Class AutoMLVideoTrainingJob (1.1.1)

Version 1.1.1

• 1.75.0 (latest)
• 1.74.0
• 1.73.0
• 1.72.0
• 1.71.1
• 1.70.0
• 1.69.0
• 1.68.0
• 1.67.1
• 1.66.0
• 1.65.0
• 1.63.0
• 1.62.0
• 1.60.0
• 1.59.0
• 1.58.0
• 1.57.0
• 1.56.0
• 1.55.0
• 1.54.1
• 1.53.0
• 1.52.0
• 1.51.0
• 1.50.0
• 1.49.0
• 1.48.0
• 1.47.0
• 1.46.0
• 1.45.0
• 1.44.0
• 1.43.0
• 1.39.0
• 1.38.1
• 1.37.0
• 1.36.4
• 1.35.0
• 1.34.0
• 1.33.1
• 1.32.0
• 1.31.1
• 1.30.1
• 1.29.0
• 1.28.1
• 1.27.1
• 1.26.1
• 1.25.0
• 1.24.1
• 1.23.0
• 1.22.1
• 1.21.0
• 1.20.0
• 1.19.1
• 1.18.3
• 1.17.1
• 1.16.1
• 1.15.1
• 1.14.0
• 1.13.1
• 1.12.1
• 1.11.0
• 1.10.0
• 1.9.0
• 1.8.1
• 1.7.1
• 1.6.2
• 1.5.0
• 1.4.3
• 1.3.0
• 1.2.0
• 1.1.1
• 1.0.1
• 0.9.0
• 0.8.0
• 0.7.1
• 0.6.0
• 0.5.1
• 0.4.0
• 0.3.1

AutoMLVideoTrainingJob(
    display_name: str,
    prediction_type: str = "classification",
    model_type: str = "CLOUD",
    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 Video 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" - A video classification model classifies shots and segments in your videos according to your own defined labels. "object_tracking" - A video object tracking model detects and tracks multiple objects in shots and segments. You can use these models to track objects in your videos according to your own pre-defined, custom labels. "action_recognition" - A video action reconition model pinpoints the location of actions with short temporal durations (1 second).
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 > AutoMLVideoTrainingJob

Methods

run

run(
    dataset: google.cloud.aiplatform.datasets.video_dataset.VideoDataset,
    training_fraction_split: float = 0.8,
    test_fraction_split: float = 0.2,
    model_display_name: Optional[str] = None,
    sync: bool = True,
)

Runs the AutoML Image training job and returns a model.

Data fraction splits: training_fraction_split, and test_fraction_split may optionally be provided, they must sum to up to 1. If none of the fractions are set, by default roughly 80% of data will be used for training, and 20% for test.

Parameters

Name	Description
dataset	datasets.VideoDataset 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.