minpy使用GPU加速Numpy科学计算方式
作者：乌拉队长
这篇文章主要介绍了minpy使用GPU加速Numpy科学计算方式，具有很好的参考价值，希望对大家有所帮助。如有错误或未考虑完全的地方，望不吝赐教
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
use 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()
C = np.dot(A,B)
d2 = time.time() - start
print('numpy:', d2)
print(C)
print("%s" , d2/d1)
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 ]
...
[ 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脚本之家。