def __init__(self, entry_point, source_dir=None, hyperparameters=None, py_version='py2',
                 framework_version=None, image_name=None, distributions=None, **kwargs):
        """
        This ``Estimator`` executes an MXNet script in a managed MXNet execution environment, within a SageMaker
        Training Job. The managed MXNet environment is an Amazon-built Docker container that executes functions
        defined in the supplied ``entry_point`` Python script.

        Training is started by calling :meth:`~sagemaker.amazon.estimator.Framework.fit` on this Estimator.
        After training is complete, calling :meth:`~sagemaker.amazon.estimator.Framework.deploy` creates a
        hosted SageMaker endpoint and returns an :class:`~sagemaker.amazon.mxnet.model.MXNetPredictor` instance
        that can be used to perform inference against the hosted model.

        Technical documentation on preparing MXNet scripts for SageMaker training and using the MXNet Estimator is
        available on the project home-page: https://github.com/aws/sagemaker-python-sdk

        Args:
            entry_point (str): Path (absolute or relative) to the Python source file which should be executed
                as the entry point to training. This should be compatible with either Python 2.7 or Python 3.5.
            source_dir (str): Path (absolute or relative) to a directory with any other training
                source code dependencies aside from tne entry point file (default: None). Structure within this
                directory are preserved when training on Amazon SageMaker.
            hyperparameters (dict): Hyperparameters that will be used for training (default: None).
                The hyperparameters are made accessible as a dict[str, str] to the training code on SageMaker.
                For convenience, this accepts other types for keys and values, but ``str()`` will be called
                to convert them before training.
            py_version (str): Python version you want to use for executing your model training code (default: 'py2').
                One of 'py2' or 'py3'.
            framework_version (str): MXNet version you want to use for executing your model training code.
                List of supported versions https://github.com/aws/sagemaker-python-sdk#mxnet-sagemaker-estimators.
                If not specified, this will default to 1.2.1.
            image_name (str): If specified, the estimator will use this image for training and hosting, instead of
                selecting the appropriate SageMaker official image based on framework_version and py_version. It can
                be an ECR url or dockerhub image and tag.

                Examples:
                    123.dkr.ecr.us-west-2.amazonaws.com/my-custom-image:1.0
                    custom-image:latest.
             distributions (dict): A dictionary with information on how to run distributed training
                (default: None).
            **kwargs: Additional kwargs passed to the :class:`~sagemaker.estimator.Framework` constructor.
        """
        if framework_version is None:
            logger.warning(empty_framework_version_warning(MXNET_VERSION, self.LATEST_VERSION))
        self.framework_version = framework_version or MXNET_VERSION

        super(MXNet, self).__init__(entry_point, source_dir, hyperparameters,
                                    image_name=image_name, **kwargs)
        self.py_version = py_version
        self._configure_distribution(distributions)
def __init__ ( self , entry_point , source_dir = None , hyperparameters = None , py_version = 'py2' , framework_version = None , image_name = None , distributions = None , ** kwargs ) : if framework_version is None : logger . warning ( empty_framework_version_warning ( MXNET_VERSION , self . LATEST_VERSION ) ) self . framework_version = framework_version or MXNET_VERSION super ( MXNet , self ) . __init__ ( entry_point , source_dir , hyperparameters , image_name = image_name , ** kwargs ) self . py_version = py_version self . _configure_distribution ( distributions )
This ``Estimator`` executes an MXNet script in a managed MXNet execution environment, within a SageMaker
        Training Job. The managed MXNet environment is an Amazon-built Docker container that executes functions
        defined in the supplied ``entry_point`` Python script.

        Training is started by calling :meth:`~sagemaker.amazon.estimator.Framework.fit` on this Estimator.
        After training is complete, calling :meth:`~sagemaker.amazon.estimator.Framework.deploy` creates a
        hosted SageMaker endpoint and returns an :class:`~sagemaker.amazon.mxnet.model.MXNetPredictor` instance
        that can be used to perform inference against the hosted model.

        Technical documentation on preparing MXNet scripts for SageMaker training and using the MXNet Estimator is
        available on the project home-page: https://github.com/aws/sagemaker-python-sdk

        Args:
            entry_point (str): Path (absolute or relative) to the Python source file which should be executed
                as the entry point to training. This should be compatible with either Python 2.7 or Python 3.5.
            source_dir (str): Path (absolute or relative) to a directory with any other training
                source code dependencies aside from tne entry point file (default: None). Structure within this
                directory are preserved when training on Amazon SageMaker.
            hyperparameters (dict): Hyperparameters that will be used for training (default: None).
                The hyperparameters are made accessible as a dict[str, str] to the training code on SageMaker.
                For convenience, this accepts other types for keys and values, but ``str()`` will be called
                to convert them before training.
            py_version (str): Python version you want to use for executing your model training code (default: 'py2').
                One of 'py2' or 'py3'.
            framework_version (str): MXNet version you want to use for executing your model training code.
                List of supported versions https://github.com/aws/sagemaker-python-sdk#mxnet-sagemaker-estimators.
                If not specified, this will default to 1.2.1.
            image_name (str): If specified, the estimator will use this image for training and hosting, instead of
                selecting the appropriate SageMaker official image based on framework_version and py_version. It can
                be an ECR url or dockerhub image and tag.

                Examples:
                    123.dkr.ecr.us-west-2.amazonaws.com/my-custom-image:1.0
                    custom-image:latest.
             distributions (dict): A dictionary with information on how to run distributed training
                (default: None).
            **kwargs: Additional kwargs passed to the :class:`~sagemaker.estimator.Framework` constructor.