Update environment from CUDA10.2 to CUDA 11.8! Adapt to 4090 and A100!#100
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HUANGYming wants to merge 1 commit intoxuebinqin:mainfrom
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Update environment from CUDA10.2 to CUDA 11.8! Adapt to 4090 and A100!#100HUANGYming wants to merge 1 commit intoxuebinqin:mainfrom
HUANGYming wants to merge 1 commit intoxuebinqin:mainfrom
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Hello author, I have been using the A100 to train the ISNet model. However, I encountered a problem with CUDA incompatibility. To resolve this issue, I spent some time upgrading the CUDA-related packages and conducting compatibility checks. Now, ISNet can run on newer architecture GPUs like the A100/4090. I have exported the upgraded environment configuration to share with you, hoping to help more people avoid the pain of environment upgrading. The two environment configuration files I am providing are compatible with CUDA 11.8. Using this environment, ISNet can run on GPUs with Ampere architecture and earlier, such as the 30 series cards, 40 series cards, A100, A10, etc. Except for the H100, which requires a CUDA 12+ environment, CUDA 11.8 currently supports the vast majority of Nvidia graphics cards.
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Thanks a lot for this @HUANGYming ! This works like a charm. Is it possible to get it merged @xuebinqin ? |
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Hello author, I have been using the A100 to train the ISNet model. However, I encountered a problem with CUDA incompatibility. To resolve this issue, I spent some time upgrading the CUDA-related packages and conducting compatibility checks. Now, ISNet can run on newer architecture GPUs like the A100/4090. I have exported the upgraded environment configuration to share with you, hoping to help more people avoid the pain of environment upgrading.
The two environment configuration files I am providing are compatible with CUDA 11.8. Using this environment, ISNet can run on GPUs with Ampere architecture and earlier, such as the 30 series cards, 40 series cards, A100, A10, etc. Except for the H100, which requires a CUDA 12+ environment, CUDA 11.8 currently supports the vast majority of Nvidia graphics cards.
In addition, I have provided three configuration methods for the CUDA 11.8 environment in my GitHub. If you are unable to configure the environment through yaml and txt, please go to my github and download the Conda environment compression package through onedrive, and directly extract it for use.