diff --git a/sources/tech/20190501 Write faster C extensions for Python with Cython.md b/sources/tech/20190501 Write faster C extensions for Python with Cython.md deleted file mode 100644 index c8c3daf779..0000000000 --- a/sources/tech/20190501 Write faster C extensions for Python with Cython.md +++ /dev/null @@ -1,83 +0,0 @@ -[#]: collector: (lujun9972) -[#]: translator: (geekpi) -[#]: reviewer: ( ) -[#]: publisher: ( ) -[#]: url: ( ) -[#]: subject: (Write faster C extensions for Python with Cython) -[#]: via: (https://opensource.com/article/19/5/python-cython) -[#]: author: (Moshe Zadka https://opensource.com/users/moshez/users/moshez/users/foundjem/users/jugmac00) - -Write faster C extensions for Python with Cython -====== -Learn more about solving common Python problems in our series covering -seven PyPI libraries. -![Hand drawing out the word "code"][1] - -Python is one of the most [popular programming languages][2] in use today—and for good reasons: it's open source, it has a wide range of uses (such as web programming, business applications, games, scientific programming, and much more), and it has a vibrant and dedicated community supporting it. This community is the reason we have such a large, diverse range of software packages available in the [Python Package Index][3] (PyPI) to extend and improve Python and solve the inevitable glitches that crop up. - -In this series, we'll look at seven PyPI libraries that can help you solve common Python problems. First up: **[**Cython**][4]** , a language that simplifies writing C extensions for Python. - -### Cython - -Python is fun to use, but sometimes, programs written in it can be slow. All the runtime dynamic dispatching comes with a steep price: sometimes it's up to 10-times slower than equivalent code written in a systems language like C or Rust. - -Moving pieces of code to a completely new language can have a big cost in both effort and reliability: All that manual rewrite work will inevitably introduce bugs. Can we have our cake and eat it too? - -To have something to optimize for this exercise, we need something slow. What can be slower than an accidentally exponential implementation of the Fibonacci sequence? - - -``` -def fib(n): - if n < 2: - return 1 - return fib(n-1) + fib(n-2) -``` - -Since a call to **fib** results in two calls, this beautifully inefficient algorithm takes a long time to execute. For example, on my new laptop, **fib(36)** takes about 4.5 seconds. These 4.5 seconds will be our baseline as we explore how Python's Cython extension can help. - -The proper way to use Cython is to integrate it into **setup.py**. However, a quick and easy way to try things out is with **pyximport**. Let's put the **fib** code above in **fib.pyx** and run it using Cython. - - -``` ->>> import pyximport; pyximport.install() ->>> import fib ->>> fib.fib(36) -``` - -Just using Cython with _no_ code changes reduced the time the algorithm takes on my laptop to around 2.5 seconds. That's a reduction of almost 50% runtime with almost no effort; certainly, a scrumptious cake to eat and have! - -Putting in a little more effort, we can make things even faster. - - -``` -cpdef int fib(int n): - if n < 2: - return 1 - return fib(n - 1) + fib(n - 2) -``` - -We moved the code in **fib** to a function defined with **cpdef** and added a couple of type annotations: it takes an integer and returns an integer. - -This makes it _much_ faster—around 0.05 seconds. It's so fast that I may start suspecting my measurement methods contain noise: previously, this noise was lost in the signal. - -So, the next time some of your Python code spends too long on the CPU, maybe spinning up some fans in the process, why not see if Cython can fix things? - -In the next article in this series, we'll look at **Black** , a project that automatically corrects format errors in your code. - --------------------------------------------------------------------------------- - -via: https://opensource.com/article/19/5/python-cython - -作者：[Moshe Zadka ][a] -选题：[lujun9972][b] -译者：[译者ID](https://github.com/译者ID) -校对：[校对者ID](https://github.com/校对者ID) - -本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出 - -[a]: https://opensource.com/users/moshez/users/moshez/users/foundjem/users/jugmac00 -[b]: https://github.com/lujun9972 -[1]: https://opensource.com/sites/default/files/styles/image-full-size/public/lead-images/code_hand_draw.png?itok=dpAf--Db (Hand drawing out the word "code") -[2]: https://opensource.com/article/18/5/numbers-python-community-trends -[3]: https://pypi.org/ -[4]: https://pypi.org/project/Cython/ diff --git a/translated/tech/20190501 Write faster C extensions for Python with Cython.md b/translated/tech/20190501 Write faster C extensions for Python with Cython.md new file mode 100644 index 0000000000..cbc2c99d62 --- /dev/null +++ b/translated/tech/20190501 Write faster C extensions for Python with Cython.md @@ -0,0 +1,82 @@ +[#]: collector: (lujun9972) +[#]: translator: (geekpi) +[#]: reviewer: ( ) +[#]: publisher: ( ) +[#]: url: ( ) +[#]: subject: (Write faster C extensions for Python with Cython) +[#]: via: (https://opensource.com/article/19/5/python-cython) +[#]: author: (Moshe Zadka https://opensource.com/users/moshez/users/moshez/users/foundjem/users/jugmac00) + +使用 Cython 为 Python 编写更快的 C 扩展 +====== +在我们覆盖 7 个 PyPI 库的系列文章中了解解决 Python 问题的更多信息。 +![Hand drawing out the word "code"][1] + +Python 是当今使用最多的[流行编程语言][2]之一，因为：它是开源的，它有广泛的用途（例如 Web 编程、业务应用、游戏、科学编程等等），它有一个充满活力和专注的社区支持它。这个社区可以让我们在 [Python Package Index][3]（PyPI）中有如此庞大、多样化的软件包，用以扩展和改进 Python 并解决不可避免的问题。 + +在本系列中，我们将介绍七个可以帮助你解决常见 Python 问题的 PyPI 库。首先是 [Cython][4]，一个简化 Python 编写 C 扩展的语言。 + +### Cython + +使用 Python 很有趣，但有时，用它编写的程序可能很慢。所有运行时动态调度会带来很大的代价：有时它比用 C 或 Rust 等系统语言编写的等效代码慢10倍。 + +将代码迁移到一种全新的语言可能会在成本和可靠性方面付出巨大代价：所有手工重写工作都将不可避免地引入错误。我们可以两者兼得么？ + +为了练习优化，我们需要一些慢代码。什么比斐波那契数列的意外指数实现更慢？ + + +``` +def fib(n): + if n < 2: + return 1 + return fib(n-1) + fib(n-2) +``` + +由于对 **fib** 的调用会导致两次调用，因此这种效率极低的算法需要很长时间才能执行。例如，在我的新笔记本电脑上，**fib(36)** 需要大约 4.5 秒。这个 4.5 秒会成为我们探索 Python 的 Cython 扩展能提供的帮助的基准。 + +使用 Cython 的正确方法是将其集成到 **setup.py** 中。然而，使用 **pyximport** 可以快速地进行尝试。让我们将 **fib** 代码放在 **fib.pyx** 中并使用 Cython 运行它。 + + +``` +>>> import pyximport; pyximport.install() +>>> import fib +>>> fib.fib(36) +``` + +只使用 Cython 不_修改_代码，这个算法在我笔记本上花费的时间减少到大约 2.5 秒。几乎无需任何努力，这几乎减少了 50％ 的运行时间。当然，得到了一个不错的成果。 + +加把劲，我们可以让它变得更快。 + + +``` +cpdef int fib(int n): + if n < 2: + return 1 + return fib(n - 1) + fib(n - 2) +``` + +我们将 **fib** 中的代码变成用 **cpdef** 定义的函数，并添加了几个类型注释：它接受一个整数并返回一个整数。 + +这个变得快_多_了，大约 0.05 秒。它是如此之快以至于我可能开始怀疑我的测量方法包含噪声：之前，这种噪声在信号中丢失了。 + +当下次你的 Python 代码花费太多 CPU 时间时，也许在这个过程中会让一些粉丝蠢蠢欲动，为何不看看 Cython 是否可以解决问题呢？ + +在本系列的下一篇文章中，我们将看一下 **Black** ，一个自动纠正代码格式错误的项目。 + +-------------------------------------------------------------------------------- + +via: https://opensource.com/article/19/5/python-cython + +作者：[Moshe Zadka ][a] +选题：[lujun9972][b] +译者：[geekpi](https://github.com/geekpi) +校对：[校对者ID](https://github.com/校对者ID) + +本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出 + +[a]: https://opensource.com/users/moshez/users/moshez/users/foundjem/users/jugmac00 +[b]: https://github.com/lujun9972 +[1]: https://opensource.com/sites/default/files/styles/image-full-size/public/lead-images/code_hand_draw.png?itok=dpAf--Db (Hand drawing out the word "code") +[2]: https://opensource.com/article/18/5/numbers-python-community-trends +[3]: https://pypi.org/ +[4]: https://pypi.org/project/Cython/