def _prepare_for_training(self, job_name=None):
        """Set any values in the estimator that need to be set before training.

        Args:
            * job_name (str): Name of the training job to be created. If not specified, one is generated,
                using the base name given to the constructor if applicable.
        """
        if job_name is not None:
            self._current_job_name = job_name
        else:
            # honor supplied base_job_name or generate it
            if self.base_job_name:
                base_name = self.base_job_name
            elif isinstance(self, sagemaker.algorithm.AlgorithmEstimator):
                base_name = self.algorithm_arn.split('/')[-1]  # pylint: disable=no-member
            else:
                base_name = base_name_from_image(self.train_image())

            self._current_job_name = name_from_base(base_name)

        # if output_path was specified we use it otherwise initialize here.
        # For Local Mode with local_code=True we don't need an explicit output_path
        if self.output_path is None:
            local_code = get_config_value('local.local_code', self.sagemaker_session.config)
            if self.sagemaker_session.local_mode and local_code:
                self.output_path = ''
            else:
                self.output_path = 's3://{}/'.format(self.sagemaker_session.default_bucket())
def _prepare_for_training ( self , job_name = None ) : if job_name is not None : self . _current_job_name = job_name else : if self . base_job_name : base_name = self . base_job_name elif isinstance ( self , sagemaker . algorithm . AlgorithmEstimator ) : base_name = self . algorithm_arn . split ( '/' ) [ - 1 ] else : base_name = base_name_from_image ( self . train_image ( ) ) self . _current_job_name = name_from_base ( base_name ) if self . output_path is None : local_code = get_config_value ( 'local.local_code' , self . sagemaker_session . config ) if self . sagemaker_session . local_mode and local_code : self . output_path = '' else : self . output_path = 's3://{}/' . format ( self . sagemaker_session . default_bucket ( ) )
Set any values in the estimator that need to be set before training.

        Args:
            * job_name (str): Name of the training job to be created. If not specified, one is generated,
                using the base name given to the constructor if applicable.