RuntimeError: Detected that PyTorch and torch_scatter were compiled with different CUDA versions. PyTorch has CUDA version 11.0 and torch_scatter has CUDA version 10.1. Please reinstall the torch_scatter that matches your PyTorch install.这个错误怎么解决

这个错误是由于 PyTorch 和 torch_scatter 使用了不同的 CUDA 版本导致的。解决方法是重新安装与你使用的 PyTorch 版本相匹配的 torch_scatter。

首先，可以使用以下命令检查 PyTorch 的 CUDA 版本：

python -c "import torch; print(torch.version.cuda)"

然后，可以在 PyTorch 的官方网站上找到与你的 CUDA 版本相匹配的 torch_scatter 版本。例如，如果你正在使用 CUDA 11.0，可以在以下链接中找到相应的 torch_scatter 版本：

https://pytorch-geometric.com/whl/torch-1.9.0%2Bcu111.html

下载与你的 PyTorch 版本相匹配的 torch_scatter 安装包，然后使用以下命令进行安装：

pip install <torch_scatter安装包路径>

安装完成后，再次运行代码，应该就能够正常运行了。

相关问题

RuntimeError: Detected that PyTorch and torchvision were compiled with different CUDA versions. PyTorch has CUDA Version=11.7 and torchvision has CUDA Version=11.8. Please reinstall the torchvision that matches your PyTorch install.

这个错误通常是因为PyTorch和torchvision使用的CUDA版本不一致导致的。你需要重新安装与你使用的PyTorch版本相匹配的torchvision。你可以通过以下命令来安装特定版本的torchvision：

pip install torchvision==0.10.0+cu110 -f https://download.pytorch.org/whl/cu110/torch_stable.html

请注意，上面的命令是安装与CUDA 11.0和PyTorch 1.9.0相匹配的torchvision。如果你使用不同版本的PyTorch或CUDA，请将`cu110`替换为你使用的CUDA版本。

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.