def run(): experiments_raw = json.loads(args.hp) hp_dicts = [ hp for x in experiments_raw for hp in xpm.get_all_hp_combinations(x) ][args.start:args.end] if args.reverse_order: hp_dicts = hp_dicts[::-1] experiments = [xpm.Experiment(hyperparameters=hp) for hp in hp_dicts] print("Running {} Experiments..\n".format(len(experiments))) for xp_count, experiment in enumerate(experiments): run_experiment(experiment, xp_count, len(experiments))
def run(): with open('federated_learning.json') as data_file: experiments_raw = json.load(data_file)[args.schedule] hp_dicts = [ hp for x in experiments_raw for hp in xpm.get_all_hp_combinations(x) ][args.start:args.end] if args.reverse_order: hp_dicts = hp_dicts[::-1] experiments = [xpm.Experiment(hyperparameters=hp) for hp in hp_dicts] print("Running {} Experiments..\n".format(len(experiments))) for xp_count, experiment in enumerate(experiments): run_experiment(experiment, xp_count, len(experiments))
parser.add_argument("--schedule", default="main", type=str) parser.add_argument("--start", default=0, type=int) parser.add_argument("--end", default=None, type=int) parser.add_argument("--reverse_order", default=False, type=bool) print("Torch Version: ", torch.__version__) device = 'cuda' if torch.cuda.is_available() else 'cpu' args = parser.parse_args() # Load the Hyperparameters of all Experiments to be performed and set up the Experiments with open('federated_learning.json') as data_file: experiments_raw = json.load(data_file)[args.schedule] hp_dicts = [ hp for x in experiments_raw for hp in xpm.get_all_hp_combinations(x) ][args.start:args.end] if args.reverse_order: hp_dicts = hp_dicts[::-1] experiments = [xpm.Experiment(hyperparameters=hp) for hp in hp_dicts] def run_experiments(experiments): print("Running {} Experiments..\n".format(len(experiments))) for xp_count, xp in enumerate(experiments): hp = dhp.get_hp(xp.hyperparameters) xp.prepare(hp) print(xp) # Load the Data and split it among the Clients client_loaders, train_loader, test_loader, stats = data_utils.get_data_loaders(