Hello, I have the following code, where I noticed a memory leak with +=, but not with + alone. import numpy m=numpy.matrix(numpy.ones((23,23))) for i in range(10000000): m+=0.0 # keeps growing in memory # m=m+0.0 # is stable in memory My version of python is 2.5, numpy 1.3.0, but it also causes memory build-up in 2.6 with numpy 1.4.0, as distributed by the Enthought Python Distribution. It's easy to work around, but could cause someone some problems. Anyone else get this? bb -- Brian Blais bblais@bryant.edu http://web.bryant.edu/~bblais http://bblais.blogspot.com/
On Fri, May 14, 2010 at 2:43 PM, Brian Blais <bblais@bryant.edu> wrote:
Hello,
I have the following code, where I noticed a memory leak with +=, but not with + alone. import numpy
m=numpy.matrix(numpy.ones((23,23)))
for i in range(10000000): m+=0.0 # keeps growing in memory # m=m+0.0 # is stable in memory
My version of python is 2.5, numpy 1.3.0, but it also causes memory build-up in 2.6 with numpy 1.4.0, as distributed by the Enthought Python Distribution.
It's easy to work around, but could cause someone some problems. Anyone else get this?
I get it also with python 2.5 numpy 1.4.0 Who owns the data ?
m=np.matrix(np.ones((3,3))) m.flags C_CONTIGUOUS : True F_CONTIGUOUS : False OWNDATA : True WRITEABLE : True ALIGNED : True UPDATEIFCOPY : False
m+=0 m.flags C_CONTIGUOUS : True F_CONTIGUOUS : False OWNDATA : False <- GONE WRITEABLE : True ALIGNED : True UPDATEIFCOPY : False
Josef
bb
-- Brian Blais bblais@bryant.edu http://web.bryant.edu/~bblais http://bblais.blogspot.com/
_______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
On Fri, May 14, 2010 at 3:26 PM, <josef.pktd@gmail.com> wrote:
On Fri, May 14, 2010 at 2:43 PM, Brian Blais <bblais@bryant.edu> wrote:
Hello,
I have the following code, where I noticed a memory leak with +=, but not with + alone. import numpy
m=numpy.matrix(numpy.ones((23,23)))
for i in range(10000000): m+=0.0 # keeps growing in memory # m=m+0.0 # is stable in memory
My version of python is 2.5, numpy 1.3.0, but it also causes memory build-up in 2.6 with numpy 1.4.0, as distributed by the Enthought Python Distribution.
It's easy to work around, but could cause someone some problems. Anyone else get this?
I get it also with python 2.5 numpy 1.4.0
Who owns the data ?
m=np.matrix(np.ones((3,3))) m.flags C_CONTIGUOUS : True F_CONTIGUOUS : False OWNDATA : True WRITEABLE : True ALIGNED : True UPDATEIFCOPY : False
m+=0 m.flags C_CONTIGUOUS : True F_CONTIGUOUS : False OWNDATA : False <- GONE WRITEABLE : True ALIGNED : True UPDATEIFCOPY : False
Josef
Maybe it's not a "true" memory leak, my python process eventually garbage collected the extra memory that was built up. Josef
bb
-- Brian Blais bblais@bryant.edu http://web.bryant.edu/~bblais http://bblais.blogspot.com/
_______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
On May 14, 2010, at 16:03 , josef.pktd@gmail.com wrote:
On Fri, May 14, 2010 at 3:26 PM, <josef.pktd@gmail.com> wrote:
On Fri, May 14, 2010 at 2:43 PM, Brian Blais <bblais@bryant.edu> wrote:
Hello,
I have the following code, where I noticed a memory leak with +=, but not with + alone. import numpy
m=numpy.matrix(numpy.ones((23,23)))
for i in range(10000000): m+=0.0 # keeps growing in memory # m=m+0.0 # is stable in memory
Maybe it's not a "true" memory leak, my python process eventually garbage collected the extra memory that was built up.
It crashed a simulator of mine (at least in Windows), until I figured out the workaround, so I would consider it a leak. :) it certainly shouldn't grow like that. if anything m=m+0.0 should chew of *more* memory than m+=0.0. bb -- Brian Blais bblais@bryant.edu http://web.bryant.edu/~bblais http://bblais.blogspot.com/
participants (2)
-
Brian Blais -
josef.pktd＠gmail.com