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# Context manager to generate batches in the background via a process pool | |
# Usage: | |
# | |
# def batch(seed): | |
# .... # generate minibatch | |
# return minibatch | |
# | |
# with BatchGenCM(batch) as bg: | |
# minibatch = next(bg) | |
# .... # do something with minibatch | |
import uuid | |
import os | |
import pickle | |
import hashlib | |
import numpy as np | |
from multiprocessing import Process, Queue | |
class BatchGenCM: | |
def __init__(self, batch_fn, seed=None, num_workers=8): | |
self.batch_fn = batch_fn | |
self.num_workers = num_workers | |
if seed is None: | |
seed = np.random.randint(4294967295) | |
self.seed = str(seed) | |
self.id = uuid.uuid4() | |
def __enter__(self): | |
self.jobq = Queue(maxsize=self.num_workers) | |
self.doneq = Queue() | |
self.processes = [] | |
self.current_batch = 0 | |
self.finished_batches = [] | |
def produce(): | |
while True: | |
n = self.jobq.get() | |
if n is None: | |
break | |
seed = hashlib.md5(self.seed + str(n)).hexdigest() | |
seed = int(seed, 16) % 4294967295 | |
batch = self.batch_fn(seed) | |
with open('/run/shm/{}-{}'.format(self.id, n), 'w') as ofile: | |
pickle.dump(batch, ofile, protocol=pickle.HIGHEST_PROTOCOL) | |
self.doneq.put(n) | |
for i in range(self.num_workers): | |
self.jobq.put(i) | |
p = Process(target=produce) | |
self.processes.append(p) | |
p.start() | |
return self | |
def __iter__(self): | |
return self | |
def next(self): | |
n = self.current_batch | |
while n not in self.finished_batches: | |
i = self.doneq.get() | |
self.finished_batches.append(i) | |
fn = '/run/shm/{}-{}'.format(self.id, n) | |
batch = pickle.load(open(fn)) | |
os.system('rm {}'.format(fn)) | |
self.jobq.put(n + self.num_workers) | |
self.current_batch += 1 | |
return batch | |
def __exit__(self, exc_type, exc_value, traceback): | |
for _ in range(self.num_workers): | |
self.jobq.put(None) | |
for process in self.processes: | |
process.join() | |
while not self.doneq.empty(): | |
_ = next(self) |
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