def handle_trial_end(self, data):
        """receive the information of trial end and generate next configuaration.

        Parameters
        ----------
        data: dict()
            it has three keys: trial_job_id, event, hyper_params
            trial_job_id: the id generated by training service
            event: the job's state
            hyper_params: the hyperparameters (a string) generated and returned by tuner
        """
        logger.debug('Tuner handle trial end, result is %s', data)

        hyper_params = json_tricks.loads(data['hyper_params'])
        s, i, _ = hyper_params['parameter_id'].split('_')
        hyper_configs = self.brackets[int(s)].inform_trial_end(int(i))

        if hyper_configs is not None:
            logger.debug(
                'bracket %s next round %s, hyper_configs: %s', s, i, hyper_configs)
            self.generated_hyper_configs = self.generated_hyper_configs + hyper_configs
            for _ in range(self.credit):
                self._request_one_trial_job()
        # Finish this bracket and generate a new bracket
        elif self.brackets[int(s)].no_more_trial:
            self.curr_s -= 1
            self.generate_new_bracket()
            for _ in range(self.credit):
                self._request_one_trial_job()
def handle_trial_end ( self , data ) : logger . debug ( 'Tuner handle trial end, result is %s' , data ) hyper_params = json_tricks . loads ( data [ 'hyper_params' ] ) s , i , _ = hyper_params [ 'parameter_id' ] . split ( '_' ) hyper_configs = self . brackets [ int ( s ) ] . inform_trial_end ( int ( i ) ) if hyper_configs is not None : logger . debug ( 'bracket %s next round %s, hyper_configs: %s' , s , i , hyper_configs ) self . generated_hyper_configs = self . generated_hyper_configs + hyper_configs for _ in range ( self . credit ) : self . _request_one_trial_job ( ) elif self . brackets [ int ( s ) ] . no_more_trial : self . curr_s -= 1 self . generate_new_bracket ( ) for _ in range ( self . credit ) : self . _request_one_trial_job ( )
receive the information of trial end and generate next configuaration.

        Parameters
        ----------
        data: dict()
            it has three keys: trial_job_id, event, hyper_params
            trial_job_id: the id generated by training service
            event: the job's state
            hyper_params: the hyperparameters (a string) generated and returned by tuner