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
Procedure:
-
Install Anaconda with Python 3.9
source: https://docs.conda.io/en/latest/miniconda.html -
Install the latest version of your GPU driver.
-
Install Cuda Toolkit 11.2.2
source: https://developer.nvidia.com/cuda-toolkit-archive -
Download Cudnn 8.1.1
source: https://developer.nvidia.com/rdp/cudnn-archive
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
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 âSystem Variablesâ click on âNewâ and create the CUDA PATH
detail:
-
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 âPython 3.9 (Tensorflow 2.5)â
- Testing your environment
jupyter notebook
detail:
thatâs all works completely fine for me.
Regards.
Ricardo.
