The prerequsities consists of a list of python packages as shown below. It is recommended to create a python virtual environment (either with `pyenv` or `conda`). The following packages can be installed using the `pip` package management tool:
```
pip install tensorflow
pip install horovod
pip install scikit-learn
pip install scikit-image
pip install pandas
```
Note: there is no guarantee of optimal performance when `tensorflow` is installed using `pip`. It is better if `tensorflow` is compiled from source, in which case the compiler will likely be able to take advantage of the advanced instruction sets supported by the processor (e.g., AVX512). An official build instruction can be found at https://www.tensorflow.org/install/source. Sometimes, an HPC center may have a tensorflow module optimized for their hardware, in which case the `pip install tensorflow` line can be replaced with a line like `module load <name_of_the_tensorflow_module>`.
