  There have been growing discussions on estimating and subsequently reducing
the operational carbon footprint of enterprise data centers. The design and
intelligent control for data centers have an important impact on data center
carbon footprint. In this paper, we showcase PyDCM, a Python library that
enables extremely fast prototyping of data center design and applies
reinforcement learning-enabled control with the purpose of evaluating key
sustainability metrics including carbon footprint, energy consumption, and
observing temperature hotspots. We demonstrate these capabilities of PyDCM and
compare them to existing works in EnergyPlus for modeling data centers. PyDCM
can also be used as a standalone Gymnasium environment for demonstrating
sustainability-focused data center control.
