine the embedding of all elements into a single embeddingusing 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 andgenerate an embedding for each index.
• Categories that appear less than 5 times in the training datasetare treated as the "unknown" category. The "unknown" categorygets its own special lookup index and resulting embedding.

Treats the column as numerical array and performs followingtransformation functions.

• All transformations for Numerical types applied to the average ofthe 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 isconsidered a missing value.
• z_score of log(value+1) when the value is greater than or equalto 0. Otherwise, this transformation is not applied and the valueis considered a missing value.
• A boolean value that indicates whether the value is valid.

Treats the column as text array and performs followingtransformation functions.

• Concatenate all text values in the array into a single text valueusing a space (" ") as a delimiter, and then treat the result asa 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 lookupindex and generate an embedding for each index. Combine theembedding 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 resultingembedding.
• 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 fromthe
• timestamp as a Categorical column.
• Invalid numerical values (for example, values that fall outsideof a typical timestamp range, or are extreme values) receive nospecial treatment and are not removed.

Model metadata specific to AutoML Tables.

A TrainingJob that trains and uploads an AutoML TextClassification Model.

A TrainingJob that trains and uploads an AutoML TextExtraction Model.

API documentation foraiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlTextExtractionInputs class.

A TrainingJob that trains and uploads an AutoML TextSentiment Model.

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

A TrainingJob that trains and uploads an AutoML VideoClassification Model.

A TrainingJob that trains and uploads an AutoML VideoObjectTracking Model.

Configuration for exporting test set predictions to aBigQuery table.

Service for generating and preparing datasets for Gen AIevaluation.

Service for generating and preparing datasets for Gen AIevaluation.

The service that manages Vertex AI Dataset and its childresources.

The service that manages Vertex AI Dataset and its childresources.

A pager for iterating throughlist_annotations requests.

This class thinly wraps an initialListAnnotationsResponse object, andprovides an__aiter__ method to iterate through itsannotations field.

If there are more pages, the__aiter__ method will make additionalListAnnotations requests and continue to iteratethrough theannotations field on thecorresponding responses.

All the usualListAnnotationsResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_annotations requests.

This class thinly wraps an initialListAnnotationsResponse object, andprovides an__iter__ method to iterate through itsannotations field.

If there are more pages, the__iter__ method will make additionalListAnnotations requests and continue to iteratethrough theannotations field on thecorresponding responses.

All the usualListAnnotationsResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_data_items requests.

This class thinly wraps an initialListDataItemsResponse object, andprovides an__aiter__ method to iterate through itsdata_items field.

If there are more pages, the__aiter__ method will make additionalListDataItems requests and continue to iteratethrough thedata_items field on thecorresponding responses.

All the usualListDataItemsResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_data_items requests.

This class thinly wraps an initialListDataItemsResponse object, andprovides an__iter__ method to iterate through itsdata_items field.

If there are more pages, the__iter__ method will make additionalListDataItems requests and continue to iteratethrough thedata_items field on thecorresponding responses.

All the usualListDataItemsResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_dataset_versions requests.

This class thinly wraps an initialListDatasetVersionsResponse object, andprovides an__aiter__ method to iterate through itsdataset_versions field.

If there are more pages, the__aiter__ method will make additionalListDatasetVersions requests and continue to iteratethrough thedataset_versions field on thecorresponding responses.

All the usualListDatasetVersionsResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_dataset_versions requests.

This class thinly wraps an initialListDatasetVersionsResponse object, andprovides an__iter__ method to iterate through itsdataset_versions field.

If there are more pages, the__iter__ method will make additionalListDatasetVersions requests and continue to iteratethrough thedataset_versions field on thecorresponding responses.

All the usualListDatasetVersionsResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_datasets requests.

This class thinly wraps an initialListDatasetsResponse object, andprovides an__aiter__ method to iterate through itsdatasets field.

If there are more pages, the__aiter__ method will make additionalListDatasets requests and continue to iteratethrough thedatasets field on thecorresponding responses.

All the usualListDatasetsResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_datasets requests.

This class thinly wraps an initialListDatasetsResponse object, andprovides an__iter__ method to iterate through itsdatasets field.

If there are more pages, the__iter__ method will make additionalListDatasets requests and continue to iteratethrough thedatasets field on thecorresponding responses.

All the usualListDatasetsResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_saved_queries requests.

This class thinly wraps an initialListSavedQueriesResponse object, andprovides an__aiter__ method to iterate through itssaved_queries field.

If there are more pages, the__aiter__ method will make additionalListSavedQueries requests and continue to iteratethrough thesaved_queries field on thecorresponding responses.

All the usualListSavedQueriesResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_saved_queries requests.

This class thinly wraps an initialListSavedQueriesResponse object, andprovides an__iter__ method to iterate through itssaved_queries field.

If there are more pages, the__iter__ method will make additionalListSavedQueries requests and continue to iteratethrough thesaved_queries field on thecorresponding responses.

All the usualListSavedQueriesResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughsearch_data_items requests.

This class thinly wraps an initialSearchDataItemsResponse object, andprovides an__aiter__ method to iterate through itsdata_item_views field.

If there are more pages, the__aiter__ method will make additionalSearchDataItems requests and continue to iteratethrough thedata_item_views field on thecorresponding responses.

All the usualSearchDataItemsResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughsearch_data_items requests.

This class thinly wraps an initialSearchDataItemsResponse object, andprovides an__iter__ method to iterate through itsdata_item_views field.

If there are more pages, the__iter__ method will make additionalSearchDataItems requests and continue to iteratethrough thedata_item_views field on thecorresponding responses.

All the usualSearchDataItemsResponseattributes are available on the pager. If multiple requests are made, onlythe 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 throughlist_deployment_resource_pools requests.

This class thinly wraps an initialListDeploymentResourcePoolsResponse object, andprovides an__aiter__ method to iterate through itsdeployment_resource_pools field.

If there are more pages, the__aiter__ method will make additionalListDeploymentResourcePools requests and continue to iteratethrough thedeployment_resource_pools field on thecorresponding responses.

All the usualListDeploymentResourcePoolsResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_deployment_resource_pools requests.

This class thinly wraps an initialListDeploymentResourcePoolsResponse object, andprovides an__iter__ method to iterate through itsdeployment_resource_pools field.

If there are more pages, the__iter__ method will make additionalListDeploymentResourcePools requests and continue to iteratethrough thedeployment_resource_pools field on thecorresponding responses.

All the usualListDeploymentResourcePoolsResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughquery_deployed_models requests.

This class thinly wraps an initialQueryDeployedModelsResponse object, andprovides an__aiter__ method to iterate through itsdeployed_models field.

If there are more pages, the__aiter__ method will make additionalQueryDeployedModels requests and continue to iteratethrough thedeployed_models field on thecorresponding responses.

All the usualQueryDeployedModelsResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughquery_deployed_models requests.

This class thinly wraps an initialQueryDeployedModelsResponse object, andprovides an__iter__ method to iterate through itsdeployed_models field.

If there are more pages, the__iter__ method will make additionalQueryDeployedModels requests and continue to iteratethrough thedeployed_models field on thecorresponding responses.

All the usualQueryDeployedModelsResponseattributes are available on the pager. If multiple requests are made, onlythe 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 throughlist_endpoints requests.

This class thinly wraps an initialListEndpointsResponse object, andprovides an__aiter__ method to iterate through itsendpoints field.

If there are more pages, the__aiter__ method will make additionalListEndpoints requests and continue to iteratethrough theendpoints field on thecorresponding responses.

All the usualListEndpointsResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_endpoints requests.

This class thinly wraps an initialListEndpointsResponse object, andprovides an__iter__ method to iterate through itsendpoints field.

If there are more pages, the__iter__ method will make additionalListEndpoints requests and continue to iteratethrough theendpoints field on thecorresponding responses.

All the usualListEndpointsResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

Vertex AI Online Evaluation Service.

Vertex AI Online Evaluation Service.

The service that handles CRUD and List for resources forFeatureOnlineStore.

The service that handles CRUD and List for resources forFeatureOnlineStore.

A pager for iterating throughlist_feature_online_stores requests.

This class thinly wraps an initialListFeatureOnlineStoresResponse object, andprovides an__aiter__ method to iterate through itsfeature_online_stores field.

If there are more pages, the__aiter__ method will make additionalListFeatureOnlineStores requests and continue to iteratethrough thefeature_online_stores field on thecorresponding responses.

All the usualListFeatureOnlineStoresResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_feature_online_stores requests.

This class thinly wraps an initialListFeatureOnlineStoresResponse object, andprovides an__iter__ method to iterate through itsfeature_online_stores field.

If there are more pages, the__iter__ method will make additionalListFeatureOnlineStores requests and continue to iteratethrough thefeature_online_stores field on thecorresponding responses.

All the usualListFeatureOnlineStoresResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_feature_view_syncs requests.

This class thinly wraps an initialListFeatureViewSyncsResponse object, andprovides an__aiter__ method to iterate through itsfeature_view_syncs field.

If there are more pages, the__aiter__ method will make additionalListFeatureViewSyncs requests and continue to iteratethrough thefeature_view_syncs field on thecorresponding responses.

All the usualListFeatureViewSyncsResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_feature_view_syncs requests.

This class thinly wraps an initialListFeatureViewSyncsResponse object, andprovides an__iter__ method to iterate through itsfeature_view_syncs field.

If there are more pages, the__iter__ method will make additionalListFeatureViewSyncs requests and continue to iteratethrough thefeature_view_syncs field on thecorresponding responses.

All the usualListFeatureViewSyncsResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_feature_views requests.

This class thinly wraps an initialListFeatureViewsResponse object, andprovides an__aiter__ method to iterate through itsfeature_views field.

If there are more pages, the__aiter__ method will make additionalListFeatureViews requests and continue to iteratethrough thefeature_views field on thecorresponding responses.

All the usualListFeatureViewsResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_feature_views requests.

This class thinly wraps an initialListFeatureViewsResponse object, andprovides an__iter__ method to iterate through itsfeature_views field.

If there are more pages, the__iter__ method will make additionalListFeatureViews requests and continue to iteratethrough thefeature_views field on thecorresponding responses.

All the usualListFeatureViewsResponseattributes are available on the pager. If multiple requests are made, onlythe 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 forFeatureRegistry.

The service that handles CRUD and List for resources forFeatureRegistry.

A pager for iterating throughlist_feature_groups requests.

This class thinly wraps an initialListFeatureGroupsResponse object, andprovides an__aiter__ method to iterate through itsfeature_groups field.

If there are more pages, the__aiter__ method will make additionalListFeatureGroups requests and continue to iteratethrough thefeature_groups field on thecorresponding responses.

All the usualListFeatureGroupsResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_feature_groups requests.

This class thinly wraps an initialListFeatureGroupsResponse object, andprovides an__iter__ method to iterate through itsfeature_groups field.

If there are more pages, the__iter__ method will make additionalListFeatureGroups requests and continue to iteratethrough thefeature_groups field on thecorresponding responses.

All the usualListFeatureGroupsResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_features requests.

This class thinly wraps an initialListFeaturesResponse object, andprovides an__aiter__ method to iterate through itsfeatures field.

If there are more pages, the__aiter__ method will make additionalListFeatures requests and continue to iteratethrough thefeatures field on thecorresponding responses.

All the usualListFeaturesResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_features requests.

This class thinly wraps an initialListFeaturesResponse object, andprovides an__iter__ method to iterate through itsfeatures field.

If there are more pages, the__iter__ method will make additionalListFeatures requests and continue to iteratethrough thefeatures field on thecorresponding responses.

All the usualListFeaturesResponseattributes are available on the pager. If multiple requests are made, onlythe 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 forFeaturestore.

The service that handles CRUD and List for resources forFeaturestore.

A pager for iterating throughlist_entity_types requests.

This class thinly wraps an initialListEntityTypesResponse object, andprovides an__aiter__ method to iterate through itsentity_types field.

If there are more pages, the__aiter__ method will make additionalListEntityTypes requests and continue to iteratethrough theentity_types field on thecorresponding responses.

All the usualListEntityTypesResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_entity_types requests.

This class thinly wraps an initialListEntityTypesResponse object, andprovides an__iter__ method to iterate through itsentity_types field.

If there are more pages, the__iter__ method will make additionalListEntityTypes requests and continue to iteratethrough theentity_types field on thecorresponding responses.

All the usualListEntityTypesResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_features requests.

This class thinly wraps an initialListFeaturesResponse object, andprovides an__aiter__ method to iterate through itsfeatures field.

If there are more pages, the__aiter__ method will make additionalListFeatures requests and continue to iteratethrough thefeatures field on thecorresponding responses.

All the usualListFeaturesResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_features requests.

This class thinly wraps an initialListFeaturesResponse object, andprovides an__iter__ method to iterate through itsfeatures field.

If there are more pages, the__iter__ method will make additionalListFeatures requests and continue to iteratethrough thefeatures field on thecorresponding responses.

All the usualListFeaturesResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_featurestores requests.

This class thinly wraps an initialListFeaturestoresResponse object, andprovides an__aiter__ method to iterate through itsfeaturestores field.

If there are more pages, the__aiter__ method will make additionalListFeaturestores requests and continue to iteratethrough thefeaturestores field on thecorresponding responses.

All the usualListFeaturestoresResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_featurestores requests.

This class thinly wraps an initialListFeaturestoresResponse object, andprovides an__iter__ method to iterate through itsfeaturestores field.

If there are more pages, the__iter__ method will make additionalListFeaturestores requests and continue to iteratethrough thefeaturestores field on thecorresponding responses.

All the usualListFeaturestoresResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughsearch_features requests.

This class thinly wraps an initialSearchFeaturesResponse object, andprovides an__aiter__ method to iterate through itsfeatures field.

If there are more pages, the__aiter__ method will make additionalSearchFeatures requests and continue to iteratethrough thefeatures field on thecorresponding responses.

All the usualSearchFeaturesResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughsearch_features requests.

This class thinly wraps an initialSearchFeaturesResponse object, andprovides an__iter__ method to iterate through itsfeatures field.

If there are more pages, the__iter__ method will make additionalSearchFeatures requests and continue to iteratethrough thefeatures field on thecorresponding responses.

All the usualSearchFeaturesResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

Service for managing Vertex AI's CachedContent resource.

Service for managing Vertex AI's CachedContent resource.

A pager for iterating throughlist_cached_contents requests.

This class thinly wraps an initialListCachedContentsResponse object, andprovides an__aiter__ method to iterate through itscached_contents field.

If there are more pages, the__aiter__ method will make additionalListCachedContents requests and continue to iteratethrough thecached_contents field on thecorresponding responses.

All the usualListCachedContentsResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_cached_contents requests.

This class thinly wraps an initialListCachedContentsResponse object, andprovides an__iter__ method to iterate through itscached_contents field.

If there are more pages, the__iter__ method will make additionalListCachedContents requests and continue to iteratethrough thecached_contents field on thecorresponding responses.

All the usualListCachedContentsResponseattributes are available on the pager. If multiple requests are made, onlythe 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 throughlist_tuning_jobs requests.

This class thinly wraps an initialListTuningJobsResponse object, andprovides an__aiter__ method to iterate through itstuning_jobs field.

If there are more pages, the__aiter__ method will make additionalListTuningJobs requests and continue to iteratethrough thetuning_jobs field on thecorresponding responses.

All the usualListTuningJobsResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_tuning_jobs requests.

This class thinly wraps an initialListTuningJobsResponse object, andprovides an__iter__ method to iterate through itstuning_jobs field.

If there are more pages, the__iter__ method will make additionalListTuningJobs requests and continue to iteratethrough thetuning_jobs field on thecorresponding responses.

All the usualListTuningJobsResponseattributes are available on the pager. If multiple requests are made, onlythe 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 throughlist_index_endpoints requests.

This class thinly wraps an initialListIndexEndpointsResponse object, andprovides an__aiter__ method to iterate through itsindex_endpoints field.

If there are more pages, the__aiter__ method will make additionalListIndexEndpoints requests and continue to iteratethrough theindex_endpoints field on thecorresponding responses.

All the usualListIndexEndpointsResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_index_endpoints requests.

This class thinly wraps an initialListIndexEndpointsResponse object, andprovides an__iter__ method to iterate through itsindex_endpoints field.

If there are more pages, the__iter__ method will make additionalListIndexEndpoints requests and continue to iteratethrough theindex_endpoints field on thecorresponding responses.

All the usualListIndexEndpointsResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

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

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

A pager for iterating throughlist_indexes requests.

This class thinly wraps an initialListIndexesResponse object, andprovides an__aiter__ method to iterate through itsindexes field.

If there are more pages, the__aiter__ method will make additionalListIndexes requests and continue to iteratethrough theindexes field on thecorresponding responses.

All the usualListIndexesResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_indexes requests.

This class thinly wraps an initialListIndexesResponse object, andprovides an__iter__ method to iterate through itsindexes field.

If there are more pages, the__iter__ method will make additionalListIndexes requests and continue to iteratethrough theindexes field on thecorresponding responses.

All the usualListIndexesResponseattributes are available on the pager. If multiple requests are made, onlythe 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 throughlist_batch_prediction_jobs requests.

This class thinly wraps an initialListBatchPredictionJobsResponse object, andprovides an__aiter__ method to iterate through itsbatch_prediction_jobs field.

If there are more pages, the__aiter__ method will make additionalListBatchPredictionJobs requests and continue to iteratethrough thebatch_prediction_jobs field on thecorresponding responses.

All the usualListBatchPredictionJobsResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_batch_prediction_jobs requests.

This class thinly wraps an initialListBatchPredictionJobsResponse object, andprovides an__iter__ method to iterate through itsbatch_prediction_jobs field.

If there are more pages, the__iter__ method will make additionalListBatchPredictionJobs requests and continue to iteratethrough thebatch_prediction_jobs field on thecorresponding responses.

All the usualListBatchPredictionJobsResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_custom_jobs requests.

This class thinly wraps an initialListCustomJobsResponse object, andprovides an__aiter__ method to iterate through itscustom_jobs field.

If there are more pages, the__aiter__ method will make additionalListCustomJobs requests and continue to iteratethrough thecustom_jobs field on thecorresponding responses.

All the usualListCustomJobsResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_custom_jobs requests.

This class thinly wraps an initialListCustomJobsResponse object, andprovides an__iter__ method to iterate through itscustom_jobs field.

If there are more pages, the__iter__ method will make additionalListCustomJobs requests and continue to iteratethrough thecustom_jobs field on thecorresponding responses.

All the usualListCustomJobsResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_data_labeling_jobs requests.

This class thinly wraps an initialListDataLabelingJobsResponse object, andprovides an__aiter__ method to iterate through itsdata_labeling_jobs field.

If there are more pages, the__aiter__ method will make additionalListDataLabelingJobs requests and continue to iteratethrough thedata_labeling_jobs field on thecorresponding responses.

All the usualListDataLabelingJobsResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_data_labeling_jobs requests.

This class thinly wraps an initialListDataLabelingJobsResponse object, andprovides an__iter__ method to iterate through itsdata_labeling_jobs field.

If there are more pages, the__iter__ method will make additionalListDataLabelingJobs requests and continue to iteratethrough thedata_labeling_jobs field on thecorresponding responses.

All the usualListDataLabelingJobsResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_hyperparameter_tuning_jobs requests.

This class thinly wraps an initialListHyperparameterTuningJobsResponse object, andprovides an__aiter__ method to iterate through itshyperparameter_tuning_jobs field.

If there are more pages, the__aiter__ method will make additionalListHyperparameterTuningJobs requests and continue to iteratethrough thehyperparameter_tuning_jobs field on thecorresponding responses.

All the usualListHyperparameterTuningJobsResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_hyperparameter_tuning_jobs requests.

This class thinly wraps an initialListHyperparameterTuningJobsResponse object, andprovides an__iter__ method to iterate through itshyperparameter_tuning_jobs field.

If there are more pages, the__iter__ method will make additionalListHyperparameterTuningJobs requests and continue to iteratethrough thehyperparameter_tuning_jobs field on thecorresponding responses.

All the usualListHyperparameterTuningJobsResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_model_deployment_monitoring_jobs requests.

This class thinly wraps an initialListModelDeploymentMonitoringJobsResponse object, andprovides an__aiter__ method to iterate through itsmodel_deployment_monitoring_jobs field.

If there are more pages, the__aiter__ method will make additionalListModelDeploymentMonitoringJobs requests and continue to iteratethrough themodel_deployment_monitoring_jobs field on thecorresponding responses.

All the usualListModelDeploymentMonitoringJobsResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_model_deployment_monitoring_jobs requests.

This class thinly wraps an initialListModelDeploymentMonitoringJobsResponse object, andprovides an__iter__ method to iterate through itsmodel_deployment_monitoring_jobs field.

If there are more pages, the__iter__ method will make additionalListModelDeploymentMonitoringJobs requests and continue to iteratethrough themodel_deployment_monitoring_jobs field on thecorresponding responses.

All the usualListModelDeploymentMonitoringJobsResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_nas_jobs requests.

This class thinly wraps an initialListNasJobsResponse object, andprovides an__aiter__ method to iterate through itsnas_jobs field.

If there are more pages, the__aiter__ method will make additionalListNasJobs requests and continue to iteratethrough thenas_jobs field on thecorresponding responses.

All the usualListNasJobsResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_nas_jobs requests.

This class thinly wraps an initialListNasJobsResponse object, andprovides an__iter__ method to iterate through itsnas_jobs field.

If there are more pages, the__iter__ method will make additionalListNasJobs requests and continue to iteratethrough thenas_jobs field on thecorresponding responses.

All the usualListNasJobsResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_nas_trial_details requests.

This class thinly wraps an initialListNasTrialDetailsResponse object, andprovides an__aiter__ method to iterate through itsnas_trial_details field.

If there are more pages, the__aiter__ method will make additionalListNasTrialDetails requests and continue to iteratethrough thenas_trial_details field on thecorresponding responses.

All the usualListNasTrialDetailsResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_nas_trial_details requests.

This class thinly wraps an initialListNasTrialDetailsResponse object, andprovides an__iter__ method to iterate through itsnas_trial_details field.

If there are more pages, the__iter__ method will make additionalListNasTrialDetails requests and continue to iteratethrough thenas_trial_details field on thecorresponding responses.

All the usualListNasTrialDetailsResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughsearch_model_deployment_monitoring_stats_anomalies requests.

This class thinly wraps an initialSearchModelDeploymentMonitoringStatsAnomaliesResponse object, andprovides an__aiter__ method to iterate through itsmonitoring_stats field.

If there are more pages, the__aiter__ method will make additionalSearchModelDeploymentMonitoringStatsAnomalies requests and continue to iteratethrough themonitoring_stats field on thecorresponding responses.

All the usualSearchModelDeploymentMonitoringStatsAnomaliesResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughsearch_model_deployment_monitoring_stats_anomalies requests.

This class thinly wraps an initialSearchModelDeploymentMonitoringStatsAnomaliesResponse object, andprovides an__iter__ method to iterate through itsmonitoring_stats field.

If there are more pages, the__iter__ method will make additionalSearchModelDeploymentMonitoringStatsAnomalies requests and continue to iteratethrough themonitoring_stats field on thecorresponding responses.

All the usualSearchModelDeploymentMonitoringStatsAnomaliesResponseattributes are available on the pager. If multiple requests are made, onlythe 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 vectorsimilarity search at scale.

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

Service for reading and writing metadata entries.

Service for reading and writing metadata entries.

A pager for iterating throughlist_artifacts requests.

This class thinly wraps an initialListArtifactsResponse object, andprovides an__aiter__ method to iterate through itsartifacts field.

If there are more pages, the__aiter__ method will make additionalListArtifacts requests and continue to iteratethrough theartifacts field on thecorresponding responses.

All the usualListArtifactsResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_artifacts requests.

This class thinly wraps an initialListArtifactsResponse object, andprovides an__iter__ method to iterate through itsartifacts field.

If there are more pages, the__iter__ method will make additionalListArtifacts requests and continue to iteratethrough theartifacts field on thecorresponding responses.

All the usualListArtifactsResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_contexts requests.

This class thinly wraps an initialListContextsResponse object, andprovides an__aiter__ method to iterate through itscontexts field.

If there are more pages, the__aiter__ method will make additionalListContexts requests and continue to iteratethrough thecontexts field on thecorresponding responses.

All the usualListContextsResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_contexts requests.

This class thinly wraps an initialListContextsResponse object, andprovides an__iter__ method to iterate through itscontexts field.

If there are more pages, the__iter__ method will make additionalListContexts requests and continue to iteratethrough thecontexts field on thecorresponding responses.

All the usualListContextsResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_executions requests.

This class thinly wraps an initialListExecutionsResponse object, andprovides an__aiter__ method to iterate through itsexecutions field.

If there are more pages, the__aiter__ method will make additionalListExecutions requests and continue to iteratethrough theexecutions field on thecorresponding responses.

All the usualListExecutionsResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_executions requests.

This class thinly wraps an initialListExecutionsResponse object, andprovides an__iter__ method to iterate through itsexecutions field.

If there are more pages, the__iter__ method will make additionalListExecutions requests and continue to iteratethrough theexecutions field on thecorresponding responses.

All the usualListExecutionsResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_metadata_schemas requests.

This class thinly wraps an initialListMetadataSchemasResponse object, andprovides an__aiter__ method to iterate through itsmetadata_schemas field.

If there are more pages, the__aiter__ method will make additionalListMetadataSchemas requests and continue to iteratethrough themetadata_schemas field on thecorresponding responses.

All the usualListMetadataSchemasResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_metadata_schemas requests.

This class thinly wraps an initialListMetadataSchemasResponse object, andprovides an__iter__ method to iterate through itsmetadata_schemas field.

If there are more pages, the__iter__ method will make additionalListMetadataSchemas requests and continue to iteratethrough themetadata_schemas field on thecorresponding responses.

All the usualListMetadataSchemasResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_metadata_stores requests.

This class thinly wraps an initialListMetadataStoresResponse object, andprovides an__aiter__ method to iterate through itsmetadata_stores field.

If there are more pages, the__aiter__ method will make additionalListMetadataStores requests and continue to iteratethrough themetadata_stores field on thecorresponding responses.

All the usualListMetadataStoresResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_metadata_stores requests.

This class thinly wraps an initialListMetadataStoresResponse object, andprovides an__iter__ method to iterate through itsmetadata_stores field.

If there are more pages, the__iter__ method will make additionalListMetadataStores requests and continue to iteratethrough themetadata_stores field on thecorresponding responses.

All the usualListMetadataStoresResponseattributes are available on the pager. If multiple requests are made, onlythe 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 throughsearch_migratable_resources requests.

This class thinly wraps an initialSearchMigratableResourcesResponse object, andprovides an__aiter__ method to iterate through itsmigratable_resources field.

If there are more pages, the__aiter__ method will make additionalSearchMigratableResources requests and continue to iteratethrough themigratable_resources field on thecorresponding responses.

All the usualSearchMigratableResourcesResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughsearch_migratable_resources requests.

This class thinly wraps an initialSearchMigratableResourcesResponse object, andprovides an__iter__ method to iterate through itsmigratable_resources field.

If there are more pages, the__iter__ method will make additionalSearchMigratableResources requests and continue to iteratethrough themigratable_resources field on thecorresponding responses.

All the usualSearchMigratableResourcesResponseattributes are available on the pager. If multiple requests are made, onlythe 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 throughlist_model_evaluation_slices requests.

This class thinly wraps an initialListModelEvaluationSlicesResponse object, andprovides an__aiter__ method to iterate through itsmodel_evaluation_slices field.

If there are more pages, the__aiter__ method will make additionalListModelEvaluationSlices requests and continue to iteratethrough themodel_evaluation_slices field on thecorresponding responses.

All the usualListModelEvaluationSlicesResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_model_evaluation_slices requests.

This class thinly wraps an initialListModelEvaluationSlicesResponse object, andprovides an__iter__ method to iterate through itsmodel_evaluation_slices field.

If there are more pages, the__iter__ method will make additionalListModelEvaluationSlices requests and continue to iteratethrough themodel_evaluation_slices field on thecorresponding responses.

All the usualListModelEvaluationSlicesResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_model_evaluations requests.

This class thinly wraps an initialListModelEvaluationsResponse object, andprovides an__aiter__ method to iterate through itsmodel_evaluations field.

If there are more pages, the__aiter__ method will make additionalListModelEvaluations requests and continue to iteratethrough themodel_evaluations field on thecorresponding responses.

All the usualListModelEvaluationsResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_model_evaluations requests.

This class thinly wraps an initialListModelEvaluationsResponse object, andprovides an__iter__ method to iterate through itsmodel_evaluations field.

If there are more pages, the__iter__ method will make additionalListModelEvaluations requests and continue to iteratethrough themodel_evaluations field on thecorresponding responses.

All the usualListModelEvaluationsResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_model_version_checkpoints requests.

This class thinly wraps an initialListModelVersionCheckpointsResponse object, andprovides an__aiter__ method to iterate through itscheckpoints field.

If there are more pages, the__aiter__ method will make additionalListModelVersionCheckpoints requests and continue to iteratethrough thecheckpoints field on thecorresponding responses.

All the usualListModelVersionCheckpointsResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_model_version_checkpoints requests.

This class thinly wraps an initialListModelVersionCheckpointsResponse object, andprovides an__iter__ method to iterate through itscheckpoints field.

If there are more pages, the__iter__ method will make additionalListModelVersionCheckpoints requests and continue to iteratethrough thecheckpoints field on thecorresponding responses.

All the usualListModelVersionCheckpointsResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_model_versions requests.

This class thinly wraps an initialListModelVersionsResponse object, andprovides an__aiter__ method to iterate through itsmodels field.

If there are more pages, the__aiter__ method will make additionalListModelVersions requests and continue to iteratethrough themodels field on thecorresponding responses.

All the usualListModelVersionsResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_model_versions requests.

This class thinly wraps an initialListModelVersionsResponse object, andprovides an__iter__ method to iterate through itsmodels field.

If there are more pages, the__iter__ method will make additionalListModelVersions requests and continue to iteratethrough themodels field on thecorresponding responses.

All the usualListModelVersionsResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_models requests.

This class thinly wraps an initialListModelsResponse object, andprovides an__aiter__ method to iterate through itsmodels field.

If there are more pages, the__aiter__ method will make additionalListModels requests and continue to iteratethrough themodels field on thecorresponding responses.

All the usualListModelsResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_models requests.

This class thinly wraps an initialListModelsResponse object, andprovides an__iter__ method to iterate through itsmodels field.

If there are more pages, the__iter__ method will make additionalListModels requests and continue to iteratethrough themodels field on thecorresponding responses.

All the usualListModelsResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

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

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

A pager for iterating throughlist_notebook_execution_jobs requests.

This class thinly wraps an initialListNotebookExecutionJobsResponse object, andprovides an__aiter__ method to iterate through itsnotebook_execution_jobs field.

If there are more pages, the__aiter__ method will make additionalListNotebookExecutionJobs requests and continue to iteratethrough thenotebook_execution_jobs field on thecorresponding responses.

All the usualListNotebookExecutionJobsResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_notebook_execution_jobs requests.

This class thinly wraps an initialListNotebookExecutionJobsResponse object, andprovides an__iter__ method to iterate through itsnotebook_execution_jobs field.

If there are more pages, the__iter__ method will make additionalListNotebookExecutionJobs requests and continue to iteratethrough thenotebook_execution_jobs field on thecorresponding responses.

All the usualListNotebookExecutionJobsResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_notebook_runtime_templates requests.

This class thinly wraps an initialListNotebookRuntimeTemplatesResponse object, andprovides an__aiter__ method to iterate through itsnotebook_runtime_templates field.

If there are more pages, the__aiter__ method will make additionalListNotebookRuntimeTemplates requests and continue to iteratethrough thenotebook_runtime_templates field on thecorresponding responses.

All the usualListNotebookRuntimeTemplatesResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_notebook_runtime_templates requests.

This class thinly wraps an initialListNotebookRuntimeTemplatesResponse object, andprovides an__iter__ method to iterate through itsnotebook_runtime_templates field.

If there are more pages, the__iter__ method will make additionalListNotebookRuntimeTemplates requests and continue to iteratethrough thenotebook_runtime_templates field on thecorresponding responses.

All the usualListNotebookRuntimeTemplatesResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_notebook_runtimes requests.

This class thinly wraps an initialListNotebookRuntimesResponse object, andprovides an__aiter__ method to iterate through itsnotebook_runtimes field.

If there are more pages, the__aiter__ method will make additionalListNotebookRuntimes requests and continue to iteratethrough thenotebook_runtimes field on thecorresponding responses.

All the usualListNotebookRuntimesResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_notebook_runtimes requests.

This class thinly wraps an initialListNotebookRuntimesResponse object, andprovides an__iter__ method to iterate through itsnotebook_runtimes field.

If there are more pages, the__iter__ method will make additionalListNotebookRuntimes requests and continue to iteratethrough thenotebook_runtimes field on thecorresponding responses.

All the usualListNotebookRuntimesResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A service for managing Vertex AI's machine learningPersistentResource.

A service for managing Vertex AI's machine learningPersistentResource.

A pager for iterating throughlist_persistent_resources requests.

This class thinly wraps an initialListPersistentResourcesResponse object, andprovides an__aiter__ method to iterate through itspersistent_resources field.

If there are more pages, the__aiter__ method will make additionalListPersistentResources requests and continue to iteratethrough thepersistent_resources field on thecorresponding responses.

All the usualListPersistentResourcesResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_persistent_resources requests.

This class thinly wraps an initialListPersistentResourcesResponse object, andprovides an__iter__ method to iterate through itspersistent_resources field.

If there are more pages, the__iter__ method will make additionalListPersistentResources requests and continue to iteratethrough thepersistent_resources field on thecorresponding responses.

All the usualListPersistentResourcesResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A service for creating and managing Vertex AI's pipelines. Thisincludes bothTrainingPipeline resources (used for AutoML andcustom training) andPipelineJob resources (used for Vertex AIPipelines).

A service for creating and managing Vertex AI's pipelines. Thisincludes bothTrainingPipeline resources (used for AutoML andcustom training) andPipelineJob resources (used for Vertex AIPipelines).

A pager for iterating throughlist_pipeline_jobs requests.

This class thinly wraps an initialListPipelineJobsResponse object, andprovides an__aiter__ method to iterate through itspipeline_jobs field.

If there are more pages, the__aiter__ method will make additionalListPipelineJobs requests and continue to iteratethrough thepipeline_jobs field on thecorresponding responses.

All the usualListPipelineJobsResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_pipeline_jobs requests.

This class thinly wraps an initialListPipelineJobsResponse object, andprovides an__iter__ method to iterate through itspipeline_jobs field.

If there are more pages, the__iter__ method will make additionalListPipelineJobs requests and continue to iteratethrough thepipeline_jobs field on thecorresponding responses.

All the usualListPipelineJobsResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_training_pipelines requests.

This class thinly wraps an initialListTrainingPipelinesResponse object, andprovides an__aiter__ method to iterate through itstraining_pipelines field.

If there are more pages, the__aiter__ method will make additionalListTrainingPipelines requests and continue to iteratethrough thetraining_pipelines field on thecorresponding responses.

All the usualListTrainingPipelinesResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_training_pipelines requests.

This class thinly wraps an initialListTrainingPipelinesResponse object, andprovides an__iter__ method to iterate through itstraining_pipelines field.

If there are more pages, the__iter__ method will make additionalListTrainingPipelines requests and continue to iteratethrough thetraining_pipelines field on thecorresponding responses.

All the usualListTrainingPipelinesResponseattributes are available on the pager. If multiple requests are made, onlythe 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 executing queries on Reasoning Engine.

A service for executing queries on Reasoning Engine.

A service for managing Vertex AI's Reasoning Engines.

A service for managing Vertex AI's Reasoning Engines.

A pager for iterating throughlist_reasoning_engines requests.

This class thinly wraps an initialListReasoningEnginesResponse object, andprovides an__aiter__ method to iterate through itsreasoning_engines field.

If there are more pages, the__aiter__ method will make additionalListReasoningEngines requests and continue to iteratethrough thereasoning_engines field on thecorresponding responses.

All the usualListReasoningEnginesResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_reasoning_engines requests.

This class thinly wraps an initialListReasoningEnginesResponse object, andprovides an__iter__ method to iterate through itsreasoning_engines field.

If there are more pages, the__iter__ method will make additionalListReasoningEngines requests and continue to iteratethrough thereasoning_engines field on thecorresponding responses.

All the usualListReasoningEnginesResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A service for creating and managing Vertex AI's Scheduleresources to periodically launch shceudled runs to make APIcalls.

A service for creating and managing Vertex AI's Scheduleresources to periodically launch shceudled runs to make APIcalls.

A pager for iterating throughlist_schedules requests.

This class thinly wraps an initialListSchedulesResponse object, andprovides an__aiter__ method to iterate through itsschedules field.

If there are more pages, the__aiter__ method will make additionalListSchedules requests and continue to iteratethrough theschedules field on thecorresponding responses.

All the usualListSchedulesResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_schedules requests.

This class thinly wraps an initialListSchedulesResponse object, andprovides an__iter__ method to iterate through itsschedules field.

If there are more pages, the__iter__ method will make additionalListSchedules requests and continue to iteratethrough theschedules field on thecorresponding responses.

All the usualListSchedulesResponseattributes are available on the pager. If multiple requests are made, onlythe 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/createSpecialist Pools to bring their own Specialists to label thedata. Customers can add/remove Managers for the Specialist Poolon Cloud console, then Managers will get email notifications tomanage Specialists and tasks on CrowdCompute console.

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

A pager for iterating throughlist_specialist_pools requests.

This class thinly wraps an initialListSpecialistPoolsResponse object, andprovides an__aiter__ method to iterate through itsspecialist_pools field.

If there are more pages, the__aiter__ method will make additionalListSpecialistPools requests and continue to iteratethrough thespecialist_pools field on thecorresponding responses.

All the usualListSpecialistPoolsResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_specialist_pools requests.

This class thinly wraps an initialListSpecialistPoolsResponse object, andprovides an__iter__ method to iterate through itsspecialist_pools field.

If there are more pages, the__iter__ method will make additionalListSpecialistPools requests and continue to iteratethrough thespecialist_pools field on thecorresponding responses.

All the usualListSpecialistPoolsResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

TensorboardService

TensorboardService

A pager for iterating throughexport_tensorboard_time_series_data requests.

This class thinly wraps an initialExportTensorboardTimeSeriesDataResponse object, andprovides an__aiter__ method to iterate through itstime_series_data_points field.

If there are more pages, the__aiter__ method will make additionalExportTensorboardTimeSeriesData requests and continue to iteratethrough thetime_series_data_points field on thecorresponding responses.

All the usualExportTensorboardTimeSeriesDataResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughexport_tensorboard_time_series_data requests.

This class thinly wraps an initialExportTensorboardTimeSeriesDataResponse object, andprovides an__iter__ method to iterate through itstime_series_data_points field.

If there are more pages, the__iter__ method will make additionalExportTensorboardTimeSeriesData requests and continue to iteratethrough thetime_series_data_points field on thecorresponding responses.

All the usualExportTensorboardTimeSeriesDataResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_tensorboard_experiments requests.

This class thinly wraps an initialListTensorboardExperimentsResponse object, andprovides an__aiter__ method to iterate through itstensorboard_experiments field.

If there are more pages, the__aiter__ method will make additionalListTensorboardExperiments requests and continue to iteratethrough thetensorboard_experiments field on thecorresponding responses.

All the usualListTensorboardExperimentsResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_tensorboard_experiments requests.

This class thinly wraps an initialListTensorboardExperimentsResponse object, andprovides an__iter__ method to iterate through itstensorboard_experiments field.

If there are more pages, the__iter__ method will make additionalListTensorboardExperiments requests and continue to iteratethrough thetensorboard_experiments field on thecorresponding responses.

All the usualListTensorboardExperimentsResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_tensorboard_runs requests.

This class thinly wraps an initialListTensorboardRunsResponse object, andprovides an__aiter__ method to iterate through itstensorboard_runs field.

If there are more pages, the__aiter__ method will make additionalListTensorboardRuns requests and continue to iteratethrough thetensorboard_runs field on thecorresponding responses.

All the usualListTensorboardRunsResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_tensorboard_runs requests.

This class thinly wraps an initialListTensorboardRunsResponse object, andprovides an__iter__ method to iterate through itstensorboard_runs field.

If there are more pages, the__iter__ method will make additionalListTensorboardRuns requests and continue to iteratethrough thetensorboard_runs field on thecorresponding responses.

All the usualListTensorboardRunsResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_tensorboard_time_series requests.

This class thinly wraps an initialListTensorboardTimeSeriesResponse object, andprovides an__aiter__ method to iterate through itstensorboard_time_series field.

If there are more pages, the__aiter__ method will make additionalListTensorboardTimeSeries requests and continue to iteratethrough thetensorboard_time_series field on thecorresponding responses.

All the usualListTensorboardTimeSeriesResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_tensorboard_time_series requests.

This class thinly wraps an initialListTensorboardTimeSeriesResponse object, andprovides an__iter__ method to iterate through itstensorboard_time_series field.

If there are more pages, the__iter__ method will make additionalListTensorboardTimeSeries requests and continue to iteratethrough thetensorboard_time_series field on thecorresponding responses.

All the usualListTensorboardTimeSeriesResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_tensorboards requests.

This class thinly wraps an initialListTensorboardsResponse object, andprovides an__aiter__ method to iterate through itstensorboards field.

If there are more pages, the__aiter__ method will make additionalListTensorboards requests and continue to iteratethrough thetensorboards field on thecorresponding responses.

All the usualListTensorboardsResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_tensorboards requests.

This class thinly wraps an initialListTensorboardsResponse object, andprovides an__iter__ method to iterate through itstensorboards field.

If there are more pages, the__iter__ method will make additionalListTensorboards requests and continue to iteratethrough thetensorboards field on thecorresponding responses.

All the usualListTensorboardsResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A service for managing user data for RAG.

A service for managing user data for RAG.

A pager for iterating throughlist_rag_corpora requests.

This class thinly wraps an initialListRagCorporaResponse object, andprovides an__aiter__ method to iterate through itsrag_corpora field.

If there are more pages, the__aiter__ method will make additionalListRagCorpora requests and continue to iteratethrough therag_corpora field on thecorresponding responses.

All the usualListRagCorporaResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_rag_corpora requests.

This class thinly wraps an initialListRagCorporaResponse object, andprovides an__iter__ method to iterate through itsrag_corpora field.

If there are more pages, the__iter__ method will make additionalListRagCorpora requests and continue to iteratethrough therag_corpora field on thecorresponding responses.

All the usualListRagCorporaResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_rag_files requests.

This class thinly wraps an initialListRagFilesResponse object, andprovides an__aiter__ method to iterate through itsrag_files field.

If there are more pages, the__aiter__ method will make additionalListRagFiles requests and continue to iteratethrough therag_files field on thecorresponding responses.

All the usualListRagFilesResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_rag_files requests.

This class thinly wraps an initialListRagFilesResponse object, andprovides an__iter__ method to iterate through itsrag_files field.

If there are more pages, the__iter__ method will make additionalListRagFiles requests and continue to iteratethrough therag_files field on thecorresponding responses.

All the usualListRagFilesResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A service for retrieving relevant contexts.

A service for retrieving relevant contexts.

Vertex AI Vizier API.

Vertex AI Vizier is a service to solve blackbox optimizationproblems, such as tuning machine learning hyperparameters andsearching over deep learning architectures.

Vertex AI Vizier API.

Vertex AI Vizier is a service to solve blackbox optimizationproblems, such as tuning machine learning hyperparameters andsearching over deep learning architectures.

A pager for iterating throughlist_studies requests.

This class thinly wraps an initialListStudiesResponse object, andprovides an__aiter__ method to iterate through itsstudies field.

If there are more pages, the__aiter__ method will make additionalListStudies requests and continue to iteratethrough thestudies field on thecorresponding responses.

All the usualListStudiesResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_studies requests.

This class thinly wraps an initialListStudiesResponse object, andprovides an__iter__ method to iterate through itsstudies field.

If there are more pages, the__iter__ method will make additionalListStudies requests and continue to iteratethrough thestudies field on thecorresponding responses.

All the usualListStudiesResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_trials requests.

This class thinly wraps an initialListTrialsResponse object, andprovides an__aiter__ method to iterate through itstrials field.

If there are more pages, the__aiter__ method will make additionalListTrials requests and continue to iteratethrough thetrials field on thecorresponding responses.

All the usualListTrialsResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

A pager for iterating throughlist_trials requests.

This class thinly wraps an initialListTrialsResponse object, andprovides an__iter__ method to iterate through itstrials field.

If there are more pages, the__iter__ method will make additionalListTrials requests and continue to iteratethrough thetrials field on thecorresponding responses.

All the usualListTrialsResponseattributes are available on the pager. If multiple requests are made, onlythe most recent response is retained, and thus used for attribute lookup.

LINT: LEGACY_NAMES Represents a hardware accelerator type.

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

This message hasoneof_ 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 othermembers.

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

Request message forMetadataService.AddContextArtifactsAndExecutions.

Response message forMetadataService.AddContextArtifactsAndExecutions.

Request message forMetadataService.AddContextChildren.

Response message forMetadataService.AddContextChildren.

Request message forMetadataService.AddExecutionEvents.

Response message forMetadataService.AddExecutionEvents.

Request message forVizierService.AddTrialMeasurement.

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

The abstract base class for a message.

Identifies a concept with which DataItems may be annotatedwith.

The generic reusable api auth config.

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

The API secret.

Instance of a general artifact.

The abstract base class for a message.

Describes the state of the Artifact.

Metadata information forNotebookService.AssignNotebookRuntime.

Request message forNotebookService.AssignNotebookRuntime.

Attribution that explains a particular prediction output.

Request message for AugmentPrompt.

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

Metadata of the backend deployed model.

Response message for AugmentPrompt.

A description of resources that to large degree are decidedby Vertex AI, and require only a modest additionalconfiguration. Each Model supporting these resources documentsits specific guidelines.

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

The storage details for Avro input content.

Runtime operation information forPipelineService.BatchCancelPipelineJobs.

Request message forPipelineService.BatchCancelPipelineJobs.

Response message forPipelineService.BatchCancelPipelineJobs.

Details of operations that perform batch create Features.

Request message forFeaturestoreService.BatchCreateFeatures.Request message forFeatureRegistryService.BatchCreateFeatures.

Response message forFeaturestoreService.BatchCreateFeatures.

Request message forTensorboardService.BatchCreateTensorboardRuns.

Response message forTensorboardService.BatchCreateTensorboardRuns.

Request message forTensorboardService.BatchCreateTensorboardTimeSeries.

Response message forTensorboardService.BatchCreateTensorboardTimeSeries.

A description of resources that are used for performing batchoperations, are dedicated to a Model, and need manualconfiguration.

Request message forPipelineService.BatchDeletePipelineJobs.

Response message forPipelineService.BatchDeletePipelineJobs.

Request message forModelService.BatchImportEvaluatedAnnotations

Response message forModelService.BatchImportEvaluatedAnnotations

Request message forModelService.BatchImportModelEvaluationSlices

Response message forModelService.BatchImportModelEvaluationSlices

Runtime operation information forMigrationService.BatchMigrateResources.

Represents a partial result in batch migration operation for oneMigrateResourceRequest.

This message hasoneof_ 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 othermembers.

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

Request message forMigrationService.BatchMigrateResources.

Response message forMigrationService.BatchMigrateResources.

A job that uses aModel toproduce predictions on multiple [inputinstances][google.cloud.aiplatform.v1.BatchPredictionJob.input_config].If predictions for significant portion of the instances fail, thejob may finish without attempting predictions for all remaininginstances.

Configures the input toBatchPredictionJob.SeeModel.supported_input_storage_formatsfor Model's supported input formats, and how instances should beexpressed via any of them.

This message hasoneof_ 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 othermembers.

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

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

The abstract base class for a message.

Configures the output ofBatchPredictionJob.SeeModel.supported_output_storage_formatsfor supported output formats, and how predictions are expressed viaany of them.

This message hasoneof_ 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 othermembers.

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

Further describes this job's output. Supplementsoutput_config.

This message hasoneof_ 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 othermembers.

.. _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 forFeaturestoreService.BatchReadFeatureValues.

This message hasoneof_ 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 othermembers.

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

Selects Features of an EntityType to read values of andspecifies read settings.

Describe pass-through fields in read_instance source.

Response message forFeaturestoreService.BatchReadFeatureValues.

Request message forTensorboardService.BatchReadTensorboardTimeSeriesData.

Response message forTensorboardService.BatchReadTensorboardTimeSeriesData.

The BigQuery location for the output content.

The BigQuery location for the input content.

Input for bleu metric.

Spec for bleu instance.

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

Bleu metric value for an instance.

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

Results for bleu metric.

Spec for bleu score metric - calculates the precision ofn-grams in the prediction as compared to reference - returns ascore ranging between 0 to 1.

Content blob.

It's preferred to send astext directly rather thanraw bytes.

Config for blur baseline.

When enabled, a linear path from the maximally blurred image tothe input image is created. Using a blurred baseline instead ofzero (black image) is motivated by the BlurIG approach explainedhere:

https://arxiv.org/abs/2004.03383

A list of boolean values.

A resource used in LLM queries for users to explicitlyspecify what to cache and how to cache.

This message hasoneof_ 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 othermembers.

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

Metadata on the usage of the cached content.

Request message forJobService.CancelBatchPredictionJob.

Request message forJobService.CancelCustomJob.

Request message forJobService.CancelDataLabelingJob.

Request message forJobService.CancelHyperparameterTuningJob.

Request message forJobService.CancelNasJob.

Request message forPipelineService.CancelPipelineJob.

Request message forPipelineService.CancelTrainingPipeline.

Request message forGenAiTuningService.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.

This message will be placed in the metadata field of agoogle.longrunning.Operation associated with aCheckTrialEarlyStoppingState request.

Request message forVizierService.CheckTrialEarlyStoppingState.

Response message forVizierService.CheckTrialEarlyStoppingState.

Describes the machine learning model version checkpoint.

Source attributions for content.

A collection of source attributions for a piece of content.

Claim that is extracted from the input text and facts thatsupport it.

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

Configurations (e.g. inference timeout) that are applied onyour endpoints.

Result of executing the [ExecutableCode].

Always follows apart containing the [ExecutableCode].

Enumeration of possible outcomes of the code execution.

Input for coherence metric.

Spec for coherence instance.

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

Spec for coherence result.

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

Spec for coherence score metric.

Colab image of the runtime.

Input for Comet metric.

Spec for Comet instance - The fields used for evaluation aredependent on the comet version.

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

Spec for Comet result - calculates the comet score for thegiven instance using the version specified in the spec.

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

Spec for Comet metric.

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

Comet version options.

Request message forVizierService.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 amessage.

AContent includes arole field designating the producer oftheContent and aparts field containing multi-part datathat contains the content of the message turn.

Instance of a general context.

The abstract base class for a message.

Details ofModelService.CopyModeloperation.

Request message forModelService.CopyModel.

This message hasoneof_ 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 othermembers.

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

Response message ofModelService.CopyModeloperation.

RagCorpus status.

RagCorpus life state.

Request message for CorroborateContent.

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

Parameters that can be overrided per request.

Response message for CorroborateContent.

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

Request message for [PredictionService.CountTokens][].

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

Response message for [PredictionService.CountTokens][].

Request message forMetadataService.CreateArtifact.

Request message forJobService.CreateBatchPredictionJob.

Request message forGenAiCacheService.CreateCachedContent.

Request message forMetadataService.CreateContext.

Request message forJobService.CreateCustomJob.

Request message forJobService.CreateDataLabelingJob.

Runtime operation information forDatasetService.CreateDataset.

Request message forDatasetService.CreateDataset.

Runtime operation information forDatasetService.CreateDatasetVersion.

Request message forDatasetService.CreateDatasetVersion.

Runtime operation information forCreateDeploymentResourcePool method.

Request message for CreateDeploymentResourcePool method.

Runtime operation information forEndpointService.CreateEndpoint.

Request message forEndpointService.CreateEndpoint.

Details of operations that perform create EntityType.

Request message forFeaturestoreService.CreateEntityType.

Request message forMetadataService.CreateExecution.

Details of operations that perform create FeatureGroup.

Request message forFeatureRegistryService.CreateFeatureGroup.

Details of operations that perform create FeatureOnlineStore.

Request message forFeatureOnlineStoreAdminService.CreateFeatureOnlineStore.

Details of operations that perform create Feature.

Request message forFeaturestoreService.CreateFeature.Request message forFeatureRegistryService.CreateFeature.

Details of operations that perform create FeatureView.

Request message forFeatureOnlineStoreAdminService.CreateFeatureView.

Details of operations that perform create Featurestore.

Request message forFeaturestoreService.CreateFeaturestore.

Request message forJobService.CreateHyperparameterTuningJob.

Runtime operation information forIndexEndpointService.CreateIndexEndpoint.

Request message forIndexEndpointService.CreateIndexEndpoint.

Runtime operation information forIndexService.CreateIndex.

Request message forIndexService.CreateIndex.

Request message forMetadataService.CreateMetadataSchema.

Details of operations that performMetadataService.CreateMetadataStore.

Request message forMetadataService.CreateMetadataStore.

Request message forJobService.CreateModelDeploymentMonitoringJob.

Request message forJobService.CreateNasJob.

Metadata information forNotebookService.CreateNotebookExecutionJob.

Request message for [NotebookService.CreateNotebookExecutionJob]

Metadata information forNotebookService.CreateNotebookRuntimeTemplate.

Request message forNotebookService.CreateNotebookRuntimeTemplate.

Details of operations that perform create PersistentResource.

Request message forPersistentResourceService.CreatePersistentResource.

Request message forPipelineService.CreatePipelineJob.

Runtime operation information forVertexRagDataService.CreateRagCorpus.

Request message forVertexRagDataService.CreateRagCorpus.

Details ofReasoningEngineService.CreateReasoningEngineoperation.

Request message forReasoningEngineService.CreateReasoningEngine.

Details of operations that perform create FeatureGroup.

Request message forScheduleService.CreateSchedule.

Runtime operation information forSpecialistPoolService.CreateSpecialistPool.

Request message forSpecialistPoolService.CreateSpecialistPool.

Request message forVizierService.CreateStudy.

Request message forTensorboardService.CreateTensorboardExperiment.

Details of operations that perform create Tensorboard.

Request message forTensorboardService.CreateTensorboard.

Request message forTensorboardService.CreateTensorboardRun.

Request message forTensorboardService.CreateTensorboardTimeSeries.

Request message forPipelineService.CreateTrainingPipeline.

Request message forVizierService.CreateTrial.

Request message forGenAiTuningService.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 Dockercontainer or a Python package. A CustomJob can have multipleworker pools and each worker pool can have its own machine andinput spec. A CustomJob will be cleaned up once the job entersterminal 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, adocument 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 onunlabeled 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 aDeployedModel, and that need a higher degree of manualconfiguration.

Request message forMetadataService.DeleteArtifact.

Request message forJobService.DeleteBatchPredictionJob.

Request message forGenAiCacheService.DeleteCachedContent.

Request message forMetadataService.DeleteContext.

Request message forJobService.DeleteCustomJob.

Request message forJobService.DeleteDataLabelingJob.

Request message forDatasetService.DeleteDataset.

Request message forDatasetService.DeleteDatasetVersion.

Request message for DeleteDeploymentResourcePool method.

Request message forEndpointService.DeleteEndpoint.

Request message forFeaturestoreService.DeleteEntityType.

Request message forMetadataService.DeleteExecution.

Request message forFeatureRegistryService.DeleteFeatureGroup.

Request message forFeatureOnlineStoreAdminService.DeleteFeatureOnlineStore.

Request message forFeaturestoreService.DeleteFeature.Request message forFeatureRegistryService.DeleteFeature.

Details of operations that delete Feature values.

Request message forFeaturestoreService.DeleteFeatureValues.

This message hasoneof_ 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 othermembers.

.. _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 valuescorresponding to the entity id will be deleted, including theentityId.

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

Response message forFeaturestoreService.DeleteFeatureValues.

This message hasoneof_ 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 othermembers.

.. _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 theSelectTimeRangeAndFeature option.

Request message forFeatureOnlineStoreAdminService.DeleteFeatureView.

Request message forFeaturestoreService.DeleteFeaturestore.

Request message forJobService.DeleteHyperparameterTuningJob.

Request message forIndexEndpointService.DeleteIndexEndpoint.

Request message forIndexService.DeleteIndex.

Details of operations that performMetadataService.DeleteMetadataStore.

Request message forMetadataService.DeleteMetadataStore.

Request message forJobService.DeleteModelDeploymentMonitoringJob.

Request message forModelService.DeleteModel.

Request message forModelService.DeleteModelVersion.

Request message forJobService.DeleteNasJob.

Request message for [NotebookService.DeleteNotebookExecutionJob]

Request message forNotebookService.DeleteNotebookRuntime.

Request message forNotebookService.DeleteNotebookRuntimeTemplate.

Details of operations that perform deletes of any entities.

Request message forPersistentResourceService.DeletePersistentResource.

Request message forPipelineService.DeletePipelineJob.

Request message forVertexRagDataService.DeleteRagCorpus.

Request message forVertexRagDataService.DeleteRagFile.

Request message forReasoningEngineService.DeleteReasoningEngine.

Request message forDatasetService.DeleteSavedQuery.

Request message forScheduleService.DeleteSchedule.

Request message forSpecialistPoolService.DeleteSpecialistPool.

Request message forVizierService.DeleteStudy.

Request message forTensorboardService.DeleteTensorboardExperiment.

Request message forTensorboardService.DeleteTensorboard.

Request message forTensorboardService.DeleteTensorboardRun.

Request message forTensorboardService.DeleteTensorboardTimeSeries.

Request message forPipelineService.DeleteTrainingPipeline.

Request message forVizierService.DeleteTrial.

Runtime operation information forIndexEndpointService.DeployIndex.

Request message forIndexEndpointService.DeployIndex.

Response message forIndexEndpointService.DeployIndex.

Runtime operation information forEndpointService.DeployModel.

Request message forEndpointService.DeployModel.

The abstract base class for a message.

Response message forEndpointService.DeployModel.

Runtime operation information forModelGardenService.Deploy.

Request message forModelGardenService.Deploy.

This message hasoneof_ 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 othermembers.

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

The deploy config to use for the deployment.

The abstract base class for a message.

The endpoint config to use for the deployment.

The model config to use for the deployment.

Response message forModelGardenService.Deploy.

A deployment of an Index. IndexEndpoints contain one or moreDeployedIndexes.

Used to set up the auth on the DeployedIndex's privateendpoint.

Configuration for an authentication provider, including support forJSON 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 moreDeployedModels.

This message hasoneof_ 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 othermembers.

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

Runtime status of the deployed model.

The abstract base class for a message.

Points to a DeployedModel.

A description of resources that can be shared by multipleDeployedModels, whose underlying specification consists of aDedicatedResources.

Stage field indicating the current progress of a deployment.

Request message forPredictionService.DirectPredict.

Response message forPredictionService.DirectPredict.

Request message forPredictionService.DirectRawPredict.

Response message forPredictionService.DirectRawPredict.

The input content is encapsulated and uploaded in therequest.

Represents the spec of disk options.

DNS peering configuration. These configurations are used tocreate DNS peering zones in the Vertex tenant project VPC,enabling resolution of records within the specified domainhosted in the target network's Cloud DNS.

A list of double values.

Describes the options to customize dynamic retrieval.

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

The mode of the predictor to be used in dynamic retrieval.

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

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

The abstract base class for a message.

The abstract base class for a message.

Tool to search public web data, powered by Vertex AI Searchand Sec4 compliance.

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 tobe modeled and have stored information about. For example,driver is an entity type, and driver0 is an instance of anentity type driver.

The abstract base class for a message.

Represents an environment variable present in a Container orPython Module.

Model error analysis for each annotation.

Attributed items for a given annotation, typicallyrepresenting neighbors from the training sets constrained by thequery type.

The query type used for finding the attributed items.

Request message for EvaluationService.EvaluateInstances.

This message hasoneof_ 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 othermembers.

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

Response message for EvaluationService.EvaluateInstances.

This message hasoneof_ 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 othermembers.

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

True positive, false positive, or false negative.

EvaluatedAnnotation is only available under ModelEvaluationSlicewith slice ofannotationSpec dimension.

Describes the type of the EvaluatedAnnotation. The type isdetermined

Explanation result of the prediction produced by the Model.

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

The abstract base class for a message.

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

Input for exact match metric.

Spec for exact match instance.

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

Exact match metric value for an instance.

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

Results for exact match metric.

Spec for exact match metric - returns 1 if prediction andreference exactly matches, otherwise 0.

Example-based explainability that returns the nearestneighbors from the provided dataset.

This message hasoneof_ 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 othermembers.

.. _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.

Overrides for example-based explanations.

Data format enum.

Restrictions namespace for example-based explanationsoverrides.

Code generated by the model that is meant to be executed, and theresult returned to the model.

Generated when using the [FunctionDeclaration] tool and[FunctionCallingConfig] mode is set to [Mode.CODE].

Supported programming languages for the generated code.

Instance of a general execution.

The abstract base class for a message.

Describes the state of the Execution.

Request message forPredictionService.Explain.

Response message forPredictionService.Explain.

Explanation of a prediction (provided inPredictResponse.predictions)produced by the Model on a giveninstance.

Metadata describing the Model's input and output forexplanation.

Metadata of the input of a feature.

Fields other thanInputMetadata.input_baselinesare applicable only for Models that are using Vertex AI-providedimages for Tensorflow.

Defines how a feature is encoded. Defaults to IDENTITY.

    ::       input = [27, 6.0, 150]       index_feature_mapping = ["age", "height", "weight"]BAG_OF_FEATURES_SPARSE (3):    The tensor represents a bag of features where each index    maps to a feature. Zero values in the tensor indicates    feature being non-existent.    <xref uid="google.cloud.aiplatform.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 numericinformation about the feature, such as its range (min, max). If thefeature has been pre-processed, for example with z-scoring, then itprovides information about how to recover the original feature. Forexample, if the input feature is an image and it has beenpre-processed to obtain 0-mean and stddev = 1 values, thenoriginal_mean, and original_stddev refer to the mean and stddev ofthe 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.

How the original image is displayed in the visualization.

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

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

The abstract base class for a message.

Metadata of the prediction output to be explained.

This message hasoneof_ 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 othermembers.

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

The abstract base class for a message.

TheExplanationMetadataentries that can be overridden at [onlineexplanation][google.cloud.aiplatform.v1.PredictionService.Explain]time.

The [inputmetadata][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 hasoneof_ 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 othermembers.

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

Specification of Model explanation.

TheExplanationSpecentries that can be overridden at [onlineexplanation][google.cloud.aiplatform.v1.PredictionService.Explain]time.

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

This message hasoneof_ 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 othermembers.

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

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

Runtime operation information forDatasetService.ExportData.

Request message forDatasetService.ExportData.

Response message forDatasetService.ExportData.

Details of operations that exports Features values.

Request message forFeaturestoreService.ExportFeatureValues.

This message hasoneof_ 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 othermembers.

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

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

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

Response message forFeaturestoreService.ExportFeatureValues.

Assigns input data to training, validation, and test setsbased on the given filters, data pieces not matched by anyfilter are ignored. Currently only supported for Datasetscontaining DataItems.If any of the filters in this message are to match nothing, thenthey can be set as '-' (the minus sign).

Supported only for unstructured Datasets.

Assigns the input data to training, validation, and test sets as perthe given fractions. Any oftraining_fraction,validation_fraction andtest_fraction may optionally beprovided, they must sum to up to 1. If the provided ones sum to lessthan 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 isused for training, 10% for validation, and 10% for test.

Details ofModelService.ExportModeloperation.

Further describes the output of the ExportModel. SupplementsExportModelRequest.OutputConfig.

Request message forModelService.ExportModel.

Output configuration for the Model export.

Response message ofModelService.ExportModeloperation.

Request message forTensorboardService.ExportTensorboardTimeSeriesData.

Response message forTensorboardService.ExportTensorboardTimeSeriesData.

The fact used in grounding.

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

Configuration for faster model deployment.

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

The abstract base class for a message.

A list of historicalSnapshotAnalysisorImportFeaturesAnalysisstats requested by user, sorted byFeatureStatsAnomaly.start_timedescending.

If the objective in the request is bothImport Feature Analysis and Snapshot Analysis, this objectivecould be one of them. Otherwise, this objective should be thesame as the objective in the request.

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

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 standarddeviation of the gaussian kernel that will be used to add noiseto interpolated inputs prior to computing gradients.

Noise sigma for a single feature.

Vertex AI Feature Online Store provides a centralizedrepository for serving ML features and embedding indexes at lowlatency. The Feature Online Store is a top-level container.

This message hasoneof_ 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 othermembers.

.. _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. Publicendpoint is provisioned by default.

The abstract base class for a message.

Optimized storage type

Possible states a featureOnlineStore can have.

Selector for Features of an EntityType.

Stats and Anomaly generated at specific timestamp for specificFeature. The start_time and end_time are used to define the timerange of the dataset that current stats belongs to, e.g. predictiontraffic is bucketed into prediction datasets by time window. If theDataset is not defined by time window, start_time = end_time.Timestamp of the stats and anomalies always refers to end_time. Rawstats and anomalies are stored in stats_uri or anomaly_uri in thetensorflow defined protos. Field data_stats contains almostidentical information with the raw stats in Vertex AI defined proto,for UI to display.

Value for a feature.

This message hasoneof_ 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 othermembers.

.. _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 hasoneof_ 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 othermembers.

.. _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 theFeatureOnlineStore will serve based on its syncConfig.

This message hasoneof_ 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 othermembers.

.. _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 syncedto 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 besynced to Online Store.

Configuration for vector indexing.

This message hasoneof_ 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 othermembers.

.. _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.

    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 FeatureViews created in OptimizedFeatureOnlineStore.

Service agent type used during data sync.

Configuration for Sync. Only one option is set.

A Vertex Rag source for features that need to be synced toOnline Store.

Format of the data in the Feature View.

Lookup key for a feature view.

This message hasoneof_ 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 othermembers.

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

ID that is comprised from several parts (columns).

Request message forFeatureOnlineStoreService.FeatureViewDirectWrite.

A data key and associated feature values to write to thefeature view.

Feature name & value pair.

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

Response message forFeatureOnlineStoreService.FeatureViewDirectWrite.

Details about the write for each key.

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

Summary from the Sync job. For continuous syncs, the summaryis updated periodically. For batch syncs, it gets updated oncompletion of the sync.

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

The abstract base class for a message.

OnlineServingConfig specifies the details for provisioningonline serving resources.

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

Possible states a featurestore can have.

Configuration of how features in Featurestore are monitored.

Configuration of the Featurestore's ImportFeature Analysis BasedMonitoring. This type of analysis generates statistics for values ofeach Feature imported by everyImportFeatureValuesoperation.

Defines the baseline to do anomaly detection for feature valuesimported by eachImportFeatureValuesoperation.

The state defines whether to enable ImportFeature analysis.

Configuration of the Featurestore's Snapshot Analysis BasedMonitoring. This type of analysis generates statistics for eachFeature based on a snapshot of the latest feature value of eachentities 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 forFeatureOnlineStoreService.FetchFeatureValues.All the features under the requested feature view will be returned.

Response message forFeatureOnlineStoreService.FetchFeatureValues

This message hasoneof_ 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 othermembers.

.. _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.

RagFile status.

RagFile state.

Assigns input data to training, validation, and test setsbased on the given filters, data pieces not matched by anyfilter are ignored. Currently only supported for Datasetscontaining DataItems.If any of the filters in this message are to match nothing, thenthey can be set as '-' (the minus sign).

Supported only for unstructured Datasets.

The request message forMatchService.FindNeighbors.

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

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

Parameters for RRF algorithm that combines search results.

The response message forMatchService.FindNeighbors.

Nearest neighbors for one query.

A neighbor of the query vector.

Input for fluency metric.

Spec for fluency instance.

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

Spec for fluency result.

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

Spec for fluency score metric.

Assigns the input data to training, validation, and test sets as perthe given fractions. Any oftraining_fraction,validation_fraction andtest_fraction may optionally beprovided, they must sum to up to 1. If the provided ones sum to lessthan 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 isused for training, 10% for validation, and 10% for test.

Input for fulfillment metric.

Spec for fulfillment instance.

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

Spec for fulfillment result.

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

Spec for fulfillment metric.

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

Function calling config.

Function calling mode.

Structured representation of a function declaration as defined bytheOpenAPI 3.0specification <https://spec.openapis.org/oas/v3.0.3>__. Included inthis declaration are the function name, description, parameters andresponse type. This FunctionDeclaration is a representation of ablock of code that can be used as aTool by the model andexecuted by the client.

The result output from a [FunctionCall] that contains a stringrepresenting the [FunctionDeclaration.name] and a structured JSONobject containing any output from the function is used as context tothe model. This should contain the result of a [FunctionCall] madebased on model prediction.

The Google Cloud Storage location where the output is to bewritten to.

The Google Cloud Storage location for the input content.

Configuration for GenAiAdvancedFeatures.

Configuration for Retrieval Augmented Generation feature.

Request message for [PredictionService.GenerateContent].

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

The abstract base class for a message.

Response message for [PredictionService.GenerateContent].

Content filter results for a prompt sent in the request.

Blocked reason enumeration.

Usage metadata about response(s).

Request message for DataFoundryService.GenerateSyntheticData.

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

The response containing the generated data.

Generation config.

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

The configuration for routing the request to a specificmodel.

This message hasoneof_ 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 othermembers.

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

When automated routing is specified, the routing will bedetermined by the pretrained routing model and customer providedmodel routing preference.

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

The model routing preference.

When manual routing is set, the specified model will be useddirectly.

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

Config for thinking features.

.. _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 generatedfrom Generative AI Studio.

Request message forDatasetService.GetAnnotationSpec.

Request message forMetadataService.GetArtifact.

Request message forJobService.GetBatchPredictionJob.

Request message forGenAiCacheService.GetCachedContent.

Request message forMetadataService.GetContext.

Request message forJobService.GetCustomJob.

Request message forJobService.GetDataLabelingJob.

Request message forDatasetService.GetDataset.Next ID: 4

Request message forDatasetService.GetDatasetVersion.Next ID: 4

Request message for GetDeploymentResourcePool method.

Request message forEndpointService.GetEndpoint

Request message forFeaturestoreService.GetEntityType.

Request message forMetadataService.GetExecution.

Request message forFeatureRegistryService.GetFeatureGroup.

Request message forFeatureOnlineStoreAdminService.GetFeatureOnlineStore.

Request message forFeaturestoreService.GetFeature.Request message forFeatureRegistryService.GetFeature.

Request message forFeatureOnlineStoreAdminService.GetFeatureView.

Request message forFeatureOnlineStoreAdminService.GetFeatureViewSync.

Request message forFeaturestoreService.GetFeaturestore.

Request message forJobService.GetHyperparameterTuningJob.

Request message forIndexEndpointService.GetIndexEndpoint

Request message forIndexService.GetIndex

Request message forMetadataService.GetMetadataSchema.

Request message forMetadataService.GetMetadataStore.

Request message forJobService.GetModelDeploymentMonitoringJob.

Request message forModelService.GetModelEvaluation.

Request message forModelService.GetModelEvaluationSlice.

Request message forModelService.GetModel.

Request message forJobService.GetNasJob.

Request message forJobService.GetNasTrialDetail.

Request message for [NotebookService.GetNotebookExecutionJob]

Request message forNotebookService.GetNotebookRuntime

Request message forNotebookService.GetNotebookRuntimeTemplate

Request message forPersistentResourceService.GetPersistentResource.

Request message forPipelineService.GetPipelineJob.

Request message forModelGardenService.GetPublisherModel

Request message forVertexRagDataService.GetRagCorpus

Request message forVertexRagDataService.GetRagEngineConfig

Request message forVertexRagDataService.GetRagFile

Request message forReasoningEngineService.GetReasoningEngine.

Request message forScheduleService.GetSchedule.

Request message forSpecialistPoolService.GetSpecialistPool.

Request message forVizierService.GetStudy.

Request message forTensorboardService.GetTensorboardExperiment.

Request message forTensorboardService.GetTensorboard.

Request message forTensorboardService.GetTensorboardRun.

Request message forTensorboardService.GetTensorboardTimeSeries.

Request message forPipelineService.GetTrainingPipeline.

Request message forVizierService.GetTrial.

Request message forGenAiTuningService.GetTuningJob.

The Google Drive location for the input content.

The type and ID of the Google Drive resource.

The type of the Google Drive resource.

Tool to retrieve public maps data for grounding, powered byGoogle.

Tool to retrieve public web data for grounding, powered byGoogle.

Input for groundedness metric.

Spec for groundedness instance.

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

Spec for groundedness result.

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

Spec for groundedness metric.

Grounding chunk.

This message hasoneof_ 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 othermembers.

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

Chunk from Google Maps.

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

Chunk from context retrieved by the retrieval tools.

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

Chunk from the web.

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

Metadata returned to client when grounding is enabled.

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

Grounding support.

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

Harm categories that will block the content.

Represents a HyperparameterTuningJob. AHyperparameterTuningJob has a Study specification and multipleCustomJobs 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 aDataset, together with the labels that will be applied to theDataItems 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 forDatasetService.ImportData.

Request message forDatasetService.ImportData.

Response message forDatasetService.ImportData.

Details of operations that perform import Feature values.

Request message forFeaturestoreService.ImportFeatureValues.

This message hasoneof_ 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 othermembers.

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

Defines the Feature value(s) to import.

Response message forFeaturestoreService.ImportFeatureValues.

Request message forModelService.ImportModelEvaluation

Config for importing RagFiles.

This message hasoneof_ 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 othermembers.

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

Runtime operation information forVertexRagDataService.ImportRagFiles.

Request message forVertexRagDataService.ImportRagFiles.

Response message forVertexRagDataService.ImportRagFiles.

This message hasoneof_ 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 othermembers.

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

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

The update method of an Index.

The abstract base class for a message.

A datapoint of Index.

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

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

This message hasoneof_ 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 othermembers.

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

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

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

Restriction of a datapoint which describe itsattributes(tokens) from each of several attributecategories(namespaces).

Feature embedding vector for sparse index. An array ofnumbers whose values are located in the specified dimensions.

Indexes are deployed into it. An IndexEndpoint can havemultiple DeployedIndexes.

The abstract base class for a message.

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

.. _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 valuetaking advantage of the model's fully differentiable structure.Refer to this paper for more details:https://arxiv.org/abs/1703.01365

The Jira source for the ImportRagFilesRequest.

JiraQueries contains the Jira queries and correspondingauthentication.

Describes the state of a job.

Contains information about the Large Model.

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

Request message forDatasetService.ListAnnotations.

Response message forDatasetService.ListAnnotations.

Request message forMetadataService.ListArtifacts.

Response message forMetadataService.ListArtifacts.

Request message forJobService.ListBatchPredictionJobs.

Response message forJobService.ListBatchPredictionJobs

Request to list CachedContents.

Response with a list of CachedContents.

Request message forMetadataService.ListContexts

Response message forMetadataService.ListContexts.

Request message forJobService.ListCustomJobs.

Response message forJobService.ListCustomJobs

Request message forDatasetService.ListDataItems.

Response message forDatasetService.ListDataItems.

Request message forJobService.ListDataLabelingJobs.

Response message forJobService.ListDataLabelingJobs.

Request message forDatasetService.ListDatasetVersions.

Response message forDatasetService.ListDatasetVersions.

Request message forDatasetService.ListDatasets.

Response message forDatasetService.ListDatasets.

Request message for ListDeploymentResourcePools method.

Response message for ListDeploymentResourcePools method.

Request message forEndpointService.ListEndpoints.

Response message forEndpointService.ListEndpoints.

Request message forFeaturestoreService.ListEntityTypes.

Response message forFeaturestoreService.ListEntityTypes.

Request message forMetadataService.ListExecutions.

Response message forMetadataService.ListExecutions.

Request message forFeatureRegistryService.ListFeatureGroups.

Response message forFeatureRegistryService.ListFeatureGroups.

Request message forFeatureOnlineStoreAdminService.ListFeatureOnlineStores.

Response message forFeatureOnlineStoreAdminService.ListFeatureOnlineStores.

Request message forFeatureOnlineStoreAdminService.ListFeatureViewSyncs.

Response message forFeatureOnlineStoreAdminService.ListFeatureViewSyncs.

Request message forFeatureOnlineStoreAdminService.ListFeatureViews.

Response message forFeatureOnlineStoreAdminService.ListFeatureViews.

Request message forFeaturestoreService.ListFeatures.Request message forFeatureRegistryService.ListFeatures.

Response message forFeaturestoreService.ListFeatures.Response message forFeatureRegistryService.ListFeatures.

Request message forFeaturestoreService.ListFeaturestores.

Response message forFeaturestoreService.ListFeaturestores.

Request message forJobService.ListHyperparameterTuningJobs.

Response message forJobService.ListHyperparameterTuningJobs

Request message forIndexEndpointService.ListIndexEndpoints.

Response message forIndexEndpointService.ListIndexEndpoints.

Request message forIndexService.ListIndexes.

Response message forIndexService.ListIndexes.

Request message forMetadataService.ListMetadataSchemas.

Response message forMetadataService.ListMetadataSchemas.

Request message forMetadataService.ListMetadataStores.

Response message forMetadataService.ListMetadataStores.

Request message forJobService.ListModelDeploymentMonitoringJobs.

Response message forJobService.ListModelDeploymentMonitoringJobs.

Request message forModelService.ListModelEvaluationSlices.

Response message forModelService.ListModelEvaluationSlices.

Request message forModelService.ListModelEvaluations.

Response message forModelService.ListModelEvaluations.

Request message forModelService.ListModelVersionCheckpoints.

Response message forModelService.ListModelVersionCheckpoints

Request message forModelService.ListModelVersions.

Response message forModelService.ListModelVersions

Request message forModelService.ListModels.

Response message forModelService.ListModels

Request message forJobService.ListNasJobs.

Response message forJobService.ListNasJobs

Request message forJobService.ListNasTrialDetails.

Response message forJobService.ListNasTrialDetails

Request message for [NotebookService.ListNotebookExecutionJobs]

Response message for [NotebookService.CreateNotebookExecutionJob]

Request message forNotebookService.ListNotebookRuntimeTemplates.

Response message forNotebookService.ListNotebookRuntimeTemplates.

Request message forNotebookService.ListNotebookRuntimes.

Response message forNotebookService.ListNotebookRuntimes.

Request message forVizierService.ListOptimalTrials.

Response message forVizierService.ListOptimalTrials.

Request message forPersistentResourceService.ListPersistentResources.

Response message forPersistentResourceService.ListPersistentResources

Request message forPipelineService.ListPipelineJobs.

Response message forPipelineService.ListPipelineJobs

Request message forVertexRagDataService.ListRagCorpora.

Response message forVertexRagDataService.ListRagCorpora.

Request message forVertexRagDataService.ListRagFiles.

Response message forVertexRagDataService.ListRagFiles.

Request message forReasoningEngineService.ListReasoningEngines.

Response message forReasoningEngineService.ListReasoningEngines

Request message forDatasetService.ListSavedQueries.

Response message forDatasetService.ListSavedQueries.

Request message forScheduleService.ListSchedules.

Response message forScheduleService.ListSchedules

Request message forSpecialistPoolService.ListSpecialistPools.

Response message forSpecialistPoolService.ListSpecialistPools.

Request message forVizierService.ListStudies.

Response message forVizierService.ListStudies.

Request message forTensorboardService.ListTensorboardExperiments.

Response message forTensorboardService.ListTensorboardExperiments.

Request message forTensorboardService.ListTensorboardRuns.

Response message forTensorboardService.ListTensorboardRuns.

Request message forTensorboardService.ListTensorboardTimeSeries.

Response message forTensorboardService.ListTensorboardTimeSeries.

Request message forTensorboardService.ListTensorboards.

Response message forTensorboardService.ListTensorboards.

Request message forPipelineService.ListTrainingPipelines.

Response message forPipelineService.ListTrainingPipelines

Request message forVizierService.ListTrials.

Response message forVizierService.ListTrials.

Request message forGenAiTuningService.ListTuningJobs.

Response message forGenAiTuningService.ListTuningJobs

Logprobs Result

Candidate for the logprobs token and score.

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

Candidates with top log probabilities at each decoding step.

Request message forVizierService.LookupStudy.

Specification of a single machine.

Manual batch tuning parameters.

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

A message representing a metric in the measurement.

Request message forModelService.MergeVersionAliases.

Instance of a general MetadataSchema.

Describes the type of the MetadataSchema.

Instance of a metadata store. Contains a set of metadata thatcan be queried.

Represents Dataplex integration settings.

Represents state information for a MetadataStore.

Input for MetricX metric.

Spec for MetricX instance - The fields used for evaluationare dependent on the MetricX version.

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

Spec for MetricX result - calculates the MetricX score forthe given instance using the version specified in the spec.

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

Spec for MetricX metric.

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

MetricX Version options.

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

This message hasoneof_ 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 othermembers.

.. _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 indatalabeling.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 hasoneof_ 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 othermembers.

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

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

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

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

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

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

Describes a successfully migrated resource.

This message hasoneof_ 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 othermembers.

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

Content Part modality

Represents token counting info for a single modality.

A trained machine learning Model.

User input field to specify the base model source. Currentlyit only supports specifing the Model Garden models and Geniemodels.

This message hasoneof_ 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 othermembers.

.. _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.

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

The Model content that can be exported.

The abstract base class for a message.

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

Configuration for Model Armor integrations of prompt andresponses.

Specification of a container for serving predictions. Some fields inthis message correspond to fields in theKubernetes Container v1corespecification <https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.23/#container-v1-core>__.

ModelDeploymentMonitoringBigQueryTable specifies the BigQuerytable name as well as some information of the logs stored inthis table.

Indicates where does the log come from.

Indicates what type of traffic does the log belong to.

Represents a job that runs periodically to monitor thedeployed models in an endpoint. It will analyze the loggedtraining & 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.

ModelDeploymentMonitoringObjectiveConfig contains the pair ofdeployed_model_id to ModelMonitoringObjectiveConfig.

The Model Monitoring Objective types.

The config for scheduling monitoring job.

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

A collection of metrics calculated by comparing Model'spredictions on a slice of the test data against ground truthannotations.

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 isexclusive.

Specification message containing the config for this SliceSpec. Whenkind is selected asvalue and/orrange, only a singleslice will be computed. Whenall_values is present, a separateslice will be computed for each possible label/value for thecorresponding key inconfig. Examples, with feature zip_codewith 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 and20000 For this example, data with the zip_code of 12345 will be inthis 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 and20000 has the country "US". For this example, data with the zip_codeof 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 thedataset.

Example 5:

::

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

Three slices are computed, one for each unique country in thedataset where the zip_code is also 12345. For this example, datawith zip_code 12345 and country "US" will be in one slice, zip_code12345 and country "Canada" in another slice, and zip_code 12345 andcountry "Mexico" in another slice, totaling 3 slices.

This message hasoneof_ 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 othermembers.

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

Single value that supports strings and floats.

This message hasoneof_ 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 othermembers.

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

Aggregated explanation metrics for a Model over a set ofinstances.

Contains information about the source of the models generatedfrom 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, includingthe information needed to detect anomalies for one particularmodel.

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

Output fromBatchPredictionJobfor Model Monitoring baseline dataset, which can be used to generatebaseline attribution scores.

This message hasoneof_ 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 othermembers.

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

The storage format of the predictions generatedBatchPrediction job.

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 hasoneof_ 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 othermembers.

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

The config for Training & Prediction data skew detection. Itspecifies the training dataset sources and the skew detectionparameters.

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 fromobjective field, thisModelSourceType enum indicates the source from which the modelwas accessed or obtained, whereas theobjective indicates theoverall aim or function of this model.

A proto representation of a Spanner-storedModelVersionCheckpoint. The meaning of the fields is equivalentto their in-Spanner counterparts.

Runtime operation information forIndexEndpointService.MutateDeployedIndex.

Request message forIndexEndpointService.MutateDeployedIndex.

Response message forIndexEndpointService.MutateDeployedIndex.

Runtime operation information forEndpointService.MutateDeployedModel.

Request message forEndpointService.MutateDeployedModel.

Response message forEndpointService.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.

The available types of multi-trial algorithms.

Represent spec for search trials.

Represent spec for train trials.

Represents a uCAIP NasJob trial.

Describes a NasTrial state.

Represents a NasTrial details along with its parameters. Ifthere is a corresponding train NasTrial, the train NasTrial isalso returned.

A query to find a number of similar entities.

This message hasoneof_ 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 othermembers.

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

The embedding vector.

Numeric filter is used to search a subset of the entities by usingboolean rules on numeric columns. For example: Database Point 0:{name: "a" value_int: 42} {name: "b" value_float: 1.0} DatabasePoint 1: {name: "a" value_int: 10} {name: "b" value_float: 2.0}Database Point 2: {name: "a" value_int: -1} {name: "b" value_float:3.0} Query: {name: "a" value_int: 12 operator: LESS} // MatchesPoint 1, 2 {name: "b" value_float: 2.0 operator: EQUAL} // MatchesPoint 1

This message hasoneof_ 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 othermembers.

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

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

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

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

Runtime operation metadata with regard to Matching EngineIndex.

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.

NotebookExecutionJob represents an instance of a notebookexecution.

This message hasoneof_ 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 othermembers.

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

Compute configuration to use for an execution job.

The Dataform Repository containing the input notebook.

The content of the input notebook in ipynb format.

The Cloud Storage uri for the input notebook.

The abstract base class for a message.

Configuration for a Workbench Instances-based environment.

Views for Get/List NotebookExecutionJob

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

A runtime is a virtual machine allocated to a particular userfor a particular Notebook file on temporary basis with lifetime.Default runtimes have a lifetime of 18 hours, while customruntimes last for 6 months from their creation or last upgrade.

The substate of the NotebookRuntime to display healthinformation.

The abstract base class for a message.

The substate of the NotebookRuntime to display state ofruntime. The resource of NotebookRuntime is in ACTIVE state forthese sub state.

A template that specifies runtime configurations such asmachine 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.

Notebook Software Config. This is passed to the backend whenuser makes software configurations in UI.

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

Defines a specification for a single output field.

The data type of the field.

PSC config that is used to automatically create PSC endpointsin the user projects.

The state of the PSC service automation.

Pairwise prediction autorater preference.

Input for pairwise metric.

Pairwise metric instance. Usually one instance corresponds toone row in an evaluation dataset.

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

Spec for pairwise metric result.

Spec for pairwise metric.

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

Input for pairwise question answering quality metric.

Spec for pairwise question answering quality instance.

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

Spec for pairwise question answering quality result.

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

Spec for pairwise question answering quality score metric.

Input for pairwise summarization quality metric.

Spec for pairwise summarization quality instance.

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

Spec for pairwise summarization quality result.

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

Spec for pairwise summarization quality score metric.

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

APart consists of data which has an associated datatype. APart can only contain one of the accepted types inPart.data.

APart must have a fixed IANA MIME type identifying the type andsubtype of the media ifinline_data orfile_data field isfilled with raw bytes.

This message hasoneof_ 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 othermembers.

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

Request message forJobService.PauseModelDeploymentMonitoringJob.

Request message forScheduleService.PauseSchedule.

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

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

The abstract base class for a message.

Describes the PersistentResource state.

Represents the failure policy of a pipeline. Currently, the defaultof a pipeline is that the pipeline will continue to run until nomore tasks can be executed, also known asPIPELINE_FAILURE_POLICY_FAIL_SLOW. However, if a pipeline is set toPIPELINE_FAILURE_POLICY_FAIL_FAST, it will stop scheduling any newtasks when a task has failed. Any scheduled tasks will continue tocompletion.

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.

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

The runtime detail of a pipeline executor.

This message hasoneof_ 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 othermembers.

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

The detail of a container execution. It contains the jobnames of the lifecycle of a container execution.

The detailed info for a custom job executor.

Pipeline template metadata ifPipelineJob.template_uriis from supported template registry. Currently, the only supportedregistry is Artifact Registry.

Input for pointwise metric.

Pointwise metric instance. Usually one instance correspondsto one row in an evaluation dataset.

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

Spec for pointwise metric result.

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

Spec for pointwise metric.

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

Represents a network port in a container.

Post startup script config.

Represents a notebook runtime post startup script behavior.

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

Supported only for tabular Datasets.

Request message forPredictionService.Predict.

Configuration for logging request-response to a BigQuerytable.

Response message forPredictionService.Predict.

Contains the schemata used in Model's predictions and explanationsviaPredictionService.Predict,PredictionService.ExplainandBatchPredictionJob.

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

Preset option controlling parameters for queryspeed-precision trade-off

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

Represents configuration for private service connect.

Probe describes a health check to be performed against acontainer to determine whether it is alive or ready to receivetraffic.

This message hasoneof_ 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 othermembers.

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

ExecAction specifies a command to execute.

GrpcAction checks the health of a container using a gRPCservice.

HttpGetAction describes an action based on HTTP Get requests.

HttpHeader describes a custom header to be used in HTTPprobes

TcpSocketAction probes the health of a container by opening aTCP socket connection.

PscAutomatedEndpoints defines the output of the forwardingrule automatically created by each PscAutomationConfig.

Configuration for PSC-I.

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 orDeployModel/CreateEndpoint requests.

This message hasoneof_ 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 othermembers.

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

Metadata information about the deployment for managingdeployment config.

The abstract base class for a message.

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.

An enum representing the open source category of aPublisherModel.

Reference to a resource.

This message hasoneof_ 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 othermembers.

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

An enum representing the state of the PublicModelVersion.

View enumeration of PublisherModel.

Details of operations that performMetadataService.PurgeArtifacts.

Request message forMetadataService.PurgeArtifacts.

Response message forMetadataService.PurgeArtifacts.

Details of operations that performMetadataService.PurgeContexts.

Request message forMetadataService.PurgeContexts.

Response message forMetadataService.PurgeContexts.

Details of operations that performMetadataService.PurgeExecutions.

Request message forMetadataService.PurgeExecutions.

Response message forMetadataService.PurgeExecutions.

The spec of a Python packaged code.

Request message forMetadataService.QueryArtifactLineageSubgraph.

Request message forMetadataService.QueryContextLineageSubgraph.

Request message for QueryDeployedModels method.

Response message for QueryDeployedModels method.

Request message forMetadataService.QueryExecutionInputsAndOutputs.

Request message for [ReasoningEngineExecutionService.Query][].

Response message for [ReasoningEngineExecutionService.Query][]

Input for question answering correctness metric.

Spec for question answering correctness instance.

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

Spec for question answering correctness result.

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

Spec for question answering correctness metric.

Input for question answering helpfulness metric.

Spec for question answering helpfulness instance.

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

Spec for question answering helpfulness result.

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

Spec for question answering helpfulness metric.

Input for question answering quality metric.

Spec for question answering quality instance.

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

Spec for question answering quality result.

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

Spec for question answering quality score metric.

Input for question answering relevance metric.

Spec for question answering relevance instance.

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

Spec for question answering relevance result.

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

Spec for question answering relevance metric.

A RagChunk includes the content of a chunk of a RagFile, andassociated metadata.

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

Represents where the chunk starts and ends in the document.

Relevant contexts for one query.

A context of the query.

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

A RagCorpus is a RagFile container and a project can havemultiple RagCorpora.

This message hasoneof_ 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 othermembers.

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

Config for the embedding model to use for RAG.

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

Config representing a model hosted on Vertex PredictionEndpoint.

Config for RagEngine.

A RagFile contains user data for chunking, embedding andindexing.

This message hasoneof_ 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 othermembers.

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

Specifies the size and overlap of chunks for RagFiles.

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

Specifies the fixed length chunking config.

Specifies the parsing config for RagFiles.

This message hasoneof_ 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 othermembers.

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

Document AI Layout Parser config.

Specifies the advanced parsing for RagFiles.

Specifies the transformation config for RagFiles.

Configuration message for RagManagedDb used by RagEngine.

This message hasoneof_ 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 othermembers.

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

Basic tier is a cost-effective and low compute tier suitable for thefollowing cases:

• Experimenting with RagManagedDb.
• Small data size.
• Latency insensitive workload.
• Only using RAG Engine with external vector DBs.

NOTE: This is the default tier if not explicitly chosen.

Scaled tier offers production grade performance along withautoscaling functionality. It is suitable for customers withlarge amounts of data or performance sensitive workloads.

Disables the RAG Engine service and deletes all your dataheld within this service. This will halt the billing of theservice.

NOTE: Once deleted the data cannot be recovered. To start usingRAG Engine again, you will need to update the tier by callingthe UpdateRagEngineConfig API.

A query to retrieve relevant contexts.

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

Specifies the context retrieval config.

Config for filters.

This message hasoneof_ 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 othermembers.

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

Config for ranking and reranking.

This message hasoneof_ 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 othermembers.

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

Config for LlmRanker.

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

Config for Rank Service.

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

Config for the Vector DB to use for RAG.

This message hasoneof_ 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 othermembers.

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

The config for the Pinecone.

The config for the default RAG-managed Vector DB.

This message hasoneof_ 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 othermembers.

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

Config for ANN search.

RagManagedDb uses a tree-based structure to partition data andfacilitate faster searches. As a tradeoff, it requires longerindexing time and manual triggering of index rebuild via theImportRagFiles and UpdateRagCorpus API.

Config for KNN search.

The config for the Vertex Vector Search.

Request message forPredictionService.RawPredict.

Configuration for the Ray OSS Logs.

Configuration for the Ray metrics.