installed cuda version 12.2 does not match the version torch was compiled with 12.1 解决方案

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  本文主要介绍了installed cuda version 12.2 does not match the version torch was compiled with 12.1 解决方案,希望能对使用deepspeed的同学们有所帮助。

1. 问题描述

  今天在运行deepspeed代码时,却遇到了installed cuda version 12.2 does not match the version torch was compiled with 12.1 解决方案的错误提示,具体报错信息如下图所示:
在这里插入图片描述

  在经过了亲身的实践后,终于找到了解决问题的方案,最终将逐步的操作过程总结如下。希望能对遇到同样bug的同学们有所帮助。

### PyTorch CUDA 12.2 Compatible Version Installation and Configuration For ensuring that the installed PyTorch version is compatible with CUDA 12.2, it's important to verify compatibility between specific versions of PyTorch and CUDA as not all combinations are supported or tested together[^1]. The Python environment setup plays a crucial role in determining which CUDA version PyTorch uses. To install a PyTorch version compatible with CUDA 12.2: #### Checking Compatibility Before proceeding with installation, check official documentation sources like the PyTorch website for tables indicating which PyTorch releases support CUDA 12.2 specifically. This ensures selecting an appropriate release without unnecessary complications from mismatched dependencies. #### Installing via Conda (Recommended Method) Using Anaconda can simplify managing environments along with their associated packages including those built against particular CUDA versions. ```bash conda install pytorch torchvision torchaudio cudatoolkit=12.2 -c pytorch -c nvidia ``` This command installs PyTorch alongside its common libraries while specifying `cudatoolkit` at version 12.2 directly through conda channels provided by both NVIDIA and PyTorch teams. #### Verifying Installation After completing the installation process, verification steps help confirm everything works correctly within your development environment. ```python import torch print(torch.cuda.is_available()) print(torch.version.cuda) ``` The first line checks whether CUDA-capable devices were detected properly; expecting output similar to `True`. Meanwhile, printing out `torch.version.cuda` should reflect "12.2", aligning with expectations set during setup. --related questions-- 1. How does one determine if existing installations conflict when trying to switch CUDA versions? 2. What alternatives exist besides using Anaconda for setting up GPU-accelerated deep learning frameworks on Linux systems? 3. Can you provide guidance on troubleshooting issues encountered after attempting these instructions? 4. Is there any performance difference noticeable between different methods used here for installing PyTorch configured for CUDA 12.2?
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