Python os.cpu_count方法代碼示例

# 需要導入模塊: import os [as 別名]
# 或者: from os import cpu_count [as 別名]
def add_base_arguments(parser, default_help):
    import os
    from os.path import join as path_join

    home = os.environ.get('HOME')
    mono_sources_default = os.environ.get('MONO_SOURCE_ROOT', '')

    parser.add_argument('--verbose-make', action='store_true', default=False, help=default_help)
    # --jobs supports not passing an argument, in which case the 'const' is used,
    # which is the number of CPU cores on the host system.
    parser.add_argument('--jobs', '-j', nargs='?', const=str(os.cpu_count()), default='1', help=default_help)
    parser.add_argument('--configure-dir', default=path_join(home, 'mono-configs'), help=default_help)
    parser.add_argument('--install-dir', default=path_join(home, 'mono-installs'), help=default_help)

    if mono_sources_default:
        parser.add_argument('--mono-sources', default=mono_sources_default, help=default_help)
    else:
        parser.add_argument('--mono-sources', required=True)

    parser.add_argument('--mxe-prefix', default='/usr', help=default_help) 

# 需要導入模塊: import os [as 別名]
# 或者: from os import cpu_count [as 別名]
def workers(master_host, master_port, relay_socket_path, num_workers):
    # Start the relay
    master_redis_cfg = {'host': master_host, 'port': master_port}
    relay_redis_cfg = {'unix_socket_path': relay_socket_path}
    if os.fork() == 0:
        RelayClient(master_redis_cfg, relay_redis_cfg).run()
        return
    # Start the workers
    noise = SharedNoiseTable()  # Workers share the same noise
    num_workers = num_workers if num_workers else os.cpu_count()
    logging.info('Spawning {} workers'.format(num_workers))
    for _ in range(num_workers):
        if os.fork() == 0:
            run_worker(relay_redis_cfg, noise=noise)
            return
    os.wait() 

# 需要導入模塊: import os [as 別名]
# 或者: from os import cpu_count [as 別名]
def auto_detect_cpus():
    try:
        from os import sched_getaffinity  # python 3 only

        def cpu_count():
            return len(sched_getaffinity(0))

    except ImportError:
        # python 2 options
        try:
            from os import cpu_count
        except ImportError:
            from multiprocessing import cpu_count

    try:
        n = cpu_count()
    except NotImplementedError:  # pragma: no cov
        n = None  # pragma: no cov
    return n if n else 1 

# 需要導入模塊: import os [as 別名]
# 或者: from os import cpu_count [as 別名]
def enable_mining(proxy):
    cores = os.cpu_count()
    if cores > 2:
        threads_count = cores - 2
    else:
        threads_count = 1
    tries = 0
    while True:
        try:
            proxy.setgenerate(True, threads_count)
            break
        except (RPCError, HttpError) as e:
            print(e, " Waiting chain startup\n")
            time.sleep(10)
            tries += 1
        if tries > 30:
            raise ChildProcessError("Node did not start correctly, aborting\n") 

# 需要導入模塊: import os [as 別名]
# 或者: from os import cpu_count [as 別名]
def cpu_count():    # pragma: no cover
    try:
        import os
        # doesn't exist in python2, and can return None
        return os.cpu_count() or 1
    except AttributeError:
        pass

    try:
        import multiprocessing
        # doesn't have to be implemented
        return multiprocessing.cpu_count()
    except NotImplementedError:
        pass

    return 1 

# 需要導入模塊: import os [as 別名]
# 或者: from os import cpu_count [as 別名]
def cpu_count(logical=True):
    """Return the number of logical CPUs in the system (same as
    os.cpu_count() in Python 3.4).

    If *logical* is False return the number of physical cores only
    (e.g. hyper thread CPUs are excluded).

    Return None if undetermined.

    The return value is cached after first call.
    If desired cache can be cleared like this:

    >>> psutil.cpu_count.cache_clear()
    """
    if logical:
        ret = _psplatform.cpu_count_logical()
    else:
        ret = _psplatform.cpu_count_physical()
    if ret is not None and ret < 1:
        ret = None
    return ret 

# 需要導入模塊: import os [as 別名]
# 或者: from os import cpu_count [as 別名]
def workers(master_host, master_port, relay_socket_path, num_workers):
    # Start the relay
    master_redis_cfg = {'host': master_host, 'port': master_port}
    relay_redis_cfg = {'unix_socket_path': relay_socket_path}
    if os.fork() == 0:
        RelayClient(master_redis_cfg, relay_redis_cfg).run()
        return
    # Start the workers
    noise = SharedNoiseTable()  # Workers share the same noise
    num_workers = num_workers if num_workers else os.cpu_count()
    print('Spawning workers')
    logging.info('Spawning {} workers'.format(num_workers))
    for _ in range(num_workers):
        if os.fork() == 0:
            run_worker(relay_redis_cfg, noise=noise)
            return
    os.wait() 

# 需要導入模塊: import os [as 別名]
# 或者: from os import cpu_count [as 別名]
def __build_qcustomplot_library(self):
        if WINDOWS_HOST:
            qcustomplot_static = join(self.build_temp, 'release', 'qcustomplot.lib')
        else:
            qcustomplot_static = join(self.build_temp, 'libqcustomplot.a')
        if exists(qcustomplot_static):
            return

        os.makedirs(self.build_temp, exist_ok=True)
        os.chdir(self.build_temp)
        print('Make static qcustomplot library...')
        self.spawn([self.qmake, join(ROOT, 'QCustomPlot/src/qcp-staticlib.pro')])
        # AFAIK only nmake does not support -j option
        has_multiprocess = not(WINDOWS_HOST and "nmake"in self.make)
        make_cmdline = [self.make]
        if has_multiprocess:
            make_cmdline.extend(('-j', str(os.cpu_count())))
        make_cmdline.append('release')
        self.spawn(make_cmdline)

        os.chdir(ROOT)
        self.static_lib = qcustomplot_static
        # Possibly it's hack
        qcustomplot_ext = self.extensions[0]
        qcustomplot_ext.extra_objects = [qcustomplot_static] 

# 需要導入模塊: import os [as 別名]
# 或者: from os import cpu_count [as 別名]
def handle_request(self, reader, writer):
        req_host, req_port = writer.get_extra_info('peername')
        peername = f'{req_host}:{req_port}'
        self._logger.info(f'Connection from {peername}')
        data = await reader.readline()
        nw, port, worker_loop, func_pickle, data_pickle = data.split()
        num_workers = int(nw) or os.cpu_count()
        self._logger.info(
            f'Starting up {num_workers} processors for {peername}')

        # start processors that will connect back to the remote server
        asyncio.gather(
            *[asyncio.create_subprocess_exec(
                'distex_proc',
                '-H', req_host,
                '-p', port,
                '-l', worker_loop,
                '-f', func_pickle,
                '-d', data_pickle,
                stdout=None, stderr=None)
                for _ in range(num_workers)])

        writer.close() 

# 需要導入模塊: import os [as 別名]
# 或者: from os import cpu_count [as 別名]
def get_num_workers(jobs):
    """
    Parameters
    ----------
    jobs How many jobs to be paralleled. Negative or 0 means number of cpu cores left.

    Returns
    -------
    How many subprocess to be used
    """
    num_workers = jobs
    if num_workers <= 0:
        num_workers = os.cpu_count() + jobs
    if num_workers < 0 or num_workers > os.cpu_count():
        raise RuntimeError("System doesn't have so many cpu cores: {} vs {}".format(jobs, os.cpu_count()))
    return num_workers 

# 需要導入模塊: import os [as 別名]
# 或者: from os import cpu_count [as 別名]
def _add_from_urls(
        self, dataset, urls, destination, destination_names, extract, progress
    ):
        files = []
        max_workers = min(os.cpu_count() - 1, 4) or 1
        with concurrent.futures.ThreadPoolExecutor(max_workers) as executor:
            futures = {
                executor.submit(
                    self._add_from_url,
                    dataset=dataset,
                    url=url,
                    destination=destination,
                    extract=extract,
                    filename=name,
                    progress=progress
                )
                for url, name in zip(urls, destination_names)
            }

            for future in concurrent.futures.as_completed(futures):
                files.extend(future.result())

        return files 

# 需要導入模塊: import os [as 別名]
# 或者: from os import cpu_count [as 別名]
def main():
  print("Main Process PID: {}".format(multiprocessing.current_process().pid))
  myProcess = MyProcess()
  myProcess.start()
  myProcess.join()

  processes = []

  for i in range(os.cpu_count()):
    process = MyProcess()
    processes.append(process)
    process.start()

  for process in processes:
    process.join() 

# 需要導入模塊: import os [as 別名]
# 或者: from os import cpu_count [as 別名]
def cpu_count():
    num_processes = os.cpu_count()
    if num_processes is None:
        num_processes = 2
    return num_processes 

# 需要導入模塊: import os [as 別名]
# 或者: from os import cpu_count [as 別名]
def build_project(project_dir, num_processes=0):
    subprocess.check_call([NDKBUILD, '-j%d' % cpu_count(), '-C', project_dir]) 

# 需要導入模塊: import os [as 別名]
# 或者: from os import cpu_count [as 別名]
def __init__(self,
                 model: BaseRLModel = PPO2,
                 policy: BasePolicy = MlpLnLstmPolicy,
                 reward_strategy: BaseRewardStrategy = IncrementalProfit,
                 exchange_args: Dict = {},
                 **kwargs):
        self.logger = kwargs.get('logger', init_logger(__name__, show_debug=kwargs.get('show_debug', True)))

        self.Model = model
        self.Policy = policy
        self.Reward_Strategy = reward_strategy
        self.exchange_args = exchange_args
        self.tensorboard_path = kwargs.get('tensorboard_path', None)
        self.input_data_path = kwargs.get('input_data_path', 'data/input/coinbase-1h-btc-usd.csv')
        self.params_db_path = kwargs.get('params_db_path', 'sqlite:///data/params.db')

        self.date_format = kwargs.get('date_format', ProviderDateFormat.DATETIME_HOUR_24)

        self.model_verbose = kwargs.get('model_verbose', 1)
        self.n_envs = kwargs.get('n_envs', os.cpu_count())
        self.n_minibatches = kwargs.get('n_minibatches', self.n_envs)
        self.train_split_percentage = kwargs.get('train_split_percentage', 0.8)
        self.data_provider = kwargs.get('data_provider', 'static')

        self.initialize_data()
        self.initialize_optuna()

        self.logger.debug(f'Initialize RLTrader: {self.study_name}')