Class AutoMLImageTrainingJob (1.14.0)

AutoMLImageTrainingJob(
    display_name: Optional[str] = None,
    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,
    labels: Optional[Dict[str, str]] = 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` Optional. 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.
labels	`Dict[str, str]` 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.
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 > builtins.object > abc.ABC > google.cloud.aiplatform.base.DoneMixin > google.cloud.aiplatform.base.StatefulResource > google.cloud.aiplatform.base.VertexAiStatefulResource > google.cloud.aiplatform.training_jobs._TrainingJob > AutoMLImageTrainingJob

Methods

run

run(
    dataset: google.cloud.aiplatform.datasets.image_dataset.ImageDataset,
    training_fraction_split: Optional[float] = None,
    validation_fraction_split: Optional[float] = None,
    test_fraction_split: Optional[float] = None,
    training_filter_split: Optional[str] = None,
    validation_filter_split: Optional[str] = None,
    test_filter_split: Optional[str] = None,
    budget_milli_node_hours: Optional[int] = None,
    model_display_name: Optional[str] = None,
    model_labels: Optional[Dict[str, str]] = None,
    disable_early_stopping: bool = False,
    sync: bool = True,
    create_request_timeout: Optional[float] = None,
)

Runs the AutoML Image training job and returns a model.

If training on a Vertex AI dataset, you can use one of the following split configurations: 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.

Data filter splits:
Assigns input data to training, validation, and test sets
based on the given filters, data pieces not matched by any
filter are ignored. Currently only supported for Datasets
containing DataItems.
If any of the filters in this message are to match nothing, then
they can be set as '-' (the minus sign).
If using filter splits, all of ``training_filter_split``, ``validation_filter_split`` and
``test_filter_split`` must be provided.
Supported only for unstructured Datasets.

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.
training_fraction_split	`float` Optional. The fraction of the input data that is to be used to train the Model. This is ignored if Dataset is not provided.
validation_fraction_split	`float` Optional. The fraction of the input data that is to be used to validate the Model. This is ignored if Dataset is not provided.
test_fraction_split	`float` Optional. The fraction of the input data that is to be used to evaluate the Model. This is ignored if Dataset is not provided.
training_filter_split	`str` 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.
validation_filter_split	`str` 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.
test_filter_split	`str` 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.
budget_milli_node_hours	`int` 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.
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.
model_labels	`Dict[str, str]` 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.
create_request_timeout	`float` Optional. The timeout for the create request in seconds.

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.