Dump Saving To Memory Error
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Pickle Dump Memory Error
ads with us Stack Overflow Questions Jobs Documentation Tags Users Badges Ask Question x Dismiss Join the Stack Overflow Community system error memory dump Stack Overflow is a community of 4.7 million programmers, just like you, helping each other. Join them; it only takes a minute: Sign up Pickle dump huge file without memory error up vote 5 system error memory dump files down vote favorite 3 I have a program where I basically adjust the probability of certain things happening based on what is already known. My file of data is already saved as a pickle Dictionary object at Dictionary.txt. The problem is, is that everytime that I run the program it pulls in the Dictionary.txt, turns it into a dictionary object, makes it's edits and overwrites Dictionary.txt. This is
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pretty memory intensive as the Dictionary.txt is 123 MB. When I dump I am getting the MemoryError, everything seems fine when I pull it in.. Is there a better (more efficient) way of doing the edits? (Perhaps w/o having to overwrite the entire file everytime) Is there a way that I can invoke garbage collection (through gc module)? (I already have it auto-enabled via gc.enable()) I know that besides readlines() you can read line-by-line. Is there a way to edit the dictionary incrementally line-by-line when I already have a fully completed Dictionary object File in the program. Any other solutions? Thank you for your time. python memory file-io pickle share|improve this question asked Jul 7 '13 at 14:31 user2543682 2813 There are a few compressive and other libraries. Personally, I like dill and H5Py for large objects. If you are using scikit learn and have to use a model base on dictionary, perhaps you could use joblib as well (only really for these models). –Andrew Scott Evans Nov 5 '15 at 22:44 add a comment| 6 Answers 6 active oldest votes up vote 2 down vote Have you tried using streaming pickle: https://code.google.com/p/streaming-pickle/ I have just solved a similar memory e
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Ask a Question Need help? Post your question and get tips & solutions from a community of 418,501 IT Pros & Developers. It's quick & easy. Re: Memory error while saving dictionary of https://bytes.com/topic/python/answers/819362-memory-error-while-saving-dictionary-size-65000x50-using-pickle size 65000X50 using pickle P: n/a Nagu I didn't have the problem with dumping as a string. When I tried to save this object to a file, memory error pops up. I am sorry for the mention of size for a dictionary. What I meant by 65000X50 is that it has 65000 keys and each key has a list of 50 tuples. I was able to save a dictionary object with 65000 system error keys and a list of 15-tuple values to a file. But I could not do the same when I have a list of 25-tuple values for 65000 keys. You exmple works just fine on my side. Thank you, Nagu Jul 7 '08 #1 Post Reply Share this Question 1 Reply P: n/a =?ISO-8859-1?Q?=22Martin_v=2E_L=F6wis=22?= I didn't have the problem with dumping as a string. When I tried to save this object to a system error memory file, memory error pops up. That's not what the backtrace says. The backtrace says that the error occurs inside pickle.dumps() (and it is consistent with the functions being called, so it's plausible). I am sorry for the mention of size for a dictionary. What I meant by 65000X50 is that it has 65000 keys and each key has a list of 50 tuples. [...] > You exmple works just fine on my side. I can get the program import pickle d = {} for i in xrange(65000): d[i]=[(x,) for x in range(50)] print "Starting dump" s = pickle.dumps(d) to complete successfully, also, however, it consumes a lot of memory. I can reduce memory usage slightly by a) dumping directly to a file, and b) using cPickle instead of pickle i.e. import cPickle as pickle d = {} for i in xrange(65000): d[i]=[(x,) for x in range(50)] print "Starting dump" pickle.dump(d,open("/tmp/t.pickle","wb")) The memory consumed originates primarily from the need to determine shared references. If you are certain that no object sharing occurs in your graph, you can do import cPickle as pickle d = {} for i in xrange(65000): d[i]=[(x,) for x in range(50)] print "Starting dump" p = pickle.Pickler(open("/tmp/t.pickle","wb")) p.fast = True p.dump(d) With that, I see no additional memory usage, and pickling comp