Class AutoMLImageTrainingJob (1.47.0)

Optional. The user-defined name of this TrainingPipeline.

The type of prediction the Model is to produce, one of: "classification" - Predict one out of multiple target values is picked for each row. "object_detection" - 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.

bool = False Required. Default is False. 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). This is only applicable for the "classification" prediction_type and will be ignored otherwise.

str = "CLOUD" Required. One of the following: "CLOUD" - Default for Image Classification. A Model best tailored to be used within Google Cloud, and which cannot be exported. "CLOUD_1" - A model type best tailored to be used within Google Cloud, which cannot be exported externally. Compared to the CLOUD model above, it is expected to have higher prediction accuracy. "CLOUD_HIGH_ACCURACY_1" - Default for Image Object Detection. 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" - 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" - A model that, in addition to being available within Google Cloud, can also be exported 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" - A model that, in addition to being available within Google Cloud, can also be exported as TensorFlow or Core ML model and used on a mobile or edge device with afterwards. "MOBILE_TF_HIGH_ACCURACY_1" - A model that, in addition to being available within Google Cloud, can also be exported 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. "EFFICIENTNET" - A model that, available in Vertex Model Garden image classification training with customizable hyperparameters. Best tailored to be used within Google Cloud, and cannot be exported externally. "VIT" - A model that, available in Vertex Model Garden image classification training with customizable hyperparameters. Best tailored to be used within Google Cloud, and cannot be exported externally. "MAXVIT" - A model that, available in Vertex Model Garden image classification training with customizable hyperparameters. Best tailored to be used within Google Cloud, and cannot be exported externally. "COCA" - A model that, available in Vertex Model Garden image classification training with customizable hyperparameters. Best tailored to be used within Google Cloud, and cannot be exported externally. "SPINENET" - A model that, available in Vertex Model Garden image object detection training with customizable hyperparameters. Best tailored to be used within Google Cloud, and cannot be exported externally. "YOLO" - A model that, available in Vertex Model Garden image object detection training with customizable hyperparameters. Best tailored to be used within Google Cloud, and cannot be exported externally.

Optional[models.Model] = None Optional. Only permitted for Image Classification models. 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 model_type.

Optional[models.Model] = None Optional for both Image Classification and Object detection models, to incrementally train a new model using an existing model as the starting point, with a reduced training time. If not specified, 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 prediction_type and model_type.

Optional. Project to run training in. Overrides project set in aiplatform.init.

Optional. Location to run training in. Overrides location set in aiplatform.init.

Optional. Custom credentials to use to run call training service. Overrides credentials set in aiplatform.init.

Optional. The labels with user-defined metadata to organize TrainingPipelines. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.

Optional. The Cloud KMS resource identifier of the customer managed encryption key used to protect the training pipeline. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created. If set, this TrainingPipeline will be secured by this key. Note: Model trained by this TrainingPipeline is also secured by this key if model_to_upload is not set separately. Overrides encryption_spec_key_name set in aiplatform.init.

Optional. The Cloud KMS resource identifier of the customer managed encryption key used to protect the model. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created. If set, the trained Model will be secured by this key. Overrides encryption_spec_key_name set in aiplatform.init.

Optional[str] = None, Optional. The field is reserved for Model Garden model training, based on the provided pre-trained model checkpoint. Only necessary for model_type==EFFICIENTNET, VIT, COCA, SPINENET, YOLO.

Optional[Dict[str, str]] = None, Optional. The field is usually used together with the Model Garden model training, when passing the customized configs for the trainer. Example with all supported parameters: trainer_config = { 'global_batch_size': '8', 'learning_rate': '0.001', 'optimizer_type': 'sgd', 'optimizer_momentum': '0.9', 'train_steps': '10000', 'accelerator_count': '1', 'anchor_size': '8', -- IOD only } trainer_config is only required for Model Garden models when model_type==EFFICIENTNET, VIT, COCA, SPINENET, YOLO.

Dict[str, str] Required. Dictionary representing metrics to optimize. The dictionary key is the metric_id, which is reported by your training job, with possible values be ('loss', 'accuracy') and the dictionary value is the optimization goal of the metric('minimize' or 'maximize'). example: metric_spec = {'loss': 'minimize', 'accuracy': 'maximize'} metric_spec is only required for Model Garden models when model_type==EFFICIENTNET, VIT, COCA, SPINENET, YOLO.

Required. Dictionary representing parameters to optimize. The dictionary key is the metric_id, which is passed into your training job as a command line key word argument, and the dictionary value is the parameter specification of the metric. from google.cloud.aiplatform import hpt as hpt parameter_spec = { 'learning_rate': hpt.DoubleParameterSpec(min=1e-7, max=1, scale='linear'), } Supported parameter specifications can be found in aiplatform.hyperparameter_tuning. These parameter specification are currently supported: Union[DoubleParameterSpec, IntegerParameterSpec, CategoricalParameterSpec, DiscreteParameterSpec] parameter_spec is only required for Model Garden models when model_type==EFFICIENTNET, VIT, COCA, SPINENET, YOLO.

The search algorithm specified for the Study. Accepts one of the following: None - If you do not specify an algorithm, your job uses the default Vertex AI algorithm. The default algorithm applies Bayesian optimization to arrive at the optimal solution with a more effective search over the parameter space. 'grid' - A simple grid search within the feasible space. This option is particularly useful if you want to specify a quantity of trials that is greater than the number of points in the feasible space. In such cases, if you do not specify a grid search, the Vertex AI default algorithm may generate duplicate suggestions. To use grid search, all parameter specs must be of type IntegerParameterSpec, CategoricalParameterSpec, or DiscreteParameterSpec. 'random' - A simple random search within the feasible space. search_algorithm is only required for Model Garden models when model_type==EFFICIENTNET, VIT, COCA, SPINENET, YOLO.

This indicates which measurement to use if/when the service automatically selects the final measurement from previously reported intermediate measurements. Accepts: 'best', 'last' Choose this based on two considerations: A) Do you expect your measurements to monotonically improve? If so, choose 'last'. On the other hand, if you're in a situation where your system can "over-train" and you expect the performance to get better for a while but then start declining, choose 'best'. B) Are your measurements significantly noisy and/or irreproducible? If so, 'best' will tend to be over-optimistic, and it may be better to choose 'last'. If both or neither of (A) and (B) apply, it doesn't matter which selection type is chosen. measurement_selection is only required for Model Garden models when model_type==EFFICIENTNET, VIT, COCA, SPINENET, YOLO.

Whether to execute this deletion synchronously. If False, this method will be executed in concurrent Future and any downstream object will be immediately returned and synced when the Future has completed.

Required. A fully-qualified resource name or ID.

Optional project to retrieve training job from. If not set, project set in aiplatform.init will be used.

Optional location to retrieve training job from. If not set, location set in aiplatform.init will be used.

Custom credentials to use to upload this model. Overrides credentials set in aiplatform.init.

Whether to execute this method synchronously. If False, this method will be executed in concurrent Future and any downstream object will be immediately returned and synced when the Future has completed.

Optional. An expression for filtering the results of the request. For field names both snake_case and camelCase are supported.

Optional. A comma-separated list of fields to order by, sorted in ascending order. Use "desc" after a field name for descending. Supported fields: display_name, create_time, update_time

Optional. Project to retrieve list from. If not set, project set in aiplatform.init will be used.

Optional. Location to retrieve list from. If not set, location set in aiplatform.init will be used.

Optional. Custom credentials to use to retrieve list. Overrides credentials set in aiplatform.init.

Required. The dataset within the same Project from which data will be used to train the Model. The Dataset must use schema compatible with Model being trained, and what is compatible should be described in the used TrainingPipeline's [training_task_definition] [google.cloud.aiplatform.v1beta1.TrainingPipeline.training_task_definition]. For tabular Datasets, all their data is exported to training, to pick and choose from.

Optional. The fraction of the input data that is to be used to train the Model. This is ignored if Dataset is not provided.

Optional. The fraction of the input data that is to be used to validate the Model. This is ignored if Dataset is not provided.

Optional. The fraction of the input data that is to be used to evaluate the Model. This is ignored if Dataset is not provided.

Optional. A filter on DataItems of the Dataset. DataItems that match this filter are used to train the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order. This is ignored if Dataset is not provided.

Optional. A filter on DataItems of the Dataset. DataItems that match this filter are used to validate the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order. This is ignored if Dataset is not provided.

Optional. A filter on DataItems of the Dataset. DataItems that match this filter are used to test the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order. This is ignored if Dataset is not provided.

Optional. The train budget of creating this Model, expressed in milli node hours i.e. 1,000 value in this field means 1 node hour. Defaults by prediction_type: classification - For Cloud models the budget must be: 8,000 - 800,000 milli node hours (inclusive). The default value is 192,000 which represents one day in wall time, assuming 8 nodes are used. object_detection - For Cloud models the budget must be: 20,000 - 900,000 milli node hours (inclusive). The default value is 216,000 which represents one day in wall time, assuming 9 nodes are used. 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 training set, the training won't be attempted and will error.

Optional. The display name of the managed Vertex AI Model. The name can be up to 128 characters long and can be consist of any UTF-8 characters. If not provided upon creation, the job's display_name is used.

Optional. The labels with user-defined metadata to organize your Models. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.

Optional. The ID to use for the Model produced by this job, which will become the final component of the model resource name. This value may be up to 63 characters, and valid characters are [a-z0-9_-]. The first character cannot be a number or hyphen.

Optional. The resource name or model ID of an existing model. The new model uploaded by this job will be a version of parent_model. Only set this field when training a new version of an existing model.

Optional. When set to True, the newly uploaded model version will automatically have alias "default" included. Subsequent uses of the model produced by this job without a version specified will use this "default" version. When set to False, the "default" alias will not be moved. Actions targeting the model version produced by this job will need to specifically reference this version by ID or alias. New model uploads, i.e. version 1, will always be "default" aliased.

Optional. User provided version aliases so that the model version uploaded by this job can be referenced via alias instead of auto-generated version ID. A default version alias will be created for the first version of the model. The format is a-z][a-zA-Z0-9-]{0,126}[a-z0-9]

Optional. The description of the model version being uploaded by this job.

bool = False Required. If true, the entire budget is used. This disables the early stopping feature. By default, the early stopping feature is enabled, which means that training might stop before the entire training budget has been used, if further training does no longer brings significant improvement to the model.

bool = True Whether to execute this method synchronously. If False, this method will be executed in concurrent Future and any downstream object will be immediately returned and synced when the Future has completed.

Optional. The timeout for the create request in seconds.