How to install CUDA in Google Colab - Cannot initialize CUDA without ATen_cuda library
I am trying to use cuda in Goolge Colab but while running my program I get the following error.


  RuntimeError: Cannot initialize CUDA without ATen_cuda library. PyTorch splits its backend into two shared libraries: a CPU library and a CUDA library; this error has occurred because you are trying to use some CUDA functionality, but the CUDA library has not been loaded by the dynamic linker for some reason.  The CUDA library MUST be loaded, EVEN IF you don't directly use any symbols from the CUDA library! One common culprit is a lack of -Wl,--no-as-needed in your link arguments; many dynamic linkers will delete dynamic library dependencies if you don't depend on any of their symbols.  You can check if this has occurred by using ldd on your binary to see if there is a dependency on *_cuda.so library.


I have the following libraries installed.

from os.path import exists
from wheel.pep425tags import get_abbr_impl, get_impl_ver, get_abi_tag
platform = '{}{}-{}'.format(get_abbr_impl(), get_impl_ver(), get_abi_tag())
cuda_output = !ldconfig -p|grep cudart.so|sed -e 's/.*\.\([0-9]*\)\.\([0-9]*\)$/cu\1\2/'
accelerator = cuda_output[0] if exists('/dev/nvidia0') else 'cpu'

!pip install -q http://download.pytorch.org/whl/{accelerator}/torch-0.4.1-

{platform}-linux_x86_64.whl torchvision
%matplotlib inline
%config InlineBackend.figure_format = 'retina'

import matplotlib.pyplot as plt
import time
import torch
from torch import nn
from torch import optim
import torch.nn.functional as F
from torchvision import datasets, transforms, models
!pip install Pillow==5.3.0
# import the new one
import PIL


And I am trying to run the following code.

for device in ['cpu', 'cuda']:

    criterion = nn.NLLLoss()
    # Only train the classifier parameters, feature parameters are frozen
    optimizer = optim.Adam(model.classifier.parameters(), lr=0.001)

    model.to(device)

    for ii, (inputs, labels) in enumerate(trainloader):

        # Move input and label tensors to the GPU
        inputs, labels = inputs.to(device), labels.to(device)

        start = time.time()

        outputs = model.forward(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        if ii==3:
            break

    print(f"Device = {device}; Time per batch: {(time.time() - start)/3:.3f} seconds")


Have you tried the following?
Go to Menu &gt; Runtime &gt; Change runtime.
Change hardware acceleration to GPU.
How to install CUDA in Google Colab GPU&#39;s
