sage_maker_magic/sage_maker_kernel/kernelmagics.py [230:243]:
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    @argument('--py_version', type=str, help='Python version')
    @argument('--instance_type', type=str, help='Type of EC2 instance to use for training, for example, ‘ml.c4.xlarge’.')
    @argument('--instance_count', type=int, help='Number of Amazon EC2 instances to use for training.')
    @argument('--output_path', type=str, help='S3 location for saving the training result (model artifacts and output files). If not specified, results are stored to a default bucket. If the bucket with the specific name does not exist, the estimator creates the bucket during the fit() method execution.')
    @argument('--hyperparameters', type=hyperparameters, help='Hyperparameters are passed to your script as arguments and can be retrieved with an argparse.', metavar='FOO:1,BAR:0.555,BAZ:ABC | \'FOO : 1, BAR : 0.555, BAZ : ABC\'')
    @argument('--channel_training', type=str, help='A string that represents the path to the directory that contains the input data for the training channel. ')
    @argument('--channel_testing', type=str, help='A string that represents the path to the directory that contains the input data for the testing channel. ')
    @argument_group(title='submit-spot', description=None)
    @argument('--use_spot_instances', type=bool, help='Specifies whether to use SageMaker Managed Spot instances for training. If enabled then the max_wait arg should also be set. More information: https://docs.aws.amazon.com/sagemaker/latest/dg/model-managed-spot-training.html ', nargs='?', const=True)
    @argument('--max_wait', type=int, help='Timeout in seconds waiting for spot training instances (default: None). After this amount of time Amazon SageMaker will stop waiting for Spot instances to become available (default: None).')
    @argument_group(title='submit-metrics', description=None)
    @argument('--enable_sagemaker_metrics', type=bool, help='Enables SageMaker Metrics Time Series. For more information see: https://docs.aws.amazon.com/sagemaker/latest/dg/API_AlgorithmSpecification.html# SageMaker-Type-AlgorithmSpecification-EnableSageMakerMetricsTimeSeries ', nargs='?', const=True)
    @argument('--metric_definitions', type=metric_definitions, nargs='*', help='A list of dictionaries that defines the metric(s) used to evaluate the training jobs. Each dictionary contains two keys: ‘Name’ for the name of the metric, and ‘Regex’ for the regular expression used to extract the metric from the logs. This should be defined only for jobs that don’t use an Amazon algorithm.', metavar="\'Name: loss, Regex: Loss = (.*?);\'")
    @argument_group(title='list', description=None)
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sage_maker_magic/sage_maker_kernel/kernelmagics.py [280:293]:
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    @argument('--py_version', type=str, help='Python version')
    @argument('--instance_type', type=str, help='Type of EC2 instance to use for training, for example, ‘ml.c4.xlarge’.')
    @argument('--instance_count', type=int, help='Number of Amazon EC2 instances to use for training.')
    @argument('--output_path', type=str, help='S3 location for saving the training result (model artifacts and output files). If not specified, results are stored to a default bucket. If the bucket with the specific name does not exist, the estimator creates the bucket during the fit() method execution.')
    @argument('--hyperparameters', type=hyperparameters, help='Hyperparameters are passed to your script as arguments and can be retrieved with an argparse.', metavar='FOO:1,BAR:0.555,BAZ:ABC | \'FOO : 1, BAR : 0.555, BAZ : ABC\'')
    @argument('--channel_training', type=str, help='A string that represents the path to the directory that contains the input data for the training channel. ')
    @argument('--channel_testing', type=str, help='A string that represents the path to the directory that contains the input data for the testing channel. ')
    @argument_group(title='submit-spot', description=None)
    @argument('--use_spot_instances', type=bool, help='Specifies whether to use SageMaker Managed Spot instances for training. If enabled then the max_wait arg should also be set. More information: https://docs.aws.amazon.com/sagemaker/latest/dg/model-managed-spot-training.html ', nargs='?', const=True)
    @argument('--max_wait', type=int, help='Timeout in seconds waiting for spot training instances (default: None). After this amount of time Amazon SageMaker will stop waiting for Spot instances to become available (default: None).')
    @argument_group(title='submit-metrics', description=None)
    @argument('--enable_sagemaker_metrics', type=bool, help='Enables SageMaker Metrics Time Series. For more information see: https://docs.aws.amazon.com/sagemaker/latest/dg/API_AlgorithmSpecification.html# SageMaker-Type-AlgorithmSpecification-EnableSageMakerMetricsTimeSeries ', nargs='?', const=True)
    @argument('--metric_definitions', type=metric_definitions, nargs='*', help='A list of dictionaries that defines the metric(s) used to evaluate the training jobs. Each dictionary contains two keys: ‘Name’ for the name of the metric, and ‘Regex’ for the regular expression used to extract the metric from the logs. This should be defined only for jobs that don’t use an Amazon algorithm.', metavar="\'Name: loss, Regex: Loss = (.*?);\'")
    @argument_group(title='list', description=None)
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