Files
ray-project--ray/python/ray/util/collective/examples/nccl_allreduce_multigpu_example.py
T
2026-07-13 13:17:40 +08:00

44 lines
1.2 KiB
Python

import cupy as cp
from cupy.cuda import Device
import ray
import ray.util.collective as collective
@ray.remote(num_gpus=2)
class Worker:
def __init__(self):
with Device(0):
self.send1 = cp.ones((4,), dtype=cp.float32)
with Device(1):
self.send2 = cp.ones((4,), dtype=cp.float32) * 2
self.recv = cp.zeros((4,), dtype=cp.float32)
def setup(self, world_size, rank):
collective.init_collective_group(world_size, rank, "nccl", "177")
return True
def compute(self):
collective.allreduce_multigpu([self.send1, self.send2], "177")
return [self.send1, self.send2], self.send1.device, self.send2.device
def destroy(self):
collective.destroy_collective_group("177")
if __name__ == "__main__":
ray.init(address="auto")
num_workers = 2
workers = []
init_rets = []
for i in range(num_workers):
w = Worker.remote()
workers.append(w)
init_rets.append(w.setup.remote(num_workers, i))
a = ray.get(init_rets)
results = ray.get([w.compute.remote() for w in workers])
print(results)
ray.get([w.destroy.remote() for w in workers])
ray.shutdown()