I want to share the procedure to work with your GPU with the new stable version of tensorflow 2.5 in case someone is having problems with the update.
I have noticed an improvement in training time.
According to official information
https://github.com/tensorflow/tensorflow/releases 98
Procedure:


Install Anaconda with Python 3.9
source: https://docs.conda.io/en/latest/miniconda.html 170


Install the latest version of your GPU driver.


Install Cuda Toolkit 11.2.2
source: https://developer.nvidia.com/cuda-toolkit-archive 434


Download Cudnn 8.1.1
source: https://developer.nvidia.com/rdp/cudnn-archive 455


Now just copy the files inside from each Cudnn folder and paste into your Cuda folder, follow this example.
source: https://docs.nvidia.com/deeplearning/cudnn/install-guide/index.html#install-windows 109
A. Copy \cuda\bin\cudnn*.dll to C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.2\bin.
B. Copy \cuda\include\cudnn*.h to C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.2\include.
C. Copy \cuda\lib\x64\cudnn*.lib to C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.2\lib\x64.


Go to your environment variables, Under “System Variables” click on “New” and create the CUDA PATH
detail:

Screenshot 2021-05-25 170730752×222 7.18 KB



Create a new conda environment


conda create --name TF-2.5 python=3.9
conda activate TF-2.5

install tensorflow and libraries

pip install tensorflow
pip install tensorflow-datasets
pip install tensorflow-hub
pip install jupyter
pip install numpy
pip install matplotlib
pip install pandas
if you need to use jupyter notebook with the new version of python and tensorflow (very important)
conda install -c conda-forge ipykernel

Register your environment

python -m ipykernel install --user --name TF-2.5 --display-name “Python 3.9 (Tensorflow 2.5)”

Testing your environment

jupyter notebook
detail:

Screenshot 2021-05-25 1727031368×1017 66.7 KB

that’s all works completely fine for me.
Regards.
Ricardo.