def worker_fn(
worker_id: int,
num_workers: int,
dataset,
batch_size: int,
stack_fn: Callable,
batch_queue: mp.Queue,
terminate_event,
exhausted_event,
):
MPWorkerInfo.set_worker_info(
num_workers=num_workers,
worker_id=worker_id,
)
for batch in batcher(dataset, batch_size):
stacked_batch = stack_fn(batch)
try:
if terminate_event.is_set():
return
buf = io.BytesIO()
ForkingPickler(buf, pickle.HIGHEST_PROTOCOL).dump(
(worker_id, stacked_batch))
batch_queue.put(buf.getvalue())
except (EOFError, BrokenPipeError):
return
exhausted_event.set()
def worker_fn(
worker_id: int,
dataset,
num_workers: int,
result_queue: mp.Queue,
):
MPWorkerInfo.set_worker_info(
num_workers=num_workers,
worker_id=worker_id,
)
for raw in map(_encode, dataset):
try:
result_queue.put(raw)
except (EOFError, BrokenPipeError):
return
def _worker_initializer(
dataset: Dataset,
transformation: Transformation,
num_workers: int,
worker_id_queue: Queue,
) -> None:
"""Initialier for processing pool."""
_WorkerData.dataset = dataset
_WorkerData.transformation = transformation
# get unique worker id
worker_id = int(worker_id_queue.get())
multiprocessing.current_process().name = f"worker_{worker_id}"
# propagate worker information
MPWorkerInfo.set_worker_info(num_workers=num_workers,
worker_id=worker_id,
worker_process=True)
def worker_fn(
worker_id: int,
dataset,
num_workers: int,
input_queue: mp.Queue,
output_queue: mp.Queue,
):
MPWorkerInfo.set_worker_info(
num_workers=num_workers,
worker_id=worker_id,
)
while True:
try:
input_queue.get()
for encoded_entry in map(_encode, dataset):
output_queue.put(encoded_entry)
output_queue.put(_encode(None))
except (EOFError, BrokenPipeError):
return