numba

Numba jit warnings interpretation in python

由 生来就可爱ヽ(ⅴ＜●) 提交于 2020-03-23 04:26:25
问题 I have defined the following recursive array generator and am using Numba jit to try and accelerate the processing (based on this SO answer) @jit("float32[:](float32,float32,intp)", nopython=False, nogil=True) def calc_func(a, b, n): res = np.empty(n, dtype="float32") res[0] = 0 for i in range(1, n): res[i] = a * res[i - 1] + (1 - a) * (b ** (i - 1)) return res a = calc_func(0.988, 0.9988, 5000) I am getting a bunch of warnings/errors that I do not quite get. Would appreciate help in

第 430 期 Python 周刊

由 限于喜欢 提交于 2020-02-26 02:31:22
文章，教程和讲座 使你的 Python 程序运行快一点 链接: https://martinheinz.dev/blog/13 不喜欢 Python 语言的人总是说它的运行速度很慢。对于特定的程序（无论使用何种编程语言）, 它的快慢，很大程度上取决于编写该程序的开发人员。让我们尝试改善 Python 程序的性能, 证明那些人是错的 Numba 使 Python 运行速度提高了1000倍！ 链接: https://www.youtube.com/watch?v=x58W9A2lnQc Numba 是Python 和 Numpy 子集的即时编译器。该视频的前半部分主要介绍 Numba 基本信息，并着重介绍了大家在使用 Numba 时的一些常见错误。剩下的视频提出了一个基于现实世界的问题，在单线程和多线程情况下，使用 Numba 最多可获得1000倍的加速效果。 如何在 gevent（uWSGI 和 Gunicorn）中使用 Flask 链接: https://iximiuz.com/en/posts/flask-gevent-tutorial 创建异步 Flask Web 应用，并在 Nginx 反向代理后使用 uWSGI 或 Gunicorn 运行它。 ASGI 简介：异步 Python Web 生态系统的出现 链接: https://florimond.dev/blog

How do I use numba on a member function of a class?

由 牧云@^-^@ 提交于 2020-02-12 09:01:09
问题 I'm using the stable version of Numba 0.30.1. I can do this: import numba as nb @nb.jit("void(f8[:])",nopython=True) def complicated(x): for a in x: b = a**2.+a**3. as a test case, and the speedup is enormous. But I don't know how to proceed if I need to speed up a function inside a class. import numba as nb def myClass(object): def __init__(self): self.k = 1 #@nb.jit(???,nopython=True) def complicated(self,x): for a in x: b = a**2.+a**3.+self.k What numba type do I use for the self object? I

提高python运行速度的几个技巧

由 笑着哭i 提交于 2020-02-08 16:36:07
使用内置数据类型 内置数据类型非常快，尤其是与我们自定义的类型相比。 这主要是因为内置的数据类型是由 C 实现的，而在 Python 中写的代码运行速度实在无法与之相比。 尽量使用内置函数,去掉属性访问 import math import time def func(): lst = [] for i in range(1, 10000000): lst.append(math.sqrt(i)) # 直接调用 sqrt return lst start = time.time() lst = func() end = time.time() print(end-start) 运行时间：4.470336198806763秒 from math import sqrt # 直接引用特定函数或属性 import time def func(): lst = [] for i in range(1, 10000000): lst.append(sqrt(i)) # 直接调用 sqrt return lst start = time.time() lst = func() end = time.time() print(end-start) 运行时间：3.7398300170898438秒 因为在进行属性访问的时候啊，会调用这个对象的 __getattribute__ 或者 _

Numba support for cuda cooperative block synchronization?? Python numba cuda grid sync

由 ∥☆過路亽．° 提交于 2020-01-24 12:44:25
问题 Numba Cuda has syncthreads() to sync all thread within a block. How can I sync all blocks in a grid without exiting the current kernel? In C-Cuda there's a cooperativeBlocks library to handle this case. I can't find something like that in the Numba Docs. Why this matters a lot ! This sort of thing happens in reductions where one computes something in each block, then you want to find the maximum over the blocks. Trivially one could push these into the stream as two separate calls. This

Numba support for cuda cooperative block synchronization?? Python numba cuda grid sync

由 六眼飞鱼酱① 提交于 2020-01-24 12:44:07
问题 Numba Cuda has syncthreads() to sync all thread within a block. How can I sync all blocks in a grid without exiting the current kernel? In C-Cuda there's a cooperativeBlocks library to handle this case. I can't find something like that in the Numba Docs. Why this matters a lot ! This sort of thing happens in reductions where one computes something in each block, then you want to find the maximum over the blocks. Trivially one could push these into the stream as two separate calls. This

RuntimeError: cannot cache function '__jaccard': no locator available for file '/usr/local/lib/python3.7/site-packages/librosa/util/matching.py'

由 此生再无相见时 提交于 2020-01-24 04:07:18
问题 I am dockerising the flask application on windows10 machine.I get the below error after the docker run RuntimeError: cannot cache function '__jaccard': no locator available for file '/usr/local/lib/python3.7/site-packages/librosa/util/matching.py' The flask application runs fine locally on my machine. i referred to kind of similar post: numba caching issue: cannot cache function / no locator available for file i have added the user access permissions for the application in the Dockerfile. 1.

How to read file with Numba

由 主宰稳场 提交于 2020-01-16 17:46:18
问题 Is it possible for Numba to read a file? I tried using the standard Numpy method save and load and got a not-supported error. Is there any other format Numba can handle? If not, what should one do when one needs to read from a file and still wants to use the power of Numba? Here is what I tried: import numpy as np from numba import njit a = np.random.randn(400, 400) np.save('test', a) @njit def f(): a = np.load('test.npy') return a b = f() > TypingError: Failed at nopython (nopython frontend)

How to read file with Numba

由 浪尽此生 提交于 2020-01-16 17:46:09
问题 Is it possible for Numba to read a file? I tried using the standard Numpy method save and load and got a not-supported error. Is there any other format Numba can handle? If not, what should one do when one needs to read from a file and still wants to use the power of Numba? Here is what I tried: import numpy as np from numba import njit a = np.random.randn(400, 400) np.save('test', a) @njit def f(): a = np.load('test.npy') return a b = f() > TypingError: Failed at nopython (nopython frontend)

numba用户手册-11 实例

由 为君一笑 提交于 2020-01-15 00:59:37
numba用户手册 1.numba基础 2.@jit 3. 使用自动并行化@jit 4. 性能提升 5.创建ufunc 6.@jitclass 7. @cfunc 8. 提前编译代码AOT 9. numba线程 10. 调试 1.19。例子 1.19.1。曼德尔布罗 #! /usr/bin/env python # -*- coding: utf-8 -*- from __future__ import print_function, division, absolute_import from timeit import default_timer as timer from matplotlib.pylab import imshow, jet, show, ion import numpy as np from numba import jit @jit def mandel(x, y, max_iters): """ Given the real and imaginary parts of a complex number, determine if it is a candidate for membership in the Mandelbrot set given a fixed number of iterations. """ i = 0 c = complex
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