TrainingPipelineArgs class

The set of arguments for TrainingPipeline. The set of arguments for TrainingPipeline.

Constructors

TrainingPipelineArgs({required Input<String> displayName, Input<GoogleCloudAiplatformV1EncryptionSpec>? encryptionSpec, Input<GoogleCloudAiplatformV1InputDataConfig>? inputDataConfig, Input<Map<String, String>>? labels, Input<String>? location, Input<String>? modelId, Input<GoogleCloudAiplatformV1Model>? modelToUpload, Input<String>? parentModel, Input<String>? project, required Input<String> trainingTaskDefinition, required Input trainingTaskInputs})
Creates a new TrainingPipelineArgs. displayName The user-defined name of this TrainingPipeline. encryptionSpec Customer-managed encryption key spec for a TrainingPipeline. 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. inputDataConfig Specifies Vertex AI owned input data that may be used for training the Model. The TrainingPipeline's training_task_definition should make clear whether this config is used and if there are any special requirements on how it should be filled. If nothing about this config is mentioned in the training_task_definition, then it should be assumed that the TrainingPipeline does not depend on this configuration. labels 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. location Optional. modelId Optional. The ID to use for the uploaded Model, 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. modelToUpload Describes the Model that may be uploaded (via ModelService.UploadModel) by this TrainingPipeline. The TrainingPipeline's training_task_definition should make clear whether this Model description should be populated, and if there are any special requirements regarding how it should be filled. If nothing is mentioned in the training_task_definition, then it should be assumed that this field should not be filled and the training task either uploads the Model without a need of this information, or that training task does not support uploading a Model as part of the pipeline. When the Pipeline's state becomes PIPELINE_STATE_SUCCEEDED and the trained Model had been uploaded into Vertex AI, then the model_to_upload's resource name is populated. The Model is always uploaded into the Project and Location in which this pipeline is. parentModel Optional. When specify this field, the model_to_upload will not be uploaded as a new model, instead, it will become a new version of this parent_model. project Optional. trainingTaskDefinition A Google Cloud Storage path to the YAML file that defines the training task which is responsible for producing the model artifact, and may also include additional auxiliary work. The definition files that can be used here are found in gs://google-cloud-aiplatform/schema/trainingjob/definition/. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access. trainingTaskInputs The training task's parameter(s), as specified in the training_task_definition's inputs.
const
TrainingPipelineArgs.fromMap(Map<String, dynamic> map)
factory

Properties

displayName → Input<String>
The user-defined name of this TrainingPipeline.
final
encryptionSpec → Input<GoogleCloudAiplatformV1EncryptionSpec>?
Customer-managed encryption key spec for a TrainingPipeline. 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.
final
hashCode → int
The hash code for this object.
no setterinherited
inputDataConfig → Input<GoogleCloudAiplatformV1InputDataConfig>?
Specifies Vertex AI owned input data that may be used for training the Model. The TrainingPipeline's training_task_definition should make clear whether this config is used and if there are any special requirements on how it should be filled. If nothing about this config is mentioned in the training_task_definition, then it should be assumed that the TrainingPipeline does not depend on this configuration.
final
labels → Input<Map<String, String>>?
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.
final
location → Input<String>?
final
modelId → Input<String>?
Optional. The ID to use for the uploaded Model, 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.
final
modelToUpload → Input<GoogleCloudAiplatformV1Model>?
Describes the Model that may be uploaded (via ModelService.UploadModel) by this TrainingPipeline. The TrainingPipeline's training_task_definition should make clear whether this Model description should be populated, and if there are any special requirements regarding how it should be filled. If nothing is mentioned in the training_task_definition, then it should be assumed that this field should not be filled and the training task either uploads the Model without a need of this information, or that training task does not support uploading a Model as part of the pipeline. When the Pipeline's state becomes PIPELINE_STATE_SUCCEEDED and the trained Model had been uploaded into Vertex AI, then the model_to_upload's resource name is populated. The Model is always uploaded into the Project and Location in which this pipeline is.
final
parentModel → Input<String>?
Optional. When specify this field, the model_to_upload will not be uploaded as a new model, instead, it will become a new version of this parent_model.
final
project → Input<String>?
final
runtimeType → Type
A representation of the runtime type of the object.
no setterinherited
trainingTaskDefinition → Input<String>
A Google Cloud Storage path to the YAML file that defines the training task which is responsible for producing the model artifact, and may also include additional auxiliary work. The definition files that can be used here are found in gs://google-cloud-aiplatform/schema/trainingjob/definition/. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
final
trainingTaskInputs → Input
The training task's parameter(s), as specified in the training_task_definition's inputs.
final

Methods

noSuchMethod(Invocation invocation) → dynamic
Invoked when a nonexistent method or property is accessed.
inherited
toMap() → Map<String, dynamic>
toString() → String
A string representation of this object.
inherited

Operators

operator ==(Object other) → bool
The equality operator.
inherited