def main(): print("-----", os.getenv("account_list_file"), os.getenv("scraping_id"), os.getenv("scraper_type")) config = Config() scraper = AccountScrapper(config) while True: scraper.scrap_one_day_in_each_account()
def test_load_config(self): config = Config() self.assertEqual('foo'.upper(), 'FOO')
def __init__(self): self.requester = Config()
def run_sensors(): config = Config() print("running sensors") camera = Camera(config) camera.shot_periodically()
def test_load_image(self): config = Config() image = Image(config,"Config.py") image.upload_image() self.assertEqual('foo'.upper(), 'FOO')
torch.backends.cudnn.deterministic = True dataset = args.datasetdir outputdir = args.outputdir embedding = '' if args.embedding == 'random': embedding = 'random' else: embedding = args.embedding model_name = args.model print(model_name) x = import_module('models.' + model_name) config = Config(dataset, outputdir, embedding) # reset config config.model_name = args.model config.save_path = os.path.join(outputdir, args.model + '.ckpt') config.log_path = os.path.join(outputdir, args.model + '.log') config.dropout = float(args.dropout) config.require_improvement = int(args.require_improvement) config.num_epochs = int(args.num_epochs) config.batch_size = int(args.batch_size) config.max_length = int(args.max_length) config.learning_rate = float(args.learning_rate) config.embed = int(args.embed_dim) config.bucket = int(args.bucket) config.wordNgrams = int(args.wordNgrams) config.lr_decay_rate = float(args.lr_decay_rate)