  The increasing global emphasis on sustainability and reducing carbon
emissions is pushing governments and corporations to rethink their approach to
data center design and operation. Given their high energy consumption and
exponentially large computational workloads, data centers are prime candidates
for optimizing power consumption, especially in areas such as cooling and IT
energy usage. A significant challenge in this pursuit is the lack of a
configurable and scalable thermal data center model that offers an end-to-end
pipeline. Data centers consist of multiple IT components whose geometric
configuration and heat dissipation make thermal modeling difficult. This paper
presents PyDCM, a customizable Data Center Model implemented in Python, that
allows users to create unique configurations of IT equipment with custom server
specifications and geometric arrangements of IT cabinets. The use of vectorized
thermal calculations makes PyDCM orders of magnitude faster (30 times) than
current Energy Plus modeling implementations and scales sublinearly with the
number of CPUs. Also, PyDCM enables the use of Deep Reinforcement Learning via
the Gymnasium wrapper to optimize data center cooling and offers a
user-friendly platform for testing various data center design prototypes.
