Pytorch CUDA安装配置

查看CUDA版本

使用nvidia-smi命令

在NVIDIA控制面板查看

安装Pytorch

打开官网PyTorch下滑到安装页面

选择对应版本复制命令进行安装

测试安装是否成功

import torch
print(torch.__version__)
print(torch.cuda.is_available())

import torch
import time
from torch import autograd
#GPU加速
print(torch.__version__)
print(torch.cuda.is_available())

a=torch.randn(10000,1000)
b=torch.randn(1000,10000)
print(a)
print(b)
t0=time.time()
c=torch.matmul(a,b)
t1=time.time()

print(a.device,t1-t0,c.norm(2))

device=torch.device('cuda')
print(device)
a=a.to(device)
b=b.to(device)

t0=time.time()
c=torch.matmul(a,b)
t2=time.time()
print(a.device,t2-t0,c.norm(2))


t0=time.time()
c=torch.matmul(a,b)
t2=time.time()

print(a.device,t2-t0,c.norm(2))

import torch
flag = torch.cuda.is_available()
print(flag)
 
ngpu= 1
# Decide which device we want to run on
device = torch.device("cuda:0" if (torch.cuda.is_available() and ngpu > 0) else "cpu")
print(device)
print(torch.cuda.get_device_name(0))
print(torch.rand(3,3).cuda())