def Main(): """Main IRC loop.""" networks = CONFIG.get("networks", type="list") log.setup_logger() LOG.info("Starting %s" % version.version_string()) LOG.info("Connecting to IRC Networks: %s" % ", ".join(networks)) procs = [] for network in networks: proc = Process(network) proc.start() procs.append(proc) try: while True: time.sleep(1) for proc in procs: if not proc.is_alive(): procs.remove(proc) if not procs: LOG.info("No longer connected to any networks, shutting down") sys.exit(0) except KeyboardInterrupt: LOG.info("Caught KeyboardInterrupt, shutting down")
def Main(): """Main IRC loop.""" networks = CONFIG.get("networks", type="list") log.setup_logger() LOG.info("Starting %s" % version.version_string()) LOG.info("Connecting to IRC Networks: %s" % ", ".join(networks)) procs = [] for network in networks: proc = Process(network) proc.start() procs.append(proc) try: while True: time.sleep(1) for proc in procs: if not proc.is_alive(): procs.remove(proc) if not procs: LOG.info("No longer connected to any networks, shutting down") sys.exit(0) except KeyboardInterrupt: LOG.info("Caught KeyboardInterrupt, shutting down")
def wrapper(self, *, config, name='monitor', **kwargs): self.name = name or f'{config["algorithm"]}' self._root_dir = root_dir = config['root_dir'] self._model_name = model_name = config['model_name'] or 'baseline' self._writer = setup_tensorboard(root_dir, model_name) tf.summary.experimental.set_step(0) self._logger = setup_logger(root_dir, model_name) init_fn(self, config=config, **kwargs)
import re import json import hashlib from urllib.parse import urlparse from core.colors import green, end from core.requester import requester from core.utils import deJSON, js_extractor, handle_anchor, getVar, updateVar from core.log import setup_logger logger = setup_logger(__name__) def is_defined(o): return o is not None def scan(data, extractor, definitions, matcher=None): matcher = matcher or _simple_match detected = [] for component in definitions: extractors = definitions[component].get("extractors", None).get(extractor, None) if (not is_defined(extractors)): continue for i in extractors: match = matcher(i, data) if (match): detected.append({ "version": match, "component": component,
def __init__(self, url_list): self.URL_LIST = url_list self.RESULT = {} self.LOGGER = setup_logger(__name__)
def __init__(self): self.LOGGER = setup_logger(__name__)
def wrapper(self, *, name=None, config, models, env, **kwargs): """ Args: name: Agent's name config: configuration for agent, should be read from config.yaml models: a dict of models kwargs: optional arguments for each specific agent """ """ For the basic configuration, see config.yaml in algo/*/ """ config_attr(self, config) # name is used in stdout/stderr as the agent's identifier # while model_name is used for logging and checkpoint # e.g., all workers share the same name, but with differnt model_names self.name = name or config["algorithm"] self._model_name = self._model_name or 'baseline' self._dtype = global_policy().compute_dtype self.model = models # track models and optimizers for Checkpoint self._ckpt_models = {} for name_, model in models.items(): setattr(self, name_, model) if isinstance(model, tf.Module) or isinstance(model, tf.Variable): self._ckpt_models[name_] = model self._env_step = tf.Variable(0, trainable=False, dtype=tf.int64) self._train_step = tf.Variable(0, trainable=False, dtype=tf.int64) self.env_step = 0 self.train_step = 0 if config.get('writer', True): self._writer = setup_tensorboard(self._root_dir, self._model_name) tf.summary.experimental.set_step(0) # Agent initialization init_fn(self, env=env, **kwargs) # save optimizers for k, v in vars(self).items(): if isinstance(v, Optimizer): self._ckpt_models[k[1:]] = v logger.info(f'ckpt models: {self._ckpt_models}') self.print_construction_complete() if config.get('display_var', True): display_model_var_info(self._ckpt_models) if config.get('save_code', True): save_code(self._root_dir, self._model_name) self._ckpt, self._ckpt_path, self._ckpt_manager = \ setup_checkpoint(self._ckpt_models, self._root_dir, self._model_name, self._env_step, self._train_step) self.restore() # to save stats to files, specify `logger: True` in config.yaml self._logger = setup_logger( config.get('logger', True) and self._root_dir, self._model_name)