Hi, with numpy '1.6.1', I have no problem.
With numpy 1.7.0b2, I can reproduce the problem. HTH Fred On Mon, Sep 24, 2012 at 1:04 PM, Gael Varoquaux <[email protected]> wrote: > Hi list, > > I think that I am hit a memory leak with numpy master. The following code > enables to reproduce it: > > ________________________________________________________________________________ > import numpy as np > n = 100 > m = np.eye(n) > for i in range(30000): > #np.linalg.slogdet(m) > > t, result_t = np.linalg.linalg._commonType(m) > a = np.linalg.linalg._fastCopyAndTranspose(t, m) > > pivots = np.zeros((n,), np.linalg.linalg.fortran_int) > results = np.linalg.lapack_lite.dgetrf(n, n, a, n, pivots, 0) > d = np.diagonal(a) > > if not i % 1000: > print i > ________________________________________________________________________________ > > If you execute this code, you'll see the memory go steadily up. > > The reason that I came up with such a strange looking code is that in my > codebase, I do repeated calls to np.linalg.slogdet. I came up with the > code above by simplifying what is done in slogdet. I don't think that I > can simplify any further and still reproduce the memory leak. > > Should I submit a bug report (in other words, can people reproduce?)? > > Cheers, > > Gaël > _______________________________________________ > NumPy-Discussion mailing list > [email protected] > http://mail.scipy.org/mailman/listinfo/numpy-discussion _______________________________________________ NumPy-Discussion mailing list [email protected] http://mail.scipy.org/mailman/listinfo/numpy-discussion
