def create_training_job(self,
TrainingJobName,
AlgorithmSpecification,
OutputDataConfig,
ResourceConfig,
InputDataConfig=None,
**kwargs):
"""
Create a training job in Local Mode
Args:
TrainingJobName (str): local training job name.
AlgorithmSpecification (dict): Identifies the training algorithm to use.
InputDataConfig (dict): Describes the training dataset and the location where it is stored.
OutputDataConfig (dict): Identifies the location where you want to save the results of model training.
ResourceConfig (dict): Identifies the resources to use for local model traininig.
HyperParameters (dict) [optional]: Specifies these algorithm-specific parameters to influence the quality of
the final model.
"""
InputDataConfig = InputDataConfig or {}
container = _SageMakerContainer(
ResourceConfig["InstanceType"],
ResourceConfig["InstanceCount"],
AlgorithmSpecification["TrainingImage"],
self.sagemaker_session,
)
training_job = _LocalTrainingJob(container)
hyperparameters = kwargs[
"HyperParameters"] if "HyperParameters" in kwargs else {}
training_job.start(InputDataConfig, OutputDataConfig, hyperparameters,
TrainingJobName)
LocalSagemakerClient._training_jobs[TrainingJobName] = training_job
def create_training_job(self,
TrainingJobName,
AlgorithmSpecification,
OutputDataConfig,
ResourceConfig,
InputDataConfig=None,
Environment=None,
**kwargs):
"""Create a training job in Local Mode.
Args:
TrainingJobName(str): local training job name.
AlgorithmSpecification(dict): Identifies the training algorithm to use.
InputDataConfig(dict, optional): Describes the training dataset and the location where
it is stored. (Default value = None)
OutputDataConfig(dict): Identifies the location where you want to save the results of
model training.
ResourceConfig(dict): Identifies the resources to use for local model training.
Environment(dict, optional): Describes the environment variables to pass
to the container. (Default value = None)
HyperParameters(dict) [optional]: Specifies these algorithm-specific parameters to
influence the quality of the final model.
**kwargs:
Returns:
"""
InputDataConfig = InputDataConfig or {}
Environment = Environment or {}
container = _SageMakerContainer(
ResourceConfig["InstanceType"],
ResourceConfig["InstanceCount"],
AlgorithmSpecification["TrainingImage"],
sagemaker_session=self.sagemaker_session,
)
training_job = _LocalTrainingJob(container)
hyperparameters = kwargs[
"HyperParameters"] if "HyperParameters" in kwargs else {}
logger.info("Starting training job")
training_job.start(InputDataConfig, OutputDataConfig, hyperparameters,
Environment, TrainingJobName)
LocalSagemakerClient._training_jobs[TrainingJobName] = training_job
