I’ll cover all those options in this article. It’s also available via Juypyter/IPython magics, or in your own code. You can obtain this by running the module from the command line against a python file. The tool that provides the most detail is the line-by-line memory usage that the module will report when profiling a single function. Memory_profiler is a set of tools for profiling a Python program’s memory usage, and the documentation gives a nice overview of those tools. It uses the psutil library (or can use tracemalloc or posix) to access process information in a cross platform way, so it works on Windows, Mac, and Linux. The package will include the library, as well as a few command line utilities. Memory_profiler is written in Python and can be installed using pip. While the tool is quite helpful, there’s a few things to know about it to use it effectively.
PYTHON FREE MEMORY CODE
This allows you to see the real impact of each line of code and get a sense where memory usage. Whereas line_profiler tells you how much time is spent on each line, memory_profiler tells you how much memory is allocated (or freed) by each line. The memory_profiler tool is similar in spirit (and inspired by) the line_profiler tool, which I’ve written about as well. In this article, I’m going to focus on one of them, memory_profiler. Learn even more techniques for reducing memory usage-read the rest of the Larger-than-memory datasets guide for Python.What do you do when your Python program is using too much memory? How do you find the spots in your code with memory allocation, especially in large chunks? It turns out that there is not usually an easy answer to these question, but a number of tools exist that can help you figure out where your code is allocating memory. Finally, you can try to only load only the data you actually care about by using indexing.If you don’t need to store all the data in memory at once, you can process data in batches, for example by returning data via a generator.The solutions I’ve covered in this article focus on compression: the same information stored with less overhead.In general, storing too many Python objects at once will waste memory.Īs always, solutions can involve compression, batching, or indexing: So you can reduce memory usage even further, to about 8MB, by using a Pandas DataFrame to store the information: it will use NumPy arrays to efficiently store the numbers internally. Memory usage is now reduced to 30MB, down 85% from the original 206MB:īonus, even-better solution: Pandas instead of dict-of-listsĪt this point most of the overhead is due to the overhead of having a Python object per floating point number. More importantly for our purposes, Python won’t create a dictionary for every object.Īll we have to do is add one line of code:įrom random import random points = for _ in range ( 1000000 ): r = random () points.Most of the time we don’t want to do that: there are a certain set of attributes we know a class will have, and that’s it.īy setting this attribute on a class, with a list of strings indicating a list of attributes: Having a dictionary for every object makes sense if you want to add arbitrary attributes to any given object. The list storing the Point objects: 4% of memory.īasically, memory usage is at least 10x as high as the actual information we care about, item 3, the random floating point numbers.The floating point numbers: 11% of memory.
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append ( point )Īnd we can see the memory usage of those three categories, plus a fourth additional one: x = x objects = for _ in range ( 1000000 ): r = random () point = Point ( r ) objects. From random import random class Point : def _init_ ( self, x ): self.
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