My PC operates on a Windows platform and is equipped with an RTX 5070 Ti graphics card, utilizing CUDA version 12. I want to use tensorflow-gpu, but the latest version of TensorFlow that is compatible with a Windows environment is 2.10, which works with CUDA 11.2. When I install CUDA 11.2 without considering GPU compatibility, I encounter the following issue. " 2025-04-03 16:25:10.834489: W tensorflow/core/common_runtime/gpu/gpu_device.cc:2027] TensorFlow was not built with CUDA kernel binaries compatible with compute capability 12.0. CUDA kernels will be jit-compiled from PTX, which could take 30 minutes or longer. 2025-04-03 16:25:10.834869: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX AVX2 To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags. 2025-04-03 16:25:10.836145: W tensorflow/core/common_runtime/gpu/gpu_device.cc:2027] TensorFlow was not built with CUDA kernel binaries compatible with compute capability 12.0. CUDA kernels will be jit-compiled from PTX, which could take 30 minutes or longer." How can I effectively utilize TensorFlow in a GPU environment on my system? Thanks in advance! Continue reading...
