Class AutoMLImageTrainingJob (1.1.1)

AutoMLImageTrainingJob(
    display_name: str,
    prediction_type: str = "classification",
    multi_label: bool = False,
    model_type: str = "CLOUD",
    base_model: Optional[google.cloud.aiplatform.models.Model] = None,
    project: Optional[str] = None,
    location: Optional[str] = None,
    credentials: Optional[google.auth.credentials.Credentials] = None,
    training_encryption_spec_key_name: Optional[str] = None,
    model_encryption_spec_key_name: Optional[str] = None,
)

Constructs a AutoML Image Training Job.

Parameters

Name	Description
display_name	`str` Required. The user-defined name of this TrainingPipeline.
prediction_type	`str` 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.
project	`str` Optional. Project to run training in. Overrides project set in aiplatform.init.
location	`str` Optional. Location to run training in. Overrides location set in aiplatform.init.
credentials	`auth_credentials.Credentials` Optional. Custom credentials to use to run call training service. Overrides credentials set in aiplatform.init.
training_encryption_spec_key_name	`Optional[str]` 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.
model_encryption_spec_key_name	`Optional[str]` 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.

Inheritance

builtins.object > google.cloud.aiplatform.base.VertexAiResourceNoun > builtins.object > google.cloud.aiplatform.base.FutureManager > google.cloud.aiplatform.base.VertexAiResourceNounWithFutureManager > google.cloud.aiplatform.training_jobs._TrainingJob > AutoMLImageTrainingJob

Methods

run

run(
    dataset: google.cloud.aiplatform.datasets.image_dataset.ImageDataset,
    training_fraction_split: float = 0.8,
    validation_fraction_split: float = 0.1,
    test_fraction_split: float = 0.1,
    budget_milli_node_hours: int = 1000,
    model_display_name: Optional[str] = None,
    disable_early_stopping: bool = False,
    sync: bool = True,
)

Runs the AutoML Image training job and returns a model.

Data fraction splits: Any of training_fraction_split, validation_fraction_split and test_fraction_split may optionally be provided, they must sum to up to 1. If the provided ones sum to less than 1, the remainder is assigned to sets as decided by Vertex AI. If none of the fractions are set, by default roughly 80% of data will be used for training, 10% for validation, and 10% for test.

Parameters

Name	Description
dataset	`datasets.ImageDataset` 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.
model_display_name	`str` 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.

Exceptions

Type	Description
RuntimeError	If Training job has already been run or is waiting to run.

Returns

Type	Description
model	The trained Vertex AI Model resource or None if training did not produce a Vertex AI Model.