SagemakerTrainTaskProps

class aws_cdk.aws_stepfunctions_tasks.SagemakerTrainTaskProps(*, algorithm_specification, input_data_config, output_data_config, training_job_name, hyperparameters=None, integration_pattern=None, resource_config=None, role=None, stopping_condition=None, tags=None, vpc_config=None)

Bases: object

__init__(*, algorithm_specification, input_data_config, output_data_config, training_job_name, hyperparameters=None, integration_pattern=None, resource_config=None, role=None, stopping_condition=None, tags=None, vpc_config=None)

Properties for creating an Amazon SageMaker training job.

Parameters
  • algorithm_specification (AlgorithmSpecification) – Identifies the training algorithm to use.

  • input_data_config (List[Channel]) – Describes the various datasets (e.g. train, validation, test) and the Amazon S3 location where stored.

  • output_data_config (OutputDataConfig) – Identifies the Amazon S3 location where you want Amazon SageMaker to save the results of model training.

  • training_job_name (str) – Training Job Name.

  • hyperparameters (Optional[Mapping[str, Any]]) – Algorithm-specific parameters that influence the quality of the model. Set hyperparameters before you start the learning process. For a list of hyperparameters provided by Amazon SageMaker Default: - No hyperparameters

  • integration_pattern (Optional[ServiceIntegrationPattern]) – The service integration pattern indicates different ways to call SageMaker APIs. The valid value is either FIRE_AND_FORGET or SYNC. Default: FIRE_AND_FORGET

  • resource_config (Optional[ResourceConfig]) – Specifies the resources, ML compute instances, and ML storage volumes to deploy for model training. Default: - 1 instance of EC2 M4.XLarge with 10GB volume

  • role (Optional[IRole]) – Role for the Training Job. The role must be granted all necessary permissions for the SageMaker training job to be able to operate. See https://docs.aws.amazon.com/fr_fr/sagemaker/latest/dg/sagemaker-roles.html#sagemaker-roles-createtrainingjob-perms Default: - a role with appropriate permissions will be created.

  • stopping_condition (Optional[StoppingCondition]) – Sets a time limit for training. Default: - max runtime of 1 hour

  • tags (Optional[Mapping[str, str]]) – Tags to be applied to the train job. Default: - No tags

  • vpc_config (Optional[VpcConfig]) – Specifies the VPC that you want your training job to connect to. Default: - No VPC

stability :stability: experimental

Attributes

algorithm_specification

Identifies the training algorithm to use.

stability :stability: experimental

Return type

AlgorithmSpecification

hyperparameters

Algorithm-specific parameters that influence the quality of the model.

Set hyperparameters before you start the learning process. For a list of hyperparameters provided by Amazon SageMaker

default :default: - No hyperparameters

see :see: https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html stability :stability: experimental

Return type

Optional[Mapping[str, Any]]

input_data_config

Describes the various datasets (e.g. train, validation, test) and the Amazon S3 location where stored.

stability :stability: experimental

Return type

List[Channel]

integration_pattern

The service integration pattern indicates different ways to call SageMaker APIs.

The valid value is either FIRE_AND_FORGET or SYNC.

default :default: FIRE_AND_FORGET

stability :stability: experimental

Return type

Optional[ServiceIntegrationPattern]

output_data_config

Identifies the Amazon S3 location where you want Amazon SageMaker to save the results of model training.

stability :stability: experimental

Return type

OutputDataConfig

resource_config

Specifies the resources, ML compute instances, and ML storage volumes to deploy for model training.

default :default: - 1 instance of EC2 M4.XLarge with 10GB volume

stability :stability: experimental

Return type

Optional[ResourceConfig]

role

Role for the Training Job.

The role must be granted all necessary permissions for the SageMaker training job to be able to operate.

See https://docs.aws.amazon.com/fr_fr/sagemaker/latest/dg/sagemaker-roles.html#sagemaker-roles-createtrainingjob-perms

default :default: - a role with appropriate permissions will be created.

stability :stability: experimental

Return type

Optional[IRole]

stopping_condition

Sets a time limit for training.

default :default: - max runtime of 1 hour

stability :stability: experimental

Return type

Optional[StoppingCondition]

tags

Tags to be applied to the train job.

default :default: - No tags

stability :stability: experimental

Return type

Optional[Mapping[str, str]]

training_job_name

Training Job Name.

stability :stability: experimental

Return type

str

vpc_config

Specifies the VPC that you want your training job to connect to.

default :default: - No VPC

stability :stability: experimental

Return type

Optional[VpcConfig]