Robin wrote: > Hi, > > I am starting to push the limits of the available memory and I'd like > to understand a bit better how Python handles memory... > This is why I switched to 64 bit linux and never looked back. > If I try to allocate something too big for the available memory I > often get a MemoryError exception. However, in other situations, > Python memory use continues to grow until the machine falls over. I > was hoping to understand the difference between those cases. I don't know what "falls over" mean. It could be that you're getting swap death -- the kernel starts attempting to use virtual memory (hard disk) for some of the RAM. This would be characterized by your CPU use dropping to near-zero, your hard disk grinding away, and your swap space use increasing.
The MemoryError simply means that Python made a request for memory that the kernel didn't grant. There's something else you might run into -- the maximum memory size of a process before the kernel kills that process. On linux i686, IIRC this limit is 3 GB. I'm not sure why you get different behavior on different runs. FWIW, with 64 bit linux the worst that happens to me now is swap death, which can be forestalled by adding lots of RAM. > From what > I've read Python never returns memory to the OS (is this right?) so > the second case, python is holding on to memory that it isn't really > using (for objects that have been destroyed). I guess my question is > why doesn't it reuse the memory freed from object deletions instead of > requesting more - and even then when requesting more, why does it > continue until the machine falls over and not cause a MemoryError? > It's hard to say without knowing what your code does. A first guess is that you're allocating lots of memory without allowing it to be freed. Specifically, you may have references to objects which you no longer need, and you should eliminate those references and allow them to be garbage collected. In some cases, circular references can be hard for python to detect, so you might want to play around with the gc module and judicious use of the del statement. Note also that IPython keeps references to past results by default (the history). > While investigating this I found this script: > http://aspn.activestate.com/ASPN/Cookbook/Python/Recipe/511474 > which does wonders for my code. I was wondering if this function > should be included in Numpy as it seems to provide an important > feature, or perhaps an entry on the wiki (in Cookbook section?) > I don't think it belongs in numpy per se, and I'm not sure of the necessity of a spot on the scipy cookbook given that it's in the python cookbook. Perhaps more useful would be starting a page called "MemoryIssues" on the scipy wiki -- I imagine this subject, as a whole, is of particular interest for many in the numpy/scipy crowd. Certainly adding a link and description to that recipe would be useful in that context. But please, feel free to add to or edit the wiki as you see fit -- if you think something will be useful, by all means, go ahead and do it. I think there are enough eyes on the wiki that it's fairly self-regulating. -Andrew _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
