How to use the dtlpy.services.list function in dtlpy

best_checkpoint_mAP = self.compute.get_metric(model_name=best_checkpoint.name, precision_to_recall_ratio=1.)
            logger.info('best checkpoint: ' + str(best_checkpoint_mAP))
            logger.info('new checkpoint: ' + str(new_checkpoint_mAP))

            # if new checkpoint performs better switch out prediction
            if new_checkpoint_mAP > best_checkpoint_mAP:
                logger.info('new checkpoint is better')
                logger.info('uploading old best checkpoint under new name')
                self.model_obj.checkpoints.upload(checkpoint_name='checkpoint_' + check0_path.split('_')[-1][:-3],
                                                  local_path=check0_path)
                logger.info('deleting old best checkpoint')
                best_checkpoint.delete()
                logger.info('uploading new best checkpoint as check0')
                new_best_checkpoint_obj = self.model_obj.checkpoints.upload(checkpoint_name='check0',
                                                                            local_path=new_checkpoint_name)
                if 'predict' not in [s.name for s in dl.services.list()]:
                    self._maybe_launch_predict(new_best_checkpoint_obj)
                else:
                    self._update_predict_service(new_best_checkpoint_obj)
                logger.info('switched with new checkpoint')

            self.compute.save_plot_metrics(save_path=graph_file_name)

            self.project.artifacts.upload(filepath=logs_file_name,
                                          package_name='zazuml',
                                          execution_id=execution_obj.id)
            self.project.artifacts.upload(filepath=graph_file_name,
                                          package_name='zazuml',
                                          execution_id=execution_obj.id)
            logger.info('waiting ' + str(time) + ' seconds for next execution . . . .')
            sleep(time)

def _maybe_launch_predict(self, new_checkpoint_obj):
        if 'predict' not in [s.name for s in dl.services.list()]:
            logger.info('predict service doesnt exist, about to deploy prediction service')
            package_obj = dl.packages.get('zazuml')
            deploy_predict_item(package=package_obj,
                                model_id=self.model_obj.id,
                                checkpoint_id=new_checkpoint_obj.id)
            logger.info('service deployed')
            logger.info('deployed prediction service')
            create_trigger()
            logger.info('created prediction trigger')
        else:
            logger.info('predict service exists, no reason to relaunch')