Ubuntu 18.04 LTS安装numba python性能优化的比较：numba，pypy， cython

本文主要是介绍Ubuntu 18.04 LTS安装numba python性能优化的比较：numba，pypy， cython，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

 

 

安装很简单, 我就不多废话了, 直接上指令:

sudo apt-get install llvm
sudo -H pip install numba

python 程序性能优化的套路一般有两种：1）jit， 即just in time compiler， 即时编译器，在运行时将某些函数编译成二进程代码，使用这种方式的有：numba 和pypy；2）将python代码转换成c++/c代码，然后编译执行，这种方式有：cython和nuitka。总而言之，转换成c++/c代码以后编译成二进制文件执行的效率比用numba和pypy即时编译执行的效率要高。

1. 首先看一下python写的求质数的函数 以及 用 numba的jit优化的函数

# main.py# 纯python语言写的求质数的代码
def primes_python(nb_primes):p = []n = 2while len(p) < nb_primes:# Is n prime?for i in p:if n % i == 0:break# If no break occurred in the loopelse:p.append(n)n += 1return p# 使用numba的jit优化的代码，只需要在上面的函数加一行代码
from numba import jit@jit
def primes_jit(nb_primes):p = []n = 2while len(p) < nb_primes:# Is n prime?for i in p:if n % i == 0:break# If no break occurred in the loopelse:p.append(n)n += 1return p

 

2. 新建一个primes.pyx文件，写一个cython函数，其中声明了变量的类型

# primes.pyxdef primes(int nb_primes):cdef int n, i, len_pcdef int p[1000]if nb_primes > 1000:nb_primes = 1000len_p = 0  # The current number of elements in p.n = 2while len_p < nb_primes:# Is n prime?for i in p[:len_p]:if n % i == 0:break# If no break occurred in the loop, we have a prime.else:p[len_p] = nlen_p += 1n += 1# Let's return the result in a python list:result_as_list  = [prime for prime in p[:len_p]]return result_as_list

再建立一个primes_python.pyx文件，新建一个和之前python里面写的一样的函数，作为对比。

# primes_python.pyxdef primes_python(nb_primes):p = []n = 2while len(p) < nb_primes:# Is n prime?for i in p:if n % i == 0:break# If no break occurred in the loopelse:p.append(n)n += 1return p

新建setup.py文件，用来编译.pyx函数

from distutils.core import setup
from Cython.Build import cythonizesetup(ext_modules=cythonize(["primes.pyx", "primes_python.pyx"],annotate=True)
)

 
# 编译命令用这个

# python setup.py build_ext --inplace

使用python setup.py build_ext --inplace编译后可以得到.pyd文件，就是可以导入的python库了。

3. 修改一下main.py， 加入函数调用和度量

# main.py 的完整内容import primes
import primes_python
import timeit
from numba import jitdef primes_python(nb_primes):p = []n = 2while len(p) < nb_primes:# Is n prime?for i in p:if n % i == 0:break# If no break occurred in the loopelse:p.append(n)n += 1return p@jit
def primes_jit(nb_primes):p = []n = 2while len(p) < nb_primes:# Is n prime?for i in p:if n % i == 0:break# If no break occurred in the loopelse:p.append(n)n += 1return pif __name__ == "__main__":repeat_times = 1000t1 = timeit.timeit(stmt="primes_python(1000)",setup="from __main__ import primes_python", number=repeat_times)print(f"run in python: {t1}s")t2 = timeit.timeit(stmt="primes.primes(1000)",setup="import primes", number=repeat_times)print(f"run cython with cdef: {t2}s")t3 = timeit.timeit(stmt="primes_jit(1000)",setup="from __main__ import primes_jit", number=repeat_times)print(f"run in python with numba jit: {t3}s")t4 = timeit.timeit(stmt="primes_python.primes_python(1000)",setup="import primes_python", number=repeat_times)print(f"run cython without cdef: {t4}s")运行一下，得到的结果如下：run in python: 28.519053545829927s
run cython with cdef: 1.6289360376895452s
run in python with numba jit: 2.0565857326599577s
run cython without cdef: 13.221758278866588s

4. 测试一下pypy的结果，建立primes_pypy.py文件：

# primes_pypy.pyimport timeitdef primes_python(nb_primes):p = []n = 2while len(p) < nb_primes:# Is n prime?for i in p:if n % i == 0:break# If no break occurred in the loopelse:p.append(n)n += 1return pif __name__ == "__main__":repeat_times = 1000t1 = timeit.timeit(stmt="primes_python(1000)",setup="from __main__ import primes_python", number=repeat_times)print(f"run in pypy: {t1}s")

使用pypy3 primes_pypy.py 运行文件， 得到结果如下：

run in pypy: 3.0445395345987682s
5.  nuitka的暂时没弄出来， 总体的运行结果如下：

run in python: 28.519053545829927s
run cython with cdef: 1.6289360376895452s
run in python with numba jit: 2.0565857326599577s
run cython without cdef: 13.221758278866588srun in pypy: 3.0445395345987682s

基本上jit的效果很明显，也不用改动python代码。

原文链接：https://blog.csdn.net/xiaozisheng2008_/article/details/85266511

这篇关于Ubuntu 18.04 LTS安装numba python性能优化的比较：numba，pypy， cython的文章就介绍到这儿，希望我们推荐的文章对编程师们有所帮助！

---