Hi.

I just installed pytorch on linuxmint 18.3. Cuda 9.0.

I am running one simple program (no cuda on it).

```
import torch
print(torch.__version__)
import numpy as np
import matplotlib.pyplot as plt
from torch.autograd import Variable
# Training Data
def get_data():
train_X = np.asarray([3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167,
7.042,10.791,5.313,7.997,5.654,9.27,3.1])
train_Y = np.asarray([1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221,
2.827,3.465,1.65,2.904,2.42,2.94,1.3])
dtype = torch.FloatTensor
X = Variable(torch.from_numpy(train_X).type(dtype),requires_grad=False).view(17,1)
y = Variable(torch.from_numpy(train_Y).type(dtype),requires_grad=False)
return X,y
def plot_variable(x,y,z='',**kwargs):
l = []
for a in [x,y]:
if type(a) == Variable:
l.append(a.data.numpy())
plt.plot(l[0],l[1],z,**kwargs)
def get_weights():
w = Variable(torch.randn(1),requires_grad = True)
b = Variable(torch.randn(1),requires_grad=True)
return w,b
def simple_network(x):
y_pred = torch.matmul(x,w)+b
return y_pred
def loss_fn(y,y_pred):
loss = (y_pred-y).pow(2).sum()
for param in [w,b]:
if not param.grad is None: param.grad.data.zero_()
loss.backward()
print("Loss.data:", loss.data)
print("Loss.data[0]:", loss.data[0])
return loss.data[0]
def optimize(learning_rate):
w.data -= learning_rate * w.grad.data
b.data -= learning_rate * b.grad.data
learning_rate = 1e-4
print("Starting")
x,y = get_data() # x - represents training data,y - represents target variables
w,b = get_weights() # w,b - Learnable parameters
for i in range(500):
print("i:", i)
y_pred = simple_network(x) # function which computes wx + b
loss = loss_fn(y, y_pred) # calculates sum of the squared differences of y and y_pred
if i % 50 == 0:
print(loss)
optimize(learning_rate) # Adjust w,b to minimize the loss
x_numpy = x.data.numpy()
plot_variable(x,y,'ro')
plot_variable(x,y_pred,label='Fitted line')
```

loss.backward() hangs.

Thanks.