import torch
import torch.multiprocessing as mp
from absl import app, flags
from torchvision.models import AlexNet
FLAGS = flags.FLAGS
flags.DEFINE_integer("num_processes", 2, "Number of subprocesses to use")
def infer(rank, queue):
"""Each subprocess will run this function on a different GPU which is indicated by the parameter `rank`."""
model = AlexNet()
device = torch.device(f"cuda:{rank}")
model.to(device)
while True:
a, b = queue.get()
if a is None: # check for sentinel value
break
x = a + b
x = x.to(device)
model(x)
del a, b # free memory
print(f"Inference on process {rank}")
def main(argv):
queue = mp.Queue()
processes = []
for rank in range(FLAGS.num_processes):
p = mp.Process(target=infer, args=(rank, queue))
p.start()
processes.append(p)
for _ in range(10):
a_1 = torch.randn(1, 3, 224, 224)
a_2 = torch.randn(1, 3, 224, 224)
b = torch.randn(1, 3, 224, 224)
queue.put((a_1, b))
queue.put((a_2, b))
for _ in range(FLAGS.num_processes):
queue.put((None, None)) # sentinel value to signal subprocesses to exit