import torch
from torch.autograd import Variable
import torch.optim


def reproduce():
	n_data = 60
	ndim = 100

	n_init = 1
	b = Variable(torch.randn(ndim, n_data))
	A = Variable(torch.randn(ndim, ndim))

	pool = torch.multiprocessing.Pool(n_init)
	res = pool.apply_async(torch.gesv, args=(b, A))
	return res.get()


if __name__ == '__main__':
	print(reproduce())

In my computer, the problem occurs when n_data = 70
but with n_data = 60, it works fine.
Thanks!