Created
June 14, 2022 13:01
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Idle gpu
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| from multiprocessing import Pool | |
| import numpy as np | |
| import torch | |
| import torch.nn as nn | |
| torch.multiprocessing.set_start_method('forkserver', force=True) | |
| # torch.multiprocessing.set_start_method('spawn') | |
| def random_size(): | |
| """ | |
| return | |
| ------- | |
| a_size, b_size | |
| """ | |
| choice = [256, 512, 1024, 2048, 4096] | |
| nums = np.random.choice(choice, size=3) | |
| a_size = (nums[0], nums[1]) | |
| b_size = (nums[1], nums[2]) | |
| return a_size, b_size | |
| def matmul(a, b): | |
| while True: | |
| result = torch.matmul(a, b) | |
| if __name__ == '__main__': | |
| gpu_num = torch.cuda.device_count() | |
| print(f"{gpu_num} GPU is/are available") | |
| # generate size | |
| size_list = [random_size() for i in range(gpu_num)] | |
| # generate random data | |
| a = [torch.rand(size_list[i][0]).cuda(i) for i in range(gpu_num)] | |
| b = [torch.rand(size_list[i][1]).cuda(i) for i in range(gpu_num)] | |
| p = Pool(gpu_num) | |
| for i in range(gpu_num): | |
| p.apply_async(matmul, args=(a[i], b[i])) | |
| p.close() | |
| p.join() |
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