benchopt/benchopt

A framework for reproducible, comparable benchmarks

What it solves

Benchopt provides a standardized framework for creating and running machine learning and optimization benchmarks. It addresses the lack of reproducibility and comparability in ML workflows by offering a structured way to test multiple solvers against the same datasets and metrics.

How it works

Implemented in Python, the framework allows users to define benchmarks that can execute algorithms written in various languages, including Python, R, Julia, and C/C++. Users can either replicate existing benchmarks from a community library or create their own using a provided template. The system utilizes a Command Line Interface (CLI) to install solvers and run benchmarks based on configuration files.

Who it’s for

It is designed for researchers and developers in the machine learning and optimization community who need to perform scientific, transparent, and reproducible performance comparisons between different algorithms.

Highlights

  • Language Agnostic: Supports solvers written in Python, R, Julia, and C/C++ binaries.
  • Robot-friendly Installation: Includes a CLI for automatic solver installation.
  • Extensible: Users can easily add new datasets, solvers, or metrics to existing benchmarks.
  • Broad Library: Maintains a wide range of pre-existing benchmarks for problems like LASSO, Logistic Regression, and ResNet Classification.

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