Types for Google Cloud Aiplatform V1beta1 Schema Trainingjob Definition v1beta1 API

class google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlForecasting(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

A TrainingJob that trains and uploads an AutoML Forecasting Model.

inputs()

The input parameters of this TrainingJob.

metadata()

The metadata information.

inputs(: [google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.automl_time_series_forecasting.AutoMlForecastingInputs](../definition_v1beta1/types.md#google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlForecastingInputs_ )

metadata(: [google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.automl_time_series_forecasting.AutoMlForecastingMetadata](../definition_v1beta1/types.md#google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlForecastingMetadata_ )

class google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlForecastingInputs(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

target_column()

The name of the column that the model is to predict.

time_series_identifier_column()

The name of the column that identifies the time series.

time_column()

The name of the column that identifies time order in the time series.

transformations()

Each transformation will apply transform function to given input column. And the result will be used for training. When creating transformation for BigQuery Struct column, the column should be flattened using “.” as the delimiter.

optimization_objective()

Objective function the model is optimizing towards. The training process creates a model that optimizes the value of the objective function over the validation set.

The supported optimization objectives:

  • “minimize-rmse” (default) - Minimize root-mean-squared error (RMSE).

  • “minimize-mae” - Minimize mean-absolute error (MAE).

  • “minimize-rmsle” - Minimize root-mean-squared log error (RMSLE).

  • “minimize-rmspe” - Minimize root-mean-squared percentage error (RMSPE).

  • “minimize-wape-mae” - Minimize the combination of weighted absolute percentage error (WAPE) and mean-absolute-error (MAE).

  • “minimize-quantile-loss” - Minimize the quantile loss at the quantiles defined in quantiles.

  • Type

    str

train_budget_milli_node_hours()

Required. The train budget of creating this model, expressed in milli node hours i.e. 1,000 value in this field means 1 node hour.

The training cost of the model will not exceed this budget. The final cost will be attempted to be close to the budget, though may end up being (even) noticeably smaller - at the backend’s discretion. This especially may happen when further model training ceases to provide any improvements.

If the budget is set to a value known to be insufficient to train a model for the given dataset, the training won’t be attempted and will error.

The train budget must be between 1,000 and 72,000 milli node hours, inclusive.

weight_column()

Column name that should be used as the weight column. Higher values in this column give more importance to the row during model training. The column must have numeric values between 0 and 10000 inclusively; 0 means the row is ignored for training. If weight column field is not set, then all rows are assumed to have equal weight of 1.

time_series_attribute_columns()

Column names that should be used as attribute columns. The value of these columns does not vary as a function of time. For example, store ID or item color.

  • Type

    MutableSequence[str]

unavailable_at_forecast_columns()

Names of columns that are unavailable when a forecast is requested. This column contains information for the given entity (identified by the time_series_identifier_column) that is unknown before the forecast For example, actual weather on a given day.

  • Type

    MutableSequence[str]

available_at_forecast_columns()

Names of columns that are available and provided when a forecast is requested. These columns contain information for the given entity (identified by the time_series_identifier_column column) that is known at forecast. For example, predicted weather for a specific day.

  • Type

    MutableSequence[str]

data_granularity()

Expected difference in time granularity between rows in the data.

forecast_horizon()

The amount of time into the future for which forecasted values for the target are returned. Expressed in number of units defined by the data_granularity field.

context_window()

The amount of time into the past training and prediction data is used for model training and prediction respectively. Expressed in number of units defined by the data_granularity field.

export_evaluated_data_items_config()

Configuration for exporting test set predictions to a BigQuery table. If this configuration is absent, then the export is not performed.

quantiles()

Quantiles to use for minimize-quantile-loss optimization_objective. Up to 5 quantiles are allowed of values between 0 and 1, exclusive. Required if the value of optimization_objective is minimize-quantile-loss. Represents the percent quantiles to use for that objective. Quantiles must be unique.

  • Type

    MutableSequence[float]

validation_options()

Validation options for the data validation component. The available options are:

  • “fail-pipeline” - default, will validate against the validation and fail the pipeline if it fails.

  • “ignore-validation” - ignore the results of the validation and continue

  • Type

    str

additional_experiments()

Additional experiment flags for the time series forcasting training.

  • Type

    MutableSequence[str]

class Granularity(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

A duration of time expressed in time granularity units.

unit()

The time granularity unit of this time period. The supported units are:

  • “minute”

  • “hour”

  • “day”

  • “week”

  • “month”

  • “year”.

  • Type

    str

quantity()

The number of granularity_units between data points in the training data. If granularity_unit is minute, can be 1, 5, 10, 15, or 30. For all other values of granularity_unit, must be 1.

quantity(: [int](https://python.readthedocs.io/en/latest/library/functions.html#int )

unit(: [str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str )

class Transformation(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

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

auto()

This field is a member of oneof transformation_detail.

numeric()

This field is a member of oneof transformation_detail.

categorical()

This field is a member of oneof transformation_detail.

timestamp()

This field is a member of oneof transformation_detail.

text()

This field is a member of oneof transformation_detail.

class AutoTransformation(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

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

column_name()

column_name(: [str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str )

class CategoricalTransformation(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Training pipeline will perform following transformation functions.

  • The categorical string as is–no change to case, punctuation, spelling, tense, and so on.

  • Convert the category name to a dictionary lookup index and generate an embedding for each index.

  • Categories that appear less than 5 times in the training dataset are treated as the “unknown” category. The “unknown” category gets its own special lookup index and resulting embedding.

column_name()

column_name(: [str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str )

class NumericTransformation(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Training pipeline will perform following transformation functions.

  • The value converted to float32.

  • The z_score of the value.

  • log(value+1) when the value is greater than or equal to 0. Otherwise, this transformation is not applied and the value is considered a missing value.

  • z_score of log(value+1) when the value is greater than or equal to 0. Otherwise, this transformation is not applied and the value is considered a missing value.

  • A boolean value that indicates whether the value is valid.

column_name()

column_name(: [str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str )

class TextTransformation(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Training pipeline will perform following transformation functions.

  • The text as is–no change to case, punctuation, spelling, tense, and so on.

  • Convert the category name to a dictionary lookup index and generate an embedding for each index.

column_name()

column_name(: [str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str )

class TimestampTransformation(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Training pipeline will perform following transformation functions.

  • Apply the transformation functions for Numerical columns.

  • Determine the year, month, day,and weekday. Treat each value from the timestamp as a Categorical column.

  • Invalid numerical values (for example, values that fall outside of a typical timestamp range, or are extreme values) receive no special treatment and are not removed.

column_name()

time_format()

The format in which that time field is expressed. The time_format must either be one of:

  • unix-seconds

  • unix-milliseconds

  • unix-microseconds

  • unix-nanoseconds

(for respectively number of seconds, milliseconds, microseconds and nanoseconds since start of the Unix epoch);

or be written in strftime syntax.

If time_format is not set, then the default format is RFC 3339 date-time format, where time-offset = "Z" (e.g. 1985-04-12T23:20:50.52Z)

column_name(: [str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str )

time_format(: [str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str )

auto(: [google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.automl_time_series_forecasting.AutoMlForecastingInputs.Transformation.AutoTransformation](../definition_v1beta1/types.md#google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlForecastingInputs.Transformation.AutoTransformation_ )

categorical(: [google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.automl_time_series_forecasting.AutoMlForecastingInputs.Transformation.CategoricalTransformation](../definition_v1beta1/types.md#google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlForecastingInputs.Transformation.CategoricalTransformation_ )

numeric(: [google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.automl_time_series_forecasting.AutoMlForecastingInputs.Transformation.NumericTransformation](../definition_v1beta1/types.md#google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlForecastingInputs.Transformation.NumericTransformation_ )

text(: [google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.automl_time_series_forecasting.AutoMlForecastingInputs.Transformation.TextTransformation](../definition_v1beta1/types.md#google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlForecastingInputs.Transformation.TextTransformation_ )

timestamp(: [google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.automl_time_series_forecasting.AutoMlForecastingInputs.Transformation.TimestampTransformation](../definition_v1beta1/types.md#google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlForecastingInputs.Transformation.TimestampTransformation_ )

additional_experiments(: MutableSequence[str )

available_at_forecast_columns(: MutableSequence[str )

context_window(: [int](https://python.readthedocs.io/en/latest/library/functions.html#int )

data_granularity(: [Granularity](../definition_v1beta1/types.md#google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlForecastingInputs.Granularity_ )

export_evaluated_data_items_config(: gcastd_export_evaluated_data_items_config.ExportEvaluatedDataItemsConfi )

forecast_horizon(: [int](https://python.readthedocs.io/en/latest/library/functions.html#int )

optimization_objective(: [str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str )

quantiles(: MutableSequence[float )

target_column(: [str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str )

time_column(: [str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str )

time_series_attribute_columns(: MutableSequence[str )

time_series_identifier_column(: [str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str )

train_budget_milli_node_hours(: [int](https://python.readthedocs.io/en/latest/library/functions.html#int )

transformations(: MutableSequence[[Transformation](../definition_v1beta1/types.md#google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlForecastingInputs.Transformation)_ )

unavailable_at_forecast_columns(: MutableSequence[str )

validation_options(: [str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str )

weight_column(: [str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str )

class google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlForecastingMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Model metadata specific to AutoML Forecasting.

train_cost_milli_node_hours()

Output only. The actual training cost of the model, expressed in milli node hours, i.e. 1,000 value in this field means 1 node hour. Guaranteed to not exceed the train budget.

train_cost_milli_node_hours(: [int](https://python.readthedocs.io/en/latest/library/functions.html#int )

class google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlImageClassification(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

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

inputs()

The input parameters of this TrainingJob.

metadata()

The metadata information.

inputs(: [google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.automl_image_classification.AutoMlImageClassificationInputs](../definition_v1beta1/types.md#google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlImageClassificationInputs_ )

metadata(: [google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.automl_image_classification.AutoMlImageClassificationMetadata](../definition_v1beta1/types.md#google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlImageClassificationMetadata_ )

class google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlImageClassificationInputs(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

model_type()

base_model_id()

The ID of the base model. If it is specified, the new model will be trained based on the base model. Otherwise, the new model will be trained from scratch. The base model must be in the same Project and Location as the new Model to train, and have the same modelType.

budget_milli_node_hours()

The training budget of creating this model, expressed in milli node hours i.e. 1,000 value in this field means 1 node hour. The actual metadata.costMilliNodeHours will be equal or less than this value. If further model training ceases to provide any improvements, it will stop without using the full budget and the metadata.successfulStopReason will be model-converged. Note, node_hour = actual_hour * number_of_nodes_involved. For modelType cloud(default), the budget must be between 8,000 and 800,000 milli node hours, inclusive. The default value is 192,000 which represents one day in wall time, considering 8 nodes are used. For model types mobile-tf-low-latency-1, mobile-tf-versatile-1, mobile-tf-high-accuracy-1, the training budget must be between 1,000 and 100,000 milli node hours, inclusive. The default value is 24,000 which represents one day in wall time on a single node that is used.

disable_early_stopping()

Use the entire training budget. This disables the early stopping feature. When false the early stopping feature is enabled, which means that AutoML Image Classification might stop training before the entire training budget has been used.

multi_label()

If false, a single-label (multi-class) Model will be trained (i.e. assuming that for each image just up to one annotation may be applicable). If true, a multi-label Model will be trained (i.e. assuming that for each image multiple annotations may be applicable).

class ModelType(value)

Bases: proto.enums.Enum

Values:

MODEL_TYPE_UNSPECIFIED (0):

    Should not be set.

CLOUD (1):

    A Model best tailored to be used within
    Google Cloud, and which cannot be exported.
    Default.

MOBILE_TF_LOW_LATENCY_1 (2):

    A model that, in addition to being available
    within Google Cloud, can also be exported (see
    ModelService.ExportModel) as TensorFlow or Core
    ML model and used on a mobile or edge device
    afterwards. Expected to have low latency, but
    may have lower prediction quality than other
    mobile models.

MOBILE_TF_VERSATILE_1 (3):

    A model that, in addition to being available
    within Google Cloud, can also be exported (see
    ModelService.ExportModel) as TensorFlow or Core
    ML model and used on a mobile or edge device
    with afterwards.

MOBILE_TF_HIGH_ACCURACY_1 (4):

    A model that, in addition to being available
    within Google Cloud, can also be exported (see
    ModelService.ExportModel) as TensorFlow or Core
    ML model and used on a mobile or edge device
    afterwards. Expected to have a higher latency,
    but should also have a higher prediction quality
    than other mobile models.

CLOUD( = )

MOBILE_TF_HIGH_ACCURACY_1( = )

MOBILE_TF_LOW_LATENCY_1( = )

MOBILE_TF_VERSATILE_1( = )

MODEL_TYPE_UNSPECIFIED( = )

base_model_id(: [str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str )

budget_milli_node_hours(: [int](https://python.readthedocs.io/en/latest/library/functions.html#int )

disable_early_stopping(: [bool](https://python.readthedocs.io/en/latest/library/functions.html#bool )

model_type(: [ModelType](../definition_v1beta1/types.md#google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlImageClassificationInputs.ModelType_ )

multi_label(: [bool](https://python.readthedocs.io/en/latest/library/functions.html#bool )

class google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlImageClassificationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

cost_milli_node_hours()

The actual training cost of creating this model, expressed in milli node hours, i.e. 1,000 value in this field means 1 node hour. Guaranteed to not exceed inputs.budgetMilliNodeHours.

successful_stop_reason()

For successful job completions, this is the reason why the job has finished.

class SuccessfulStopReason(value)

Bases: proto.enums.Enum

Values:

SUCCESSFUL_STOP_REASON_UNSPECIFIED (0):

    Should not be set.

BUDGET_REACHED (1):

    The inputs.budgetMilliNodeHours had been
    reached.

MODEL_CONVERGED (2):

    Further training of the Model ceased to
    increase its quality, since it already has
    converged.

BUDGET_REACHED( = )

MODEL_CONVERGED( = )

SUCCESSFUL_STOP_REASON_UNSPECIFIED( = )

cost_milli_node_hours(: [int](https://python.readthedocs.io/en/latest/library/functions.html#int )

successful_stop_reason(: [SuccessfulStopReason](../definition_v1beta1/types.md#google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlImageClassificationMetadata.SuccessfulStopReason_ )

class google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlImageObjectDetection(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

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

inputs()

The input parameters of this TrainingJob.

metadata()

The metadata information

inputs(: [google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.automl_image_object_detection.AutoMlImageObjectDetectionInputs](../definition_v1beta1/types.md#google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlImageObjectDetectionInputs_ )

metadata(: [google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.automl_image_object_detection.AutoMlImageObjectDetectionMetadata](../definition_v1beta1/types.md#google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlImageObjectDetectionMetadata_ )

class google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlImageObjectDetectionInputs(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

model_type()

budget_milli_node_hours()

The training budget of creating this model, expressed in milli node hours i.e. 1,000 value in this field means 1 node hour. The actual metadata.costMilliNodeHours will be equal or less than this value. If further model training ceases to provide any improvements, it will stop without using the full budget and the metadata.successfulStopReason will be model-converged. Note, node_hour = actual_hour * number_of_nodes_involved. For modelType cloud(default), the budget must be between 20,000 and 900,000 milli node hours, inclusive. The default value is 216,000 which represents one day in wall time, considering 9 nodes are used. For model types mobile-tf-low-latency-1, mobile-tf-versatile-1, mobile-tf-high-accuracy-1 the training budget must be between 1,000 and 100,000 milli node hours, inclusive. The default value is 24,000 which represents one day in wall time on a single node that is used.

disable_early_stopping()

Use the entire training budget. This disables the early stopping feature. When false the early stopping feature is enabled, which means that AutoML Image Object Detection might stop training before the entire training budget has been used.

class ModelType(value)

Bases: proto.enums.Enum

Values:

MODEL_TYPE_UNSPECIFIED (0):

    Should not be set.

CLOUD_HIGH_ACCURACY_1 (1):

    A model best tailored to be used within
    Google Cloud, and which cannot be exported.
    Expected to have a higher latency, but should
    also have a higher prediction quality than other
    cloud models.

CLOUD_LOW_LATENCY_1 (2):

    A model best tailored to be used within
    Google Cloud, and which cannot be exported.
    Expected to have a low latency, but may have
    lower prediction quality than other cloud
    models.

MOBILE_TF_LOW_LATENCY_1 (3):

    A model that, in addition to being available
    within Google Cloud can also be exported (see
    ModelService.ExportModel) and used on a mobile
    or edge device with TensorFlow afterwards.
    Expected to have low latency, but may have lower
    prediction quality than other mobile models.

MOBILE_TF_VERSATILE_1 (4):

    A model that, in addition to being available
    within Google Cloud can also be exported (see
    ModelService.ExportModel) and used on a mobile
    or edge device with TensorFlow afterwards.

MOBILE_TF_HIGH_ACCURACY_1 (5):

    A model that, in addition to being available
    within Google Cloud, can also be exported (see
    ModelService.ExportModel) and used on a mobile
    or edge device with TensorFlow afterwards.
    Expected to have a higher latency, but should
    also have a higher prediction quality than other
    mobile models.

CLOUD_HIGH_ACCURACY_1( = )

CLOUD_LOW_LATENCY_1( = )

MOBILE_TF_HIGH_ACCURACY_1( = )

MOBILE_TF_LOW_LATENCY_1( = )

MOBILE_TF_VERSATILE_1( = )

MODEL_TYPE_UNSPECIFIED( = )

budget_milli_node_hours(: [int](https://python.readthedocs.io/en/latest/library/functions.html#int )

disable_early_stopping(: [bool](https://python.readthedocs.io/en/latest/library/functions.html#bool )

model_type(: [ModelType](../definition_v1beta1/types.md#google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlImageObjectDetectionInputs.ModelType_ )

class google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlImageObjectDetectionMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

cost_milli_node_hours()

The actual training cost of creating this model, expressed in milli node hours, i.e. 1,000 value in this field means 1 node hour. Guaranteed to not exceed inputs.budgetMilliNodeHours.

successful_stop_reason()

For successful job completions, this is the reason why the job has finished.

class SuccessfulStopReason(value)

Bases: proto.enums.Enum

Values:

SUCCESSFUL_STOP_REASON_UNSPECIFIED (0):

    Should not be set.

BUDGET_REACHED (1):

    The inputs.budgetMilliNodeHours had been
    reached.

MODEL_CONVERGED (2):

    Further training of the Model ceased to
    increase its quality, since it already has
    converged.

BUDGET_REACHED( = )

MODEL_CONVERGED( = )

SUCCESSFUL_STOP_REASON_UNSPECIFIED( = )

cost_milli_node_hours(: [int](https://python.readthedocs.io/en/latest/library/functions.html#int )

successful_stop_reason(: [SuccessfulStopReason](../definition_v1beta1/types.md#google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlImageObjectDetectionMetadata.SuccessfulStopReason_ )

class google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlImageSegmentation(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

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

inputs()

The input parameters of this TrainingJob.

metadata()

The metadata information.

inputs(: [google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.automl_image_segmentation.AutoMlImageSegmentationInputs](../definition_v1beta1/types.md#google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlImageSegmentationInputs_ )

metadata(: [google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.automl_image_segmentation.AutoMlImageSegmentationMetadata](../definition_v1beta1/types.md#google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlImageSegmentationMetadata_ )

class google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlImageSegmentationInputs(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

model_type()

budget_milli_node_hours()

The training budget of creating this model, expressed in milli node hours i.e. 1,000 value in this field means 1 node hour. The actual metadata.costMilliNodeHours will be equal or less than this value. If further model training ceases to provide any improvements, it will stop without using the full budget and the metadata.successfulStopReason will be model-converged. Note, node_hour = actual_hour * number_of_nodes_involved. Or actaul_wall_clock_hours = train_budget_milli_node_hours / (number_of_nodes_involved * 1000) For modelType cloud-high-accuracy-1(default), the budget must be between 20,000 and 2,000,000 milli node hours, inclusive. The default value is 192,000 which represents one day in wall time (1000 milli * 24 hours * 8 nodes).

base_model_id()

The ID of the base model. If it is specified, the new model will be trained based on the base model. Otherwise, the new model will be trained from scratch. The base model must be in the same Project and Location as the new Model to train, and have the same modelType.

class ModelType(value)

Bases: proto.enums.Enum

Values:

MODEL_TYPE_UNSPECIFIED (0):

    Should not be set.

CLOUD_HIGH_ACCURACY_1 (1):

    A model to be used via prediction calls to
    uCAIP API. Expected to have a higher latency,
    but should also have a higher prediction quality
    than other models.

CLOUD_LOW_ACCURACY_1 (2):

    A model to be used via prediction calls to
    uCAIP API. Expected to have a lower latency but
    relatively lower prediction quality.

MOBILE_TF_LOW_LATENCY_1 (3):

    A model that, in addition to being available
    within Google Cloud, can also be exported (see
    ModelService.ExportModel) as TensorFlow model
    and used on a mobile or edge device afterwards.
    Expected to have low latency, but may have lower
    prediction quality than other mobile models.

CLOUD_HIGH_ACCURACY_1( = )

CLOUD_LOW_ACCURACY_1( = )

MOBILE_TF_LOW_LATENCY_1( = )

MODEL_TYPE_UNSPECIFIED( = )

base_model_id(: [str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str )

budget_milli_node_hours(: [int](https://python.readthedocs.io/en/latest/library/functions.html#int )

model_type(: [ModelType](../definition_v1beta1/types.md#google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlImageSegmentationInputs.ModelType_ )

class google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlImageSegmentationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

cost_milli_node_hours()

The actual training cost of creating this model, expressed in milli node hours, i.e. 1,000 value in this field means 1 node hour. Guaranteed to not exceed inputs.budgetMilliNodeHours.

successful_stop_reason()

For successful job completions, this is the reason why the job has finished.

class SuccessfulStopReason(value)

Bases: proto.enums.Enum

Values:

SUCCESSFUL_STOP_REASON_UNSPECIFIED (0):

    Should not be set.

BUDGET_REACHED (1):

    The inputs.budgetMilliNodeHours had been
    reached.

MODEL_CONVERGED (2):

    Further training of the Model ceased to
    increase its quality, since it already has
    converged.

BUDGET_REACHED( = )

MODEL_CONVERGED( = )

SUCCESSFUL_STOP_REASON_UNSPECIFIED( = )

cost_milli_node_hours(: [int](https://python.readthedocs.io/en/latest/library/functions.html#int )

successful_stop_reason(: [SuccessfulStopReason](../definition_v1beta1/types.md#google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlImageSegmentationMetadata.SuccessfulStopReason_ )

class google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlTables(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

A TrainingJob that trains and uploads an AutoML Tables Model.

inputs()

The input parameters of this TrainingJob.

metadata()

The metadata information.

inputs(: [google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.automl_tables.AutoMlTablesInputs](../definition_v1beta1/types.md#google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlTablesInputs_ )

metadata(: [google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.automl_tables.AutoMlTablesMetadata](../definition_v1beta1/types.md#google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlTablesMetadata_ )

class google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlTablesInputs(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

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

optimization_objective_recall_value()

Required when optimization_objective is “maximize-precision-at-recall”. Must be between 0 and 1, inclusive.

This field is a member of oneof additional_optimization_objective_config.

optimization_objective_precision_value()

Required when optimization_objective is “maximize-recall-at-precision”. Must be between 0 and 1, inclusive.

This field is a member of oneof additional_optimization_objective_config.

prediction_type()

The type of prediction the Model is to produce. “classification” - Predict one out of multiple target values is picked for each row.

“regression” - Predict a value based on its

relation to other values. This type is available only to columns that contain semantically numeric values, i.e. integers or floating point number, even if stored as e.g. strings.

target_column()

The column name of the target column that the model is to predict.

transformations()

Each transformation will apply transform function to given input column. And the result will be used for training. When creating transformation for BigQuery Struct column, the column should be flattened using “.” as the delimiter.

optimization_objective()

Objective function the model is optimizing towards. The training process creates a model that maximizes/minimizes the value of the objective function over the validation set.

The supported optimization objectives depend on the prediction type. If the field is not set, a default objective function is used.

classification (binary):

“maximize-au-roc” (default) - Maximize the

area under the receiver operating characteristic (ROC) curve. “minimize-log-loss” - Minimize log loss.

“maximize-au-prc” - Maximize the area under

the precision-recall curve. “maximize-precision-at-recall” - Maximize precision for a specified recall value. “maximize-recall-at-precision” - Maximize recall for a specified precision value.

classification (multi-class):

“minimize-log-loss” (default) - Minimize log

loss.

regression:

“minimize-rmse” (default) - Minimize

root-mean-squared error (RMSE). “minimize-mae”

  • Minimize mean-absolute error (MAE). “minimize-rmsle” - Minimize root-mean-squared log error (RMSLE).

  • Type

    str

train_budget_milli_node_hours()

Required. The train budget of creating this model, expressed in milli node hours i.e. 1,000 value in this field means 1 node hour.

The training cost of the model will not exceed this budget. The final cost will be attempted to be close to the budget, though may end up being (even) noticeably smaller - at the backend’s discretion. This especially may happen when further model training ceases to provide any improvements.

If the budget is set to a value known to be insufficient to train a model for the given dataset, the training won’t be attempted and will error.

The train budget must be between 1,000 and 72,000 milli node hours, inclusive.

disable_early_stopping()

Use the entire training budget. This disables the early stopping feature. By default, the early stopping feature is enabled, which means that AutoML Tables might stop training before the entire training budget has been used.

weight_column_name()

Column name that should be used as the weight column. Higher values in this column give more importance to the row during model training. The column must have numeric values between 0 and 10000 inclusively; 0 means the row is ignored for training. If weight column field is not set, then all rows are assumed to have equal weight of 1.

export_evaluated_data_items_config()

Configuration for exporting test set predictions to a BigQuery table. If this configuration is absent, then the export is not performed.

additional_experiments()

Additional experiment flags for the Tables training pipeline.

  • Type

    MutableSequence[str]

class Transformation(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

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

auto()

This field is a member of oneof transformation_detail.

numeric()

This field is a member of oneof transformation_detail.

categorical()

This field is a member of oneof transformation_detail.

timestamp()

This field is a member of oneof transformation_detail.

text()

This field is a member of oneof transformation_detail.

repeated_numeric()

This field is a member of oneof transformation_detail.

repeated_categorical()

This field is a member of oneof transformation_detail.

repeated_text()

This field is a member of oneof transformation_detail.

class AutoTransformation(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

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

column_name()

column_name(: [str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str )

class CategoricalArrayTransformation(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

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

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

  • Empty arrays treated as an embedding of zeroes.

column_name()

column_name(: [str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str )

class CategoricalTransformation(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Training pipeline will perform following transformation functions.

  • The categorical string as is–no change to case, punctuation, spelling, tense, and so on.

  • Convert the category name to a dictionary lookup index and generate an embedding for each index.

  • Categories that appear less than 5 times in the training dataset are treated as the “unknown” category. The “unknown” category gets its own special lookup index and resulting embedding.

column_name()

column_name(: [str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str )

class NumericArrayTransformation(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

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

  • All transformations for Numerical types applied to the average of the all elements.

  • The average of empty arrays is treated as zero.

column_name()

invalid_values_allowed()

If invalid values is allowed, the training pipeline will create a boolean feature that indicated whether the value is valid. Otherwise, the training pipeline will discard the input row from trainining data.

column_name(: [str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str )

invalid_values_allowed(: [bool](https://python.readthedocs.io/en/latest/library/functions.html#bool )

class NumericTransformation(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Training pipeline will perform following transformation functions.

  • The value converted to float32.

  • The z_score of the value.

  • log(value+1) when the value is greater than or equal to 0. Otherwise, this transformation is not applied and the value is considered a missing value.

  • z_score of log(value+1) when the value is greater than or equal to 0. Otherwise, this transformation is not applied and the value is considered a missing value.

  • A boolean value that indicates whether the value is valid.

column_name()

invalid_values_allowed()

If invalid values is allowed, the training pipeline will create a boolean feature that indicated whether the value is valid. Otherwise, the training pipeline will discard the input row from trainining data.

column_name(: [str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str )

invalid_values_allowed(: [bool](https://python.readthedocs.io/en/latest/library/functions.html#bool )

class TextArrayTransformation(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

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

  • Concatenate all text values in the array into a single text value using a space (” “) as a delimiter, and then treat the result as a single text value. Apply the transformations for Text columns.

  • Empty arrays treated as an empty text.

column_name()

column_name(: [str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str )

class TextTransformation(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Training pipeline will perform following transformation functions.

  • The text as is–no change to case, punctuation, spelling, tense, and so on.

  • Tokenize text to words. Convert each words to a dictionary lookup index and generate an embedding for each index. Combine the embedding of all elements into a single embedding using the mean.

  • Tokenization is based on unicode script boundaries.

  • Missing values get their own lookup index and resulting embedding.

  • Stop-words receive no special treatment and are not removed.

column_name()

column_name(: [str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str )

class TimestampTransformation(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Training pipeline will perform following transformation functions.

  • Apply the transformation functions for Numerical columns.

  • Determine the year, month, day,and weekday. Treat each value from the

  • timestamp as a Categorical column.

  • Invalid numerical values (for example, values that fall outside of a typical timestamp range, or are extreme values) receive no special treatment and are not removed.

column_name()

time_format()

The format in which that time field is expressed. The time_format must either be one of:

  • unix-seconds

  • unix-milliseconds

  • unix-microseconds

  • unix-nanoseconds (for respectively number of seconds, milliseconds, microseconds and nanoseconds since start of the Unix epoch); or be written in strftime syntax. If time_format is not set, then the default format is RFC 3339 date-time format, where time-offset = "Z" (e.g. 1985-04-12T23:20:50.52Z)

  • Type

    str

invalid_values_allowed()

If invalid values is allowed, the training pipeline will create a boolean feature that indicated whether the value is valid. Otherwise, the training pipeline will discard the input row from trainining data.

column_name(: [str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str )

invalid_values_allowed(: [bool](https://python.readthedocs.io/en/latest/library/functions.html#bool )

time_format(: [str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str )

auto(: [google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.automl_tables.AutoMlTablesInputs.Transformation.AutoTransformation](../definition_v1beta1/types.md#google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlTablesInputs.Transformation.AutoTransformation_ )

categorical(: [google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.automl_tables.AutoMlTablesInputs.Transformation.CategoricalTransformation](../definition_v1beta1/types.md#google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlTablesInputs.Transformation.CategoricalTransformation_ )

numeric(: [google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.automl_tables.AutoMlTablesInputs.Transformation.NumericTransformation](../definition_v1beta1/types.md#google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlTablesInputs.Transformation.NumericTransformation_ )

repeated_categorical(: [google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.automl_tables.AutoMlTablesInputs.Transformation.CategoricalArrayTransformation](../definition_v1beta1/types.md#google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlTablesInputs.Transformation.CategoricalArrayTransformation_ )

repeated_numeric(: [google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.automl_tables.AutoMlTablesInputs.Transformation.NumericArrayTransformation](../definition_v1beta1/types.md#google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlTablesInputs.Transformation.NumericArrayTransformation_ )

repeated_text(: [google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.automl_tables.AutoMlTablesInputs.Transformation.TextArrayTransformation](../definition_v1beta1/types.md#google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlTablesInputs.Transformation.TextArrayTransformation_ )

text(: [google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.automl_tables.AutoMlTablesInputs.Transformation.TextTransformation](../definition_v1beta1/types.md#google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlTablesInputs.Transformation.TextTransformation_ )

timestamp(: [google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.automl_tables.AutoMlTablesInputs.Transformation.TimestampTransformation](../definition_v1beta1/types.md#google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlTablesInputs.Transformation.TimestampTransformation_ )

additional_experiments(: MutableSequence[str )

disable_early_stopping(: [bool](https://python.readthedocs.io/en/latest/library/functions.html#bool )

export_evaluated_data_items_config(: gcastd_export_evaluated_data_items_config.ExportEvaluatedDataItemsConfi )

optimization_objective(: [str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str )

optimization_objective_precision_value(: [float](https://python.readthedocs.io/en/latest/library/functions.html#float )

optimization_objective_recall_value(: [float](https://python.readthedocs.io/en/latest/library/functions.html#float )

prediction_type(: [str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str )

target_column(: [str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str )

train_budget_milli_node_hours(: [int](https://python.readthedocs.io/en/latest/library/functions.html#int )

transformations(: MutableSequence[[Transformation](../definition_v1beta1/types.md#google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlTablesInputs.Transformation)_ )

weight_column_name(: [str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str )

class google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlTablesMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Model metadata specific to AutoML Tables.

train_cost_milli_node_hours()

Output only. The actual training cost of the model, expressed in milli node hours, i.e. 1,000 value in this field means 1 node hour. Guaranteed to not exceed the train budget.

train_cost_milli_node_hours(: [int](https://python.readthedocs.io/en/latest/library/functions.html#int )

class google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlTextClassification(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

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

inputs()

The input parameters of this TrainingJob.

inputs(: [google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.automl_text_classification.AutoMlTextClassificationInputs](../definition_v1beta1/types.md#google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlTextClassificationInputs_ )

class google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlTextClassificationInputs(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

multi_label()

multi_label(: [bool](https://python.readthedocs.io/en/latest/library/functions.html#bool )

class google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlTextExtraction(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

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

inputs()

The input parameters of this TrainingJob.

  • Type

    google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlTextExtractionInputs

inputs(: google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.automl_text_extraction.AutoMlTextExtractionInput )

class google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlTextExtractionInputs(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

class google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlTextSentiment(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

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

inputs()

The input parameters of this TrainingJob.

inputs(: [google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.automl_text_sentiment.AutoMlTextSentimentInputs](../definition_v1beta1/types.md#google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlTextSentimentInputs_ )

class google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlTextSentimentInputs(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

sentiment_max()

A sentiment is expressed as an integer ordinal, where higher value means a more positive sentiment. The range of sentiments that will be used is between 0 and sentimentMax (inclusive on both ends), and all the values in the range must be represented in the dataset before a model can be created. Only the Annotations with this sentimentMax will be used for training. sentimentMax value must be between 1 and 10 (inclusive).

sentiment_max(: [int](https://python.readthedocs.io/en/latest/library/functions.html#int )

class google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlVideoActionRecognition(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

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

inputs()

The input parameters of this TrainingJob.

inputs(: [google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.automl_video_action_recognition.AutoMlVideoActionRecognitionInputs](../definition_v1beta1/types.md#google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlVideoActionRecognitionInputs_ )

class google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlVideoActionRecognitionInputs(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

model_type()

class ModelType(value)

Bases: proto.enums.Enum

Values:

MODEL_TYPE_UNSPECIFIED (0):

    Should not be set.

CLOUD (1):

    A model best tailored to be used within
    Google Cloud, and which c annot be exported.
    Default.

MOBILE_VERSATILE_1 (2):

    A model that, in addition to being available
    within Google Cloud, can also be exported (see
    ModelService.ExportModel) as a TensorFlow or
    TensorFlow Lite model and used on a mobile or
    edge device afterwards.

MOBILE_JETSON_VERSATILE_1 (3):

    A model that, in addition to being available
    within Google Cloud, can also be exported (see
    ModelService.ExportModel) to a Jetson device
    afterwards.

MOBILE_CORAL_VERSATILE_1 (4):

    A model that, in addition to being available
    within Google Cloud, can also be exported (see
    ModelService.ExportModel) as a TensorFlow or
    TensorFlow Lite model and used on a Coral device
    afterwards.

CLOUD( = )

MOBILE_CORAL_VERSATILE_1( = )

MOBILE_JETSON_VERSATILE_1( = )

MOBILE_VERSATILE_1( = )

MODEL_TYPE_UNSPECIFIED( = )

model_type(: [ModelType](../definition_v1beta1/types.md#google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlVideoActionRecognitionInputs.ModelType_ )

class google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlVideoClassification(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

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

inputs()

The input parameters of this TrainingJob.

inputs(: [google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.automl_video_classification.AutoMlVideoClassificationInputs](../definition_v1beta1/types.md#google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlVideoClassificationInputs_ )

class google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlVideoClassificationInputs(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

model_type()

class ModelType(value)

Bases: proto.enums.Enum

Values:

MODEL_TYPE_UNSPECIFIED (0):

    Should not be set.

CLOUD (1):

    A model best tailored to be used within
    Google Cloud, and which cannot be exported.
    Default.

MOBILE_VERSATILE_1 (2):

    A model that, in addition to being available
    within Google Cloud, can also be exported (see
    ModelService.ExportModel) as a TensorFlow or
    TensorFlow Lite model and used on a mobile or
    edge device afterwards.

MOBILE_JETSON_VERSATILE_1 (3):

    A model that, in addition to being available
    within Google Cloud, can also be exported (see
    ModelService.ExportModel) to a Jetson device
    afterwards.

CLOUD( = )

MOBILE_JETSON_VERSATILE_1( = )

MOBILE_VERSATILE_1( = )

MODEL_TYPE_UNSPECIFIED( = )

model_type(: [ModelType](../definition_v1beta1/types.md#google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlVideoClassificationInputs.ModelType_ )

class google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlVideoObjectTracking(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

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

inputs()

The input parameters of this TrainingJob.

inputs(: [google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.automl_video_object_tracking.AutoMlVideoObjectTrackingInputs](../definition_v1beta1/types.md#google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlVideoObjectTrackingInputs_ )

class google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlVideoObjectTrackingInputs(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

model_type()

class ModelType(value)

Bases: proto.enums.Enum

Values:

MODEL_TYPE_UNSPECIFIED (0):

    Should not be set.

CLOUD (1):

    A model best tailored to be used within
    Google Cloud, and which c annot be exported.
    Default.

MOBILE_VERSATILE_1 (2):

    A model that, in addition to being available
    within Google Cloud, can also be exported (see
    ModelService.ExportModel) as a TensorFlow or
    TensorFlow Lite model and used on a mobile or
    edge device afterwards.

MOBILE_CORAL_VERSATILE_1 (3):

    A versatile model that is meant to be
    exported (see ModelService.ExportModel) and used
    on a Google Coral device.

MOBILE_CORAL_LOW_LATENCY_1 (4):

    A model that trades off quality for low
    latency, to be exported (see
    ModelService.ExportModel) and used on a Google
    Coral device.

MOBILE_JETSON_VERSATILE_1 (5):

    A versatile model that is meant to be
    exported (see ModelService.ExportModel) and used
    on an NVIDIA Jetson device.

MOBILE_JETSON_LOW_LATENCY_1 (6):

    A model that trades off quality for low
    latency, to be exported (see
    ModelService.ExportModel) and used on an NVIDIA
    Jetson device.

CLOUD( = )

MOBILE_CORAL_LOW_LATENCY_1( = )

MOBILE_CORAL_VERSATILE_1( = )

MOBILE_JETSON_LOW_LATENCY_1( = )

MOBILE_JETSON_VERSATILE_1( = )

MOBILE_VERSATILE_1( = )

MODEL_TYPE_UNSPECIFIED( = )

model_type(: [ModelType](../definition_v1beta1/types.md#google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlVideoObjectTrackingInputs.ModelType_ )

class google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.ExportEvaluatedDataItemsConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Bases: proto.message.Message

Configuration for exporting test set predictions to a BigQuery table.

destination_bigquery_uri()

URI of desired destination BigQuery table. Expected format: bq://<project_id>:<dataset_id>:

If not specified, then results are exported to the following auto-created BigQuery table: <project_id>:export_evaluated_examples_<model_name>_<yyyy_MM_dd’T’HH_mm_ss_SSS’Z’>.evaluated_examples

override_existing_table()

If true and an export destination is specified, then the contents of the destination are overwritten. Otherwise, if the export destination already exists, then the export operation fails.

destination_bigquery_uri(: [str](https://python.readthedocs.io/en/latest/library/stdtypes.html#str )

override_existing_table(: [bool](https://python.readthedocs.io/en/latest/library/functions.html#bool )