  Alchemist is a system that allows Apache Spark to achieve better performance
by interfacing with HPC libraries for large-scale distributed computations. In
this paper, we highlight some recent developments in Alchemist that are of
interest to Cray users and the scientific community in general. We discuss our
experience porting Alchemist to container images and deploying it on Cray XC
(using Shifter) and CS (using Singularity) series supercomputers and on a local
Kubernetes cluster.
  Newly developed interfaces for Python, Dask, and PySpark enable the use of
Alchemist with additional data analysis frameworks. We also briefly discuss the
combination of Alchemist with RLlib, an increasingly popular library for
reinforcement learning, and consider the benefits of leveraging HPC simulations
in reinforcement learning. Finally, since data transfer between the client
applications and Alchemist are the main overhead Alchemist encounters, we give
a qualitative assessment of these transfer times with respect to
different~factors.
