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