An AssertionError: Torch not compiled with CUDA enabled is a common error that occurs when a user tries to run a PyTorch script on a machine with a CUDA-enabled GPU, but PyTorch has not been properly installed with CUDA support. In this article, we will discuss what causes this error, how to check if your machine has a CUDA-enabled GPU, and how to properly install PyTorch with CUDA support.
What Causes the Error?
PyTorch is a powerful machine learning library that allows for the creation and training of deep learning models. One of its main features is its ability to utilize CUDA, a parallel computing platform and API for GPUs, to greatly speed up the training process. However, in order to take advantage of CUDA, PyTorch must be installed with CUDA support.
If PyTorch is installed without CUDA support, and a user tries to run a script that utilizes CUDA functionality, the script will raise an AssertionError: Torch not compiled with CUDA enabled because the library was not compiled to work with CUDA.
Check for CUDA-enabled GPU
Before installing PyTorch, you should check if your machine has a CUDA-enabled GPU. On Windows, you can check this by going to the Device Manager and looking for an entry under Display adapters. On Linux, you can use the command lspci | grep -i nvidia to check for NVIDIA GPUs. On MacOS, you can use the command system_profiler SPDisplaysDataType to check for NVIDIA GPUs.
Install PyTorch with CUDA Support
If your machine has a CUDA-enabled GPU, you can install PyTorch with CUDA support by running the following command:
pip install torch torchvision torchaudio -f https://download.pytorch.org/whl/cu111/torch_stable.html
The above command installs the latest version of PyTorch and its dependencies with CUDA 11.1 support. Replace cu111 with the version of CUDA that your GPU supports.
Alternatively, you can also install PyTorch using conda by running the following command:
conda install pytorch torchvision torchaudio -c pytorch
Example
Here's an example of a PyTorch script that raises the AssertionError: Torch not compiled with CUDA enabled if PyTorch is not installed with CUDA support:
import torch
if not torch.cuda.is_available():
raise AssertionError("Torch not compiled with CUDA enabled")
else:
print("PyTorch with CUDA support is available!")
If you run this script and PyTorch is installed with CUDA support, you should see the message "PyTorch with CUDA support is available!" printed to the console. If PyTorch is not installed with CUDA support, the script will raise the AssertionError: Torch not compiled with CUDA enabled.
In conclusion, the AssertionError: Torch not compiled with CUDA enabled occurs when a user tries to run a PyTorch script on a machine with a CUDA-enabled GPU, but PyTorch has not been properly installed with CUDA support. To avoid this error, you should check if your machine has a CUDA-enabled GPU, and then install PyTorch with CUDA support using pip
How to Check Which CUDA Version is Installed
Once PyTorch is installed with CUDA support, it's a good idea to check which version of CUDA is installed on your machine. This can be done by running the following command:
nvcc --version
This will print the version of CUDA that is currently installed on your machine. It's important to ensure that the version of CUDA installed on your machine matches the version specified when installing PyTorch.
Utilizing CUDA with PyTorch
Once PyTorch is installed with CUDA support, you can utilize CUDA to speed up the training process of your deep learning models. One of the easiest ways to do this is by moving your model and data to a CUDA-enabled device using the .to() method. Here's an example:
import torch
# Create a model
model = torch.nn.Linear(10, 10)
# Move the model to the GPU
model.to('cuda')
# Create some data
data = torch.randn(5, 10)
# Move the data to the GPU
data = data.to('cuda')
# Perform computations on the GPU
output = model(data)
In this example, we create a simple linear model and move it to the GPU using the .to() method. We also create some data and move it to the GPU. All computations performed on the model and data will now be done on the GPU, greatly speeding up the training process.
Limitations
It's important to note that not all PyTorch operations can be accelerated with CUDA. Some operations, such as indexing and slicing, are not supported on the GPU and will need to be performed on the CPU. Additionally, not all hardware configurations support CUDA acceleration. It's important to check the CUDA capabilities of your GPU before attempting to utilize CUDA with PyTorch.
Conclusion
In this article, we have discussed the AssertionError: Torch not compiled with CUDA enabled error and how to properly install PyTorch with CUDA support. We also discussed how to check which version of CUDA is installed, how to utilize CUDA with PyTorch, and some of the limitations of CUDA acceleration. By following the steps outlined in this article, you can ensure that your PyTorch scripts can take full advantage of CUDA acceleration to speed up the training process of your deep learning models.
Popular questions
- What causes the "AssertionError: Torch not compiled with CUDA enabled" error?
This error occurs when a user tries to run a PyTorch script on a machine with a CUDA-enabled GPU, but PyTorch has not been properly installed with CUDA support. PyTorch must be installed with CUDA support in order to take advantage of CUDA acceleration.
- How do I check if my machine has a CUDA-enabled GPU?
On Windows, you can check this by going to the Device Manager and looking for an entry under Display adapters. On Linux, you can use the command "lspci | grep -i nvidia" to check for NVIDIA GPUs. On MacOS, you can use the command "system_profiler SPDisplaysDataType" to check for NVIDIA GPUs.
- How do I install PyTorch with CUDA support?
You can install PyTorch with CUDA support by running the command "pip install torch torchvision torchaudio -f https://download.pytorch.org/whl/cu111/torch_stable.html", where cu111 should be replaced with the version of CUDA that your GPU supports.
- How do I check which version of CUDA is installed on my machine?
You can check which version of CUDA is installed on your machine by running the command "nvcc –version".
- How do I utilize CUDA with PyTorch?
Once PyTorch is installed with CUDA support, you can utilize CUDA to speed up the training process of your deep learning models. One of the easiest ways to do this is by moving your model and data to a CUDA-enabled device using the .to() method. You can also use the torch.cuda.is_available() to check if CUDA is available and use torch.backends.cudnn.enabled = True to enable the cuDNN library which will make the computation even faster.
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PyTorch
