Python multiprocessing.log_to_stderr方法代碼示例

# 需要導入模塊: import multiprocessing [as 別名]
# 或者: from multiprocessing import log_to_stderr [as 別名]
def start_service(self):
        """
        start speaker training service.
        """
        # prevent signal from propagating to child process
        handler = signal.getsignal(signal.SIGINT)
        signal.signal(signal.SIGINT, signal.SIG_IGN)

        if self.debug:
            self.sprecog.debug = True
            mp.log_to_stderr(logging.DEBUG)

        self.sprecog.speaker_name = self.speaker_name
        self.proc = mp.Process(name="watchdog", target=self.__run,
                               args=(self.event,))
        self.proc.setDaemon = False
        self.proc.start()
        # restore signal
        signal.signal(signal.SIGINT, handler) 

# 需要導入模塊: import multiprocessing [as 別名]
# 或者: from multiprocessing import log_to_stderr [as 別名]
def main():
    """Starts several processes

    This must be kept to the bare minimum
    """
    multiprocessing.log_to_stderr()
    logger = multiprocessing.get_logger()
    logger.setLevel(logging.INFO)

    jobs = []

    try:
        bfs = BrundleFuzzServer()
        jobs.append(bfs)
        bfs.start()

        for j in jobs:
            j.join()

    except KeyboardInterrupt:
        pass 

# 需要導入模塊: import multiprocessing [as 別名]
# 或者: from multiprocessing import log_to_stderr [as 別名]
def __apply_func_with_worker_stream(args):
    """
    Call func, using ``queue`` to redirect stdout and stderr, with a tuple of args because multiprocessing.Pool.map
    only accepts one argument for the function.

    This function is called _inside_ a separate process.
    """

    # set up logging
    logger = multiprocessing.log_to_stderr()
    logger.setLevel(logging.WARNING)
    from cea import suppres_3rd_party_debug_loggers
    suppres_3rd_party_debug_loggers()

    # unpack the arguments
    func, queue, on_complete, i_queue, n, args = args[0], args[1], args[2], args[3], args[4], args[5:]

    # set up printing to stderr and stdout to go through the queue
    sys.stdout = QueueWorkerStream('stdout', queue)
    sys.stderr = QueueWorkerStream('stderr', queue)

    # CALL
    result = func(*args)

    if on_complete:
        on_complete(i_queue.get(), n, args, result)

    return result 

# 需要導入模塊: import multiprocessing [as 別名]
# 或者: from multiprocessing import log_to_stderr [as 別名]
def init_filler(dummynet, filler_cbs, in_train_mode):  # pragma: no cover
    """Initialize a filler thread."""
    # pylint: disable=global-variable-undefined, global-variable-not-assigned
    global net, cbs, train_mode, initialized, logger
    logger = _log_to_stderr(_logging.WARN)
    logger.debug("Initializing filler. Train mode: %s.", in_train_mode)
    net = dummynet
    cbs = filler_cbs
    train_mode = in_train_mode
    initialized = False 

# 需要導入模塊: import multiprocessing [as 別名]
# 或者: from multiprocessing import log_to_stderr [as 別名]
def run(self):
        '''
        Starts running the Gaussian process learner. When the new parameters event is triggered, reads the cost information provided and updates the Gaussian process with the information. Then searches the Gaussian process for new optimal parameters to test based on the biased cost. Parameters to test next are put on the output parameters queue.
        '''
        #logging to the main log file from a process (as apposed to a thread) in cpython is currently buggy on windows and/or python 2.7
        #current solution is to only log to the console for warning and above from a process
        self.log = mp.log_to_stderr(logging.WARNING)
        
        try:
            while not self.end_event.is_set():
                #self.log.debug('Learner waiting for new params event')
                self.save_archive()
                self.wait_for_new_params_event()
                #self.log.debug('Gaussian process learner reading costs')
                self.get_params_and_costs()
                self.fit_gaussian_process()
                for _ in range(self.generation_num):
                    self.log.debug('Gaussian process learner generating parameter:'+ str(self.params_count+1))
                    next_params = self.find_next_parameters()
                    self.params_out_queue.put(next_params)
                    if self.end_event.is_set():
                        raise LearnerInterrupt()
        except LearnerInterrupt:
            pass
            
        end_dict = {}
        if self.predict_global_minima_at_end:
            self.get_params_and_costs()
            self.fit_gaussian_process()
            self.find_global_minima()
            end_dict.update({'predicted_best_parameters':self.predicted_best_parameters,
                             'predicted_best_cost':self.predicted_best_cost,
                             'predicted_best_uncertainty':self.predicted_best_uncertainty})
        self.params_out_queue.put(end_dict)
        self._shut_down()
        self.log.debug('Ended Gaussian Process Learner') 

# 需要導入模塊: import multiprocessing [as 別名]
# 或者: from multiprocessing import log_to_stderr [as 別名]
def __init__(self, blockchain, mempool):
        mp.log_to_stderr()
        mp_logger = mp.get_logger()
        mp_logger.setLevel(logging.DEBUG)
        self.blockchain = blockchain
        self.mempool = mempool 

# 需要導入模塊: import multiprocessing [as 別名]
# 或者: from multiprocessing import log_to_stderr [as 別名]
def __init__(self, peers, api_client, blockchain, mempool, validator):
        super(FullNode, self).__init__(peers, api_client)
        mp.log_to_stderr()
        mp_logger = mp.get_logger()
        mp_logger.setLevel(logging.DEBUG)
        self.app = Bottle()
        self.app.merge(public_app)
        self.app.merge(permissioned_app)
        self.blockchain = blockchain
        self.mempool = mempool
        self.validator = validator 

# 需要導入模塊: import multiprocessing [as 別名]
# 或者: from multiprocessing import log_to_stderr [as 別名]
def _consume_and_write(queue, path, store, sharr):
    """Insert :var:`row` received from the queue into the shared memory array
    at the current index and increment. Empty rows are always written to disk
    (keeps stores 'call-index-aligned').
    """
    proc = mp.current_process()
    slog = utils.get_logger(proc.name)
    log = mp.log_to_stderr(slog.getEffectiveLevel())
    log.debug("starting storage writer '{}'".format(proc.name))
    log.info("storage path is '{}'".format(path))
    log.debug("sharr is '{}'".format(sharr))

    # set up a new store instance for writing
    with store.writer(path, dtypes=store.dtypes) as store:
        # notify parent that file has been created
        queue.put(path)

        # handle no pandas/np case
        buff = store if sharr is None else sharr
        bufftype = type(buff)
        log.debug('buffer type is {}'.format(bufftype))

        for row in iter(queue.get, Terminate):  # consume and process
            now = time.time()

            # write frame to disk on buffer fill
            if sharr and sharr.is_full():
                log.debug('writing to {} storage...'.format(store.ext))
                try:
                    # push a data frame
                    store.put(pd.DataFrame.from_records(buff.read()))
                except ValueError:
                    log.error(traceback.format_exc())
                log.debug("storage put took '{}'".format(time.time() - now))

            try:  # push to ring buffer (or store if no pd)
                buff.put(row)
                log.debug("{} insert took '{}'".format(
                          bufftype, time.time() - now))
            except ValueError:
                log.error(traceback.format_exc())

    log.debug("terminating frame writer '{}'".format(proc.name))