sage_maker_magic/sage_maker_kernel/kernelmagics.py [176:187]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    @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)
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -



sage_maker_magic/sage_maker_kernel/kernelmagics.py [230:241]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    @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)
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -