January 2018
Beginner to intermediate
548 pages
12h 11m
English
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Profiling the memory usage of your code with memory_profiler
The methods described in the previous recipe were about CPU time profiling. That may be the most obvious factor when it comes to code profiling. However, memory is also a critical factor. Writing memory-optimized code is not trivial and can really make your program faster. This is particularly important when dealing with large NumPy arrays, as we will see later in this chapter.
In this recipe, we will look at a simple memory profiler unsurprisingly named memory_profiler. Its usage is very similar to line_profiler, and it can be conveniently used from IPython.
Getting ready
You can install memory_profiler with conda install memory_profiler.
How to do it...
- We load the
memory_profilerIPython ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.