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Oct. 22, 2018
Mar. 12, 2018

Mt. Rainier with lenticular clouds (credit: US National Park Service)


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Rainier provides an idiomatic, high-performance functional Scala API for bayesian inference via Markov Chain Monte Carlo.

Rainier allows you to describe a complex prior distribution by composing primitive distributions using familiar combinators like map, flatMap, and zip; condition that prior on your observed data; and, after an inference step, sample from the resulting posterior distribution.

Underlying this is a static scalar compute graph with auto-differentiation and very fast CPU-based execution.

It is implemented in pure Scala, with minimal external dependencies and no JNI libs, and as such is convenient to deploy, including to Spark or Hadoop clusters.

Rainier currently provides two samplers: affine-invariant MCMC, an ensemble method popularized by the Emcee package in Python, and Hamiltonian Monte Carlo, a gradient-based method used in Stan and PyMC3.

Depending on your background, you might think of Rainier as aspiring to be either "Stan, but on the JVM", or "TensorFlow, but for small data".


Here's what it looks like to fit a simple linear regression with poisson noise and log-normal priors in Rainier:

val data: List[(Int,Int)] = ???
val model = for {
    slope <- LogNormal(0,1).param
    intercept <- LogNormal(0,1).param
    regression <- Predictor.from{x: Int => Poisson(x*slope + intercept)}.fit(data)
} yield regression

Performance and Scale

Rainier requires that all of the observations or training data for a given model fit comfortably into RAM on a single machine. It does not make use of GPUs or of SIMD instructions.

Within those constraints, however, it is extremely fast. Rainier takes advantage of knowing all of your data ahead of time by aggressively precomputing as much as it can, which is a significant practical benefit relative to systems that compile a data-agnostic model. It produces optimized, unboxed, JIT-friendly JVM bytecode for all numerical calculations. This compilation happens in-process and is fast enough for interactive use at a REPL.

For example, on a MacBook Pro, gradient evaluation for Neal's funnel takes under a microsecond, and end-to-end compilation and sampling for 10,000 iterations of HMC with 5 leapfrog steps each takes around 50ms.

As a rough comparison, Rainier seems to yield a 10x or more speedup relative to the equivalent Stan models. This is promising, though please keep in mind that benchmarking is hard, micro-benchmarks are often meaningless, and Stan's sampler implementation is much more sophisticated and much, much, much better tested than Rainier's!


A good starting point is the Tour of Rainier's Core.

If you want to dig deeper, there's a tour of the underlying compute graph, as well as some detailed implementation notes.

If you're more familiar with deep learning systems like TensorFlow or PyTorch, you might also be interested in this brief summary of some of the similarities and differences between DL and MCMC.


Rainier uses SBT to build. If you have SBT installed, you can build Rainier and test that it's working by executing sbt "project rainierExample" run and then selecting rainier.example.FitNormal. You should see output something like this:

[info] Running rainier.example.FitNormal
    2.19 |
         |                                 ·           ··
         |               ·           ·  · ·· ·  ···  ·  ·· ·  ·    · · ·
         |                ·   ·   ··························· ··· ·      ·  ···
         |                   · ·············································      ··
    2.10 |             ·····   ···············································  ···
         |          · ·  ···················································· ····      ·
         |·   ·  ·   ···························∘··∘·····························  ·
         |      ··························∘·∘∘∘∘∘∘∘∘∘∘∘∘∘∘∘∘∘···················· ··
         |     ·      ··················∘∘∘∘∘∘∘∘∘○○○○∘○○∘∘∘∘∘∘···················· · ·
    2.00 |        ·····················∘∘∘∘∘∘○○○○○○○○○○○○○∘∘∘∘∘∘······················ ·
         |           ··················∘∘∘∘∘∘○○○○○○○○○○○○○∘∘∘∘∘∘···················
         |         ·····················∘∘∘∘∘∘∘○○○○○∘∘○○∘∘∘∘∘∘∘∘················ ·····
         |          ·······················∘∘∘∘∘∘∘∘∘∘∘∘∘∘∘∘······················· ··  ·
         |       ·· ··························································   ·  ·    ·
    1.90 |          ·  ························································     ·
         |                ············································· ···· ·    · ·
         |                      ·   · ··· ··················· ···· ·  ·    ·
         |                           ··· ·  · ·· ·· ·  ··  · ·· ··
         |                              ·        ··
    1.81 |                                       ·
       2.691    2.750    2.810    2.869    2.929    2.988    3.048    3.107    3.167


Contributions to Rainier are welcomed. Please note that fatal warnings are enabled in the CI environment so be sure to fix all warnings before submitting a pull request for final review.

You can enable fatal warnings locally by either setting SCALAC_FATAL_WARNINGS=true before running SBT or entering scalacFatalWarnings := true in the SBT prompt.

Using Rainier from SBT or Maven

Rainier is published on sonatype. To use it in your SBT project, you can add the following to your build.sbt:

libraryDependencies += "com.stripe" %% "rainier-core" % "0.1.1"


Rainier was written primarily by Avi Bryant, with guidance and contributions from:

Thanks also to Aaron Steele and Michael Manapat for organizational support, and Travis Brown and Andy Scott for build wrangling and other dev infrastructure.

Latest Releases
Mixture params and performance improvements
 Jul. 20 2018
 Jul. 13 2018
 Jun. 12 2018
 Jun. 5 2018
 May. 23 2018