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 properly installed or is not available on your system. To solve this issue, you can try the following steps: 1. Make sure you have a compatible NVIDIA GPU installed on your system. PyTorch requires CUDA-compatible GPUs for GPU acceleration. 2. Install the CUDA toolkit that is compatible with your GPU. You can download the CUDA toolkit from the NVIDIA website. Make sure to select the appropriate version that matches your GPU and operating system. 3. After installing the CUDA toolkit, make sure to add the CUDA binaries to your system's PATH environment variable. This will allow PyTorch to locate the CUDA library. 4. Install the appropriate version of PyTorch that supports CUDA. You can install PyTorch with CUDA support using pip or conda. For example, if you are using pip, you can run the following command: ``` pip install torch torchvision torchaudio cudatoolkit=<CUDA_VERSION> ``` Replace `<CUDA_VERSION>` with the version of CUDA toolkit you installed. 5. Restart your Python interpreter or Jupyter notebook after installing PyTorch to ensure the changes take effect. By following these steps, you should be able to resolve the "Cannot initialize CUDA without ATen_cuda library" error and use CUDA functionality in PyTorch.