def method1_subroutine(model, layer_size, mode): result = list() query = 'python3 /home/changseok/layerwise_quantization/slave/main.py -b 1 -m ' + model for i in reversed(range(23)): if 0 in mode: query += ' -wb ' + init_parameter(i + 1, layer_size) if 1 in mode: query += ' -fb ' + init_parameter(i + 1, layer_size) if 2 in mode: query += ' -pt ' + init_parameter(i + 1, layer_size) print('running command is : ', query) result.append(run(query)) print(result[-1]) return result
def method2_subroutine(model, layer_size, mode, order): result = list() query = 'python3 /home/changseok/layerwise_quantization/slave/main.py -b 1 -m ' + model processing_factor = [0 for i in range(layer_size)] for i in order: for j in reversed(range(23)): processing_factor[i] = j + 1 if 0 in mode: query += ' -wb ' + init_parameter(processing_factor, layer_size) if 1 in mode: query += ' -fb ' + init_parameter(processing_factor, layer_size) if 2 in mode: query += ' -pt ' + init_parameter(processing_factor, layer_size) print('running command is : ', query) result.append(run(query)) print(result[-1]) return result
start_time = time.time() # time the beginning of the simulation configs_list = [] number_of_runs = 25 #define the number of replications to run each configuration bounds_list = [[1, 3], [2, 4], [3, 5], [4, 6]] for bounds in bounds_list: config = { "number_of_agents": 1000, "rate_of_infection_per_contact": 0.03, "recovery_rate": 0.08, "incubation_period": 3, "number_of_hubs": 10, "degree_of_homophily": 0.89, "hub_densities": [8, 8, 8, 8, 8, 12, 12, 12, 12, 12], "hub_sizes": [80, 80, 80, 80, 80, 120, 120, 120, 120, 120], "infection_costs": [bounds] * 10, "infection_cost_key": "uniform", "starting_vaccination_rate": 0.15, "number_of_seasons": 25, "vax_choice_key": "seasonal_learning", "vax_choice_params": { "discount_factor": 1 }, "log_time_period_data": False } configs_list.append(config) if __name__ == '__main__': # this line of code is needed to make concurrent.futures work on Windows run(configs_list, number_of_runs)