Class AutoMLTextTrainingJob (1.26.1)

AutoMLTextTrainingJob(
    display_name: str,
    prediction_type: str,
    multi_label: bool = False,
    sentiment_max: int = 10,
    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 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.

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 > AutoMLTextTrainingJob

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

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.

Exceptions
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.

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()

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.

Parameters
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.

Exceptions
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.

Parameter
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.

Exceptions
Type	Description
`RuntimeError`	If training failed or if a model was not produced by this training.

Returns
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' )

Parameters
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: `display_name`, `create_time`, `update_time`
`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.text_dataset.TextDataset,
    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,
    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 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()

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.