  We present the first version of CYJAX, a package for machine learning
Calabi-Yau metrics using JAX. It is meant to be accessible both as a top-level
tool and as a library of modular functions. CYJAX is currently centered around
the algebraic ansatz for the K\"ahler potential which automatically satisfies
K\"ahlerity and compatibility on patch overlaps. As of now, this implementation
is limited to varieties defined by a single defining equation on one complex
projective space. We comment on some planned generalizations.
