Inquiry Regarding TensorFlow GPU Installation on RHEL 9.3

Hello TensorFlow community,

I’m currently using RHEL 9.3 and aiming to enable GPU compute support for TensorFlow on my system. I’d appreciate some guidance regarding the specific versions of Python, TensorFlow, and supporting packages required for this setup.

1.Python Version: Could you please advise on the compatible version(s) of Python for TensorFlow GPU installation on RHEL 9.3?
2.TensorFlow Version: Which version of TensorFlow is recommended for use with GPU compute on RHEL 9.3? Are there any known compatibility issues I should be aware of?
3.CUDA Toolkit: What version of the CUDA Toolkit is compatible with the recommended TensorFlow version for RHEL 9.3? Additionally, any specific considerations or tips for installing CUDA Toolkit on this platform?
4.cuDNN: For enabling GPU acceleration with TensorFlow, do you have recommendations regarding the compatible version of cuDNN for RHEL 9.3?
5.GPU Driver: Which GPU driver version(s) are recommended for use with TensorFlow GPU on RHEL 9.3?
6.Supporting Packages: Apart from TensorFlow, CUDA Toolkit, cuDNN, and GPU drivers, are there any other supporting packages or dependencies I should consider installing for optimal performance and compatibility?
7.Configuration and Troubleshooting: Are there any specific configuration steps or troubleshooting tips I should be aware of during the installation process or while using TensorFlow with GPU compute on RHEL 9.3?
I’m eager to harness the power of GPU acceleration for my TensorFlow workflows on RHEL 9.3, and any insights or recommendations from the community would be greatly appreciated.

Thank you in advance for your assistance!

Krishnan S

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