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 commentheartbeat (
Optional
[Duration
]) – Timeout for the heartbeat. Default: - Noneinput_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 ‘$’)integration_pattern (
Optional
[IntegrationPattern
]) – AWS Step Functions integrates with services directly in the Amazon States Language. You can control these AWS services using service integration patterns Default: IntegrationPattern.REQUEST_RESPONSEoutput_path (
Optional
[str
]) – JSONPath expression to select select a portion of the state output to pass to the next state. May also be the special value JsonPath.DISCARD, which will cause the effective output to be the empty object {}. Default: - The entire JSON node determined by the state input, the task result, and resultPath is passed to the next state (JSON path ‘$’)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: - Nonealgorithm_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 hyperparametersresource_config (
Optional
[ResourceConfig
]) – (experimental) Specifies the resources, ML compute instances, and ML storage volumes to deploy for model training. Default: - 1 instance of EC2M4.XLarge
with10GB
volumerole (
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 hourtags (
Optional
[Mapping
[str
,str
]]) – (experimental) Tags to be applied to the train job. Default: - No tagsvpc_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
-
comment
¶ An optional description for this state.
- Default
No comment
- Return type
Optional
[str
]
-
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
- 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
-
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 ‘$’)
- Return type
Optional
[str
]
-
resource_config
¶ (experimental) Specifies the resources, ML compute instances, and ML storage volumes to deploy for model training.
- Default
1 instance of EC2
M4.XLarge
with10GB
volume
- Stability
experimental
- Return type
Optional
[ResourceConfig
]
-
result_path
¶ JSONPath expression to indicate where to inject the state’s output.
May also be the special value JsonPath.DISCARD, which will cause the state’s input to become its output.
- Default
Replaces the entire input with the result (JSON path ‘$’)
- 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.
- 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
]
(experimental) Tags to be applied to the train job.
- Default
No tags
- Stability
experimental
- Return type
Optional
[Mapping
[str
,str
]]
-
training_job_name
¶ (experimental) Training Job Name.
- Stability
experimental
- Return type
str