RuntimeError: NVTX functions not installed. Are you sure you have a CUDA build? #215
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Hello everyone, I have used RF diffusion once, however, once I installed Protein MPNN and AF2 for further processing, I keep getting the error "RuntimeError: NVTX functions not installed. Are you sure you have a CUDA build?" when I try to use RF diffusion, is there any suggestion as to how I can overcome it? |
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Replies: 2 comments 5 replies
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Generally this means that you have the CPU-only build of PyTorch installed in your Conda environment. First off, do you have an NVIDIA CPU with CUDA support? Usually you can run If you do have at least CUDA 11.1 support, then try a |
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Hi, thank you so much. I had Nvidia CPU with cuda support but I had multiple cuda toolkit versions which led to this error. I had cuda toolkit version clashes, just changed the conda env it worked for me, however, I do not know how to go about it for DLbinder design, in all likelihood I will run into this issue again. |
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Once you have your Conda environment set up, you should be able to keep using it as-is. If you need to re-install the Conda environment (e.g. for a different user or on a different machine), you'll unfortunately need to debug the CUDA version issues again. But if you keep track of which re-installation commands you used for a particular machine/CUDA version, that hopefully should be easier next time. |
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Hello, sorry to bother you , my environment was working fine until it wasn't, I am not sure why .. I ran RFdiffusion to generate adjacency pytorch and secondary structure files and once I got them I started my diffusion protocol and I keep getting this error I have not changed a thing : File "/home/zbukhari/RFdiffusion/scripts/../scripts/run_inference.py", line 94, in main Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace. when I do conda list | grep torch I have these: conda list | grep torch |
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The "cpu" there is your issue. It's looking for the CUDA version of pytorch and you don't have it. I'm not sure why it worked for you previously, but it may be that you're using slightly different settings this time (or doing a more extensive computation). I would indeed recommend trying to install the CUDA versions of pytorch if you can. Not only will that speed the computation, RFDiffusion uses NVIDIA's version of se3_transformer, which limits the incentive to make working on CPUs easy. -- That said, I think there's ways of forcing a CPU-only installation, but I'm not personally familiar with them, and I'm not sure how well those work if you also have a CUDA-capable GPU installed. As mentioned, you likely need to try re-installing pytorch in the conda environment, trying to get the cuda version. You may have luck using |
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It worked, thanks |
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Thanks for the tip it worked!
I just did conda install -c pytorch pytorch
…On Mon, Apr 8, 2024 at 6:16 AM Rocco Moretti ***@***.***> wrote:
pytorch 1.9.1 cpu_py39hc5866cc_3 conda-forge
The "cpu" there is your issue. It's looking for the CUDA version of
pytorch and you don't have it. I'm not sure why it worked for you
previously, but it may be that you're using slightly different settings
this time (or doing a more extensive computation). I would indeed recommend
trying to install the CUDA versions of pytorch if you can. Not only will
that speed the computation, RFDiffusion uses NVIDIA's version of
se3_transformer, which limits the incentive to make working on CPUs easy.
-- That said, I think there's ways of forcing a CPU-only installation, but
I'm not personally familiar with them, and I'm not sure how well those work
if you also have a CUDA-capable GPU installed.
As mentioned, you likely need to try re-installing pytorch in the conda
environment, trying to get the cuda version. You may have luck using -c
pytorch on the installation command line to try to force use of the
pytorch channel (versus the conda-forge channel it looks like you're
currently using).
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Generally this means that you have the CPU-only build of PyTorch installed in your Conda environment.
First off, do you have an NVIDIA CPU with CUDA support? Usually you can run
nvidia-smito get a report, and see the maximum CUDA version your hardware supports. For the provided environment files, this should be 11.1 or greater. If your max supported CUDA version is less than that, or you don't have an CUDA-capable NVIDIA GPU, then you'll have to adjust your conda installation for it.If you do have at least CUDA 11.1 support, then try a
conda list | grep torchwith the RFDiffusion environment activated. You should see "pytorch" as one of the lines. You should be able to also see the deta…