memory_profiler exposes a number of functions to be used in third-partycode.

memory_usage(proc=-1, interval=.1, timeout=None) returns the memory usageover a time interval. The first argument,proc represents whatshould be monitored. This can either be the PID of a process (notnecessarily a Python program), a string containing some python code tobe evaluated or a tuple(f, args, kw) containing a function and itsarguments to be evaluated asf(*args, **kw). For example,

>>>from memory_profilerimport memory_usage>>> mem_usage= memory_usage(-1,interval=.2,timeout=1)>>>print(mem_usage)    [7.296875, 7.296875, 7.296875, 7.296875, 7.296875]

Here I've told memory_profiler to get the memory consumption of thecurrent process over a period of 1 second with a time interval of 0.2seconds. As PID I've given it -1, which is a special number (PIDs areusually positive) that means current process, that is, I'm getting thememory usage of the current Python interpreter. Thus I'm gettingaround 7MB of memory usage from a plain python interpreter. If I trythe same thing on IPython (console) I get 29MB, and if I try the samething on the IPython notebook it scales up to 44MB.

If you'd like to get the memory consumption of a Python function, thenyou should specify the function and its arguments in the tuple(f,args, kw). For example:

>>> # define a simple function>>> def f(a, n=100):    ...     import time    ...     time.sleep(2)    ...     b = [a] * n    ...     time.sleep(1)    ...     return b    ...>>> from memory_profiler import memory_usage>>> memory_usage((f, (1,), {'n' : int(1e6)}))

This will execute the codef(1, n=int(1e6)) and return the memoryconsumption during this execution.