Types for Google Cloud Aiplatform V1 Schema Trainingjob Definition v1 API¶
- class google.cloud.aiplatform.v1.schema.trainingjob.definition_v1.types.AutoMlImageClassification(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
MessageA TrainingJob that trains and uploads an AutoML Image Classification Model.
- inputs¶
The input parameters of this TrainingJob.
- metadata¶
The metadata information.
- class google.cloud.aiplatform.v1.schema.trainingjob.definition_v1.types.AutoMlImageClassificationInputs(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
Message- model_type¶
- base_model_id¶
The ID of the
basemodel. If it is specified, the new model will be trained based on thebasemodel. Otherwise, the new model will be trained from scratch. Thebasemodel must be in the same Project and Location as the new Model to train, and have the same modelType.- Type:
- budget_milli_node_hours¶
The training budget of creating this model, expressed in milli node hours i.e. 1,000 value in this field means 1 node hour. The actual metadata.costMilliNodeHours will be equal or less than this value. If further model training ceases to provide any improvements, it will stop without using the full budget and the metadata.successfulStopReason will be
model-converged. Note, node_hour = actual_hour * number_of_nodes_involved. For modelTypecloud(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 typesmobile-tf-low-latency-1,mobile-tf-versatile-1,mobile-tf-high-accuracy-1, the training budget must be between 1,000 and 100,000 milli node hours, inclusive. The default value is 24,000 which represents one day in wall time on a single node that is used.- Type:
- disable_early_stopping¶
Use the entire training budget. This disables the early stopping feature. When false the early stopping feature is enabled, which means that AutoML Image Classification might stop training before the entire training budget has been used.
- Type:
- multi_label¶
If false, a single-label (multi-class) Model will be trained (i.e. assuming that for each image just up to one annotation may be applicable). If true, a multi-label Model will be trained (i.e. assuming that for each image multiple annotations may be applicable).
- Type:
- class ModelType(value)[source]¶
Bases:
Enum- Values:
- MODEL_TYPE_UNSPECIFIED (0):
Should not be set.
- CLOUD (1):
A Model best tailored to be used within Google Cloud, and which cannot be exported. Default.
- MOBILE_TF_LOW_LATENCY_1 (2):
A model that, in addition to being available within Google Cloud, can also be exported (see ModelService.ExportModel) as TensorFlow or Core ML model and used on a mobile or edge device afterwards. Expected to have low latency, but may have lower prediction quality than other mobile models.
- MOBILE_TF_VERSATILE_1 (3):
A model that, in addition to being available within Google Cloud, can also be exported (see ModelService.ExportModel) as TensorFlow or Core ML model and used on a mobile or edge device with afterwards.
- MOBILE_TF_HIGH_ACCURACY_1 (4):
A model that, in addition to being available within Google Cloud, can also be exported (see ModelService.ExportModel) as TensorFlow or Core ML model and used on a mobile or edge device afterwards. Expected to have a higher latency, but should also have a higher prediction quality than other mobile models.
- class google.cloud.aiplatform.v1.schema.trainingjob.definition_v1.types.AutoMlImageClassificationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
Message- cost_milli_node_hours¶
The actual training cost of creating this model, expressed in milli node hours, i.e. 1,000 value in this field means 1 node hour. Guaranteed to not exceed inputs.budgetMilliNodeHours.
- Type:
- successful_stop_reason¶
For successful job completions, this is the reason why the job has finished.
- class SuccessfulStopReason(value)[source]¶
Bases:
Enum- Values:
- SUCCESSFUL_STOP_REASON_UNSPECIFIED (0):
Should not be set.
- BUDGET_REACHED (1):
The inputs.budgetMilliNodeHours had been reached.
- MODEL_CONVERGED (2):
Further training of the Model ceased to increase its quality, since it already has converged.
- class google.cloud.aiplatform.v1.schema.trainingjob.definition_v1.types.AutoMlImageObjectDetection(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
MessageA TrainingJob that trains and uploads an AutoML Image Object Detection Model.
- inputs¶
The input parameters of this TrainingJob.
- metadata¶
The metadata information
- class google.cloud.aiplatform.v1.schema.trainingjob.definition_v1.types.AutoMlImageObjectDetectionInputs(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
Message- model_type¶
- budget_milli_node_hours¶
The training budget of creating this model, expressed in milli node hours i.e. 1,000 value in this field means 1 node hour. The actual metadata.costMilliNodeHours will be equal or less than this value. If further model training ceases to provide any improvements, it will stop without using the full budget and the metadata.successfulStopReason will be
model-converged. Note, node_hour = actual_hour * number_of_nodes_involved. For modelTypecloud(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 typesmobile-tf-low-latency-1,mobile-tf-versatile-1,mobile-tf-high-accuracy-1the training budget must be between 1,000 and 100,000 milli node hours, inclusive. The default value is 24,000 which represents one day in wall time on a single node that is used.- Type:
- disable_early_stopping¶
Use the entire training budget. This disables the early stopping feature. When false the early stopping feature is enabled, which means that AutoML Image Object Detection might stop training before the entire training budget has been used.
- Type:
- class ModelType(value)[source]¶
Bases:
Enum- Values:
- MODEL_TYPE_UNSPECIFIED (0):
Should not be set.
- CLOUD_HIGH_ACCURACY_1 (1):
A model best tailored to be used within Google Cloud, and which cannot be exported. Expected to have a higher latency, but should also have a higher prediction quality than other cloud models.
- CLOUD_LOW_LATENCY_1 (2):
A model best tailored to be used within Google Cloud, and which cannot be exported. Expected to have a low latency, but may have lower prediction quality than other cloud models.
- MOBILE_TF_LOW_LATENCY_1 (3):
A model that, in addition to being available within Google Cloud can also be exported (see ModelService.ExportModel) and used on a mobile or edge device with TensorFlow afterwards. Expected to have low latency, but may have lower prediction quality than other mobile models.
- MOBILE_TF_VERSATILE_1 (4):
A model that, in addition to being available within Google Cloud can also be exported (see ModelService.ExportModel) and used on a mobile or edge device with TensorFlow afterwards.
- MOBILE_TF_HIGH_ACCURACY_1 (5):
A model that, in addition to being available within Google Cloud, can also be exported (see ModelService.ExportModel) and used on a mobile or edge device with TensorFlow afterwards. Expected to have a higher latency, but should also have a higher prediction quality than other mobile models.
- class google.cloud.aiplatform.v1.schema.trainingjob.definition_v1.types.AutoMlImageObjectDetectionMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
Message- cost_milli_node_hours¶
The actual training cost of creating this model, expressed in milli node hours, i.e. 1,000 value in this field means 1 node hour. Guaranteed to not exceed inputs.budgetMilliNodeHours.
- Type:
- successful_stop_reason¶
For successful job completions, this is the reason why the job has finished.
- class SuccessfulStopReason(value)[source]¶
Bases:
Enum- Values:
- SUCCESSFUL_STOP_REASON_UNSPECIFIED (0):
Should not be set.
- BUDGET_REACHED (1):
The inputs.budgetMilliNodeHours had been reached.
- MODEL_CONVERGED (2):
Further training of the Model ceased to increase its quality, since it already has converged.
- class google.cloud.aiplatform.v1.schema.trainingjob.definition_v1.types.AutoMlImageSegmentation(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
MessageA TrainingJob that trains and uploads an AutoML Image Segmentation Model.
- inputs¶
The input parameters of this TrainingJob.
- metadata¶
The metadata information.
- class google.cloud.aiplatform.v1.schema.trainingjob.definition_v1.types.AutoMlImageSegmentationInputs(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
Message- model_type¶
- budget_milli_node_hours¶
The training budget of creating this model, expressed in milli node hours i.e. 1,000 value in this field means 1 node hour. The actual metadata.costMilliNodeHours will be equal or less than this value. If further model training ceases to provide any improvements, it will stop without using the full budget and the metadata.successfulStopReason will be
model-converged. Note, node_hour = actual_hour * number_of_nodes_involved. Or actaul_wall_clock_hours = train_budget_milli_node_hours / (number_of_nodes_involved * 1000) For modelTypecloud-high-accuracy-1(default), the budget must be between 20,000 and 2,000,000 milli node hours, inclusive. The default value is 192,000 which represents one day in wall time (1000 milli * 24 hours * 8 nodes).- Type:
- base_model_id¶
The ID of the
basemodel. If it is specified, the new model will be trained based on thebasemodel. Otherwise, the new model will be trained from scratch. Thebasemodel must be in the same Project and Location as the new Model to train, and have the same modelType.- Type:
- class ModelType(value)[source]¶
Bases:
Enum- Values:
- MODEL_TYPE_UNSPECIFIED (0):
Should not be set.
- CLOUD_HIGH_ACCURACY_1 (1):
A model to be used via prediction calls to uCAIP API. Expected to have a higher latency, but should also have a higher prediction quality than other models.
- CLOUD_LOW_ACCURACY_1 (2):
A model to be used via prediction calls to uCAIP API. Expected to have a lower latency but relatively lower prediction quality.
- MOBILE_TF_LOW_LATENCY_1 (3):
A model that, in addition to being available within Google Cloud, can also be exported (see ModelService.ExportModel) as TensorFlow model and used on a mobile or edge device afterwards. Expected to have low latency, but may have lower prediction quality than other mobile models.
- class google.cloud.aiplatform.v1.schema.trainingjob.definition_v1.types.AutoMlImageSegmentationMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
Message- cost_milli_node_hours¶
The actual training cost of creating this model, expressed in milli node hours, i.e. 1,000 value in this field means 1 node hour. Guaranteed to not exceed inputs.budgetMilliNodeHours.
- Type:
- successful_stop_reason¶
For successful job completions, this is the reason why the job has finished.
- class SuccessfulStopReason(value)[source]¶
Bases:
Enum- Values:
- SUCCESSFUL_STOP_REASON_UNSPECIFIED (0):
Should not be set.
- BUDGET_REACHED (1):
The inputs.budgetMilliNodeHours had been reached.
- MODEL_CONVERGED (2):
Further training of the Model ceased to increase its quality, since it already has converged.
- class google.cloud.aiplatform.v1.schema.trainingjob.definition_v1.types.AutoMlTables(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
MessageA TrainingJob that trains and uploads an AutoML Tables Model.
- inputs¶
The input parameters of this TrainingJob.
- metadata¶
The metadata information.
- class google.cloud.aiplatform.v1.schema.trainingjob.definition_v1.types.AutoMlTablesInputs(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
MessageThis message has oneof fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.
- optimization_objective_recall_value¶
Required when optimization_objective is “maximize-precision-at-recall”. Must be between 0 and 1, inclusive.
This field is a member of oneof
additional_optimization_objective_config.- Type:
- optimization_objective_precision_value¶
Required when optimization_objective is “maximize-recall-at-precision”. Must be between 0 and 1, inclusive.
This field is a member of oneof
additional_optimization_objective_config.- Type:
- prediction_type¶
The type of prediction the Model is to produce. “classification” - Predict one out of multiple target values is picked for each row.
“regression” - Predict a value based on its
relation to other values. This type is available only to columns that contain semantically numeric values, i.e. integers or floating point number, even if stored as e.g. strings.
- Type:
- transformations¶
Each transformation will apply transform function to given input column. And the result will be used for training. When creating transformation for BigQuery Struct column, the column should be flattened using “.” as the delimiter.
- optimization_objective¶
Objective function the model is optimizing towards. The training process creates a model that maximizes/minimizes the value of the objective function over the validation set.
The supported optimization objectives depend on the prediction type. If the field is not set, a default objective function is used.
classification (binary):
“maximize-au-roc” (default) - Maximize the
area under the receiver operating characteristic (ROC) curve. “minimize-log-loss” - Minimize log loss.
“maximize-au-prc” - Maximize the area under
the precision-recall curve. “maximize-precision-at-recall” - Maximize precision for a specified recall value. “maximize-recall-at-precision” - Maximize recall for a specified precision value.
classification (multi-class):
“minimize-log-loss” (default) - Minimize log
loss.
regression:
“minimize-rmse” (default) - Minimize
root-mean-squared error (RMSE). “minimize-mae” - Minimize mean-absolute error (MAE). “minimize-rmsle” - Minimize root-mean-squared log error (RMSLE).
- Type:
- train_budget_milli_node_hours¶
Required. The train budget of creating this model, expressed in milli node hours i.e. 1,000 value in this field means 1 node hour.
The training cost of the model will not exceed this budget. The final cost will be attempted to be close to the budget, though may end up being (even) noticeably smaller - at the backend’s discretion. This especially may happen when further model training ceases to provide any improvements.
If the budget is set to a value known to be insufficient to train a model for the given dataset, the training won’t be attempted and will error.
The train budget must be between 1,000 and 72,000 milli node hours, inclusive.
- Type:
- disable_early_stopping¶
Use the entire training budget. This disables the early stopping feature. By default, the early stopping feature is enabled, which means that AutoML Tables might stop training before the entire training budget has been used.
- Type:
- weight_column_name¶
Column name that should be used as the weight column. Higher values in this column give more importance to the row during model training. The column must have numeric values between 0 and 10000 inclusively; 0 means the row is ignored for training. If weight column field is not set, then all rows are assumed to have equal weight of 1.
- Type:
- export_evaluated_data_items_config¶
Configuration for exporting test set predictions to a BigQuery table. If this configuration is absent, then the export is not performed.
- additional_experiments¶
Additional experiment flags for the Tables training pipeline.
- Type:
MutableSequence[str]
- class Transformation(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
MessageThis message has oneof fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members.
- class AutoTransformation(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
MessageTraining pipeline will infer the proper transformation based on the statistic of dataset.
- class CategoricalArrayTransformation(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
MessageTreats 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.
- class CategoricalTransformation(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
MessageTraining 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.
- class NumericArrayTransformation(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
MessageTreats the column as numerical array and performs following transformation functions.
All transformations for Numerical types applied to the average of the all elements.
The average of empty arrays is treated as zero.
- class NumericTransformation(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
MessageTraining pipeline will perform following transformation functions.
The value converted to float32.
The z_score of the value.
log(value+1) when the value is greater than or equal to 0. Otherwise, this transformation is not applied and the value is considered a missing value.
z_score of log(value+1) when the value is greater than or equal to 0. Otherwise, this transformation is not applied and the value is considered a missing value.
A boolean value that indicates whether the value is valid.
- class TextArrayTransformation(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
MessageTreats 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.
- class TextTransformation(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
MessageTraining 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.
- class TimestampTransformation(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
MessageTraining pipeline will perform following transformation functions.
Apply the transformation functions for Numerical columns.
Determine the year, month, day,and weekday. Treat each value from the
timestamp as a Categorical column.
Invalid numerical values (for example, values that fall outside of a typical timestamp range, or are extreme values) receive no special treatment and are not removed.
- time_format¶
The format in which that time field is expressed. The time_format must either be one of:
unix-secondsunix-millisecondsunix-microsecondsunix-nanoseconds(for respectively number of seconds, milliseconds, microseconds and nanoseconds since start of the Unix epoch); or be written instrftimesyntax. If time_format is not set, then the default format is RFC 3339date-timeformat, wheretime-offset="Z"(e.g. 1985-04-12T23:20:50.52Z)
- Type:
- class google.cloud.aiplatform.v1.schema.trainingjob.definition_v1.types.AutoMlTablesMetadata(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
MessageModel metadata specific to AutoML Tables.
- class google.cloud.aiplatform.v1.schema.trainingjob.definition_v1.types.AutoMlTextClassification(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
MessageA TrainingJob that trains and uploads an AutoML Text Classification Model.
- inputs¶
The input parameters of this TrainingJob.
- class google.cloud.aiplatform.v1.schema.trainingjob.definition_v1.types.AutoMlTextClassificationInputs(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
Message
- class google.cloud.aiplatform.v1.schema.trainingjob.definition_v1.types.AutoMlTextExtraction(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
MessageA TrainingJob that trains and uploads an AutoML Text Extraction Model.
- inputs¶
The input parameters of this TrainingJob.
- class google.cloud.aiplatform.v1.schema.trainingjob.definition_v1.types.AutoMlTextSentiment(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
MessageA TrainingJob that trains and uploads an AutoML Text Sentiment Model.
- inputs¶
The input parameters of this TrainingJob.
- class google.cloud.aiplatform.v1.schema.trainingjob.definition_v1.types.AutoMlTextSentimentInputs(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
Message- sentiment_max¶
A sentiment is expressed as an integer ordinal, where higher value means a more positive sentiment. The range of sentiments that will be used is between 0 and sentimentMax (inclusive on both ends), and all the values in the range must be represented in the dataset before a model can be created. Only the Annotations with this sentimentMax will be used for training. sentimentMax value must be between 1 and 10 (inclusive).
- Type:
- class google.cloud.aiplatform.v1.schema.trainingjob.definition_v1.types.AutoMlVideoActionRecognition(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
MessageA TrainingJob that trains and uploads an AutoML Video Action Recognition Model.
- inputs¶
The input parameters of this TrainingJob.
- class google.cloud.aiplatform.v1.schema.trainingjob.definition_v1.types.AutoMlVideoActionRecognitionInputs(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
Message- model_type¶
- class ModelType(value)[source]¶
Bases:
Enum- Values:
- MODEL_TYPE_UNSPECIFIED (0):
Should not be set.
- CLOUD (1):
A model best tailored to be used within Google Cloud, and which c annot be exported. Default.
- MOBILE_VERSATILE_1 (2):
A model that, in addition to being available within Google Cloud, can also be exported (see ModelService.ExportModel) as a TensorFlow or TensorFlow Lite model and used on a mobile or edge device afterwards.
- MOBILE_JETSON_VERSATILE_1 (3):
A model that, in addition to being available within Google Cloud, can also be exported (see ModelService.ExportModel) to a Jetson device afterwards.
- MOBILE_CORAL_VERSATILE_1 (4):
A model that, in addition to being available within Google Cloud, can also be exported (see ModelService.ExportModel) as a TensorFlow or TensorFlow Lite model and used on a Coral device afterwards.
- class google.cloud.aiplatform.v1.schema.trainingjob.definition_v1.types.AutoMlVideoClassification(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
MessageA TrainingJob that trains and uploads an AutoML Video Classification Model.
- inputs¶
The input parameters of this TrainingJob.
- class google.cloud.aiplatform.v1.schema.trainingjob.definition_v1.types.AutoMlVideoClassificationInputs(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
Message- model_type¶
- class ModelType(value)[source]¶
Bases:
Enum- Values:
- MODEL_TYPE_UNSPECIFIED (0):
Should not be set.
- CLOUD (1):
A model best tailored to be used within Google Cloud, and which cannot be exported. Default.
- MOBILE_VERSATILE_1 (2):
A model that, in addition to being available within Google Cloud, can also be exported (see ModelService.ExportModel) as a TensorFlow or TensorFlow Lite model and used on a mobile or edge device afterwards.
- MOBILE_JETSON_VERSATILE_1 (3):
A model that, in addition to being available within Google Cloud, can also be exported (see ModelService.ExportModel) to a Jetson device afterwards.
- class google.cloud.aiplatform.v1.schema.trainingjob.definition_v1.types.AutoMlVideoObjectTracking(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
MessageA TrainingJob that trains and uploads an AutoML Video ObjectTracking Model.
- inputs¶
The input parameters of this TrainingJob.
- class google.cloud.aiplatform.v1.schema.trainingjob.definition_v1.types.AutoMlVideoObjectTrackingInputs(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
Message- model_type¶
- class ModelType(value)[source]¶
Bases:
Enum- Values:
- MODEL_TYPE_UNSPECIFIED (0):
Should not be set.
- CLOUD (1):
A model best tailored to be used within Google Cloud, and which c annot be exported. Default.
- MOBILE_VERSATILE_1 (2):
A model that, in addition to being available within Google Cloud, can also be exported (see ModelService.ExportModel) as a TensorFlow or TensorFlow Lite model and used on a mobile or edge device afterwards.
- MOBILE_CORAL_VERSATILE_1 (3):
A versatile model that is meant to be exported (see ModelService.ExportModel) and used on a Google Coral device.
- MOBILE_CORAL_LOW_LATENCY_1 (4):
A model that trades off quality for low latency, to be exported (see ModelService.ExportModel) and used on a Google Coral device.
- MOBILE_JETSON_VERSATILE_1 (5):
A versatile model that is meant to be exported (see ModelService.ExportModel) and used on an NVIDIA Jetson device.
- MOBILE_JETSON_LOW_LATENCY_1 (6):
A model that trades off quality for low latency, to be exported (see ModelService.ExportModel) and used on an NVIDIA Jetson device.
- class google.cloud.aiplatform.v1.schema.trainingjob.definition_v1.types.ExportEvaluatedDataItemsConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)[source]¶
Bases:
MessageConfiguration for exporting test set predictions to a BigQuery table.
- destination_bigquery_uri¶
URI of desired destination BigQuery table. Expected format: bq://<project_id>:<dataset_id>:
If not specified, then results are exported to the following auto-created BigQuery table: <project_id>:export_evaluated_examples_<model_name>_<yyyy_MM_dd’T’HH_mm_ss_SSS’Z’>.evaluated_examples
- Type:
