Pytorch says that CUDA is not available Troubleshooting Guide for Data Scientists

If you’re a data scientist working with PyTorch, you may have encountered the following error message: RuntimeError: CUDA error: no CUDA-capable device is detected. This error indicates that PyTorch is unable to detect a CUDA-capable GPU on your system. In this blog post, we’ll explore some common causes of this error and provide troubleshooting tips to help you resolve it.

Table of Contents

1. What is CUDA?
2. Common Causes of the no CUDA-capable device is detected Error
3. Troubleshooting Tips
4. Conclusion

What is CUDA?

CUDA is a parallel computing platform and programming model developed by NVIDIA. It enables developers to use NVIDIA GPUs for general-purpose computing, including deep learning. PyTorch relies on CUDA to accelerate computations on GPUs, which can significantly speed up training of deep learning models.

Common Causes of the no CUDA-capable device is detected Error

1. Missing or incompatible CUDA driver: PyTorch requires a compatible version of the CUDA driver to be installed on your system. If the driver is missing or incompatible, PyTorch will not be able to detect a CUDA-capable device. You can check if you have a compatible CUDA driver installed by running the following command in your terminal:

   nvcc --version
   

   If you don’t have a compatible CUDA driver installed, you can download and install the latest version from the NVIDIA website.

2. Missing or incompatible CUDA toolkit: In addition to the CUDA driver, PyTorch also requires a compatible version of the CUDA toolkit to be installed. The CUDA toolkit includes libraries and tools that are needed for GPU computing. You can check if you have a compatible CUDA toolkit installed by running the following command in your terminal:

   nvcc --version
   

   If you don’t have a compatible CUDA toolkit installed, you can download and install the latest version from the NVIDIA website.

3. Incorrect PyTorch installation: If you have multiple versions of PyTorch installed on your system, it’s possible that you’re using a version that doesn’t support CUDA. Make sure that you have installed the correct version of PyTorch that supports CUDA by running the following command in your Python environment:

   import torch
   print(torch.version.cuda)
   

   This will print the version of CUDA that is supported by your version of PyTorch.

4. Missing or incorrect environment variables: PyTorch requires several environment variables to be set correctly in order to detect CUDA devices. These include CUDA_HOME, LD_LIBRARY_PATH, and PATH. Make sure that these environment variables are set correctly by running the following command in your terminal:

   echo $CUDA_HOME $LD_LIBRARY_PATH $PATH
   

   This will print the values of these environment variables. If any of the variables are not set correctly, you can set them manually by adding the following lines to your ~/.bashrc file:

   export CUDA_HOME=/usr/local/cuda
   export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CUDA_HOME/lib64
   export PATH=$PATH:$CUDA_HOME/bin
   

Troubleshooting Tips

If you’ve checked the common causes listed above and you’re still encountering the “no CUDA-capable device is detected” error, here are some additional troubleshooting tips:

1. Check your GPU: Make sure that your GPU is properly installed and configured. You can check if your GPU is detected by running the following command in your terminal:

   lspci | grep -i nvidia
   

   This will list all NVIDIA GPUs that are detected on your system.

2. Check your GPU memory: PyTorch requires a minimum amount of GPU memory to be available in order to run. Make sure that your GPU has enough memory available by running the following command in your Python environment:

   import torch
   print(torch.cuda.get_device_properties(0).total_memory)
   

   This will print the total amount of memory available on your GPU.

3. Check your PyTorch installation: If you’ve installed PyTorch using a package manager (such as pip or conda), try uninstalling and reinstalling PyTorch to ensure that it’s installed correctly.

4. Check your system logs: Check your system logs for any errors related to CUDA or your GPU. You can view system logs by running the following command in your terminal:

   dmesg | grep -i cuda
   

   This will list any CUDA-related errors in your system logs.

Conclusion

The no CUDA-capable device is detected error in PyTorch can be caused by a variety of issues, including missing or incompatible CUDA drivers or toolkits, incorrect PyTorch installation, missing or incorrect environment variables, GPU issues, and insufficient GPU memory. By following the troubleshooting tips outlined in this blog post, you should be able to resolve this error and get back to training your deep learning models on GPUs.

SHARE:

About Saturn Cloud

Saturn Cloud is your all-in-one solution for data science & ML development, deployment, and data pipelines in the cloud. Spin up a notebook with 4TB of RAM, add a GPU, connect to a distributed cluster of workers, and more. Request a demo today to learn more.