AssertionError: torch not compiled with cuda enabled ( Fix )

AssertionError torch not compiled with cuda enabled

AssertionError: torch not compiled with Cuda enabled error occurs because of using cuda GPU enable syntax over normal PyTorch (CPU only ). There are multiple scenarios where you can get this error. Sometimes CUDA enablement is clear and visible. This is easy to fix by making it false or removing the same. But in some scenarios, It is indirectly calling Cuda which is explicitly not visible. Hence There we need to understand the internal working of such parameter or function which is causing the issue. Anyways in this article, we will go throw the most common reasons.


assertionerror: torch not compiled with cuda enabled ( Solution ) –

Solution 1: Switching from CUDA to normal version  –

Usually while compiling any neural network in PyTorch, we can pass cuda enable. If we simply remove the same it will remove the error. Refer to the below example, If you are using a similar syntax pattern then remove Cuda while compiling the neural network.

from torch import nn
net = nn.Sequential(
    nn.Linear(18*18, 80),
    nn.Linear(80, 80),
    nn.Linear(80, 10),

The correct way is –

assertionerror torch not compiled with cuda enabled solution
assertionerror torch not compiled with cuda enabled solution

Solution 2: Installing cuda supported Pytorch –

See the bottom line is that if you are facing such an incompatibility issue either you adjust your code according to available libraries in the system. Or we install the compatible libraries in our system to get rid of the same error.

You may any package managers to install cuda supported pytorch. Use any of the below commands –

conda –

conda install pytorch==1.11.0 torchvision==0.12.0 torchaudio==0.11.0 cudatoolkit=11.3 -c pytorch

pip –

pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 torchaudio==0.11.0 --extra-index-url

Solution 3:  set pin_memory=False –

This is one of the same categories where CUDA is not visible directly. But Internally if it is True then it copies the tensors into CUDA space for processing. To Avoid the same we have to make it False. Once more thing, By Default it is True. Hence we have to explicitly make it False while using the get_iterator function in DataLoader class.


torch not compiled with cuda enabled ( Similar Error )-

There are so many errors that have similar solutions but because of the specification added it looks bit different. Hence to avoid confusion, Here are some variations:

  1. Platform specifications: This error has generic solution with most of the platform like win10, mac, linux etc.
  2. Addition Modules: Sometimes we get this error in intermediate modules like detectron2 etc. But the solution will be generic in all the cases.
  3. Hardware Specifications: Not Only the Platform but the Underlying hardware like processors like AMD, Jetson, etc have the same impact and solution.
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Benefits of CUDA with Torch –

CUDA is a parallel processing framework which provides an application interface to deal with the graphic card utility of the system. In complex operations like deep learning model training where we have to run operations like backpropagation, we need multiprocessing. GPU provides great support for multiprocessing for that we need CUDA (NVIDIA). PyTorch or Tensorflow or any other deep learning framework required GPU handling for high performance. However, it works fine with the CPU in case of small datasets, fewer epochs, etc. But Typically the dataset for any state of art algorithm is usually large in volume. Hence we need CUDA with PyTorch ( Python binding of Torch).


Data Science Learner Team


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