SageMakerCreateTrainingJobProps

class aws_cdk.aws_stepfunctions_tasks.SageMakerCreateTrainingJobProps(*, comment=None, heartbeat=None, input_path=None, integration_pattern=None, output_path=None, result_path=None, timeout=None, algorithm_specification, input_data_config, output_data_config, training_job_name, hyperparameters=None, resource_config=None, role=None, stopping_condition=None, tags=None, vpc_config=None)

Bases: aws_cdk.aws_stepfunctions.TaskStateBaseProps

(experimental) Properties for creating an Amazon SageMaker training job.

Parameters
  • comment (Optional[str]) – An optional description for this state. Default: - No comment

  • heartbeat (Optional[Duration]) – Timeout for the heartbeat. Default: - None

  • input_path (Optional[str]) – JSONPath expression to select part of the state to be the input to this state. May also be the special value JsonPath.DISCARD, which will cause the effective input to be the empty object {}. Default: - The entire task input (JSON path ‘$’)

  • integration_pattern (Optional[IntegrationPattern]) – AWS Step Functions integrates with services directly in the Amazon States Language. You can control these AWS services using service integration patterns Default: IntegrationPattern.REQUEST_RESPONSE

  • output_path (Optional[str]) – JSONPath expression to select select a portion of the state output to pass to the next state. May also be the special value JsonPath.DISCARD, which will cause the effective output to be the empty object {}. Default: - The entire JSON node determined by the state input, the task result, and resultPath is passed to the next state (JSON path ‘$’)

  • result_path (Optional[str]) – JSONPath expression to indicate where to inject the state’s output. May also be the special value JsonPath.DISCARD, which will cause the state’s input to become its output. Default: - Replaces the entire input with the result (JSON path ‘$’)

  • timeout (Optional[Duration]) – Timeout for the state machine. Default: - None

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

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

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

  • training_job_name (str) – (experimental) Training Job Name.

  • hyperparameters (Optional[Mapping[str, Any]]) – (experimental) 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

  • resource_config (Optional[ResourceConfig]) – (experimental) 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]) – (experimental) 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 will be created.

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

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

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

Stability

experimental

Attributes

algorithm_specification

(experimental) Identifies the training algorithm to use.

Stability

experimental

Return type

AlgorithmSpecification

comment

An optional description for this state.

Default
  • No comment

Return type

Optional[str]

heartbeat

Timeout for the heartbeat.

Default
  • None

Return type

Optional[Duration]

hyperparameters

(experimental) 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

See

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

Stability

experimental

Return type

Optional[Mapping[str, Any]]

input_data_config

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

Stability

experimental

Return type

List[Channel]

input_path

JSONPath expression to select part of the state to be the input to this state.

May also be the special value JsonPath.DISCARD, which will cause the effective input to be the empty object {}.

Default
  • The entire task input (JSON path ‘$’)

Return type

Optional[str]

integration_pattern

AWS Step Functions integrates with services directly in the Amazon States Language.

You can control these AWS services using service integration patterns

Default

IntegrationPattern.REQUEST_RESPONSE

See

https://docs.aws.amazon.com/step-functions/latest/dg/connect-to-resource.html#connect-wait-token

Return type

Optional[IntegrationPattern]

output_data_config

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

Stability

experimental

Return type

OutputDataConfig

output_path

JSONPath expression to select select a portion of the state output to pass to the next state.

May also be the special value JsonPath.DISCARD, which will cause the effective output to be the empty object {}.

Default

  • The entire JSON node determined by the state input, the task result,

and resultPath is passed to the next state (JSON path ‘$’)

Return type

Optional[str]

resource_config

(experimental) 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

Stability

experimental

Return type

Optional[ResourceConfig]

result_path

JSONPath expression to indicate where to inject the state’s output.

May also be the special value JsonPath.DISCARD, which will cause the state’s input to become its output.

Default
  • Replaces the entire input with the result (JSON path ‘$’)

Return type

Optional[str]

role

(experimental) 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 will be created.

Stability

experimental

Return type

Optional[IRole]

stopping_condition

(experimental) Sets a time limit for training.

Default
  • max runtime of 1 hour

Stability

experimental

Return type

Optional[StoppingCondition]

tags

(experimental) Tags to be applied to the train job.

Default
  • No tags

Stability

experimental

Return type

Optional[Mapping[str, str]]

timeout

Timeout for the state machine.

Default
  • None

Return type

Optional[Duration]

training_job_name

(experimental) Training Job Name.

Stability

experimental

Return type

str

vpc_config

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

Default
  • No VPC

Stability

experimental

Return type

Optional[VpcConfig]