Scientific applications are increasingly complex and domain specific, and the underlying architectures of the parallel and distributed systems on which they are executed also continue to grow in complexity. As these high performance parallel and distributed computing applications and environments continue to grow both in complexity and computing power, there is an increasing financial cost associated with both the acquisition and maintenance of those systems. Therefore, the ability to model the performance of these applications and systems before and during their development and deployment to guide cost-effective decisions about their resources and configurations is highly important to the designers of those applications and systems. Performance Evaluation Process Algebra (PEPA) is a modeling language and framework for modeling parallel and distributed computing and communication applications and systems, and numerous examples are present in the literature where PEPA has been utilized to model these systems for evaluating or predicting their performance using various metrics, including throughput, utilization, and robustness. Since its development, the PEPA modeling framework has been expanded to model biological systems and networks (Bio-PEPA), and massive (on the order of ~10^129 components) homogeneous systems with Grouped PEPA (GPEPA). PEPA and its derivatives are implemented in a variety of ways, ranging from plug-ins integrated with the Eclipse integrated development environment to standalone command-line based interpreters, each with their own unique and often challenging installation and configuration requirements. To help enable other researchers to more easily utilize these frameworks and facilitate increased and robust reproducibility across end-user platforms, we present and make available containerized versions of a number of these PEPA frameworks. We have validated the functionality of these containers by testing them with models available from the research community that utilizes PEPA. These containers serve as a readily available resource for the community and can be executed on any environment capable of executing the underlying containerization framework.