  A standard practice in extragalactic population studies is the fitting of
parametric models to galaxy images. From such fits, key structural parameters
of galaxies such as total flux and effective radius (size) can be extracted.
One of the most popular parametric forms is that of the S\'ersic profile, which
is flexible enough to reasonably fit the light distribution of nearly all
galaxies. Here we present pysersic, a Bayesian framework created to facilitate
the inference of structural parameters from galaxy images. Pysersic is written
in pure Python, and is built using the jax framework, allowing for just-in-time
compilation, auto-differentiation and seamless execution on CPUs, GPUs or TPUs.
Inference is performed with the numpyro package using gradient based methods,
e.g., No U-Turn Sampling, for efficient and robust posterior estimation in only
a few minutes on a modern laptop. Pysersic is designed to have a user-friendly
interface, allowing users to fit single or multiple sources in a few lines of
code, while also being flexible enough for integration into current and future
analysis pipelines. In addition to sampling, pysersic can produce point
estimates of the best model via optimization in several seconds, and
approximate the posterior via stochastic variational inference. The use of the
numpyro probabilistic language provides future extensibility to arbitrary
models beyond the S\'ersic.
