def main(): """ Main function """ log = logging.getLogger(__name__) log.info("Starting AutoTV") usage = "%prog -c FILE [--debug]" # the version of Auto TV. version = "0.1" parser = optparse.OptionParser(usage=usage, version=version) parser.add_option( "-c", "--config", dest="config_file", help="the config file(e.g. autotv.cfg)", metavar="FILE") parser.add_option( "--debug", action="store_true", dest="verbose", default=False, help="print more info") options, _ = parser.parse_args() # setup logging if options.verbose: # set logging to debug utils.setup_logging(logging.DEBUG) else: utils.setup_logging(logging.INFO) log.debug("Input arguments: {%s}".format(options)) # the config file is mandatory if not options.config_file: parser.error("Missing mandatory argument: '-c/--config'") log.info('Starting Auto TV...') # read the config cfg = Config(options.config_file) # cfg_options = cfg.cfg_options # Start TV Parsing tvp = TVParser(cfg) if options.verbose: tvp.store_debug_info() copy_tvs = tvp.get_unstored_tv_contents() tvp.transfer_tv_contents(copy_tvs) # Start Movie Parsing movieparser = MovieParser(cfg) if options.verbose: movieparser.store_debug_info() log.info("exit!")
def main(): setup_logging('REPROSERVER-RUNNER') # SQL database global SQLSession engine, SQLSession = database.connect() # AMQP tasks = TaskQueues() # Object storage global object_store object_store = get_object_store() logging.info("Ready, listening for requests") tasks.consume_run_tasks(run_request)
from flask import Flask from flask_restful import Api from common.utils import setup_logging from resources.fruit import Fruit from resources.fruits import Fruits from resources.nested_fruits import NestedFruits app = Flask(__name__) app.secret_key = "dev" app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///data.db' app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False api = Api(app) api.add_resource(Fruits, "/api/v1/fruits") api.add_resource(Fruit, "/api/v1/fruit/<int:_id>") api.add_resource(NestedFruits, "/api/v1/fruit/nest") if __name__ == "__main__": @app.before_first_request def create_tables(): db.create_all() from db import db setup_logging() db.init_app(app) app.run(port=5000, host="0.0.0.0")
if _args.aicrowd_challenge: from aicrowd import utils_pytorch as pyu, aicrowd_helpers # Export the representation extractor path_to_saved = pyu.export_model(pyu.RepresentationExtractor(model.model.encoder, 'mean'), input_shape=(1, model.num_channels, model.image_size, model.image_size)) logging.info(f'A copy of the model saved in {path_to_saved}') if on_aicrowd_server: # AICrowd will handle the evaluation aicrowd_helpers.register_progress(1.0) aicrowd_helpers.submit() else: # Run evaluation locally # The local_evaluation is implemented by aicrowd in the global namespace, so importing it suffices. # todo: implement a modular version of local_evaluation # noinspection PyUnresolvedReferences from aicrowd import local_evaluation if __name__ == "__main__": _args = get_args(sys.argv[1:]) setup_logging(_args.verbose) initialize_seeds(_args.seed) # set the environment variables for dataset directory and name, and check if the root dataset directory exists. set_environment_variables(_args.dset_dir, _args.dset_name) assert os.path.exists(os.environ.get('DISENTANGLEMENT_LIB_DATA', '')), \ 'Root dataset directory does not exist at: \"{}\"'.format(_args.dset_dir) main(_args)
import os import sys sys.path.insert(0, os.path.dirname(os.path.dirname(__file__))) from common.utils import setup_logging # noqa setup_logging('REPROSERVER-WEB') from web.main import main # noqa main()