A guide to setup tensorflow 2.8 with conda on stour. In short, you should be able to setup the environment by running from a bash shell in which conda has been setup using conda init bash
./setup_env.sh <env_name>You may need to chmod +x setup_env.sh to get the right permissions to run.
In the environment.yaml file, we setup the conda environment by installing CUDA from the nvidia channel and some other things from the standard anaconda channel. Then it installs tensorflow 2.8.0 using pip because tensorflow 2.8.0 is not avaiable through conda as of writing.
This, however, isn't enough because the tensorflow install will not be able to find the CUDA libraries by default.
I'm not sure exactly why this doesn't happen inherently but we can make it work by including the lib folder from the conda environment in LD_LIBRARY_PATH.
In order to make sure that the correct libraries are automatically included when an environment is activated, we can use the instructions from https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#saving-environment-variables for setting environment variables. Essentially we create the <conda_dir>/etc/conda/activate.d and <conda_dir>/etc/conda/deactivate.d files which set LD_LIBRARY_PATH when the environment is activated and reset it back when the environment is deactivated.
You should be able to test if the install was succesful by running
import tensorflow as tf
print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU')))Which will print a number >0 if tensorflow can see the GPU(s) on the machine.