# Install Tensorflow-gpu on Windows
https://www.tensorflow.org/install/pip?hl=zh-tw
tensorflow version link at bottom

### 1. install nvidia driver
### 2. install cuda tool kit
### 3. install CUDNN
https://docs.nvidia.com/deeplearning/cudnn/install-guide/index.html#install-windows
### version check
https://docs.nvidia.com/deeplearning/cudnn/support-matrix/index.html

### 4. install conda or virtualenv
## Example
python\==3.8
cuda\==11.8
https://developer.nvidia.com/cuda-11-8-0-download-archive?target_os=Windows&target_arch=x86_64&target_version=11&target_type=exe_local
cudnn\==8.9.x for cuda 11.x
https://developer.nvidia.com/rdp/cudnn-download

# unzip downloaded file
# move /include, /bin, lib to C:\Program Files\NVIDIA\CUDNN\v8.x
# add C:\Program Files\NVIDIA\CUDNN\v8.x\bin to path 

tensorflow-gpu\==2.6.0

# install nvidia driver
# install nvidia toolkit
# install CUDNN
conda create --name tensorflow-gpu python=3.8
conda activate tensorflow-gpu
pip install https://storage.googleapis.com/tensorflow/windows/gpu/tensorflow_gpu-2.6.0-cp38-cp38-win_amd64.whl
pip install protobuf==3.19.0 keras==2.6.0 tensorflow==2.6.0
# if necessary 
# pip uninstall anything not compatiable with tensorflow
# pip install tensorflow again
python 
>>> import tensorflow as tf
>>> tf.test.is_gpu_available() # true