Installations

We will be using the OSCER supercomputer this semester. If you wish to also have your own local configuration, then you have two good options for setting this up: pip and anaconda. In either case, you want to be using Tensorflow 2.x for this class (not 1.8!).

Preliminaries

It is very useful to have multiple, independent python configurations installed on your machine. These can be managed with virtual environements. Here is a quick introduction to this tool: Using Venv

Tensorflow Installation

Pip is a package manager for python.
Anaconda is a general environment manager, which includes python packages (via pip) as well as other components (such as specific executables).
- The set of packages/software that comes with anaconda is a curated list, so (hopefully) everything that is installed is mutually compatible.
- Once you have installed anaconda, you can use pip to add other python packages that you need.

Needed Python Packages

You will also want a variety of packages over the course of the semester. You can use the pip install command to add any that you do not already have.

Keras
sklearn
pandas
gym
pypng
matplotlib
pydot
bitarray

Juptyer Lab

Juptyer Lab is an interactive development environment that you should only be using on your local machine (never on the supercomputer!). It is useful for quickly testing out ideas and code before you execute experiments.

Jupyter Lab installation

Useful software to support jupyter:

pandoc (need executable and python package)
xelatex (executable)

fagg@cs.ou.edu

Last modified: Mon Jan 25 18:28:55 2021