  The interest and demand for training deep neural networks have been
experiencing rapid growth, spanning a wide range of applications in both
academia and industry. However, training them distributed and at scale remains
difficult due to the complex ecosystem of tools and hardware involved. One
consequence is that the responsibility of orchestrating these complex
components is often left to one-off scripts and glue code customized for
specific problems. To address these restrictions, we introduce \emph{Alchemist}
- an internal service built at Apple from the ground up for \emph{easy},
\emph{fast}, and \emph{scalable} distributed training. We discuss its design,
implementation, and examples of running different flavors of distributed
training. We also present case studies of its internal adoption in the
development of autonomous systems, where training times have been reduced by
10x to keep up with the ever-growing data collection.
