Enum value maps for AutoMlImageClassificationInputs_ModelType.
var (
AutoMlImageClassificationInputs_ModelType_name = map[int32]string{
0: "MODEL_TYPE_UNSPECIFIED",
1: "CLOUD",
2: "MOBILE_TF_LOW_LATENCY_1",
3: "MOBILE_TF_VERSATILE_1",
4: "MOBILE_TF_HIGH_ACCURACY_1",
}
AutoMlImageClassificationInputs_ModelType_value = map[string]int32{
"MODEL_TYPE_UNSPECIFIED": 0,
"CLOUD": 1,
"MOBILE_TF_LOW_LATENCY_1": 2,
"MOBILE_TF_VERSATILE_1": 3,
"MOBILE_TF_HIGH_ACCURACY_1": 4,
}
)
Enum value maps for AutoMlImageClassificationMetadata_SuccessfulStopReason.
var (
AutoMlImageClassificationMetadata_SuccessfulStopReason_name = map[int32]string{
0: "SUCCESSFUL_STOP_REASON_UNSPECIFIED",
1: "BUDGET_REACHED",
2: "MODEL_CONVERGED",
}
AutoMlImageClassificationMetadata_SuccessfulStopReason_value = map[string]int32{
"SUCCESSFUL_STOP_REASON_UNSPECIFIED": 0,
"BUDGET_REACHED": 1,
"MODEL_CONVERGED": 2,
}
)
Enum value maps for AutoMlImageObjectDetectionInputs_ModelType.
var (
AutoMlImageObjectDetectionInputs_ModelType_name = map[int32]string{
0: "MODEL_TYPE_UNSPECIFIED",
1: "CLOUD_HIGH_ACCURACY_1",
2: "CLOUD_LOW_LATENCY_1",
3: "MOBILE_TF_LOW_LATENCY_1",
4: "MOBILE_TF_VERSATILE_1",
5: "MOBILE_TF_HIGH_ACCURACY_1",
}
AutoMlImageObjectDetectionInputs_ModelType_value = map[string]int32{
"MODEL_TYPE_UNSPECIFIED": 0,
"CLOUD_HIGH_ACCURACY_1": 1,
"CLOUD_LOW_LATENCY_1": 2,
"MOBILE_TF_LOW_LATENCY_1": 3,
"MOBILE_TF_VERSATILE_1": 4,
"MOBILE_TF_HIGH_ACCURACY_1": 5,
}
)
Enum value maps for AutoMlImageObjectDetectionMetadata_SuccessfulStopReason.
var (
AutoMlImageObjectDetectionMetadata_SuccessfulStopReason_name = map[int32]string{
0: "SUCCESSFUL_STOP_REASON_UNSPECIFIED",
1: "BUDGET_REACHED",
2: "MODEL_CONVERGED",
}
AutoMlImageObjectDetectionMetadata_SuccessfulStopReason_value = map[string]int32{
"SUCCESSFUL_STOP_REASON_UNSPECIFIED": 0,
"BUDGET_REACHED": 1,
"MODEL_CONVERGED": 2,
}
)
Enum value maps for AutoMlImageSegmentationInputs_ModelType.
var (
AutoMlImageSegmentationInputs_ModelType_name = map[int32]string{
0: "MODEL_TYPE_UNSPECIFIED",
1: "CLOUD_HIGH_ACCURACY_1",
2: "CLOUD_LOW_ACCURACY_1",
3: "MOBILE_TF_LOW_LATENCY_1",
}
AutoMlImageSegmentationInputs_ModelType_value = map[string]int32{
"MODEL_TYPE_UNSPECIFIED": 0,
"CLOUD_HIGH_ACCURACY_1": 1,
"CLOUD_LOW_ACCURACY_1": 2,
"MOBILE_TF_LOW_LATENCY_1": 3,
}
)
Enum value maps for AutoMlImageSegmentationMetadata_SuccessfulStopReason.
var (
AutoMlImageSegmentationMetadata_SuccessfulStopReason_name = map[int32]string{
0: "SUCCESSFUL_STOP_REASON_UNSPECIFIED",
1: "BUDGET_REACHED",
2: "MODEL_CONVERGED",
}
AutoMlImageSegmentationMetadata_SuccessfulStopReason_value = map[string]int32{
"SUCCESSFUL_STOP_REASON_UNSPECIFIED": 0,
"BUDGET_REACHED": 1,
"MODEL_CONVERGED": 2,
}
)
Enum value maps for AutoMlVideoActionRecognitionInputs_ModelType.
var (
AutoMlVideoActionRecognitionInputs_ModelType_name = map[int32]string{
0: "MODEL_TYPE_UNSPECIFIED",
1: "CLOUD",
2: "MOBILE_VERSATILE_1",
3: "MOBILE_JETSON_VERSATILE_1",
4: "MOBILE_CORAL_VERSATILE_1",
}
AutoMlVideoActionRecognitionInputs_ModelType_value = map[string]int32{
"MODEL_TYPE_UNSPECIFIED": 0,
"CLOUD": 1,
"MOBILE_VERSATILE_1": 2,
"MOBILE_JETSON_VERSATILE_1": 3,
"MOBILE_CORAL_VERSATILE_1": 4,
}
)
Enum value maps for AutoMlVideoClassificationInputs_ModelType.
var (
AutoMlVideoClassificationInputs_ModelType_name = map[int32]string{
0: "MODEL_TYPE_UNSPECIFIED",
1: "CLOUD",
2: "MOBILE_VERSATILE_1",
3: "MOBILE_JETSON_VERSATILE_1",
}
AutoMlVideoClassificationInputs_ModelType_value = map[string]int32{
"MODEL_TYPE_UNSPECIFIED": 0,
"CLOUD": 1,
"MOBILE_VERSATILE_1": 2,
"MOBILE_JETSON_VERSATILE_1": 3,
}
)
Enum value maps for AutoMlVideoObjectTrackingInputs_ModelType.
var (
AutoMlVideoObjectTrackingInputs_ModelType_name = map[int32]string{
0: "MODEL_TYPE_UNSPECIFIED",
1: "CLOUD",
2: "MOBILE_VERSATILE_1",
3: "MOBILE_CORAL_VERSATILE_1",
4: "MOBILE_CORAL_LOW_LATENCY_1",
5: "MOBILE_JETSON_VERSATILE_1",
6: "MOBILE_JETSON_LOW_LATENCY_1",
}
AutoMlVideoObjectTrackingInputs_ModelType_value = map[string]int32{
"MODEL_TYPE_UNSPECIFIED": 0,
"CLOUD": 1,
"MOBILE_VERSATILE_1": 2,
"MOBILE_CORAL_VERSATILE_1": 3,
"MOBILE_CORAL_LOW_LATENCY_1": 4,
"MOBILE_JETSON_VERSATILE_1": 5,
"MOBILE_JETSON_LOW_LATENCY_1": 6,
}
)
var File_google_cloud_aiplatform_v1_schema_trainingjob_definition_automl_image_classification_proto protoreflect.FileDescriptor
var File_google_cloud_aiplatform_v1_schema_trainingjob_definition_automl_image_object_detection_proto protoreflect.FileDescriptor
var File_google_cloud_aiplatform_v1_schema_trainingjob_definition_automl_image_segmentation_proto protoreflect.FileDescriptor
var File_google_cloud_aiplatform_v1_schema_trainingjob_definition_automl_tables_proto protoreflect.FileDescriptor
var File_google_cloud_aiplatform_v1_schema_trainingjob_definition_automl_text_classification_proto protoreflect.FileDescriptor
var File_google_cloud_aiplatform_v1_schema_trainingjob_definition_automl_text_extraction_proto protoreflect.FileDescriptor
var File_google_cloud_aiplatform_v1_schema_trainingjob_definition_automl_text_sentiment_proto protoreflect.FileDescriptor
var File_google_cloud_aiplatform_v1_schema_trainingjob_definition_automl_time_series_forecasting_proto protoreflect.FileDescriptor
var File_google_cloud_aiplatform_v1_schema_trainingjob_definition_automl_video_action_recognition_proto protoreflect.FileDescriptor
var File_google_cloud_aiplatform_v1_schema_trainingjob_definition_automl_video_classification_proto protoreflect.FileDescriptor
var File_google_cloud_aiplatform_v1_schema_trainingjob_definition_automl_video_object_tracking_proto protoreflect.FileDescriptor
var File_google_cloud_aiplatform_v1_schema_trainingjob_definition_export_evaluated_data_items_config_proto protoreflect.FileDescriptor
A TrainingJob that trains and uploads an AutoML Forecasting Model.
type AutoMlForecasting struct {
// The input parameters of this TrainingJob.
Inputs *AutoMlForecastingInputs `protobuf:"bytes,1,opt,name=inputs,proto3" json:"inputs,omitempty"`
// The metadata information.
Metadata *AutoMlForecastingMetadata `protobuf:"bytes,2,opt,name=metadata,proto3" json:"metadata,omitempty"`
// contains filtered or unexported fields
}
func (*AutoMlForecasting) Descriptor() ([]byte, []int)
Deprecated: Use AutoMlForecasting.ProtoReflect.Descriptor instead.
func (x *AutoMlForecasting) GetInputs() *AutoMlForecastingInputs
func (x *AutoMlForecasting) GetMetadata() *AutoMlForecastingMetadata
func (*AutoMlForecasting) ProtoMessage()
func (x *AutoMlForecasting) ProtoReflect() protoreflect.Message
func (x *AutoMlForecasting) Reset()
func (x *AutoMlForecasting) String() string
type AutoMlForecastingInputs struct {
// The name of the column that the model is to predict.
TargetColumn string `protobuf:"bytes,1,opt,name=target_column,json=targetColumn,proto3" json:"target_column,omitempty"`
// The name of the column that identifies the time series.
TimeSeriesIdentifierColumn string `protobuf:"bytes,2,opt,name=time_series_identifier_column,json=timeSeriesIdentifierColumn,proto3" json:"time_series_identifier_column,omitempty"`
// The name of the column that identifies time order in the time series.
TimeColumn string `protobuf:"bytes,3,opt,name=time_column,json=timeColumn,proto3" json:"time_column,omitempty"`
// 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.
Transformations []*AutoMlForecastingInputs_Transformation `protobuf:"bytes,4,rep,name=transformations,proto3" json:"transformations,omitempty"`
// 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`.
OptimizationObjective string `protobuf:"bytes,5,opt,name=optimization_objective,json=optimizationObjective,proto3" json:"optimization_objective,omitempty"`
// 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.
TrainBudgetMilliNodeHours int64 `protobuf:"varint,6,opt,name=train_budget_milli_node_hours,json=trainBudgetMilliNodeHours,proto3" json:"train_budget_milli_node_hours,omitempty"`
// 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.
WeightColumn string `protobuf:"bytes,7,opt,name=weight_column,json=weightColumn,proto3" json:"weight_column,omitempty"`
// Column names that should be used as static columns.
// The value of these columns are static per time series.
StaticColumns []string `protobuf:"bytes,8,rep,name=static_columns,json=staticColumns,proto3" json:"static_columns,omitempty"`
// Column names that should be used as time variant past only columns.
// This column contains information for the given entity (identified by the
// time_series_identifier_column) that is known for the past but not the
// future (e.g. population of a city in a given year, or weather on a given
// day).
TimeVariantPastOnlyColumns []string `protobuf:"bytes,9,rep,name=time_variant_past_only_columns,json=timeVariantPastOnlyColumns,proto3" json:"time_variant_past_only_columns,omitempty"`
// Column names that should be used as time variant past and future columns.
// This column contains information for the given entity (identified by the
// key column) that is known for the past and the future
TimeVariantPastAndFutureColumns []string `protobuf:"bytes,10,rep,name=time_variant_past_and_future_columns,json=timeVariantPastAndFutureColumns,proto3" json:"time_variant_past_and_future_columns,omitempty"`
// Expected difference in time granularity between rows in the data. If it is
// not set, the period is inferred from data.
Period *AutoMlForecastingInputs_Period `protobuf:"bytes,11,opt,name=period,proto3" json:"period,omitempty"`
// The number of periods offset into the future as the start of the forecast
// window (the window of future values to predict, relative to the present.),
// where each period is one unit of granularity as defined by the `period`
// field above. Default to 0. Inclusive.
ForecastWindowStart int64 `protobuf:"varint,12,opt,name=forecast_window_start,json=forecastWindowStart,proto3" json:"forecast_window_start,omitempty"`
// The number of periods offset into the future as the end of the forecast
// window (the window of future values to predict, relative to the present.),
// where each period is one unit of granularity as defined by the `period`
// field above. Inclusive.
ForecastWindowEnd int64 `protobuf:"varint,13,opt,name=forecast_window_end,json=forecastWindowEnd,proto3" json:"forecast_window_end,omitempty"`
// The number of periods offset into the past to restrict past sequence, where
// each period is one unit of granularity as defined by the `period`. Default
// value 0 means that it lets algorithm to define the value. Inclusive.
PastHorizon int64 `protobuf:"varint,14,opt,name=past_horizon,json=pastHorizon,proto3" json:"past_horizon,omitempty"`
// Configuration for exporting test set predictions to a BigQuery table. If
// this configuration is absent, then the export is not performed.
ExportEvaluatedDataItemsConfig *ExportEvaluatedDataItemsConfig `protobuf:"bytes,15,opt,name=export_evaluated_data_items_config,json=exportEvaluatedDataItemsConfig,proto3" json:"export_evaluated_data_items_config,omitempty"`
// 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.
Quantiles []float64 `protobuf:"fixed64,16,rep,packed,name=quantiles,proto3" json:"quantiles,omitempty"`
// 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
ValidationOptions string `protobuf:"bytes,17,opt,name=validation_options,json=validationOptions,proto3" json:"validation_options,omitempty"`
// contains filtered or unexported fields
}
func (*AutoMlForecastingInputs) Descriptor() ([]byte, []int)
Deprecated: Use AutoMlForecastingInputs.ProtoReflect.Descriptor instead.
func (x *AutoMlForecastingInputs) GetExportEvaluatedDataItemsConfig() *ExportEvaluatedDataItemsConfig
func (x *AutoMlForecastingInputs) GetForecastWindowEnd() int64
func (x *AutoMlForecastingInputs) GetForecastWindowStart() int64
func (x *AutoMlForecastingInputs) GetOptimizationObjective() string
func (x *AutoMlForecastingInputs) GetPastHorizon() int64
func (x *AutoMlForecastingInputs) GetPeriod() *AutoMlForecastingInputs_Period
func (x *AutoMlForecastingInputs) GetQuantiles() []float64
func (x *AutoMlForecastingInputs) GetStaticColumns() []string
func (x *AutoMlForecastingInputs) GetTargetColumn() string
func (x *AutoMlForecastingInputs) GetTimeColumn() string
func (x *AutoMlForecastingInputs) GetTimeSeriesIdentifierColumn() string
func (x *AutoMlForecastingInputs) GetTimeVariantPastAndFutureColumns() []string
func (x *AutoMlForecastingInputs) GetTimeVariantPastOnlyColumns() []string
func (x *AutoMlForecastingInputs) GetTrainBudgetMilliNodeHours() int64
func (x *AutoMlForecastingInputs) GetTransformations() []*AutoMlForecastingInputs_Transformation
func (x *AutoMlForecastingInputs) GetValidationOptions() string
func (x *AutoMlForecastingInputs) GetWeightColumn() string
func (*AutoMlForecastingInputs) ProtoMessage()
func (x *AutoMlForecastingInputs) ProtoReflect() protoreflect.Message
func (x *AutoMlForecastingInputs) Reset()
func (x *AutoMlForecastingInputs) String() string
A duration of time expressed in time granularity units.
type AutoMlForecastingInputs_Period struct {
// The time granularity unit of this time period.
// The supported unit are:
// "minute"
// "hour"
// "day"
// "week"
// "month"
// "year"
Unit string `protobuf:"bytes,1,opt,name=unit,proto3" json:"unit,omitempty"`
// The number of units per period, e.g. 3 weeks or 2 months.
Quantity int64 `protobuf:"varint,2,opt,name=quantity,proto3" json:"quantity,omitempty"`
// contains filtered or unexported fields
}
func (*AutoMlForecastingInputs_Period) Descriptor() ([]byte, []int)
Deprecated: Use AutoMlForecastingInputs_Period.ProtoReflect.Descriptor instead.
func (x *AutoMlForecastingInputs_Period) GetQuantity() int64
func (x *AutoMlForecastingInputs_Period) GetUnit() string
func (*AutoMlForecastingInputs_Period) ProtoMessage()
func (x *AutoMlForecastingInputs_Period) ProtoReflect() protoreflect.Message
func (x *AutoMlForecastingInputs_Period) Reset()
func (x *AutoMlForecastingInputs_Period) String() string
type AutoMlForecastingInputs_Transformation struct {
// The transformation that the training pipeline will apply to the input
// columns.
//
// Types that are assignable to TransformationDetail:
// *AutoMlForecastingInputs_Transformation_Auto
// *AutoMlForecastingInputs_Transformation_Numeric
// *AutoMlForecastingInputs_Transformation_Categorical
// *AutoMlForecastingInputs_Transformation_Timestamp
// *AutoMlForecastingInputs_Transformation_Text
// *AutoMlForecastingInputs_Transformation_RepeatedNumeric
// *AutoMlForecastingInputs_Transformation_RepeatedCategorical
// *AutoMlForecastingInputs_Transformation_RepeatedText
TransformationDetail isAutoMlForecastingInputs_Transformation_TransformationDetail `protobuf_oneof:"transformation_detail"`
// contains filtered or unexported fields
}
func (*AutoMlForecastingInputs_Transformation) Descriptor() ([]byte, []int)
Deprecated: Use AutoMlForecastingInputs_Transformation.ProtoReflect.Descriptor instead.
func (x *AutoMlForecastingInputs_Transformation) GetAuto() *AutoMlForecastingInputs_Transformation_AutoTransformation
func (x *AutoMlForecastingInputs_Transformation) GetCategorical() *AutoMlForecastingInputs_Transformation_CategoricalTransformation
func (x *AutoMlForecastingInputs_Transformation) GetNumeric() *AutoMlForecastingInputs_Transformation_NumericTransformation
func (x *AutoMlForecastingInputs_Transformation) GetRepeatedCategorical() *AutoMlForecastingInputs_Transformation_CategoricalArrayTransformation
func (x *AutoMlForecastingInputs_Transformation) GetRepeatedNumeric() *AutoMlForecastingInputs_Transformation_NumericArrayTransformation
func (x *AutoMlForecastingInputs_Transformation) GetRepeatedText() *AutoMlForecastingInputs_Transformation_TextArrayTransformation
func (x *AutoMlForecastingInputs_Transformation) GetText() *AutoMlForecastingInputs_Transformation_TextTransformation
func (x *AutoMlForecastingInputs_Transformation) GetTimestamp() *AutoMlForecastingInputs_Transformation_TimestampTransformation
func (m *AutoMlForecastingInputs_Transformation) GetTransformationDetail() isAutoMlForecastingInputs_Transformation_TransformationDetail
func (*AutoMlForecastingInputs_Transformation) ProtoMessage()
func (x *AutoMlForecastingInputs_Transformation) ProtoReflect() protoreflect.Message
func (x *AutoMlForecastingInputs_Transformation) Reset()
func (x *AutoMlForecastingInputs_Transformation) String() string
type AutoMlForecastingInputs_Transformation_Auto struct {
Auto *AutoMlForecastingInputs_Transformation_AutoTransformation `protobuf:"bytes,1,opt,name=auto,proto3,oneof"`
}
Training pipeline will infer the proper transformation based on the statistic of dataset.
type AutoMlForecastingInputs_Transformation_AutoTransformation struct {
ColumnName string `protobuf:"bytes,1,opt,name=column_name,json=columnName,proto3" json:"column_name,omitempty"`
// contains filtered or unexported fields
}
func (*AutoMlForecastingInputs_Transformation_AutoTransformation) Descriptor() ([]byte, []int)
Deprecated: Use AutoMlForecastingInputs_Transformation_AutoTransformation.ProtoReflect.Descriptor instead.
func (x *AutoMlForecastingInputs_Transformation_AutoTransformation) GetColumnName() string
func (*AutoMlForecastingInputs_Transformation_AutoTransformation) ProtoMessage()
func (x *AutoMlForecastingInputs_Transformation_AutoTransformation) ProtoReflect() protoreflect.Message
func (x *AutoMlForecastingInputs_Transformation_AutoTransformation) Reset()
func (x *AutoMlForecastingInputs_Transformation_AutoTransformation) String() string
type AutoMlForecastingInputs_Transformation_Categorical struct {
Categorical *AutoMlForecastingInputs_Transformation_CategoricalTransformation `protobuf:"bytes,3,opt,name=categorical,proto3,oneof"`
}
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.
type AutoMlForecastingInputs_Transformation_CategoricalArrayTransformation struct {
ColumnName string `protobuf:"bytes,1,opt,name=column_name,json=columnName,proto3" json:"column_name,omitempty"`
// contains filtered or unexported fields
}
func (*AutoMlForecastingInputs_Transformation_CategoricalArrayTransformation) Descriptor() ([]byte, []int)
Deprecated: Use AutoMlForecastingInputs_Transformation_CategoricalArrayTransformation.ProtoReflect.Descriptor instead.
func (x *AutoMlForecastingInputs_Transformation_CategoricalArrayTransformation) GetColumnName() string
func (*AutoMlForecastingInputs_Transformation_CategoricalArrayTransformation) ProtoMessage()
func (x *AutoMlForecastingInputs_Transformation_CategoricalArrayTransformation) ProtoReflect() protoreflect.Message
func (x *AutoMlForecastingInputs_Transformation_CategoricalArrayTransformation) Reset()
func (x *AutoMlForecastingInputs_Transformation_CategoricalArrayTransformation) String() string
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.
type AutoMlForecastingInputs_Transformation_CategoricalTransformation struct {
ColumnName string `protobuf:"bytes,1,opt,name=column_name,json=columnName,proto3" json:"column_name,omitempty"`
// contains filtered or unexported fields
}
func (*AutoMlForecastingInputs_Transformation_CategoricalTransformation) Descriptor() ([]byte, []int)
Deprecated: Use AutoMlForecastingInputs_Transformation_CategoricalTransformation.ProtoReflect.Descriptor instead.
func (x *AutoMlForecastingInputs_Transformation_CategoricalTransformation) GetColumnName() string
func (*AutoMlForecastingInputs_Transformation_CategoricalTransformation) ProtoMessage()
func (x *AutoMlForecastingInputs_Transformation_CategoricalTransformation) ProtoReflect() protoreflect.Message
func (x *AutoMlForecastingInputs_Transformation_CategoricalTransformation) Reset()
func (x *AutoMlForecastingInputs_Transformation_CategoricalTransformation) String() string
type AutoMlForecastingInputs_Transformation_Numeric struct {
Numeric *AutoMlForecastingInputs_Transformation_NumericTransformation `protobuf:"bytes,2,opt,name=numeric,proto3,oneof"`
}
Treats the column as numerical array and performs following transformation functions.
type AutoMlForecastingInputs_Transformation_NumericArrayTransformation struct {
ColumnName string `protobuf:"bytes,1,opt,name=column_name,json=columnName,proto3" json:"column_name,omitempty"`
// 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.
InvalidValuesAllowed bool `protobuf:"varint,2,opt,name=invalid_values_allowed,json=invalidValuesAllowed,proto3" json:"invalid_values_allowed,omitempty"`
// contains filtered or unexported fields
}
func (*AutoMlForecastingInputs_Transformation_NumericArrayTransformation) Descriptor() ([]byte, []int)
Deprecated: Use AutoMlForecastingInputs_Transformation_NumericArrayTransformation.ProtoReflect.Descriptor instead.
func (x *AutoMlForecastingInputs_Transformation_NumericArrayTransformation) GetColumnName() string
func (x *AutoMlForecastingInputs_Transformation_NumericArrayTransformation) GetInvalidValuesAllowed() bool
func (*AutoMlForecastingInputs_Transformation_NumericArrayTransformation) ProtoMessage()
func (x *AutoMlForecastingInputs_Transformation_NumericArrayTransformation) ProtoReflect() protoreflect.Message
func (x *AutoMlForecastingInputs_Transformation_NumericArrayTransformation) Reset()
func (x *AutoMlForecastingInputs_Transformation_NumericArrayTransformation) String() string
Training pipeline will perform following transformation functions.
type AutoMlForecastingInputs_Transformation_NumericTransformation struct {
ColumnName string `protobuf:"bytes,1,opt,name=column_name,json=columnName,proto3" json:"column_name,omitempty"`
// 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.
InvalidValuesAllowed bool `protobuf:"varint,2,opt,name=invalid_values_allowed,json=invalidValuesAllowed,proto3" json:"invalid_values_allowed,omitempty"`
// contains filtered or unexported fields
}
func (*AutoMlForecastingInputs_Transformation_NumericTransformation) Descriptor() ([]byte, []int)
Deprecated: Use AutoMlForecastingInputs_Transformation_NumericTransformation.ProtoReflect.Descriptor instead.
func (x *AutoMlForecastingInputs_Transformation_NumericTransformation) GetColumnName() string
func (x *AutoMlForecastingInputs_Transformation_NumericTransformation) GetInvalidValuesAllowed() bool
func (*AutoMlForecastingInputs_Transformation_NumericTransformation) ProtoMessage()
func (x *AutoMlForecastingInputs_Transformation_NumericTransformation) ProtoReflect() protoreflect.Message
func (x *AutoMlForecastingInputs_Transformation_NumericTransformation) Reset()
func (x *AutoMlForecastingInputs_Transformation_NumericTransformation) String() string
type AutoMlForecastingInputs_Transformation_RepeatedCategorical struct {
RepeatedCategorical *AutoMlForecastingInputs_Transformation_CategoricalArrayTransformation `protobuf:"bytes,7,opt,name=repeated_categorical,json=repeatedCategorical,proto3,oneof"`
}
type AutoMlForecastingInputs_Transformation_RepeatedNumeric struct {
RepeatedNumeric *AutoMlForecastingInputs_Transformation_NumericArrayTransformation `protobuf:"bytes,6,opt,name=repeated_numeric,json=repeatedNumeric,proto3,oneof"`
}
type AutoMlForecastingInputs_Transformation_RepeatedText struct {
RepeatedText *AutoMlForecastingInputs_Transformation_TextArrayTransformation `protobuf:"bytes,8,opt,name=repeated_text,json=repeatedText,proto3,oneof"`
}
type AutoMlForecastingInputs_Transformation_Text struct {
Text *AutoMlForecastingInputs_Transformation_TextTransformation `protobuf:"bytes,5,opt,name=text,proto3,oneof"`
}
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.
type AutoMlForecastingInputs_Transformation_TextArrayTransformation struct {
ColumnName string `protobuf:"bytes,1,opt,name=column_name,json=columnName,proto3" json:"column_name,omitempty"`
// contains filtered or unexported fields
}
func (*AutoMlForecastingInputs_Transformation_TextArrayTransformation) Descriptor() ([]byte, []int)
Deprecated: Use AutoMlForecastingInputs_Transformation_TextArrayTransformation.ProtoReflect.Descriptor instead.
func (x *AutoMlForecastingInputs_Transformation_TextArrayTransformation) GetColumnName() string
func (*AutoMlForecastingInputs_Transformation_TextArrayTransformation) ProtoMessage()
func (x *AutoMlForecastingInputs_Transformation_TextArrayTransformation) ProtoReflect() protoreflect.Message
func (x *AutoMlForecastingInputs_Transformation_TextArrayTransformation) Reset()
func (x *AutoMlForecastingInputs_Transformation_TextArrayTransformation) String() string
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.
type AutoMlForecastingInputs_Transformation_TextTransformation struct {
ColumnName string `protobuf:"bytes,1,opt,name=column_name,json=columnName,proto3" json:"column_name,omitempty"`
// contains filtered or unexported fields
}
func (*AutoMlForecastingInputs_Transformation_TextTransformation) Descriptor() ([]byte, []int)
Deprecated: Use AutoMlForecastingInputs_Transformation_TextTransformation.ProtoReflect.Descriptor instead.
func (x *AutoMlForecastingInputs_Transformation_TextTransformation) GetColumnName() string
func (*AutoMlForecastingInputs_Transformation_TextTransformation) ProtoMessage()
func (x *AutoMlForecastingInputs_Transformation_TextTransformation) ProtoReflect() protoreflect.Message
func (x *AutoMlForecastingInputs_Transformation_TextTransformation) Reset()
func (x *AutoMlForecastingInputs_Transformation_TextTransformation) String() string
type AutoMlForecastingInputs_Transformation_Timestamp struct {
Timestamp *AutoMlForecastingInputs_Transformation_TimestampTransformation `protobuf:"bytes,4,opt,name=timestamp,proto3,oneof"`
}
Training pipeline will perform following transformation functions.
type AutoMlForecastingInputs_Transformation_TimestampTransformation struct {
ColumnName string `protobuf:"bytes,1,opt,name=column_name,json=columnName,proto3" json:"column_name,omitempty"`
// 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)
TimeFormat string `protobuf:"bytes,2,opt,name=time_format,json=timeFormat,proto3" json:"time_format,omitempty"`
// 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.
InvalidValuesAllowed bool `protobuf:"varint,3,opt,name=invalid_values_allowed,json=invalidValuesAllowed,proto3" json:"invalid_values_allowed,omitempty"`
// contains filtered or unexported fields
}
func (*AutoMlForecastingInputs_Transformation_TimestampTransformation) Descriptor() ([]byte, []int)
Deprecated: Use AutoMlForecastingInputs_Transformation_TimestampTransformation.ProtoReflect.Descriptor instead.
func (x *AutoMlForecastingInputs_Transformation_TimestampTransformation) GetColumnName() string
func (x *AutoMlForecastingInputs_Transformation_TimestampTransformation) GetInvalidValuesAllowed() bool
func (x *AutoMlForecastingInputs_Transformation_TimestampTransformation) GetTimeFormat() string
func (*AutoMlForecastingInputs_Transformation_TimestampTransformation) ProtoMessage()
func (x *AutoMlForecastingInputs_Transformation_TimestampTransformation) ProtoReflect() protoreflect.Message
func (x *AutoMlForecastingInputs_Transformation_TimestampTransformation) Reset()
func (x *AutoMlForecastingInputs_Transformation_TimestampTransformation) String() string
Model metadata specific to AutoML Forecasting.
type AutoMlForecastingMetadata struct {
// 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.
TrainCostMilliNodeHours int64 `protobuf:"varint,1,opt,name=train_cost_milli_node_hours,json=trainCostMilliNodeHours,proto3" json:"train_cost_milli_node_hours,omitempty"`
// contains filtered or unexported fields
}
func (*AutoMlForecastingMetadata) Descriptor() ([]byte, []int)
Deprecated: Use AutoMlForecastingMetadata.ProtoReflect.Descriptor instead.
func (x *AutoMlForecastingMetadata) GetTrainCostMilliNodeHours() int64
func (*AutoMlForecastingMetadata) ProtoMessage()
func (x *AutoMlForecastingMetadata) ProtoReflect() protoreflect.Message
func (x *AutoMlForecastingMetadata) Reset()
func (x *AutoMlForecastingMetadata) String() string
A TrainingJob that trains and uploads an AutoML Image Classification Model.
type AutoMlImageClassification struct {
// The input parameters of this TrainingJob.
Inputs *AutoMlImageClassificationInputs `protobuf:"bytes,1,opt,name=inputs,proto3" json:"inputs,omitempty"`
// The metadata information.
Metadata *AutoMlImageClassificationMetadata `protobuf:"bytes,2,opt,name=metadata,proto3" json:"metadata,omitempty"`
// contains filtered or unexported fields
}
func (*AutoMlImageClassification) Descriptor() ([]byte, []int)
Deprecated: Use AutoMlImageClassification.ProtoReflect.Descriptor instead.
func (x *AutoMlImageClassification) GetInputs() *AutoMlImageClassificationInputs
func (x *AutoMlImageClassification) GetMetadata() *AutoMlImageClassificationMetadata
func (*AutoMlImageClassification) ProtoMessage()
func (x *AutoMlImageClassification) ProtoReflect() protoreflect.Message
func (x *AutoMlImageClassification) Reset()
func (x *AutoMlImageClassification) String() string
type AutoMlImageClassificationInputs struct {
ModelType AutoMlImageClassificationInputs_ModelType `protobuf:"varint,1,opt,name=model_type,json=modelType,proto3,enum=google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlImageClassificationInputs_ModelType" json:"model_type,omitempty"`
// 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.
BaseModelId string `protobuf:"bytes,2,opt,name=base_model_id,json=baseModelId,proto3" json:"base_model_id,omitempty"`
// 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.
BudgetMilliNodeHours int64 `protobuf:"varint,3,opt,name=budget_milli_node_hours,json=budgetMilliNodeHours,proto3" json:"budget_milli_node_hours,omitempty"`
// 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.
DisableEarlyStopping bool `protobuf:"varint,4,opt,name=disable_early_stopping,json=disableEarlyStopping,proto3" json:"disable_early_stopping,omitempty"`
// 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).
MultiLabel bool `protobuf:"varint,5,opt,name=multi_label,json=multiLabel,proto3" json:"multi_label,omitempty"`
// contains filtered or unexported fields
}
func (*AutoMlImageClassificationInputs) Descriptor() ([]byte, []int)
Deprecated: Use AutoMlImageClassificationInputs.ProtoReflect.Descriptor instead.
func (x *AutoMlImageClassificationInputs) GetBaseModelId() string
func (x *AutoMlImageClassificationInputs) GetBudgetMilliNodeHours() int64
func (x *AutoMlImageClassificationInputs) GetDisableEarlyStopping() bool
func (x *AutoMlImageClassificationInputs) GetModelType() AutoMlImageClassificationInputs_ModelType
func (x *AutoMlImageClassificationInputs) GetMultiLabel() bool
func (*AutoMlImageClassificationInputs) ProtoMessage()
func (x *AutoMlImageClassificationInputs) ProtoReflect() protoreflect.Message
func (x *AutoMlImageClassificationInputs) Reset()
func (x *AutoMlImageClassificationInputs) String() string
type AutoMlImageClassificationInputs_ModelType int32
const (
// Should not be set.
AutoMlImageClassificationInputs_MODEL_TYPE_UNSPECIFIED AutoMlImageClassificationInputs_ModelType = 0
// A Model best tailored to be used within Google Cloud, and which cannot
// be exported.
// Default.
AutoMlImageClassificationInputs_CLOUD AutoMlImageClassificationInputs_ModelType = 1
// 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.
AutoMlImageClassificationInputs_MOBILE_TF_LOW_LATENCY_1 AutoMlImageClassificationInputs_ModelType = 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 with afterwards.
AutoMlImageClassificationInputs_MOBILE_TF_VERSATILE_1 AutoMlImageClassificationInputs_ModelType = 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 afterwards.
// Expected to have a higher latency, but should also have a higher
// prediction quality than other mobile models.
AutoMlImageClassificationInputs_MOBILE_TF_HIGH_ACCURACY_1 AutoMlImageClassificationInputs_ModelType = 4
)
func (AutoMlImageClassificationInputs_ModelType) Descriptor() protoreflect.EnumDescriptor
func (x AutoMlImageClassificationInputs_ModelType) Enum() *AutoMlImageClassificationInputs_ModelType
func (AutoMlImageClassificationInputs_ModelType) EnumDescriptor() ([]byte, []int)
Deprecated: Use AutoMlImageClassificationInputs_ModelType.Descriptor instead.
func (x AutoMlImageClassificationInputs_ModelType) Number() protoreflect.EnumNumber
func (x AutoMlImageClassificationInputs_ModelType) String() string
func (AutoMlImageClassificationInputs_ModelType) Type() protoreflect.EnumType
type AutoMlImageClassificationMetadata struct {
// 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.
CostMilliNodeHours int64 `protobuf:"varint,1,opt,name=cost_milli_node_hours,json=costMilliNodeHours,proto3" json:"cost_milli_node_hours,omitempty"`
// For successful job completions, this is the reason why the job has
// finished.
SuccessfulStopReason AutoMlImageClassificationMetadata_SuccessfulStopReason `protobuf:"varint,2,opt,name=successful_stop_reason,json=successfulStopReason,proto3,enum=google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlImageClassificationMetadata_SuccessfulStopReason" json:"successful_stop_reason,omitempty"`
// contains filtered or unexported fields
}
func (*AutoMlImageClassificationMetadata) Descriptor() ([]byte, []int)
Deprecated: Use AutoMlImageClassificationMetadata.ProtoReflect.Descriptor instead.
func (x *AutoMlImageClassificationMetadata) GetCostMilliNodeHours() int64
func (x *AutoMlImageClassificationMetadata) GetSuccessfulStopReason() AutoMlImageClassificationMetadata_SuccessfulStopReason
func (*AutoMlImageClassificationMetadata) ProtoMessage()
func (x *AutoMlImageClassificationMetadata) ProtoReflect() protoreflect.Message
func (x *AutoMlImageClassificationMetadata) Reset()
func (x *AutoMlImageClassificationMetadata) String() string
type AutoMlImageClassificationMetadata_SuccessfulStopReason int32
const (
// Should not be set.
AutoMlImageClassificationMetadata_SUCCESSFUL_STOP_REASON_UNSPECIFIED AutoMlImageClassificationMetadata_SuccessfulStopReason = 0
// The inputs.budgetMilliNodeHours had been reached.
AutoMlImageClassificationMetadata_BUDGET_REACHED AutoMlImageClassificationMetadata_SuccessfulStopReason = 1
// Further training of the Model ceased to increase its quality, since it
// already has converged.
AutoMlImageClassificationMetadata_MODEL_CONVERGED AutoMlImageClassificationMetadata_SuccessfulStopReason = 2
)
func (AutoMlImageClassificationMetadata_SuccessfulStopReason) Descriptor() protoreflect.EnumDescriptor
func (x AutoMlImageClassificationMetadata_SuccessfulStopReason) Enum() *AutoMlImageClassificationMetadata_SuccessfulStopReason
func (AutoMlImageClassificationMetadata_SuccessfulStopReason) EnumDescriptor() ([]byte, []int)
Deprecated: Use AutoMlImageClassificationMetadata_SuccessfulStopReason.Descriptor instead.
func (x AutoMlImageClassificationMetadata_SuccessfulStopReason) Number() protoreflect.EnumNumber
func (x AutoMlImageClassificationMetadata_SuccessfulStopReason) String() string
func (AutoMlImageClassificationMetadata_SuccessfulStopReason) Type() protoreflect.EnumType
A TrainingJob that trains and uploads an AutoML Image Object Detection Model.
type AutoMlImageObjectDetection struct {
// The input parameters of this TrainingJob.
Inputs *AutoMlImageObjectDetectionInputs `protobuf:"bytes,1,opt,name=inputs,proto3" json:"inputs,omitempty"`
// The metadata information
Metadata *AutoMlImageObjectDetectionMetadata `protobuf:"bytes,2,opt,name=metadata,proto3" json:"metadata,omitempty"`
// contains filtered or unexported fields
}
func (*AutoMlImageObjectDetection) Descriptor() ([]byte, []int)
Deprecated: Use AutoMlImageObjectDetection.ProtoReflect.Descriptor instead.
func (x *AutoMlImageObjectDetection) GetInputs() *AutoMlImageObjectDetectionInputs
func (x *AutoMlImageObjectDetection) GetMetadata() *AutoMlImageObjectDetectionMetadata
func (*AutoMlImageObjectDetection) ProtoMessage()
func (x *AutoMlImageObjectDetection) ProtoReflect() protoreflect.Message
func (x *AutoMlImageObjectDetection) Reset()
func (x *AutoMlImageObjectDetection) String() string
type AutoMlImageObjectDetectionInputs struct {
ModelType AutoMlImageObjectDetectionInputs_ModelType `protobuf:"varint,1,opt,name=model_type,json=modelType,proto3,enum=google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlImageObjectDetectionInputs_ModelType" json:"model_type,omitempty"`
// 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.
BudgetMilliNodeHours int64 `protobuf:"varint,2,opt,name=budget_milli_node_hours,json=budgetMilliNodeHours,proto3" json:"budget_milli_node_hours,omitempty"`
// 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.
DisableEarlyStopping bool `protobuf:"varint,3,opt,name=disable_early_stopping,json=disableEarlyStopping,proto3" json:"disable_early_stopping,omitempty"`
// contains filtered or unexported fields
}
func (*AutoMlImageObjectDetectionInputs) Descriptor() ([]byte, []int)
Deprecated: Use AutoMlImageObjectDetectionInputs.ProtoReflect.Descriptor instead.
func (x *AutoMlImageObjectDetectionInputs) GetBudgetMilliNodeHours() int64
func (x *AutoMlImageObjectDetectionInputs) GetDisableEarlyStopping() bool
func (x *AutoMlImageObjectDetectionInputs) GetModelType() AutoMlImageObjectDetectionInputs_ModelType
func (*AutoMlImageObjectDetectionInputs) ProtoMessage()
func (x *AutoMlImageObjectDetectionInputs) ProtoReflect() protoreflect.Message
func (x *AutoMlImageObjectDetectionInputs) Reset()
func (x *AutoMlImageObjectDetectionInputs) String() string
type AutoMlImageObjectDetectionInputs_ModelType int32
const (
// Should not be set.
AutoMlImageObjectDetectionInputs_MODEL_TYPE_UNSPECIFIED AutoMlImageObjectDetectionInputs_ModelType = 0
// 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.
AutoMlImageObjectDetectionInputs_CLOUD_HIGH_ACCURACY_1 AutoMlImageObjectDetectionInputs_ModelType = 1
// 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.
AutoMlImageObjectDetectionInputs_CLOUD_LOW_LATENCY_1 AutoMlImageObjectDetectionInputs_ModelType = 2
// 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.
AutoMlImageObjectDetectionInputs_MOBILE_TF_LOW_LATENCY_1 AutoMlImageObjectDetectionInputs_ModelType = 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.
AutoMlImageObjectDetectionInputs_MOBILE_TF_VERSATILE_1 AutoMlImageObjectDetectionInputs_ModelType = 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.
// Expected to have a higher latency, but should also have a higher
// prediction quality than other mobile models.
AutoMlImageObjectDetectionInputs_MOBILE_TF_HIGH_ACCURACY_1 AutoMlImageObjectDetectionInputs_ModelType = 5
)
func (AutoMlImageObjectDetectionInputs_ModelType) Descriptor() protoreflect.EnumDescriptor
func (x AutoMlImageObjectDetectionInputs_ModelType) Enum() *AutoMlImageObjectDetectionInputs_ModelType
func (AutoMlImageObjectDetectionInputs_ModelType) EnumDescriptor() ([]byte, []int)
Deprecated: Use AutoMlImageObjectDetectionInputs_ModelType.Descriptor instead.
func (x AutoMlImageObjectDetectionInputs_ModelType) Number() protoreflect.EnumNumber
func (x AutoMlImageObjectDetectionInputs_ModelType) String() string
func (AutoMlImageObjectDetectionInputs_ModelType) Type() protoreflect.EnumType
type AutoMlImageObjectDetectionMetadata struct {
// 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.
CostMilliNodeHours int64 `protobuf:"varint,1,opt,name=cost_milli_node_hours,json=costMilliNodeHours,proto3" json:"cost_milli_node_hours,omitempty"`
// For successful job completions, this is the reason why the job has
// finished.
SuccessfulStopReason AutoMlImageObjectDetectionMetadata_SuccessfulStopReason `protobuf:"varint,2,opt,name=successful_stop_reason,json=successfulStopReason,proto3,enum=google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlImageObjectDetectionMetadata_SuccessfulStopReason" json:"successful_stop_reason,omitempty"`
// contains filtered or unexported fields
}
func (*AutoMlImageObjectDetectionMetadata) Descriptor() ([]byte, []int)
Deprecated: Use AutoMlImageObjectDetectionMetadata.ProtoReflect.Descriptor instead.
func (x *AutoMlImageObjectDetectionMetadata) GetCostMilliNodeHours() int64
func (x *AutoMlImageObjectDetectionMetadata) GetSuccessfulStopReason() AutoMlImageObjectDetectionMetadata_SuccessfulStopReason
func (*AutoMlImageObjectDetectionMetadata) ProtoMessage()
func (x *AutoMlImageObjectDetectionMetadata) ProtoReflect() protoreflect.Message
func (x *AutoMlImageObjectDetectionMetadata) Reset()
func (x *AutoMlImageObjectDetectionMetadata) String() string
type AutoMlImageObjectDetectionMetadata_SuccessfulStopReason int32
const (
// Should not be set.
AutoMlImageObjectDetectionMetadata_SUCCESSFUL_STOP_REASON_UNSPECIFIED AutoMlImageObjectDetectionMetadata_SuccessfulStopReason = 0
// The inputs.budgetMilliNodeHours had been reached.
AutoMlImageObjectDetectionMetadata_BUDGET_REACHED AutoMlImageObjectDetectionMetadata_SuccessfulStopReason = 1
// Further training of the Model ceased to increase its quality, since it
// already has converged.
AutoMlImageObjectDetectionMetadata_MODEL_CONVERGED AutoMlImageObjectDetectionMetadata_SuccessfulStopReason = 2
)
func (AutoMlImageObjectDetectionMetadata_SuccessfulStopReason) Descriptor() protoreflect.EnumDescriptor
func (x AutoMlImageObjectDetectionMetadata_SuccessfulStopReason) Enum() *AutoMlImageObjectDetectionMetadata_SuccessfulStopReason
func (AutoMlImageObjectDetectionMetadata_SuccessfulStopReason) EnumDescriptor() ([]byte, []int)
Deprecated: Use AutoMlImageObjectDetectionMetadata_SuccessfulStopReason.Descriptor instead.
func (x AutoMlImageObjectDetectionMetadata_SuccessfulStopReason) Number() protoreflect.EnumNumber
func (x AutoMlImageObjectDetectionMetadata_SuccessfulStopReason) String() string
func (AutoMlImageObjectDetectionMetadata_SuccessfulStopReason) Type() protoreflect.EnumType
A TrainingJob that trains and uploads an AutoML Image Segmentation Model.
type AutoMlImageSegmentation struct {
// The input parameters of this TrainingJob.
Inputs *AutoMlImageSegmentationInputs `protobuf:"bytes,1,opt,name=inputs,proto3" json:"inputs,omitempty"`
// The metadata information.
Metadata *AutoMlImageSegmentationMetadata `protobuf:"bytes,2,opt,name=metadata,proto3" json:"metadata,omitempty"`
// contains filtered or unexported fields
}
func (*AutoMlImageSegmentation) Descriptor() ([]byte, []int)
Deprecated: Use AutoMlImageSegmentation.ProtoReflect.Descriptor instead.
func (x *AutoMlImageSegmentation) GetInputs() *AutoMlImageSegmentationInputs
func (x *AutoMlImageSegmentation) GetMetadata() *AutoMlImageSegmentationMetadata
func (*AutoMlImageSegmentation) ProtoMessage()
func (x *AutoMlImageSegmentation) ProtoReflect() protoreflect.Message
func (x *AutoMlImageSegmentation) Reset()
func (x *AutoMlImageSegmentation) String() string
type AutoMlImageSegmentationInputs struct {
ModelType AutoMlImageSegmentationInputs_ModelType `protobuf:"varint,1,opt,name=model_type,json=modelType,proto3,enum=google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlImageSegmentationInputs_ModelType" json:"model_type,omitempty"`
// 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).
BudgetMilliNodeHours int64 `protobuf:"varint,2,opt,name=budget_milli_node_hours,json=budgetMilliNodeHours,proto3" json:"budget_milli_node_hours,omitempty"`
// 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.
BaseModelId string `protobuf:"bytes,3,opt,name=base_model_id,json=baseModelId,proto3" json:"base_model_id,omitempty"`
// contains filtered or unexported fields
}
func (*AutoMlImageSegmentationInputs) Descriptor() ([]byte, []int)
Deprecated: Use AutoMlImageSegmentationInputs.ProtoReflect.Descriptor instead.
func (x *AutoMlImageSegmentationInputs) GetBaseModelId() string
func (x *AutoMlImageSegmentationInputs) GetBudgetMilliNodeHours() int64
func (x *AutoMlImageSegmentationInputs) GetModelType() AutoMlImageSegmentationInputs_ModelType
func (*AutoMlImageSegmentationInputs) ProtoMessage()
func (x *AutoMlImageSegmentationInputs) ProtoReflect() protoreflect.Message
func (x *AutoMlImageSegmentationInputs) Reset()
func (x *AutoMlImageSegmentationInputs) String() string
type AutoMlImageSegmentationInputs_ModelType int32
const (
// Should not be set.
AutoMlImageSegmentationInputs_MODEL_TYPE_UNSPECIFIED AutoMlImageSegmentationInputs_ModelType = 0
// 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.
AutoMlImageSegmentationInputs_CLOUD_HIGH_ACCURACY_1 AutoMlImageSegmentationInputs_ModelType = 1
// A model to be used via prediction calls to uCAIP API. Expected
// to have a lower latency but relatively lower prediction quality.
AutoMlImageSegmentationInputs_CLOUD_LOW_ACCURACY_1 AutoMlImageSegmentationInputs_ModelType = 2
// 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.
AutoMlImageSegmentationInputs_MOBILE_TF_LOW_LATENCY_1 AutoMlImageSegmentationInputs_ModelType = 3
)
func (AutoMlImageSegmentationInputs_ModelType) Descriptor() protoreflect.EnumDescriptor
func (x AutoMlImageSegmentationInputs_ModelType) Enum() *AutoMlImageSegmentationInputs_ModelType
func (AutoMlImageSegmentationInputs_ModelType) EnumDescriptor() ([]byte, []int)
Deprecated: Use AutoMlImageSegmentationInputs_ModelType.Descriptor instead.
func (x AutoMlImageSegmentationInputs_ModelType) Number() protoreflect.EnumNumber
func (x AutoMlImageSegmentationInputs_ModelType) String() string
func (AutoMlImageSegmentationInputs_ModelType) Type() protoreflect.EnumType
type AutoMlImageSegmentationMetadata struct {
// 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.
CostMilliNodeHours int64 `protobuf:"varint,1,opt,name=cost_milli_node_hours,json=costMilliNodeHours,proto3" json:"cost_milli_node_hours,omitempty"`
// For successful job completions, this is the reason why the job has
// finished.
SuccessfulStopReason AutoMlImageSegmentationMetadata_SuccessfulStopReason `protobuf:"varint,2,opt,name=successful_stop_reason,json=successfulStopReason,proto3,enum=google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlImageSegmentationMetadata_SuccessfulStopReason" json:"successful_stop_reason,omitempty"`
// contains filtered or unexported fields
}
func (*AutoMlImageSegmentationMetadata) Descriptor() ([]byte, []int)
Deprecated: Use AutoMlImageSegmentationMetadata.ProtoReflect.Descriptor instead.
func (x *AutoMlImageSegmentationMetadata) GetCostMilliNodeHours() int64
func (x *AutoMlImageSegmentationMetadata) GetSuccessfulStopReason() AutoMlImageSegmentationMetadata_SuccessfulStopReason
func (*AutoMlImageSegmentationMetadata) ProtoMessage()
func (x *AutoMlImageSegmentationMetadata) ProtoReflect() protoreflect.Message
func (x *AutoMlImageSegmentationMetadata) Reset()
func (x *AutoMlImageSegmentationMetadata) String() string
type AutoMlImageSegmentationMetadata_SuccessfulStopReason int32
const (
// Should not be set.
AutoMlImageSegmentationMetadata_SUCCESSFUL_STOP_REASON_UNSPECIFIED AutoMlImageSegmentationMetadata_SuccessfulStopReason = 0
// The inputs.budgetMilliNodeHours had been reached.
AutoMlImageSegmentationMetadata_BUDGET_REACHED AutoMlImageSegmentationMetadata_SuccessfulStopReason = 1
// Further training of the Model ceased to increase its quality, since it
// already has converged.
AutoMlImageSegmentationMetadata_MODEL_CONVERGED AutoMlImageSegmentationMetadata_SuccessfulStopReason = 2
)
func (AutoMlImageSegmentationMetadata_SuccessfulStopReason) Descriptor() protoreflect.EnumDescriptor
func (x AutoMlImageSegmentationMetadata_SuccessfulStopReason) Enum() *AutoMlImageSegmentationMetadata_SuccessfulStopReason
func (AutoMlImageSegmentationMetadata_SuccessfulStopReason) EnumDescriptor() ([]byte, []int)
Deprecated: Use AutoMlImageSegmentationMetadata_SuccessfulStopReason.Descriptor instead.
func (x AutoMlImageSegmentationMetadata_SuccessfulStopReason) Number() protoreflect.EnumNumber
func (x AutoMlImageSegmentationMetadata_SuccessfulStopReason) String() string
func (AutoMlImageSegmentationMetadata_SuccessfulStopReason) Type() protoreflect.EnumType
A TrainingJob that trains and uploads an AutoML Tables Model.
type AutoMlTables struct {
// The input parameters of this TrainingJob.
Inputs *AutoMlTablesInputs `protobuf:"bytes,1,opt,name=inputs,proto3" json:"inputs,omitempty"`
// The metadata information.
Metadata *AutoMlTablesMetadata `protobuf:"bytes,2,opt,name=metadata,proto3" json:"metadata,omitempty"`
// contains filtered or unexported fields
}
func (*AutoMlTables) Descriptor() ([]byte, []int)
Deprecated: Use AutoMlTables.ProtoReflect.Descriptor instead.
func (x *AutoMlTables) GetInputs() *AutoMlTablesInputs
func (x *AutoMlTables) GetMetadata() *AutoMlTablesMetadata
func (*AutoMlTables) ProtoMessage()
func (x *AutoMlTables) ProtoReflect() protoreflect.Message
func (x *AutoMlTables) Reset()
func (x *AutoMlTables) String() string
type AutoMlTablesInputs struct {
// Additional optimization objective configuration. Required for
// `maximize-precision-at-recall` and `maximize-recall-at-precision`,
// otherwise unused.
//
// Types that are assignable to AdditionalOptimizationObjectiveConfig:
//
// *AutoMlTablesInputs_OptimizationObjectiveRecallValue
// *AutoMlTablesInputs_OptimizationObjectivePrecisionValue
AdditionalOptimizationObjectiveConfig isAutoMlTablesInputs_AdditionalOptimizationObjectiveConfig `protobuf_oneof:"additional_optimization_objective_config"`
// 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.
PredictionType string `protobuf:"bytes,1,opt,name=prediction_type,json=predictionType,proto3" json:"prediction_type,omitempty"`
// The column name of the target column that the model is to predict.
TargetColumn string `protobuf:"bytes,2,opt,name=target_column,json=targetColumn,proto3" json:"target_column,omitempty"`
// 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.
Transformations []*AutoMlTablesInputs_Transformation `protobuf:"bytes,3,rep,name=transformations,proto3" json:"transformations,omitempty"`
// 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).
OptimizationObjective string `protobuf:"bytes,4,opt,name=optimization_objective,json=optimizationObjective,proto3" json:"optimization_objective,omitempty"`
// 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.
TrainBudgetMilliNodeHours int64 `protobuf:"varint,7,opt,name=train_budget_milli_node_hours,json=trainBudgetMilliNodeHours,proto3" json:"train_budget_milli_node_hours,omitempty"`
// 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.
DisableEarlyStopping bool `protobuf:"varint,8,opt,name=disable_early_stopping,json=disableEarlyStopping,proto3" json:"disable_early_stopping,omitempty"`
// 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.
WeightColumnName string `protobuf:"bytes,9,opt,name=weight_column_name,json=weightColumnName,proto3" json:"weight_column_name,omitempty"`
// Configuration for exporting test set predictions to a BigQuery table. If
// this configuration is absent, then the export is not performed.
ExportEvaluatedDataItemsConfig *ExportEvaluatedDataItemsConfig `protobuf:"bytes,10,opt,name=export_evaluated_data_items_config,json=exportEvaluatedDataItemsConfig,proto3" json:"export_evaluated_data_items_config,omitempty"`
// Additional experiment flags for the Tables training pipeline.
AdditionalExperiments []string `protobuf:"bytes,11,rep,name=additional_experiments,json=additionalExperiments,proto3" json:"additional_experiments,omitempty"`
// contains filtered or unexported fields
}
func (*AutoMlTablesInputs) Descriptor() ([]byte, []int)
Deprecated: Use AutoMlTablesInputs.ProtoReflect.Descriptor instead.
func (x *AutoMlTablesInputs) GetAdditionalExperiments() []string
func (m *AutoMlTablesInputs) GetAdditionalOptimizationObjectiveConfig() isAutoMlTablesInputs_AdditionalOptimizationObjectiveConfig
func (x *AutoMlTablesInputs) GetDisableEarlyStopping() bool
func (x *AutoMlTablesInputs) GetExportEvaluatedDataItemsConfig() *ExportEvaluatedDataItemsConfig
func (x *AutoMlTablesInputs) GetOptimizationObjective() string
func (x *AutoMlTablesInputs) GetOptimizationObjectivePrecisionValue() float32
func (x *AutoMlTablesInputs) GetOptimizationObjectiveRecallValue() float32
func (x *AutoMlTablesInputs) GetPredictionType() string
func (x *AutoMlTablesInputs) GetTargetColumn() string
func (x *AutoMlTablesInputs) GetTrainBudgetMilliNodeHours() int64
func (x *AutoMlTablesInputs) GetTransformations() []*AutoMlTablesInputs_Transformation
func (x *AutoMlTablesInputs) GetWeightColumnName() string
func (*AutoMlTablesInputs) ProtoMessage()
func (x *AutoMlTablesInputs) ProtoReflect() protoreflect.Message
func (x *AutoMlTablesInputs) Reset()
func (x *AutoMlTablesInputs) String() string
type AutoMlTablesInputs_OptimizationObjectivePrecisionValue struct {
// Required when optimization_objective is "maximize-recall-at-precision".
// Must be between 0 and 1, inclusive.
OptimizationObjectivePrecisionValue float32 `protobuf:"fixed32,6,opt,name=optimization_objective_precision_value,json=optimizationObjectivePrecisionValue,proto3,oneof"`
}
type AutoMlTablesInputs_OptimizationObjectiveRecallValue struct {
// Required when optimization_objective is "maximize-precision-at-recall".
// Must be between 0 and 1, inclusive.
OptimizationObjectiveRecallValue float32 `protobuf:"fixed32,5,opt,name=optimization_objective_recall_value,json=optimizationObjectiveRecallValue,proto3,oneof"`
}
type AutoMlTablesInputs_Transformation struct {
// The transformation that the training pipeline will apply to the input
// columns.
//
// Types that are assignable to TransformationDetail:
//
// *AutoMlTablesInputs_Transformation_Auto
// *AutoMlTablesInputs_Transformation_Numeric
// *AutoMlTablesInputs_Transformation_Categorical
// *AutoMlTablesInputs_Transformation_Timestamp
// *AutoMlTablesInputs_Transformation_Text
// *AutoMlTablesInputs_Transformation_RepeatedNumeric
// *AutoMlTablesInputs_Transformation_RepeatedCategorical
// *AutoMlTablesInputs_Transformation_RepeatedText
TransformationDetail isAutoMlTablesInputs_Transformation_TransformationDetail `protobuf_oneof:"transformation_detail"`
// contains filtered or unexported fields
}
func (*AutoMlTablesInputs_Transformation) Descriptor() ([]byte, []int)
Deprecated: Use AutoMlTablesInputs_Transformation.ProtoReflect.Descriptor instead.
func (x *AutoMlTablesInputs_Transformation) GetAuto() *AutoMlTablesInputs_Transformation_AutoTransformation
func (x *AutoMlTablesInputs_Transformation) GetCategorical() *AutoMlTablesInputs_Transformation_CategoricalTransformation
func (x *AutoMlTablesInputs_Transformation) GetNumeric() *AutoMlTablesInputs_Transformation_NumericTransformation
func (x *AutoMlTablesInputs_Transformation) GetRepeatedCategorical() *AutoMlTablesInputs_Transformation_CategoricalArrayTransformation
func (x *AutoMlTablesInputs_Transformation) GetRepeatedNumeric() *AutoMlTablesInputs_Transformation_NumericArrayTransformation
func (x *AutoMlTablesInputs_Transformation) GetRepeatedText() *AutoMlTablesInputs_Transformation_TextArrayTransformation
func (x *AutoMlTablesInputs_Transformation) GetText() *AutoMlTablesInputs_Transformation_TextTransformation
func (x *AutoMlTablesInputs_Transformation) GetTimestamp() *AutoMlTablesInputs_Transformation_TimestampTransformation
func (m *AutoMlTablesInputs_Transformation) GetTransformationDetail() isAutoMlTablesInputs_Transformation_TransformationDetail
func (*AutoMlTablesInputs_Transformation) ProtoMessage()
func (x *AutoMlTablesInputs_Transformation) ProtoReflect() protoreflect.Message
func (x *AutoMlTablesInputs_Transformation) Reset()
func (x *AutoMlTablesInputs_Transformation) String() string
type AutoMlTablesInputs_Transformation_Auto struct {
Auto *AutoMlTablesInputs_Transformation_AutoTransformation `protobuf:"bytes,1,opt,name=auto,proto3,oneof"`
}
Training pipeline will infer the proper transformation based on the statistic of dataset.
type AutoMlTablesInputs_Transformation_AutoTransformation struct {
ColumnName string `protobuf:"bytes,1,opt,name=column_name,json=columnName,proto3" json:"column_name,omitempty"`
// contains filtered or unexported fields
}
func (*AutoMlTablesInputs_Transformation_AutoTransformation) Descriptor() ([]byte, []int)
Deprecated: Use AutoMlTablesInputs_Transformation_AutoTransformation.ProtoReflect.Descriptor instead.
func (x *AutoMlTablesInputs_Transformation_AutoTransformation) GetColumnName() string
func (*AutoMlTablesInputs_Transformation_AutoTransformation) ProtoMessage()
func (x *AutoMlTablesInputs_Transformation_AutoTransformation) ProtoReflect() protoreflect.Message
func (x *AutoMlTablesInputs_Transformation_AutoTransformation) Reset()
func (x *AutoMlTablesInputs_Transformation_AutoTransformation) String() string
type AutoMlTablesInputs_Transformation_Categorical struct {
Categorical *AutoMlTablesInputs_Transformation_CategoricalTransformation `protobuf:"bytes,3,opt,name=categorical,proto3,oneof"`
}
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.
type AutoMlTablesInputs_Transformation_CategoricalArrayTransformation struct {
ColumnName string `protobuf:"bytes,1,opt,name=column_name,json=columnName,proto3" json:"column_name,omitempty"`
// contains filtered or unexported fields
}
func (*AutoMlTablesInputs_Transformation_CategoricalArrayTransformation) Descriptor() ([]byte, []int)
Deprecated: Use AutoMlTablesInputs_Transformation_CategoricalArrayTransformation.ProtoReflect.Descriptor instead.
func (x *AutoMlTablesInputs_Transformation_CategoricalArrayTransformation) GetColumnName() string
func (*AutoMlTablesInputs_Transformation_CategoricalArrayTransformation) ProtoMessage()
func (x *AutoMlTablesInputs_Transformation_CategoricalArrayTransformation) ProtoReflect() protoreflect.Message
func (x *AutoMlTablesInputs_Transformation_CategoricalArrayTransformation) Reset()
func (x *AutoMlTablesInputs_Transformation_CategoricalArrayTransformation) String() string
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.
type AutoMlTablesInputs_Transformation_CategoricalTransformation struct {
ColumnName string `protobuf:"bytes,1,opt,name=column_name,json=columnName,proto3" json:"column_name,omitempty"`
// contains filtered or unexported fields
}
func (*AutoMlTablesInputs_Transformation_CategoricalTransformation) Descriptor() ([]byte, []int)
Deprecated: Use AutoMlTablesInputs_Transformation_CategoricalTransformation.ProtoReflect.Descriptor instead.
func (x *AutoMlTablesInputs_Transformation_CategoricalTransformation) GetColumnName() string
func (*AutoMlTablesInputs_Transformation_CategoricalTransformation) ProtoMessage()
func (x *AutoMlTablesInputs_Transformation_CategoricalTransformation) ProtoReflect() protoreflect.Message
func (x *AutoMlTablesInputs_Transformation_CategoricalTransformation) Reset()
func (x *AutoMlTablesInputs_Transformation_CategoricalTransformation) String() string
type AutoMlTablesInputs_Transformation_Numeric struct {
Numeric *AutoMlTablesInputs_Transformation_NumericTransformation `protobuf:"bytes,2,opt,name=numeric,proto3,oneof"`
}
Treats the column as numerical array and performs following transformation functions.
type AutoMlTablesInputs_Transformation_NumericArrayTransformation struct {
ColumnName string `protobuf:"bytes,1,opt,name=column_name,json=columnName,proto3" json:"column_name,omitempty"`
// 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.
InvalidValuesAllowed bool `protobuf:"varint,2,opt,name=invalid_values_allowed,json=invalidValuesAllowed,proto3" json:"invalid_values_allowed,omitempty"`
// contains filtered or unexported fields
}
func (*AutoMlTablesInputs_Transformation_NumericArrayTransformation) Descriptor() ([]byte, []int)
Deprecated: Use AutoMlTablesInputs_Transformation_NumericArrayTransformation.ProtoReflect.Descriptor instead.
func (x *AutoMlTablesInputs_Transformation_NumericArrayTransformation) GetColumnName() string
func (x *AutoMlTablesInputs_Transformation_NumericArrayTransformation) GetInvalidValuesAllowed() bool
func (*AutoMlTablesInputs_Transformation_NumericArrayTransformation) ProtoMessage()
func (x *AutoMlTablesInputs_Transformation_NumericArrayTransformation) ProtoReflect() protoreflect.Message
func (x *AutoMlTablesInputs_Transformation_NumericArrayTransformation) Reset()
func (x *AutoMlTablesInputs_Transformation_NumericArrayTransformation) String() string
Training pipeline will perform following transformation functions.
type AutoMlTablesInputs_Transformation_NumericTransformation struct {
ColumnName string `protobuf:"bytes,1,opt,name=column_name,json=columnName,proto3" json:"column_name,omitempty"`
// 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.
InvalidValuesAllowed bool `protobuf:"varint,2,opt,name=invalid_values_allowed,json=invalidValuesAllowed,proto3" json:"invalid_values_allowed,omitempty"`
// contains filtered or unexported fields
}
func (*AutoMlTablesInputs_Transformation_NumericTransformation) Descriptor() ([]byte, []int)
Deprecated: Use AutoMlTablesInputs_Transformation_NumericTransformation.ProtoReflect.Descriptor instead.
func (x *AutoMlTablesInputs_Transformation_NumericTransformation) GetColumnName() string
func (x *AutoMlTablesInputs_Transformation_NumericTransformation) GetInvalidValuesAllowed() bool
func (*AutoMlTablesInputs_Transformation_NumericTransformation) ProtoMessage()
func (x *AutoMlTablesInputs_Transformation_NumericTransformation) ProtoReflect() protoreflect.Message
func (x *AutoMlTablesInputs_Transformation_NumericTransformation) Reset()
func (x *AutoMlTablesInputs_Transformation_NumericTransformation) String() string
type AutoMlTablesInputs_Transformation_RepeatedCategorical struct {
RepeatedCategorical *AutoMlTablesInputs_Transformation_CategoricalArrayTransformation `protobuf:"bytes,7,opt,name=repeated_categorical,json=repeatedCategorical,proto3,oneof"`
}
type AutoMlTablesInputs_Transformation_RepeatedNumeric struct {
RepeatedNumeric *AutoMlTablesInputs_Transformation_NumericArrayTransformation `protobuf:"bytes,6,opt,name=repeated_numeric,json=repeatedNumeric,proto3,oneof"`
}
type AutoMlTablesInputs_Transformation_RepeatedText struct {
RepeatedText *AutoMlTablesInputs_Transformation_TextArrayTransformation `protobuf:"bytes,8,opt,name=repeated_text,json=repeatedText,proto3,oneof"`
}
type AutoMlTablesInputs_Transformation_Text struct {
Text *AutoMlTablesInputs_Transformation_TextTransformation `protobuf:"bytes,5,opt,name=text,proto3,oneof"`
}
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.
type AutoMlTablesInputs_Transformation_TextArrayTransformation struct {
ColumnName string `protobuf:"bytes,1,opt,name=column_name,json=columnName,proto3" json:"column_name,omitempty"`
// contains filtered or unexported fields
}
func (*AutoMlTablesInputs_Transformation_TextArrayTransformation) Descriptor() ([]byte, []int)
Deprecated: Use AutoMlTablesInputs_Transformation_TextArrayTransformation.ProtoReflect.Descriptor instead.
func (x *AutoMlTablesInputs_Transformation_TextArrayTransformation) GetColumnName() string
func (*AutoMlTablesInputs_Transformation_TextArrayTransformation) ProtoMessage()
func (x *AutoMlTablesInputs_Transformation_TextArrayTransformation) ProtoReflect() protoreflect.Message
func (x *AutoMlTablesInputs_Transformation_TextArrayTransformation) Reset()
func (x *AutoMlTablesInputs_Transformation_TextArrayTransformation) String() string
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.
type AutoMlTablesInputs_Transformation_TextTransformation struct {
ColumnName string `protobuf:"bytes,1,opt,name=column_name,json=columnName,proto3" json:"column_name,omitempty"`
// contains filtered or unexported fields
}
func (*AutoMlTablesInputs_Transformation_TextTransformation) Descriptor() ([]byte, []int)
Deprecated: Use AutoMlTablesInputs_Transformation_TextTransformation.ProtoReflect.Descriptor instead.
func (x *AutoMlTablesInputs_Transformation_TextTransformation) GetColumnName() string
func (*AutoMlTablesInputs_Transformation_TextTransformation) ProtoMessage()
func (x *AutoMlTablesInputs_Transformation_TextTransformation) ProtoReflect() protoreflect.Message
func (x *AutoMlTablesInputs_Transformation_TextTransformation) Reset()
func (x *AutoMlTablesInputs_Transformation_TextTransformation) String() string
type AutoMlTablesInputs_Transformation_Timestamp struct {
Timestamp *AutoMlTablesInputs_Transformation_TimestampTransformation `protobuf:"bytes,4,opt,name=timestamp,proto3,oneof"`
}
Training pipeline will perform following transformation functions.
type AutoMlTablesInputs_Transformation_TimestampTransformation struct {
ColumnName string `protobuf:"bytes,1,opt,name=column_name,json=columnName,proto3" json:"column_name,omitempty"`
// 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)
TimeFormat string `protobuf:"bytes,2,opt,name=time_format,json=timeFormat,proto3" json:"time_format,omitempty"`
// 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.
InvalidValuesAllowed bool `protobuf:"varint,3,opt,name=invalid_values_allowed,json=invalidValuesAllowed,proto3" json:"invalid_values_allowed,omitempty"`
// contains filtered or unexported fields
}
func (*AutoMlTablesInputs_Transformation_TimestampTransformation) Descriptor() ([]byte, []int)
Deprecated: Use AutoMlTablesInputs_Transformation_TimestampTransformation.ProtoReflect.Descriptor instead.
func (x *AutoMlTablesInputs_Transformation_TimestampTransformation) GetColumnName() string
func (x *AutoMlTablesInputs_Transformation_TimestampTransformation) GetInvalidValuesAllowed() bool
func (x *AutoMlTablesInputs_Transformation_TimestampTransformation) GetTimeFormat() string
func (*AutoMlTablesInputs_Transformation_TimestampTransformation) ProtoMessage()
func (x *AutoMlTablesInputs_Transformation_TimestampTransformation) ProtoReflect() protoreflect.Message
func (x *AutoMlTablesInputs_Transformation_TimestampTransformation) Reset()
func (x *AutoMlTablesInputs_Transformation_TimestampTransformation) String() string
Model metadata specific to AutoML Tables.
type AutoMlTablesMetadata struct {
// 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.
TrainCostMilliNodeHours int64 `protobuf:"varint,1,opt,name=train_cost_milli_node_hours,json=trainCostMilliNodeHours,proto3" json:"train_cost_milli_node_hours,omitempty"`
// contains filtered or unexported fields
}
func (*AutoMlTablesMetadata) Descriptor() ([]byte, []int)
Deprecated: Use AutoMlTablesMetadata.ProtoReflect.Descriptor instead.
func (x *AutoMlTablesMetadata) GetTrainCostMilliNodeHours() int64
func (*AutoMlTablesMetadata) ProtoMessage()
func (x *AutoMlTablesMetadata) ProtoReflect() protoreflect.Message
func (x *AutoMlTablesMetadata) Reset()
func (x *AutoMlTablesMetadata) String() string
A TrainingJob that trains and uploads an AutoML Text Classification Model.
type AutoMlTextClassification struct {
// The input parameters of this TrainingJob.
Inputs *AutoMlTextClassificationInputs `protobuf:"bytes,1,opt,name=inputs,proto3" json:"inputs,omitempty"`
// contains filtered or unexported fields
}
func (*AutoMlTextClassification) Descriptor() ([]byte, []int)
Deprecated: Use AutoMlTextClassification.ProtoReflect.Descriptor instead.
func (x *AutoMlTextClassification) GetInputs() *AutoMlTextClassificationInputs
func (*AutoMlTextClassification) ProtoMessage()
func (x *AutoMlTextClassification) ProtoReflect() protoreflect.Message
func (x *AutoMlTextClassification) Reset()
func (x *AutoMlTextClassification) String() string
type AutoMlTextClassificationInputs struct {
MultiLabel bool `protobuf:"varint,1,opt,name=multi_label,json=multiLabel,proto3" json:"multi_label,omitempty"`
// contains filtered or unexported fields
}
func (*AutoMlTextClassificationInputs) Descriptor() ([]byte, []int)
Deprecated: Use AutoMlTextClassificationInputs.ProtoReflect.Descriptor instead.
func (x *AutoMlTextClassificationInputs) GetMultiLabel() bool
func (*AutoMlTextClassificationInputs) ProtoMessage()
func (x *AutoMlTextClassificationInputs) ProtoReflect() protoreflect.Message
func (x *AutoMlTextClassificationInputs) Reset()
func (x *AutoMlTextClassificationInputs) String() string
A TrainingJob that trains and uploads an AutoML Text Extraction Model.
type AutoMlTextExtraction struct {
// The input parameters of this TrainingJob.
Inputs *AutoMlTextExtractionInputs `protobuf:"bytes,1,opt,name=inputs,proto3" json:"inputs,omitempty"`
// contains filtered or unexported fields
}
func (*AutoMlTextExtraction) Descriptor() ([]byte, []int)
Deprecated: Use AutoMlTextExtraction.ProtoReflect.Descriptor instead.
func (x *AutoMlTextExtraction) GetInputs() *AutoMlTextExtractionInputs
func (*AutoMlTextExtraction) ProtoMessage()
func (x *AutoMlTextExtraction) ProtoReflect() protoreflect.Message
func (x *AutoMlTextExtraction) Reset()
func (x *AutoMlTextExtraction) String() string
type AutoMlTextExtractionInputs struct {
// contains filtered or unexported fields
}
func (*AutoMlTextExtractionInputs) Descriptor() ([]byte, []int)
Deprecated: Use AutoMlTextExtractionInputs.ProtoReflect.Descriptor instead.
func (*AutoMlTextExtractionInputs) ProtoMessage()
func (x *AutoMlTextExtractionInputs) ProtoReflect() protoreflect.Message
func (x *AutoMlTextExtractionInputs) Reset()
func (x *AutoMlTextExtractionInputs) String() string
A TrainingJob that trains and uploads an AutoML Text Sentiment Model.
type AutoMlTextSentiment struct {
// The input parameters of this TrainingJob.
Inputs *AutoMlTextSentimentInputs `protobuf:"bytes,1,opt,name=inputs,proto3" json:"inputs,omitempty"`
// contains filtered or unexported fields
}
func (*AutoMlTextSentiment) Descriptor() ([]byte, []int)
Deprecated: Use AutoMlTextSentiment.ProtoReflect.Descriptor instead.
func (x *AutoMlTextSentiment) GetInputs() *AutoMlTextSentimentInputs
func (*AutoMlTextSentiment) ProtoMessage()
func (x *AutoMlTextSentiment) ProtoReflect() protoreflect.Message
func (x *AutoMlTextSentiment) Reset()
func (x *AutoMlTextSentiment) String() string
type AutoMlTextSentimentInputs struct {
// 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).
SentimentMax int32 `protobuf:"varint,1,opt,name=sentiment_max,json=sentimentMax,proto3" json:"sentiment_max,omitempty"`
// contains filtered or unexported fields
}
func (*AutoMlTextSentimentInputs) Descriptor() ([]byte, []int)
Deprecated: Use AutoMlTextSentimentInputs.ProtoReflect.Descriptor instead.
func (x *AutoMlTextSentimentInputs) GetSentimentMax() int32
func (*AutoMlTextSentimentInputs) ProtoMessage()
func (x *AutoMlTextSentimentInputs) ProtoReflect() protoreflect.Message
func (x *AutoMlTextSentimentInputs) Reset()
func (x *AutoMlTextSentimentInputs) String() string
A TrainingJob that trains and uploads an AutoML Video Action Recognition Model.
type AutoMlVideoActionRecognition struct {
// The input parameters of this TrainingJob.
Inputs *AutoMlVideoActionRecognitionInputs `protobuf:"bytes,1,opt,name=inputs,proto3" json:"inputs,omitempty"`
// contains filtered or unexported fields
}
func (*AutoMlVideoActionRecognition) Descriptor() ([]byte, []int)
Deprecated: Use AutoMlVideoActionRecognition.ProtoReflect.Descriptor instead.
func (x *AutoMlVideoActionRecognition) GetInputs() *AutoMlVideoActionRecognitionInputs
func (*AutoMlVideoActionRecognition) ProtoMessage()
func (x *AutoMlVideoActionRecognition) ProtoReflect() protoreflect.Message
func (x *AutoMlVideoActionRecognition) Reset()
func (x *AutoMlVideoActionRecognition) String() string
type AutoMlVideoActionRecognitionInputs struct {
ModelType AutoMlVideoActionRecognitionInputs_ModelType `protobuf:"varint,1,opt,name=model_type,json=modelType,proto3,enum=google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlVideoActionRecognitionInputs_ModelType" json:"model_type,omitempty"`
// contains filtered or unexported fields
}
func (*AutoMlVideoActionRecognitionInputs) Descriptor() ([]byte, []int)
Deprecated: Use AutoMlVideoActionRecognitionInputs.ProtoReflect.Descriptor instead.
func (x *AutoMlVideoActionRecognitionInputs) GetModelType() AutoMlVideoActionRecognitionInputs_ModelType
func (*AutoMlVideoActionRecognitionInputs) ProtoMessage()
func (x *AutoMlVideoActionRecognitionInputs) ProtoReflect() protoreflect.Message
func (x *AutoMlVideoActionRecognitionInputs) Reset()
func (x *AutoMlVideoActionRecognitionInputs) String() string
type AutoMlVideoActionRecognitionInputs_ModelType int32
const (
// Should not be set.
AutoMlVideoActionRecognitionInputs_MODEL_TYPE_UNSPECIFIED AutoMlVideoActionRecognitionInputs_ModelType = 0
// A model best tailored to be used within Google Cloud, and which c annot
// be exported. Default.
AutoMlVideoActionRecognitionInputs_CLOUD AutoMlVideoActionRecognitionInputs_ModelType = 1
// 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.
AutoMlVideoActionRecognitionInputs_MOBILE_VERSATILE_1 AutoMlVideoActionRecognitionInputs_ModelType = 2
// A model that, in addition to being available within Google Cloud, can
// also be exported (see ModelService.ExportModel) to a Jetson device
// afterwards.
AutoMlVideoActionRecognitionInputs_MOBILE_JETSON_VERSATILE_1 AutoMlVideoActionRecognitionInputs_ModelType = 3
// 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.
AutoMlVideoActionRecognitionInputs_MOBILE_CORAL_VERSATILE_1 AutoMlVideoActionRecognitionInputs_ModelType = 4
)
func (AutoMlVideoActionRecognitionInputs_ModelType) Descriptor() protoreflect.EnumDescriptor
func (x AutoMlVideoActionRecognitionInputs_ModelType) Enum() *AutoMlVideoActionRecognitionInputs_ModelType
func (AutoMlVideoActionRecognitionInputs_ModelType) EnumDescriptor() ([]byte, []int)
Deprecated: Use AutoMlVideoActionRecognitionInputs_ModelType.Descriptor instead.
func (x AutoMlVideoActionRecognitionInputs_ModelType) Number() protoreflect.EnumNumber
func (x AutoMlVideoActionRecognitionInputs_ModelType) String() string
func (AutoMlVideoActionRecognitionInputs_ModelType) Type() protoreflect.EnumType
A TrainingJob that trains and uploads an AutoML Video Classification Model.
type AutoMlVideoClassification struct {
// The input parameters of this TrainingJob.
Inputs *AutoMlVideoClassificationInputs `protobuf:"bytes,1,opt,name=inputs,proto3" json:"inputs,omitempty"`
// contains filtered or unexported fields
}
func (*AutoMlVideoClassification) Descriptor() ([]byte, []int)
Deprecated: Use AutoMlVideoClassification.ProtoReflect.Descriptor instead.
func (x *AutoMlVideoClassification) GetInputs() *AutoMlVideoClassificationInputs
func (*AutoMlVideoClassification) ProtoMessage()
func (x *AutoMlVideoClassification) ProtoReflect() protoreflect.Message
func (x *AutoMlVideoClassification) Reset()
func (x *AutoMlVideoClassification) String() string
type AutoMlVideoClassificationInputs struct {
ModelType AutoMlVideoClassificationInputs_ModelType `protobuf:"varint,1,opt,name=model_type,json=modelType,proto3,enum=google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlVideoClassificationInputs_ModelType" json:"model_type,omitempty"`
// contains filtered or unexported fields
}
func (*AutoMlVideoClassificationInputs) Descriptor() ([]byte, []int)
Deprecated: Use AutoMlVideoClassificationInputs.ProtoReflect.Descriptor instead.
func (x *AutoMlVideoClassificationInputs) GetModelType() AutoMlVideoClassificationInputs_ModelType
func (*AutoMlVideoClassificationInputs) ProtoMessage()
func (x *AutoMlVideoClassificationInputs) ProtoReflect() protoreflect.Message
func (x *AutoMlVideoClassificationInputs) Reset()
func (x *AutoMlVideoClassificationInputs) String() string
type AutoMlVideoClassificationInputs_ModelType int32
const (
// Should not be set.
AutoMlVideoClassificationInputs_MODEL_TYPE_UNSPECIFIED AutoMlVideoClassificationInputs_ModelType = 0
// A model best tailored to be used within Google Cloud, and which cannot
// be exported. Default.
AutoMlVideoClassificationInputs_CLOUD AutoMlVideoClassificationInputs_ModelType = 1
// 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.
AutoMlVideoClassificationInputs_MOBILE_VERSATILE_1 AutoMlVideoClassificationInputs_ModelType = 2
// A model that, in addition to being available within Google Cloud, can
// also be exported (see ModelService.ExportModel) to a Jetson device
// afterwards.
AutoMlVideoClassificationInputs_MOBILE_JETSON_VERSATILE_1 AutoMlVideoClassificationInputs_ModelType = 3
)
func (AutoMlVideoClassificationInputs_ModelType) Descriptor() protoreflect.EnumDescriptor
func (x AutoMlVideoClassificationInputs_ModelType) Enum() *AutoMlVideoClassificationInputs_ModelType
func (AutoMlVideoClassificationInputs_ModelType) EnumDescriptor() ([]byte, []int)
Deprecated: Use AutoMlVideoClassificationInputs_ModelType.Descriptor instead.
func (x AutoMlVideoClassificationInputs_ModelType) Number() protoreflect.EnumNumber
func (x AutoMlVideoClassificationInputs_ModelType) String() string
func (AutoMlVideoClassificationInputs_ModelType) Type() protoreflect.EnumType
A TrainingJob that trains and uploads an AutoML Video ObjectTracking Model.
type AutoMlVideoObjectTracking struct {
// The input parameters of this TrainingJob.
Inputs *AutoMlVideoObjectTrackingInputs `protobuf:"bytes,1,opt,name=inputs,proto3" json:"inputs,omitempty"`
// contains filtered or unexported fields
}
func (*AutoMlVideoObjectTracking) Descriptor() ([]byte, []int)
Deprecated: Use AutoMlVideoObjectTracking.ProtoReflect.Descriptor instead.
func (x *AutoMlVideoObjectTracking) GetInputs() *AutoMlVideoObjectTrackingInputs
func (*AutoMlVideoObjectTracking) ProtoMessage()
func (x *AutoMlVideoObjectTracking) ProtoReflect() protoreflect.Message
func (x *AutoMlVideoObjectTracking) Reset()
func (x *AutoMlVideoObjectTracking) String() string
type AutoMlVideoObjectTrackingInputs struct {
ModelType AutoMlVideoObjectTrackingInputs_ModelType `protobuf:"varint,1,opt,name=model_type,json=modelType,proto3,enum=google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlVideoObjectTrackingInputs_ModelType" json:"model_type,omitempty"`
// contains filtered or unexported fields
}
func (*AutoMlVideoObjectTrackingInputs) Descriptor() ([]byte, []int)
Deprecated: Use AutoMlVideoObjectTrackingInputs.ProtoReflect.Descriptor instead.
func (x *AutoMlVideoObjectTrackingInputs) GetModelType() AutoMlVideoObjectTrackingInputs_ModelType
func (*AutoMlVideoObjectTrackingInputs) ProtoMessage()
func (x *AutoMlVideoObjectTrackingInputs) ProtoReflect() protoreflect.Message
func (x *AutoMlVideoObjectTrackingInputs) Reset()
func (x *AutoMlVideoObjectTrackingInputs) String() string
type AutoMlVideoObjectTrackingInputs_ModelType int32
const (
// Should not be set.
AutoMlVideoObjectTrackingInputs_MODEL_TYPE_UNSPECIFIED AutoMlVideoObjectTrackingInputs_ModelType = 0
// A model best tailored to be used within Google Cloud, and which c annot
// be exported. Default.
AutoMlVideoObjectTrackingInputs_CLOUD AutoMlVideoObjectTrackingInputs_ModelType = 1
// 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.
AutoMlVideoObjectTrackingInputs_MOBILE_VERSATILE_1 AutoMlVideoObjectTrackingInputs_ModelType = 2
// A versatile model that is meant to be exported (see
// ModelService.ExportModel) and used on a Google Coral device.
AutoMlVideoObjectTrackingInputs_MOBILE_CORAL_VERSATILE_1 AutoMlVideoObjectTrackingInputs_ModelType = 3
// A model that trades off quality for low latency, to be exported (see
// ModelService.ExportModel) and used on a Google Coral device.
AutoMlVideoObjectTrackingInputs_MOBILE_CORAL_LOW_LATENCY_1 AutoMlVideoObjectTrackingInputs_ModelType = 4
// A versatile model that is meant to be exported (see
// ModelService.ExportModel) and used on an NVIDIA Jetson device.
AutoMlVideoObjectTrackingInputs_MOBILE_JETSON_VERSATILE_1 AutoMlVideoObjectTrackingInputs_ModelType = 5
// A model that trades off quality for low latency, to be exported (see
// ModelService.ExportModel) and used on an NVIDIA Jetson device.
AutoMlVideoObjectTrackingInputs_MOBILE_JETSON_LOW_LATENCY_1 AutoMlVideoObjectTrackingInputs_ModelType = 6
)
func (AutoMlVideoObjectTrackingInputs_ModelType) Descriptor() protoreflect.EnumDescriptor
func (x AutoMlVideoObjectTrackingInputs_ModelType) Enum() *AutoMlVideoObjectTrackingInputs_ModelType
func (AutoMlVideoObjectTrackingInputs_ModelType) EnumDescriptor() ([]byte, []int)
Deprecated: Use AutoMlVideoObjectTrackingInputs_ModelType.Descriptor instead.
func (x AutoMlVideoObjectTrackingInputs_ModelType) Number() protoreflect.EnumNumber
func (x AutoMlVideoObjectTrackingInputs_ModelType) String() string
func (AutoMlVideoObjectTrackingInputs_ModelType) Type() protoreflect.EnumType
Configuration for exporting test set predictions to a BigQuery table.
type ExportEvaluatedDataItemsConfig struct {
// URI of desired destination BigQuery table. Expected format:
// bq://<project_id>:<dataset_id>:<table>
//
// 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
DestinationBigqueryUri string `protobuf:"bytes,1,opt,name=destination_bigquery_uri,json=destinationBigqueryUri,proto3" json:"destination_bigquery_uri,omitempty"`
// 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.
OverrideExistingTable bool `protobuf:"varint,2,opt,name=override_existing_table,json=overrideExistingTable,proto3" json:"override_existing_table,omitempty"`
// contains filtered or unexported fields
}
func (*ExportEvaluatedDataItemsConfig) Descriptor() ([]byte, []int)
Deprecated: Use ExportEvaluatedDataItemsConfig.ProtoReflect.Descriptor instead.
func (x *ExportEvaluatedDataItemsConfig) GetDestinationBigqueryUri() string
func (x *ExportEvaluatedDataItemsConfig) GetOverrideExistingTable() bool
func (*ExportEvaluatedDataItemsConfig) ProtoMessage()
func (x *ExportEvaluatedDataItemsConfig) ProtoReflect() protoreflect.Message
func (x *ExportEvaluatedDataItemsConfig) Reset()
func (x *ExportEvaluatedDataItemsConfig) String() string