# Versions
Python 3.10
Tensorflow 2.16.1
CUDA 12.3
CUDNN 8.9.7
skimage 0.24
sklearn 1.5.2
numpy 1.26.4
Official instructions to install Tensorflow: https://www.tensorflow.org/install/pip
conda create -n cas_main_dl python=3.10
conda activate cas_main_dl
# Either: GPU
pip install tensorflow[and-cuda]==2.16.1
# Or: CPU
pip install tensorflow==2.16.1
pip install wget notebook matplotlib pandas tqdm ipywidgets scikit-learn scikit-image
For detailed installation instructions, see https://www.tensorflow.org/install/pip
If you have forked the repository, you can run a notebook directly in Google Colab by using a link like this:
For Windows (in particular when using a GPU), it is strongly recommended to install and use Tensorflow within WSL2 (Windows Subsystem for Linux). Without GPU (CPU only), you can still also use native Windows if you want.
Install WSL2, see https://docs.microsoft.com/windows/wsl/install
To enable GPU support, one must also install CUDA Toolkit and CUDNN:
Good summary of needed steps: https://medium.com/@ali.abulhawa/installing-tensorflow-2-16-gpu-on-windows-wsl2-df73ac3446c9
wget https://developer.download.nvidia.com/compute/cuda/repos/wsl-ubuntu/x86_64/cuda-keyring_1.1-1_all.deb
sudo dpkg -i cuda-keyring_1.1-1_all.deb
sudo apt-get update
sudo apt-get -y install cuda-toolkit-12-3
- download https://developer.nvidia.com/downloads/compute/cudnn/secure/8.9.7/local_installers/12.x/cudnn-local-repo-ubuntu2204-8.9.7.29_1.0-1_amd64.deb/
- copy file to WSL home dir
sudo dpkg -i cudnn-local-repo-ubuntu2204-8.9.7.29_1.0-1_amd64.deb
sudo cp /var/cudnn-local-repo-ubuntu2204-8.9.7.29/cudnn-local-08A7D361-keyring.gpg /usr/share/keyrings/
sudo apt-get update
sudo apt-get install libcudnn8=8.9.7.29-1+cuda12.2