Why tinygp?#

There are many Python libraries that exist for Gaussian Process (GP) modeling, so one might ask: (a) why does tinygp exist and (b) why might someone want to use it?

Why does tinygp exist?#

Its development started as an experiment because I wanted to figure out how to best leverage jax to build a minimal, but flexible and high performance GP library. I also wanted to learn some subtleties of designing a jax-based library.

A fundamental design decision is that tinygp does not offer implementations of any inference routines. Instead, it only provides an expressive interface for designing GP kernels and defining the relevant jax operations. Because of the composable nature of jax code, this high-level interface is compatible with other jax-based modeling frameworks such as numpyro and flax. This design has some benefits (it can take advantage of these excellent existing libraries and any fast linear algebra available in jax) and some shortcomings (it won’t necessarily support all the state-of-the-art GP inference algorithms out of the box).

Other Gaussian process libraries#

There are a lot of other libraries for using GPs in Python, so I won’t list them all here. Some of the most popular libraries are:

These all aim to “do it all”, in the sense that they provide a modeling framework, inference algorithms, and a lot of other nice features. For problems with extremely large datasets, GPyTorch is particularly interesting, since it includes novel scalable linear algebra and approximate inference techniques that allow black-box models to operate at scale. These could all be good choices if you’re looking for a general purpose GP library.

I’m the lead developer of two other GP libraries for Python:

that aim to sit at a lower level from the popular libraries above. These packages primarily provide methods for evaluating GP likelihoods that can be integrated into other data analysis pipelines. tinygp is meant as a george replacement, and I think it’s unlikely that you would ever want to use george instead (more on that below). celerite is (currently) restricted to 1-dimensional datasets with a specific type of kernel function, but if your problem fits into that framework, I think it would be hard to beat. I’m hoping to implement an interface to celerite as part of tinygp at some point.

There are some other new GP libraries built on top of jax, including:

At the time of writing these libraries don’t seem to be ready for public consumption either, but they are worth keeping an eye on!

What about george?#

As mentioned above, I am also the lead developer of the george library, which fills much the same niche as tinygp, so I thought it would be worth saying a few words about that. In fact, I started developing tinygp in large part because I wanted to stop maintaining george, and I wanted to have something else to point users to. The main reason I want to retire george is that I made some fundamental design decisions that made sense at that time, but have since been obviated by libraries like jax. In particular, george requires kernel functions to be implemented in C++, via an awkward YAML specification. This allowed high performance kernel function evaluation, but also meant that adding a custom kernel required re-compiling the library. The JIT-compilation provided by jax provides these features with a much more ergonomic API. george also includes a homebrewed modeling framework with limited and awkward support for named parameters, differentiation, and some other domain-specific features. The automatic differentiation provided by jax, and the rich ecosystem of modeling and inference frameworks built on top of it (including numpyro, flax, blackjax, etc.) offer all of these same features and much more. Either way, it’s probably time to move on from george!

The only feature that george has that is not yet implemented in tinygp is the “HODLR” approximate linear algebra technique. This has somewhat limited applicability (in particular it is really only useful for 1-dimensional data, where celerite is probably a better choice anyway), and using the GPU-accelerated version of tinygp will often provide even better performance, see Benchmarks.

With these points in mind, much of this documentation will discuss tinygp in the context of george and give advice on porting models.