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 , ** kwargs ) : 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
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.