Dear team, I am installing tensorflow on my gpu laptop as per the installation instructions given in the website. I get the following error.
023-04-03 15:35:42.735382: I tensorflow/core/util/port.cc:110] 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.
2023-04-03 15:35:42.760248: 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 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-04-03 15:35:43.175060: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
2023-04-03 15:35:43.612999: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:996] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at linux/sysfs-bus-pci at v6.0 · torvalds/linux · GitHub
2023-04-03 15:35:43.629685: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1956] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at Install TensorFlow with pip for how to download and setup the required libraries for your platform.
Skipping registering GPU devices…
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I am installing on Ubuntu 22.04 having nvidia 3050 rtx graphics card.
For your convenience it is recommended that you automate it with the following commands. The system paths will be automatically configured when you activate this conda environment.
On Windows 11, WSL 2, the installation procedure fails at the following step:
>pip install nvidia-cudnn-cu11==8.6.0.163
ERROR: Could not find a version that satisfies the requirement nvidia-cudnn-cu11==8.6.0.163 (from versions: 0.0.1.dev5)
ERROR: No matching distribution found for nvidia-cudnn-cu11==8.6.0.163
I have tried copying the cudann files from C:\Program Files\NVIDIA\CUDNN\v8.6.0.163 into C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8 but that makes no difference. I wonder if it’s the pip install of nvidia-cudnn-cu11==8.6.0.163 that is out of date?
>pip install nvidia-cudnn-cu11==8.6.0.163
ERROR: Could not find a version that satisfies the requirement nvidia-cudnn-cu11==8.6.0.163 (from versions: 0.0.1.dev5)
ERROR: No matching distribution found for nvidia-cudnn-cu11==8.6.0.163
We are successfully able to install nvidia-cudnn-cu11==8.6.0.163. Please refer to the below screesnshot