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There’s no easy way to find out the memory size of a python object. One of the problems you may find is that Python objects – like lists and dicts – may have references to other python objects (in this case, what would your size be? The size containing the size of each object or not?). There are some pointers overhead and internal structures related to object types and garbage collection. Finally, some python objects have non-obvious behaviors. For instance, lists reserve space for more objects than they have, most of the time; dicts are even more complicated since they can operate in different ways (they have a different implementation for small number of keys and sometimes they over allocate entries).
There is a big chunk of code (and an updated big chunk of code) out there to try to best approximate the size of a python object in memory. There’s also some simpler approximations. But they will always be approximations.
You may also want to check some old description about PyObject (the internal C struct that represents virtually all python objects).
getsizeof() calls the object’s
__sizeof__ method and adds an additional garbage collector overhead if the object is managed by the garbage collector.
Another approach is to use pickle. See this answer to a duplicate of this question.
I haven’t any personal experience with either of the following, but a simple search for a “Python [memory] profiler” yield:
PySizer, “a memory profiler for Python,” found at http://pysizer.8325.org/. However the page seems to indicate that the project hasn’t been updated for a while, and refers to…
Heapy, “support[ing] debugging and optimization regarding memory related issues in Python programs,” found at http://guppy-pe.sourceforge.net/#Heapy.
Hope that helps.
This must be used with care because an override on the objects __sizeof__ might be misleading.
Using the bregman.suite, some tests with sys.getsizeof output a copy of an array object (data) in an object instance as being bigger than the object itself (mfcc).
>>> mfcc = MelFrequencyCepstrum(filepath, params) >>> data = mfcc.X[:] >>> sys.getsizeof(mfcc) 64 >>> sys.getsizeof(mfcc.X) >>>80 >>> sys.getsizeof(data) 80 >>> mfcc <bregman.features.MelFrequencyCepstrum object at 0x104ad3e90>
For big objects you may use a somewhat crude but effective method:
check how much memory your Python process occupies in the system, then delete the object and compare.
This method has many drawbacks but it will give you a very fast estimate for very big objects.