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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.
Type
google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlForecastingInputs
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.
Type
time_series_identifier_column()
The name of the column that identifies the time series.
Type
time_column()
The name of the column that identifies time order in the time series.
Type
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.
Type
MutableSequence[google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlForecastingInputs.Transformation]
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
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.
Type
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.
Type
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.
Type
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.
Type
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
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
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.
Type
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()
Type
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()
Type
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()
Type
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()
Type
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()
Type
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
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.
Type
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.
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 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.
Type
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.
Type
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).
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_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.
Type
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.
Type
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.
Type
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.
Type
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).
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.
Type
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.
Type
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.
Type
google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlTablesMetadata
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
.
Type
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
.
Type
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.
Type
target_column()
The column name of the target column that the model is to predict.
Type
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.
Type
MutableSequence[google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlTablesInputs.Transformation]
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
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.
Type
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.
Type
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.
Type
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()
Type
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()
Type
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()
Type
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()
Type
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.
Type
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()
Type
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.
Type
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()
Type
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()
Type
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()
Type
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 instrftime
syntax. If time_format is not set, then the default format is RFC 3339date-time
format, wheretime-offset
="Z"
(e.g. 1985-04-12T23:20:50.52Z)Type
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.
Type
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.
Type
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()
Type
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).
Type
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
Type
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.
Type