Best Python code snippet using yandex-tank
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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