Python multiprocessing.SimpleQueue方法代码示例

# 需要导入模块: from torch import multiprocessing [as 别名]
# 或者: from torch.multiprocessing import SimpleQueue [as 别名]
def __init__(self, config):
        self.config = config
        self.neat_config = self.load_neat_config()
        self.neat_config.pop_size = config.pop_size
        self.task_q = mp.SimpleQueue()
        self.result_q = mp.SimpleQueue()
        self.total_steps = 0
        stop = mp.Value('i', False)
        stats = SharedStats(config.state_dim)
        normalizers = [StaticNormalizer(config.state_dim) for _ in range(config.num_workers)]
        for normalizer in normalizers:
            normalizer.offline_stats.load(stats)
        workers = [Worker(id, normalizers[id], self.task_q, self.result_q, stop,
                          config, self.neat_config) for id in range(config.num_workers)]
        for w in workers: w.start()
        self.normalizers = normalizers
        self.stats = stats
        self.stop = stop 

# 需要导入模块: from torch import multiprocessing [as 别名]
# 或者: from torch.multiprocessing import SimpleQueue [as 别名]
def __init__(self, loader):
        self.dataset = loader.dataset
        self.collate_fn = loader.collate_fn
        self.batch_sampler = loader.batch_sampler
        self.num_workers = loader.num_workers
        self.pin_memory = loader.pin_memory
        self.done_event = threading.Event()

        self.sample_iter = iter(self.batch_sampler)

        if self.num_workers > 0:
            self.index_queue = multiprocessing.SimpleQueue()
            self.data_queue = multiprocessing.SimpleQueue()
            self.batches_outstanding = 0
            self.shutdown = False
            self.send_idx = 0
            self.rcvd_idx = 0
            self.reorder_dict = {}

            self.workers = [
                multiprocessing.Process(
                    target=_worker_loop,
                    args=(self.dataset, self.index_queue, self.data_queue, self.collate_fn, np.random.randint(0, 4294967296, dtype='uint32')))
                for _ in range(self.num_workers)]

            for w in self.workers:
                w.daemon = True  # ensure that the worker exits on process exit
                w.start()

            if self.pin_memory:
                in_data = self.data_queue
                self.data_queue = queue.Queue()
                self.pin_thread = threading.Thread(
                    target=_pin_memory_loop,
                    args=(in_data, self.data_queue, self.done_event))
                self.pin_thread.daemon = True
                self.pin_thread.start()

            # prime the prefetch loop
            for _ in range(2 * self.num_workers):
                self._put_indices() 

# 需要导入模块: from torch import multiprocessing [as 别名]
# 或者: from torch.multiprocessing import SimpleQueue [as 别名]
def get_batch(
    flags,
    free_queue: mp.SimpleQueue,
    full_queue: mp.SimpleQueue,
    buffers: Buffers,
    initial_agent_state_buffers,
    timings,
    lock=threading.Lock(),
):
    with lock:
        timings.time("lock")
        indices = [full_queue.get() for _ in range(flags.batch_size)]
        timings.time("dequeue")
    batch = {
        key: torch.stack([buffers[key][m] for m in indices], dim=1) for key in buffers
    }
    initial_agent_state = (
        torch.cat(ts, dim=1)
        for ts in zip(*[initial_agent_state_buffers[m] for m in indices])
    )
    timings.time("batch")
    for m in indices:
        free_queue.put(m)
    timings.time("enqueue")
    batch = {k: t.to(device=flags.device, non_blocking=True) for k, t in batch.items()}
    initial_agent_state = tuple(
        t.to(device=flags.device, non_blocking=True) for t in initial_agent_state
    )
    timings.time("device")
    return batch, initial_agent_state 

# 需要导入模块: from torch import multiprocessing [as 别名]
# 或者: from torch.multiprocessing import SimpleQueue [as 别名]
def __init__(self, loader):
        self.dataset = loader.dataset
        self.collate_fn = loader.collate_fn
        self.batch_sampler = loader.batch_sampler
        self.num_workers = loader.num_workers
        self.pin_memory = loader.pin_memory
        self.done_event = threading.Event()

        self.sample_iter = iter(self.batch_sampler)

        if self.num_workers > 0:
            self.index_queue = multiprocessing.SimpleQueue()
            self.data_queue = multiprocessing.SimpleQueue()
            self.batches_outstanding = 0
            self.shutdown = False
            self.send_idx = 0
            self.rcvd_idx = 0
            self.reorder_dict = {}

            self.workers = [
                multiprocessing.Process(
                    target=_worker_loop,
                    args=(self.dataset, self.index_queue, self.data_queue, self.collate_fn))
                for _ in range(self.num_workers)]

            for w in self.workers:
                w.daemon = True  # ensure that the worker exits on process exit
                w.start()

            if self.pin_memory:
                in_data = self.data_queue
                self.data_queue = queue.Queue()
                self.pin_thread = threading.Thread(
                    target=_pin_memory_loop,
                    args=(in_data, self.data_queue, self.done_event))
                self.pin_thread.daemon = True
                self.pin_thread.start()

            # prime the prefetch loop
            for _ in range(2 * self.num_workers):
                self._put_indices() 

# 需要导入模块: from torch import multiprocessing [as 别名]
# 或者: from torch.multiprocessing import SimpleQueue [as 别名]
def __init__(self, threadsafe=False, shared=None):
        self._threadsafe = threadsafe
        if self._threadsafe and shared is None:
            # Threadsafe metrics tracking works by keeping a queue that workers can
            # push updates to. the main worker works through the queue at report
            # time. We could add some buffering to improve performance, but we
            # are deprioritizing hogwild performance at this time.
            self._buffer = None
            self._queue = multiprocessing.SimpleQueue()
            self._worker = False
            self._data = {}
        elif shared and 'queue' in shared:
            # This is a clone, in threadsafe mode
            self._buffer = {}
            self._queue = shared['queue']
            self._worker = True
            self._data = None
        elif shared and 'data' in shared:
            # This is a clone, in non-threadsafe mode
            self._buffer = None
            self._queue = None
            self._worker = False
            self._data = shared['data']
        else:
            # The original in non-threadsafe mode
            self._buffer = None
            self._queue = None
            self._worker = False
            self._data = {} 

# 需要导入模块: from torch import multiprocessing [as 别名]
# 或者: from torch.multiprocessing import SimpleQueue [as 别名]
def __init__(self, loader):
        self.dataset = loader.dataset
        self.collate_fn = loader.collate_fn
        self.batch_sampler = loader.batch_sampler
        self.num_workers = loader.num_workers
        self.pin_memory = loader.pin_memory and torch.cuda.is_available()
        self.timeout = loader.timeout
        self.done_event = threading.Event()

        self.sample_iter = iter(self.batch_sampler)

        if self.num_workers > 0:
            self.worker_init_fn = loader.worker_init_fn
            self.index_queue = multiprocessing.SimpleQueue()
            self.worker_result_queue = multiprocessing.SimpleQueue()
            self.batches_outstanding = 0
            self.worker_pids_set = False
            self.shutdown = False
            self.send_idx = 0
            self.rcvd_idx = 0
            self.reorder_dict = {}

            base_seed = torch.LongTensor(1).random_(0, 2**31-1)[0]
            self.workers = [
                multiprocessing.Process(
                    target=_worker_loop,
                    args=(self.dataset, self.index_queue, self.worker_result_queue, self.collate_fn,
                          base_seed + i, self.worker_init_fn, i))
                for i in range(self.num_workers)]

            if self.pin_memory or self.timeout > 0:
                self.data_queue = queue.Queue()
                if self.pin_memory:
                    maybe_device_id = torch.cuda.current_device()
                else:
                    # do not initialize cuda context if not necessary
                    maybe_device_id = None
                self.worker_manager_thread = threading.Thread(
                    target=_worker_manager_loop,
                    args=(self.worker_result_queue, self.data_queue, self.done_event, self.pin_memory,
                          maybe_device_id))
                self.worker_manager_thread.daemon = True
                self.worker_manager_thread.start()
            else:
                self.data_queue = self.worker_result_queue

            for w in self.workers:
                w.daemon = True  # ensure that the worker exits on process exit
                w.start()

            _update_worker_pids(id(self), tuple(w.pid for w in self.workers))
            _set_SIGCHLD_handler()
            self.worker_pids_set = True

            # prime the prefetch loop
            for _ in range(2 * self.num_workers):
                self._put_indices() 

# 需要导入模块: from torch import multiprocessing [as 别名]
# 或者: from torch.multiprocessing import SimpleQueue [as 别名]
def __init__(self, loader):
        self.dataset = loader.dataset
        self.collate_fn = loader.collate_fn
        self.batch_sampler = loader.batch_sampler
        self.num_workers = loader.num_workers
        self.pin_memory = loader.pin_memory and torch.cuda.is_available()
        self.timeout = loader.timeout
        self.done_event = threading.Event()

        self.sample_iter = iter(self.batch_sampler)

        if self.num_workers > 0:
            self.worker_init_fn = loader.worker_init_fn
            self.index_queue = multiprocessing.SimpleQueue()
            self.worker_result_queue = multiprocessing.SimpleQueue()
            self.batches_outstanding = 0
            self.worker_pids_set = False
            self.shutdown = False
            self.send_idx = 0
            self.rcvd_idx = 0
            self.reorder_dict = {}

            base_seed = torch.LongTensor(1).random_(0, 2**31-1)[0]
            self.workers = [
                multiprocessing.Process(
                    target=_worker_loop,
                    args=(self.dataset, self.index_queue, self.worker_result_queue, self.collate_fn,
                          base_seed + i, self.worker_init_fn, i))
                for i in range(self.num_workers)]

            if self.pin_memory or self.timeout > 0:
                self.data_queue = queue.Queue()
                if self.pin_memory:
                    maybe_device_id = torch.cuda.current_device()
                else:
                    # do not initialize cuda context if not necessary
                    maybe_device_id = None
                self.worker_manager_thread = threading.Thread(
                    target=_worker_manager_loop,
                    args=(self.worker_result_queue, self.data_queue, self.done_event, self.pin_memory,
                          maybe_device_id))
                self.worker_manager_thread.daemon = True
                self.worker_manager_thread.start()
            else:
                self.data_queue = self.worker_result_queue

            for w in self.workers:
                w.daemon = True  # ensure that the worker exits on process exit
                w.start()

            _set_worker_pids(id(self), tuple(w.pid for w in self.workers))
            _set_SIGCHLD_handler()
            self.worker_pids_set = True

            # prime the prefetch loop
            for _ in range(2 * self.num_workers):
                self._put_indices() 

# 需要导入模块: from torch import multiprocessing [as 别名]
# 或者: from torch.multiprocessing import SimpleQueue [as 别名]
def __init__(self, loader):
        self.dataset = loader.dataset
        self.collate_fn = loader.collate_fn
        self.batch_sampler = loader.batch_sampler
        self.num_workers = loader.num_workers
        self.pin_memory = loader.pin_memory
        self.use_thread = loader.use_thread
        self.pin_done_event = threading.Event()

        self.sample_iter = iter(self.batch_sampler)

        if self.num_workers > 0:
            self.index_queue = queue.Queue() if self.use_thread else multiprocessing.SimpleQueue()
            self.data_queue = queue.Queue() if self.use_thread else multiprocessing.SimpleQueue()
            self.batches_outstanding = 0
            self.shutdown = False
            self.send_idx = 0
            self.rcvd_idx = 0
            self.reorder_dict = {}

            if self.use_thread:
                self.workers = [
                    threading.Thread(
                        target=_worker_thread_loop,
                        args=(self.dataset, self.index_queue, self.data_queue, self.collate_fn))
                    for _ in range(self.num_workers)]
            else:
                self.workers = [
                    multiprocessing.Process(
                        target=_worker_process_loop,
                        args=(self.dataset, self.index_queue, self.data_queue, self.collate_fn))
                    for _ in range(self.num_workers)]

            for w in self.workers:
                w.daemon = True
                w.start()

            if self.pin_memory:
                in_data = self.data_queue
                self.data_queue = queue.Queue()
                self.pin_thread = threading.Thread(
                    target=_pin_memory_loop,
                    args=(in_data, self.data_queue, self.pin_done_event))
                self.pin_thread.daemon = True
                self.pin_thread.start()

            for _ in range(2 * self.num_workers):
                self._put_indices() 

# 需要导入模块: from torch import multiprocessing [as 别名]
# 或者: from torch.multiprocessing import SimpleQueue [as 别名]
def __init__(self, loader):
        self.dataset = loader.dataset
        self.collate_fn = loader.collate_fn
        self.batch_sampler = loader.batch_sampler
        self.num_workers = loader.num_workers
        self.pin_memory = loader.pin_memory and torch.cuda.is_available()
        self.timeout = loader.timeout
        self.done_event = threading.Event()

        self.sample_iter = iter(self.batch_sampler)

        if self.num_workers > 0:
            self.worker_init_fn = loader.worker_init_fn
            self.index_queues = [multiprocessing.SimpleQueue() for _ in range(self.num_workers)]
            self.worker_queue_idx = 0
            self.worker_result_queue = multiprocessing.SimpleQueue()
            self.batches_outstanding = 0
            self.worker_pids_set = False
            self.shutdown = False
            self.send_idx = 0
            self.rcvd_idx = 0
            self.reorder_dict = {}

            base_seed = torch.LongTensor(1).random_()[0]
            self.workers = [
                multiprocessing.Process(
                    target=_worker_loop,
                    args=(self.dataset, self.index_queues[i],
                          self.worker_result_queue, self.collate_fn, base_seed + i,
                          self.worker_init_fn, i))
                for i in range(self.num_workers)]

            if self.pin_memory or self.timeout > 0:
                self.data_queue = queue.Queue()
                if self.pin_memory:
                    maybe_device_id = torch.cuda.current_device()
                else:
                    # do not initialize cuda context if not necessary
                    maybe_device_id = None
                self.worker_manager_thread = threading.Thread(
                    target=_worker_manager_loop,
                    args=(self.worker_result_queue, self.data_queue, self.done_event, self.pin_memory,
                          maybe_device_id))
                self.worker_manager_thread.daemon = True
                self.worker_manager_thread.start()
            else:
                self.data_queue = self.worker_result_queue

            for w in self.workers:
                w.daemon = True  # ensure that the worker exits on process exit
                w.start()

            _update_worker_pids(id(self), tuple(w.pid for w in self.workers))
            _set_SIGCHLD_handler()
            self.worker_pids_set = True

            # prime the prefetch loop
            for _ in range(2 * self.num_workers):
                self._put_indices() 

# 需要导入模块: from torch import multiprocessing [as 别名]
# 或者: from torch.multiprocessing import SimpleQueue [as 别名]
def __init__(self, loader):
        self.dataset = loader.dataset
        self.collate_fn = loader.collate_fn
        self.batch_sampler = loader.batch_sampler
        self.num_workers = loader.num_workers
        self.pin_memory = loader.pin_memory and torch.cuda.is_available()
        self.timeout = loader.timeout
        self.done_event = threading.Event()

        self.sample_iter = iter(self.batch_sampler)

        if self.num_workers > 0:
            self.worker_init_fn = loader.worker_init_fn
            self.index_queues = [multiprocessing.Queue() for _ in range(self.num_workers)]
            self.worker_queue_idx = 0
            self.worker_result_queue = multiprocessing.SimpleQueue()
            self.batches_outstanding = 0
            self.worker_pids_set = False
            self.shutdown = False
            self.send_idx = 0
            self.rcvd_idx = 0
            self.reorder_dict = {}

            self.workers = [
                multiprocessing.Process(
                    target=_worker_loop,
                    args=(self.dataset, self.index_queues[i],
                          self.worker_result_queue, self.collate_fn, self.worker_init_fn, i))
                for i in range(self.num_workers)]

            if self.pin_memory or self.timeout > 0:
                self.data_queue = queue.Queue()
                if self.pin_memory:
                    maybe_device_id = torch.cuda.current_device()
                else:
                    # do not initialize cuda context if not necessary
                    maybe_device_id = None
                self.worker_manager_thread = threading.Thread(
                    target=_worker_manager_loop,
                    args=(self.worker_result_queue, self.data_queue, self.done_event, self.pin_memory,
                          maybe_device_id))
                self.worker_manager_thread.daemon = True
                self.worker_manager_thread.start()
            else:
                self.data_queue = self.worker_result_queue

            for w in self.workers:
                w.daemon = True  # ensure that the worker exits on process exit
                w.start()

            _set_worker_pids(id(self), tuple(w.pid for w in self.workers))
            _set_SIGCHLD_handler()
            self.worker_pids_set = True

            # prime the prefetch loop
            for _ in range(2 * self.num_workers):
                self._put_indices() 

# 需要导入模块: from torch import multiprocessing [as 别名]
# 或者: from torch.multiprocessing import SimpleQueue [as 别名]
def __init__(self, loader):
        self.random_vars = loader.random_vars
        self.dataset = loader.dataset
        self.collate_fn = loader.collate_fn
        self.batch_sampler = loader.batch_sampler
        self.num_workers = loader.num_workers
        self.pin_memory = loader.pin_memory and torch.cuda.is_available()
        self.timeout = loader.timeout
        self.done_event = threading.Event()

        self.sample_iter = iter(self.batch_sampler)

        base_seed = torch.LongTensor(1).random_().item()

        if self.num_workers > 0:
            self.worker_init_fn = loader.worker_init_fn
            self.index_queues = [multiprocessing.Queue() for _ in range(self.num_workers)]
            self.worker_queue_idx = 0
            self.worker_result_queue = multiprocessing.SimpleQueue()
            self.batches_outstanding = 0
            self.worker_pids_set = False
            self.shutdown = False
            self.send_idx = 0
            self.rcvd_idx = 0
            self.reorder_dict = {}

            self.workers = [
                multiprocessing.Process(
                    target=_worker_loop,
                    args=(self.dataset, self.index_queues[i],
                          self.worker_result_queue, self.collate_fn, base_seed + i,
                          self.worker_init_fn, i))
                for i in range(self.num_workers)]

            if self.pin_memory or self.timeout > 0:
                self.data_queue = queue.Queue()
                if self.pin_memory:
                    maybe_device_id = torch.cuda.current_device()
                else:
                    # do not initialize cuda context if not necessary
                    maybe_device_id = None
                self.worker_manager_thread = threading.Thread(
                    target=_worker_manager_loop,
                    args=(self.worker_result_queue, self.data_queue, self.done_event, self.pin_memory,
                          maybe_device_id))
                self.worker_manager_thread.daemon = True
                self.worker_manager_thread.start()
            else:
                self.data_queue = self.worker_result_queue

            for w in self.workers:
                w.daemon = True  # ensure that the worker exits on process exit
                w.start()

            _update_worker_pids(id(self), tuple(w.pid for w in self.workers))
            _set_SIGCHLD_handler()
            self.worker_pids_set = True

            # prime the prefetch loop
            for _ in range(2 * self.num_workers):
                self._put_indices()