Setting up Conda Virtual Environment for Tensorflow

These steps are for create a Python virtual environment for running Tensorflow on GPU. The steps work on Fedora Linux 38 and Ubuntu 22.04 LTS:

To install miniconda, we can do as a regular user:

curl -s "https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh" | bash

Following that, we create a conda virtual environment for Python.

# create conda virtual environment
conda create -n tf213 python=3.11 pip

# activate the environment in order to install packages and libraries
conda activate tf213

#
# the following are from Tensorflow pip installation guide
#
# install CUDA Toolkit 
conda install -c conda-forge cudatoolkit=11.8.0

# install python packages
pip install nvidia-cudnn-cu11==8.6.0.163 tensorflow==2.13.*

#
# setting up library and tool search paths
# scripts in activate.d shall be run when the environment
# is being activated
#
mkdir -p $CONDA_PREFIX/etc/conda/activate.d
# get CUDNN_PATH
echo 'CUDNN_PATH=$(dirname $(python -c "import nvidia.cudnn;print(nvidia.cudnn.__file__)"))' >> $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh
# set LD_LIBRARY_PATH
echo 'export LD_LIBRARY_PATH=$CUDNN_PATH/lib:$CONDA_PREFIX/lib/:$LD_LIBRARY_PATH' >> $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh
# set XLA_FLAGS (for some systems, without this, it will lead to a 'libdevice not found at ./libdevice.10.bc' error
echo 'export XLA_FLAGS=--xla_gpu_cuda_data_dir=$CONDA_PREFIX' >> $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh

To test it, we can run

source $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh
python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"

Enjoy!