Class AutoMLTextTrainingJob (1.58.0)

AutoMLTextTrainingJob(
    display_name: str,
    prediction_type: str,
    multi_label: bool = False,
    sentiment_max: int = 10,
    project: typing.Optional[str] = None,
    location: typing.Optional[str] = None,
    credentials: typing.Optional[google.auth.credentials.Credentials] = None,
    labels: typing.Optional[typing.Dict[str, str]] = None,
    training_encryption_spec_key_name: typing.Optional[str] = None,
    model_encryption_spec_key_name: typing.Optional[str] = None,
)

Constructs a AutoML Text 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 classification model analyzes text data and returns a list of categories that apply to the text found in the data. Vertex AI offers both single-label and multi-label text classification models. "extraction" - An entity extraction model inspects text data for known entities referenced in the data and labels those entities in the text. "sentiment" - A sentiment analysis model inspects text data and identifies the prevailing emotional opinion within it, especially to determine a writer's attitude as positive, negative, or neutral.
`multi_label`	`bool` Required and only applicable for text classification task. If false, a single-label (multi-class) Model will be trained (i.e. assuming that for each text snippet just up to one annotation may be applicable). If true, a multi-label Model will be trained (i.e. assuming that for each text snippet multiple annotations may be applicable).
`sentiment_max`	`int` Required and only applicable for sentiment task. A sentiment is expressed as an integer ordinal, where higher value means a more positive sentiment. The range of sentiments that will be used is between 0 and sentimentMax (inclusive on both ends), and all the values in the range must be represented in the dataset before a model can be created. Only the Annotations with this sentimentMax will be used for training. sentimentMax value must be between 1 and 10 (inclusive).
`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.

Properties

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

Optional. The time when the training job entered the PIPELINE_STATE_SUCCEEDED, PIPELINE_STATE_FAILED, or PIPELINE_STATE_CANCELLED state.

error

Optional. Detailed error information for this training job resource. Error information is created only when the state of the training job is PIPELINE_STATE_FAILED or PIPELINE_STATE_CANCELLED.

gca_resource

The underlying resource proto representation.

has_failed

Returns true if the training job failed, otherwise false.

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

Optional. The time when the training job first entered the PIPELINE_STATE_RUNNING state.

state

Current training state.

update_time

Time this resource was last updated.

Methods

cancel

cancel() -> None

Asynchronously attempts to cancel a training job.

The server makes a best effort to cancel the job, but the training job can't always be cancelled. If the training job is canceled, its state transitions to CANCELLED and it's not deleted.

Exceptions
Type	Description
`RuntimeError`	If this training job isn't running, then a runtime error is raised.

delete

delete(sync: bool = True) -> None

Deletes this Vertex AI resource. WARNING: This deletion is permanent.

Parameter
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() -> bool

Method indicating whether a job has completed.

get

get(
    resource_name: str,
    project: typing.Optional[str] = None,
    location: typing.Optional[str] = None,
    credentials: typing.Optional[google.auth.credentials.Credentials] = None,
) -> google.cloud.aiplatform.training_jobs._TrainingJob

Gets a training job using the resource_name that's passed in.

Parameters
Name	Description
`resource_name`	`str` Required. A fully-qualified resource name or ID.
`project`	`str` Optional. The name of the Google Cloud project to retrieve the training job from. This overrides the project that was set by `aiplatform.init`.
`location`	`str` Optional. The Google Cloud region from where the training job is retrieved. This region overrides the region that was set by `aiplatform.init`.
`credentials`	`auth_credentials.Credentials` Optional. The credentials that are used to upload this model. These credentials override the credentials set by `aiplatform.init`.

Exceptions
Type	Description
`ValueError`	A `ValueError` is raised if the task definition of the retrieved training job doesn't match the custom training task definition.

get_model

get_model(sync=True) -> google.cloud.aiplatform.models.Model

Returns the Vertex AI model produced by this training job.

Parameter
Name	Description
`sync`	`bool` If set to `true`, this method runs synchronously. If `false`, this method runs asynchronously.

Exceptions
Type	Description
`RuntimeError`	A runtime error is raised if the training job failed or if a model wasn't produced by the training job.

list

list(
    filter: typing.Optional[str] = None,
    order_by: typing.Optional[str] = None,
    project: typing.Optional[str] = None,
    location: typing.Optional[str] = None,
    credentials: typing.Optional[google.auth.credentials.Credentials] = None,
) -> typing.List[google.cloud.aiplatform.base.VertexAiResourceNoun]

Lists all instances of this training job resource.

The following shows an example of how to call CustomTrainingJob.list:

aiplatform.CustomTrainingJob.list(
    filter='display_name="experiment_a27"',
    order_by='create_time desc'
)

Parameters
Name	Description
`filter`	`str` Optional. An expression for filtering the results of the request. For field names, snake_case and camelCase are supported.
`order_by`	`str` Optional. A comma-separated list of fields used to sort the returned traing job resources. The defauilt sorting order is ascending. To sort by a field name in descending order, use `desc` after the field name. The following fields are supported: `display_name`, `create_time`, `update_time`.
`project`	`str` Optional. The name of the Google Cloud project to which to retrieve the list of training job resources. This overrides the project that was set by `aiplatform.init`.
`location`	`str` Optional. The Google Cloud region from where the training job resources are retrieved. This region overrides the region that was set by `aiplatform.init`.
`credentials`	`auth_credentials.Credentials` Optional. The credentials that are used to retrieve list. These credentials override the credentials set by `aiplatform.init`.

Returns
Type	Description
`List[VertexAiResourceNoun]`	A list of training job resources.

run

run(
    dataset: google.cloud.aiplatform.datasets.text_dataset.TextDataset,
    training_fraction_split: typing.Optional[float] = None,
    validation_fraction_split: typing.Optional[float] = None,
    test_fraction_split: typing.Optional[float] = None,
    training_filter_split: typing.Optional[str] = None,
    validation_filter_split: typing.Optional[str] = None,
    test_filter_split: typing.Optional[str] = None,
    model_display_name: typing.Optional[str] = None,
    model_labels: typing.Optional[typing.Dict[str, str]] = None,
    model_id: typing.Optional[str] = None,
    parent_model: typing.Optional[str] = None,
    is_default_version: typing.Optional[bool] = True,
    model_version_aliases: typing.Optional[typing.Sequence[str]] = None,
    model_version_description: typing.Optional[str] = None,
    sync: bool = True,
    create_request_timeout: typing.Optional[float] = None,
) -> google.cloud.aiplatform.models.Model

Runs the 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.TextDataset` 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].
`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.
`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 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 `[a-z0-9_-]`. The first character cannot be a number or hyphen.
`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 `parent_model`. Only set this field when training a new version of an existing model.
`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 `a-z][a-zA-Z0-9-]`{0,126}[a-z0-9]
`model_version_description`	`str` Optional. The description of the model version being uploaded by this job.
`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.
`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.

to_dict

to_dict() -> typing.Dict[str, typing.Any]

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() -> None

Waits until the resource has been created.