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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 ( |
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: |
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: |
Inheritance
builtins.object > google.cloud.aiplatform.base.AiPlatformResourceNoun > builtins.object > google.cloud.aiplatform.base.FutureManager > google.cloud.aiplatform.base.AiPlatformResourceNounWithFutureManager > google.cloud.aiplatform.training_jobs._TrainingJob > AutoMLVideoTrainingJobMethods
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.
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 AI Platform 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. |
Type | Description |
RuntimeError | If Training job has already been run or is waiting to run. |
Type | Description |
model | The trained AI Platform Model resource or None if training did not produce an AI Platform Model. |