How to use get_progress_logger method in yandex-tank

Best Python code snippet using yandex-tank

benchmark_pipeline_interval_trenscend.py

Source:benchmark_pipeline_interval_trenscend.py

...28            self.list_of_parameter_to_modified_value_dictionary = self.generate_parameter_to_modified_value_dictionary_for_each_pipeline(base_nameserver_port=self.base_nameserver_port, max_tuning_interval=self.max_tuning_interval, number_of_repeat_for_each_pipeline=self.number_of_repeat_for_each_pipeline)29            if self.run_multiprocessing:30                self.start_multiprocessing()31    def do_a_run_of_pipeline(self, parameter_to_modified_value_dictionary, file_path_for_saving_result, do_multiprocessing_dry_run=False):32        progress_logger = get_progress_logger()33        progress_logger.info('start doing one run of pipeline: ' + file_path_for_saving_result)34        progress_logger.info('the param dict for ' + file_path_for_saving_result + ' is: ' + str(parameter_to_modified_value_dictionary))35        print('start running pipeline: ')36        print(parameter_to_modified_value_dictionary)37        if not do_multiprocessing_dry_run:38            pipeLineTuningIntervalTranscend = PipeLineTuningIntervalTranscend(**parameter_to_modified_value_dictionary)39            AnalyzerPipeLineAutoForecastVA.save_analyzer_result_in_a_memory_efficient_way(pipeLineTuningIntervalTranscend.pipeLineAutoForecastVA, file_name=file_path_for_saving_result)40        progress_logger.info('end doing one run of pipeline: ' + file_path_for_saving_result)41        print('completed: ' + file_path_for_saving_result)42        print(parameter_to_modified_value_dictionary)43    def start_multiprocessing(self):44        progress_logger = get_progress_logger()45        progress_logger.info('starting multiprocessing')46        multiprocess_manager = ProcessLoom(max_runner_cap=self.max_runner_cap)47        for parameter_to_modified_value_dictionary in self.list_of_parameter_to_modified_value_dictionary:48            md5_code = ComputeCharacterizationCodeAndMd5Code.get_class_MD5_code(target_class=PipeLineTuningIntervalTranscend, parameter_to_modified_value_dictionary=parameter_to_modified_value_dictionary, store=True, md5_to_parameter_to_value_mapping_file_path=self.md5_to_parameter_to_value_mapping_file_path)49            file_path_for_saving_result = 'results/'+md5_code50            multiprocess_manager.add_function(self.do_a_run_of_pipeline, kwargs={'parameter_to_modified_value_dictionary': parameter_to_modified_value_dictionary, 'file_path_for_saving_result': file_path_for_saving_result, 'do_multiprocessing_dry_run': self.do_multiprocessing_dry_run}, key=md5_code)51        self.output = multiprocess_manager.execute()52    @staticmethod53    def generate_parameter_to_modified_value_dictionary_for_each_pipeline(base_nameserver_port=65100, max_tuning_interval=999999999, number_of_repeat_for_each_pipeline=5):54        '''Generate a list of kwargs, one for each pipeline'''55        # tuning interval56        list_of_tuning_interval_dict = []57        for interval in [5]:58            parameter_to_modified_value_dictionary = {}...

benchmark_pipeline_autova.py

Source:benchmark_pipeline_autova.py

...27        if not self.lazy:28            if self.run_multiprocessing:29                self.start_multiprocessing()30    def do_a_run_of_pipeline(self, parameter_to_modified_value_dictionary, file_path_for_saving_result, do_multiprocessing_dry_run=False):31        progress_logger = get_progress_logger(logger_name=self.logger_name, log_output_file_path=os.path.join(self.folder_name_to_save_results, self.log_output_file_name))32        progress_logger.info('start doing one run of pipeline: ' + file_path_for_saving_result)33        progress_logger.info('the param dict for ' + file_path_for_saving_result + ' is: ' + str(parameter_to_modified_value_dictionary))34        print('start running pipeline: ')35        print(parameter_to_modified_value_dictionary)36        timerDefault = TimerDefault(time_unit='hour')37        timerDefault.start()38        if not do_multiprocessing_dry_run:39            pipeLineTuningInterval = PipeLineTuningInterval(**parameter_to_modified_value_dictionary)40            AnalyzerPipeLineAutoForecastVA.save_analyzer_result_in_a_memory_efficient_way(pipeLineTuningInterval.pipeLineAutoForecastVA, file_name=file_path_for_saving_result)41        timerDefault.end()42        progress_logger.info('end doing one run of pipeline: ' + file_path_for_saving_result + ' ' + 'duration(hours): ' + str(timerDefault.get_duration()))43        print('completed: ' + file_path_for_saving_result)44        print(parameter_to_modified_value_dictionary)45    def start_multiprocessing(self):46        # progress_logger = get_progress_logger(logger_name=self.logger_name, log_output_file_path=os.path.join(self.folder_name_to_save_results, self.log_output_file_name))47        # progress_logger.info('starting multiprocessing')  # cannot do logging untill creates the folder48        multiprocess_manager = ProcessLoom(max_runner_cap=self.max_runner_cap)49        for parameter_to_modified_value_dictionary in self.list_of_input_dictionary:50            md5_code = ComputeCharacterizationCodeAndMd5Code.get_class_MD5_code(target_class=PipeLineTuningInterval, parameter_to_modified_value_dictionary=parameter_to_modified_value_dictionary, store=True, md5_to_parameter_to_value_mapping_file_path=os.path.join(self.folder_name_to_save_results, self.md5_to_parameter_to_value_mapping_file_name))51            file_path_for_saving_result = self.folder_name_to_save_results + '/' + md5_code + '.dat'52            multiprocess_manager.add_function(self.do_a_run_of_pipeline, kwargs={'parameter_to_modified_value_dictionary': parameter_to_modified_value_dictionary, 'file_path_for_saving_result': file_path_for_saving_result, 'do_multiprocessing_dry_run': self.do_multiprocessing_dry_run}, key=md5_code)...

queue_deduplicate.py

Source:queue_deduplicate.py

...27                ),28            )29        original_size, new_size = backend.deduplicate(30            queue,31            progress_logger=self.get_progress_logger(),32        )33        if original_size == new_size:34            self.stdout.write(35                "No duplicate jobs detected (queue length remains {})".format(36                    original_size,37                ),38            )39        else:40            self.stdout.write(41                "Deduplication reduced the queue from {} jobs to {} job(s)".format(42                    original_size,43                    new_size,44                ),45            )46    def get_progress_logger(self) -> ProgressLogger:47        try:48            import tqdm49            progress = tqdm.tqdm50        except ImportError:51            def progress(iterable: T) -> T:52                return iterable...

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