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