minpy使用GPU加速Numpy科学计算方式
目录
- minpy使用GPU加速Numpy科学计算
- Install mxnet and install minpy
- 总结
minpy使用GPU加速Numpy科学计算
minpy是一个基于MXNet的支持GPU的加速Numpy计算的库,用法和Numpy几乎一样,使用:
import scipy.io as sio import matplotlib as plt import minpy.numpy as np
使用时只需要在Numpy前面加上minpy,就可以像Numpy一开发者_Python学习样使用它进行矩阵运算。
甩一条MXNet官网链接
minpy安装起来也很简单:
先安装MXNet依赖
# 安装cuda10.1版本的MXNet pip install mxnet-cu101 # 如果你的cuda版本为10.0,则执行下面的命令,其他版本同理 pip install mxnet-cu100
然后安装minpy:
pip install minpy
然后就可以正常使用了。
Install mxnet and install minpy
make sure the things below.
- 1.your MAChine has a nvidia gpu.
- 2.installed gpu cuda and cudnn.
how to install mxnet?
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple --upgrade mxnet-cu90
us编程e cu90 because my cuda version is 9.0
then install minpy
pip install minpy
then is a demo to test minpy gpu acceleration.
import time import numpy as np import numpy.random as random import minpy.numpy as mnp def main(): random.seed(0) X = random.randn(10000, 16000) A = np.array(X,dtype=np.float32) Y = random.randn(16000, 5000) B = np.array(Y,dtype=np.float32) print("A.shape:%s" ,A.shape) print("B.shape:%s" ,B.shape) start = time.time() C = mnp.dot(A,B) d1 = time.time() - start print('minpy numpy:', d1) print(C) start = time.time() js C = np.dot(A,B) d2 = time.time() - start print('numpyjs:', d2) print(C) print("%s" , d2/d1) php if __name__ == '__main__': main()
output
A.shape:%s (10000, 16000) B.shape:%s (16000, 5000) minpy numpy: 0.3046295642852783 [[-129.23964 34.24473 205.77763 ... 64.57458 -134.04288 -282.5226 ] [ 56.055874 151.66455 4.534541 ... -59.855354 77.807755 102.97847 ] [ 53.7853 -133.20685 -114.16803 ... -78.15841 -22.429447 -100.71634 ] ... 编程[ 18.944311 -179.30074 -114.42271 ... -22.20309 -29.131681 16.166618 ] [ -5.1453457 -11.761197 -28.63139 ... -236.34016 -67.44423 -50.811813 ] [ 137.46251 -77.67743 -74.262535 ... -25.249132 83.94517 -14.008699 ]] numpy: 3.323066234588623 [[-129.23964 34.24473 205.77763 ... 64.57458 -134.04288 -282.5226 ] [ 56.055874 151.66455 4.534541 ... -59.855354 77.807755 102.97847 ] [ 53.7853 -133.20685 -114.16803 ... -78.15841 -22.429447 -100.71634 ] ... [ 18.944311 -179.30074 -114.42271 ... -22.20309 -29.131681 16.166618 ] [ -5.1453457 -11.761197 -28.63139 ... -236.34016 -67.44423 -50.811813 ] [ 137.46251 -77.67743 -74.262535 ... -25.249132 83.94517 -14.008699 ]] %s 10.908548034020265
总结
以上为个人经验,希望能给大家一个参考,也希望大家多多支持我们。
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