Hey sorry in advanced if this is not how I should post a question. I am trying to get my tensorflow working on my gaming rig so I can get codes running faster than just Jupyter-lab on my macbook.

GPU : RTX 4070TI

CPU : AMD Ryzen 9 7900X 12-core

Ram : 32 GB DDR5

OS : POP!_OS 22.04 LTS

So I did pip install tensorflow[and-cuda], and also downloaded Cuda and Cudnn. I’ve tried using both cudnn8.7 and cuda 11.8 and even went as low as cuda 11.5 as listed from build from sources. when I run nvcc --version it also shows the cuda version being 11.8, and nvidia-smi it shows cuda 11.8 but tf still gives the following errors.

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>>> import tensorflow as tf
2023-11-15 21:41:00.977036: I tensorflow/core/util/port.cc:111] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2023-11-15 21:41:01.000163: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2023-11-15 21:41:01.000182: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2023-11-15 21:41:01.000205: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
2023-11-15 21:41:01.004826: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI AVX512_BF16 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.

Have anyone gotten cuda, and cudnn working on their pop os, if so which build and what did you do?