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AutoMLVideoTrainingJob(
display_name: Optional[str] = None,
prediction_type: str = "classification",
model_type: str = "CLOUD",
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 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 recognition 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. |
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: |
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.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 > AutoMLVideoTrainingJobProperties
create_time
Time this resource was created.
display_name
Display name of this resource.
encryption_spec
Customer-managed encryption key options for this Vertex AI resource.
If this is set, then all resources created by this Vertex AI resource will be encrypted with the provided encryption key.
end_time
Time when the TrainingJob resource entered the PIPELINE_STATE_SUCCEEDED
,
PIPELINE_STATE_FAILED
, PIPELINE_STATE_CANCELLED
state.
error
Detailed error info for this TrainingJob resource. Only populated when
the TrainingJob's state is PIPELINE_STATE_FAILED
or
PIPELINE_STATE_CANCELLED
.
gca_resource
The underlying resource proto representation.
has_failed
Returns True if training has failed.
False otherwise.
labels
User-defined labels containing metadata about this resource.
Read more about labels at https://goo.gl/xmQnxf
name
Name of this resource.
resource_name
Full qualified resource name.
start_time
Time when the TrainingJob entered the PIPELINE_STATE_RUNNING
for
the first time.
state
Current training state.
update_time
Time this resource was last updated.
Methods
cancel
cancel()
Starts asynchronous cancellation on the TrainingJob. The server
makes a best effort to cancel the job, but success is not guaranteed.
On successful cancellation, the TrainingJob is not deleted; instead it
becomes a job with state set to CANCELLED
.
Type | Description |
RuntimeError | If this TrainingJob has not started running. |
delete
delete(sync: bool = True)
Deletes this Vertex AI resource. WARNING: This deletion is permanent.
Name | Description |
sync |
bool
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. |
done
done()
Method indicating whether a job has completed.
get
get(
resource_name: str,
project: Optional[str] = None,
location: Optional[str] = None,
credentials: Optional[google.auth.credentials.Credentials] = None,
)
Get Training Job for the given resource_name.
Name | Description |
resource_name |
str
Required. A fully-qualified resource name or ID. |
project |
str
Optional project to retrieve training job from. If not set, project set in aiplatform.init will be used. |
location |
str
Optional location to retrieve training job from. If not set, location set in aiplatform.init will be used. |
credentials |
auth_credentials.Credentials
Custom credentials to use to upload this model. Overrides credentials set in aiplatform.init. |
Type | Description |
ValueError | If the retrieved training job's training task definition doesn't match the custom training task definition. |
get_model
get_model(sync=True)
Vertex AI Model produced by this training, if one was produced.
Name | Description |
sync |
bool
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. |
Type | Description |
RuntimeError | If training failed or if a model was not produced by this training. |
Type | Description |
model | Vertex AI Model produced by this training |
list
list(
filter: Optional[str] = None,
order_by: Optional[str] = None,
project: Optional[str] = None,
location: Optional[str] = None,
credentials: Optional[google.auth.credentials.Credentials] = None,
)
List all instances of this TrainingJob resource.
Example Usage:
aiplatform.CustomTrainingJob.list( filter='display_name="experiment_a27"', order_by='create_time desc' )
Name | Description |
filter |
str
Optional. An expression for filtering the results of the request. For field names both snake_case and camelCase are supported. |
order_by |
str
Optional. A comma-separated list of fields to order by, sorted in ascending order. Use "desc" after a field name for descending. Supported fields: |
project |
str
Optional. Project to retrieve list from. If not set, project set in aiplatform.init will be used. |
location |
str
Optional. Location to retrieve list from. If not set, location set in aiplatform.init will be used. |
credentials |
auth_credentials.Credentials
Optional. Custom credentials to use to retrieve list. Overrides credentials set in aiplatform.init. |
run
run(
dataset: google.cloud.aiplatform.datasets.video_dataset.VideoDataset,
training_fraction_split: Optional[float] = None,
test_fraction_split: Optional[float] = None,
training_filter_split: Optional[str] = None,
test_filter_split: Optional[str] = None,
model_display_name: Optional[str] = None,
model_labels: Optional[Dict[str, str]] = None,
model_id: Optional[str] = None,
parent_model: Optional[str] = None,
is_default_version: Optional[bool] = True,
model_version_aliases: Optional[Sequence[str]] = None,
model_version_description: Optional[str] = None,
sync: bool = True,
create_request_timeout: Optional[float] = None,
)
Runs the AutoML Video 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:
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.
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.
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. |
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. |
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. |
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. |
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. |
model_id |
str
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 |
parent_model |
str
Optional. The resource name or model ID of an existing model. The new model uploaded by this job will be a version of |
is_default_version |
bool
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. |
model_version_aliases |
Sequence[str]
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 |
model_version_description |
str
Optional. The description of the model version being uploaded by this job. |
create_request_timeout |
float
Optional. The timeout for the create request in seconds. |
Type | Description |
RuntimeError | If Training job has already been run or is waiting to run. |
Type | Description |
model | The trained Vertex AI Model resource or None if training did not produce a Vertex AI Model. |
to_dict
to_dict()
Returns the resource proto as a dictionary.
wait
wait()
Helper method that blocks until all futures are complete.
wait_for_resource_creation
wait_for_resource_creation()
Waits until resource has been created.