我试图在 Nvidia GPU 上训练一些神经网络，但似乎桌面环境（KDE）占用了 GPU：
$ nvidia-smi
Sat Apr 22 09:04:16 2017
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 375.39 Driver Version: 375.39 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 GeForce GTX 960M Off | 0000:01:00.0 Off | N/A |
| N/A 52C P0 N/A / N/A | 1295MiB / 2002MiB | 4% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 1139 G /usr/lib/xorg/Xorg 681MiB |
| 0 1591 G kwin_x11 50MiB |
| 0 1594 G /usr/bin/krunner 13MiB |
| 0 1596 G /usr/bin/plasmashell 126MiB |
| 0 2267 G ...el-token=FF7F1AB0E04D51461A7E5E08B2463625 136MiB |
+-----------------------------------------------------------------------------+
下面是我正在运行的 Python 代码：
import torch
import torchvision
import torchvision.transforms as transforms
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,
shuffle=True, num_workers=2)
testset = torchvision.datasets.CIFAR10(root='./data', train=False,
download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=4,
shuffle=False, num_workers=2)
classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
import matplotlib.pyplot as plt
import numpy as np
def imshow(img):
img = img / 2 + 0.5 # unnormalize
npimg = img.numpy()
plt.imshow(np.transpose(npimg, (1, 2, 0)))
# get some random training images
dataiter = iter(trainloader)
images, labels = dataiter.next()
imshow(torchvision.utils.make_grid(images))
print(' '.join('%5s' % classes[labels[j]] for j in range(4)))
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = x.view(-1, 16 * 5 * 5)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
net = Net()
net.cuda()
import torch.optim as optim
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
for epoch in range(2): # loop over the dataset multiple times
running_loss = 0.0
for i, data in enumerate(trainloader, 0):
# get the inputs
inputs, labels = data
# wrap them in Variable
inputs, labels = Variable(inputs.cuda()), Variable(labels.cuda())
# zero the parameter gradients
optimizer.zero_grad()
# forward + backward + optimize
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
# print statistics
running_loss += loss.data[0]
if i % 2000 == 1999: # print every 2000 mini-batches
print('[%d, %5d] loss: %.3f' %
(epoch + 1, i + 1, running_loss / 2000))
running_loss = 0.0
print('Finished Training')
错误：
Traceback (most recent call last):
File "<input>", line 64, in <module>
File "/home/kaiyin/virtualenvs/pytorch/lib/python3.5/site-packages/torch/nn/modules/module.py", line 147, in cuda
return self._apply(lambda t: t.cuda(device_id))
File "/home/kaiyin/virtualenvs/pytorch/lib/python3.5/site-packages/torch/nn/modules/module.py", line 118, in _apply
module._apply(fn)
File "/home/kaiyin/virtualenvs/pytorch/lib/python3.5/site-packages/torch/nn/modules/module.py", line 124, in _apply
param.data = fn(param.data)
File "/home/kaiyin/virtualenvs/pytorch/lib/python3.5/site-packages/torch/nn/modules/module.py", line 147, in <lambda>
return self._apply(lambda t: t.cuda(device_id))
File "/home/kaiyin/virtualenvs/pytorch/lib/python3.5/site-packages/torch/_utils.py", line 65, in _cuda
return new_type(self.size()).copy_(self, async)
RuntimeError: cuda runtime error (46) : all CUDA-capable devices are busy or unavailable at /b/wheel/pytorch-src/torch/lib/THC/generic/THCStorage.cu:66
car cat bird dog
如何禁止那些与 kde 相关的进程使用 GPU，而让它们使用英特尔显卡？