Finding bottlenecks and increasing your speed potência by magnitudes com some tips I come along over the past year.

Você está assistindo: Como melhorar a performance do notebook


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While we all know the premature micro optimizations estão the root of tudo de evil, many thanks to Donald Knuth’s file “Structured Programming with Go come Statements” <1>, ultimately at some suggest in your data exploration process you grasp para more than just a current “working” solution.

The heuristic technique we commonly follow considers:

Make the work.Make it right.Make it fast.

Before jumping right to ns third point and starting with refactoring our code, that is important to recognize where the atuação bottlenecks are, to do an informed decision on a course of activity we desire to follow.

This is a fundamental action if we must achieve a greatest advantage with a least lot of work. Truth be told, one of a most voluminous mistakes in this setup would be to do an informed conjecture e fine-tune what we think is the main driver of ns issue. By profiling our code, we remove this vulnerability since we will certainly know exactly where ns issues are.

Since we’re utilizing Jupyter Notebooks here, us may too want to remover advantage that conveniences the come along com it, such as magic commands. Magic commands estão with no decorrer doubt 1 of ns sweet enhancements ao extending der notebook’s capabilities. In detail, we will take der look at:

%time and %timeit%prun and %lprun%mprun and %memit%%heatvisualizing ns output of der profiling session with snakeviz

and study the following:

pythonic caminho of codingloop optimization method com vectorizationoptimization with different algorithms

Our primeiro goal is to recognize what’s resulting in us headaches. In general, profiling involves measuring a resource you want to optimize for, whether it is memory consumption or CPU time.

In the next instances we will certainly consider how to strive for optimization if our tasks ser estar “CPU-bound” (thus time spent in the CPU) and, in contrast to it, how e why come pay privado attention to memory intensive tasks.

Note: In this article I’m not diving right into parallel or high-performance computing para tackling concerns regarding atuação bottlenecks. This is fora of scope para this post and might be an opinião to write around in ns future.

Approximating π with monte Carlo integration

A agrupar Carlo simulation is a method ao estimating solution to naquela problem through randomly generating samples. They ser estar primarily suited porque o calculating a “brute force” approximation to a solution of a system which may be of high dimension, such together DeepMind’s AlphaGo inválido where monte Carlo tree search was being utilized.

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We will definir a slow method which evaluate pi using random generated data points and then look ao ways come optimize. Remember that the area covered through a círculo with radius one inscribed in naquela square, equals exactly to a quarter the pi.

We get the value of pi through taking the ratio of area the circle to area of ns square,