pplbench.org

What It Is

PPL Bench is a probabilistic programming language benchmark tool from a Facebook Research-related GitHub project. Its goal is to compare the performance of different PPLs and their inference methods through a unified workflow. Models, iteration counts, trial counts, PPLs, inference algorithms, and visualization legends are defined via JSON configuration; after running, it outputs charts such as Predictive Log Likelihood (PLL).

Core Capabilities

Its core design emphasizes modularity and reuse: users can plug in new models and add implementations for new probabilistic programming languages; the same benchmark workflow can be reused across multiple PPLs. The documentation explicitly supports Stan, Jags, PyMC3, Pyro, and NumPyro, and provides installation instructions for each. By using Predictive Log Likelihood as a unified metric, it enables comparison across different PPLs. The tool is mainly used through the pplbench command line and JSON configuration files, making it suitable for scripted experiments and reproducing results from papers.

Pricing and Deployment

The documentation does not mention commercial pricing or paid editions. It can be installed via pip install pplbench or pip install 'pplbench[ppls]', or installed locally after cloning the source code from GitHub. As such, it is closer to a free open-source tool than a SaaS service. For self-hosting, users can run it in a local virtualenv or conda environment, but system-level dependencies for different PPLs must be handled manually; for example, Jags requires additional system packages.

Pros and Cons

Its strengths are a clear positioning, a high degree of configurability, good support for reproducible experiments, and coverage of several typical PPL ecosystems. The documentation includes sections such as Getting Started, Working with PPLs, Models, and System Overview, with relatively clear onboarding commands and examples. The downsides are that dependency versions are quite specific and somewhat dated: PyMC3, Pyro, and NumPyro are all listed with fixed older versions, which may lead to compatibility issues. Dependencies such as Jags also increase environment setup costs. The documentation does not describe maintenance activity, release cadence, support channels, or any cloud service.

Who It’s For and Access from China

It is best suited to developers or research teams working on Bayesian modeling, probabilistic programming research, inference algorithm comparisons, and reproducing experiments from academic papers. It is not very suitable as a general-purpose application monitoring tool or a standard ML training platform. The documentation does not provide information about access from China. In practice, availability of pplbench.org, GitHub, and pip-related resources may depend on the local network environment. If access is unstable, users can consider local package mirrors, GitHub mirror strategies, or building their own benchmark workflow based on ArviZ, Stan/PyMC/NumPyro.