I just started with PyTorch and tried working my way through the tutorials.
When I run the standard neural_networks_tutorial.py I get the following error:
Traceback (most recent call last):
  File "src/pytorch_tests/neural_networks_tutorial.py", line 98, in <module>
    out = net(input)
  File "/home/ssudholt/checkouts/pytorch-tests/env/local/lib/python2.7/site-packages/torch/nn/modules/module.py", line 206, in __call__
    result = self.forward(*input, **kwargs)
  File "src/pytorch_tests/neural_networks_tutorial.py", line 61, in forward
    x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))
  File "/home/ssudholt/checkouts/pytorch-tests/env/local/lib/python2.7/site-packages/torch/nn/modules/module.py", line 206, in __call__
    result = self.forward(*input, **kwargs)
  File "/home/ssudholt/checkouts/pytorch-tests/env/local/lib/python2.7/site-packages/torch/nn/modules/conv.py", line 237, in forward
    self.padding, self.dilation, self.groups)
  File "/home/ssudholt/checkouts/pytorch-tests/env/local/lib/python2.7/site-packages/torch/nn/functional.py", line 40, in conv2d
    return f(input, weight, bias)
TypeError: argument 0 is not a Variable

I ran the tutorial without any modifications:
import torch
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__()
        # 1 input image channel, 6 output channels, 5x5 square convolution
        # kernel
        self.conv1 = nn.Conv2d(1, 6, 5)
        self.conv2 = nn.Conv2d(6, 16, 5)
        # an affine operation: y = Wx + b
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        # Max pooling over a (2, 2) window
        x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))
        # If the size is a square you can only specify a single number
        x = F.max_pool2d(F.relu(self.conv2(x)), 2)
        x = x.view(-1, self.num_flat_features(x))
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

    def num_flat_features(self, x):
        size = x.size()[1:]  # all dimensions except the batch dimension
        num_features = 1
        for s in size:
            num_features *= s
        return num_features


net = Net()
net
print(net)

input = Variable(torch.randn(1, 1, 32, 32))
out = net(input)
print(out)

PyTorch was installed in a clean virtualenv through pip (version 0.1.12_2).
Google did non know anything about this error so I was hoping that maybe someone here can point me in the right direction.