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# Copyright (c) 2024 Bytedance Ltd. and/or its affiliates
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
import os
import torch.multiprocessing as mp
import time
def check_mem(cuda_device):
devices_info = (
os.popen('"/usr/bin/nvidia-smi" --query-gpu=memory.total,memory.used --format=csv,nounits,noheader')
.read()
.strip()
.split("\n")
)
total, used = devices_info[int(cuda_device)].split(",")
return total, used
def loop(cuda_device):
cuda_i = torch.device(f"cuda:{cuda_device}")
total, used = check_mem(cuda_device)
total = int(total)
used = int(used)
max_mem = int(total * 0.9)
block_mem = max_mem - used
while True:
x = torch.rand(20, 512, 512, dtype=torch.float, device=cuda_i)
y = torch.rand(20, 512, 512, dtype=torch.float, device=cuda_i)
time.sleep(0.001)
x = torch.matmul(x, y)
def main():
if torch.cuda.is_available():
num_processes = torch.cuda.device_count()
processes = list()
for i in range(num_processes):
p = mp.Process(target=loop, args=(i,))
p.start()
processes.append(p)
for p in processes:
p.join()
if __name__ == "__main__":
torch.multiprocessing.set_start_method("spawn")
main()
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