Skip to content


Repository files navigation


—Making your ML and optimization benchmarks simple and open—

Test Status codecov Documentation install-per-months discord SWH

Benchopt is a benchmarking suite tailored for machine learning workflows. It is built for simplicity, transparency, and reproducibility. It is implemented in Python but can run algorithms written in many programming languages.

So far, benchopt has been tested with Python, R, Julia and C/C++ (compiled binaries with a command line interface). Programs available via conda should be compatible as well. See for instance an example of usage with R.


It is recommended to use benchopt within a conda environment to fully-benefit from benchopt Command Line Interface (CLI).

To install benchopt, start by creating a new conda environment and then activate it

Then run the following command to install the latest release of benchopt

It is also possible to use the latest development version. To do so, run instead

Getting started

After installing benchopt, you can

  • replicate/modify an existing benchmark
  • create your own benchmark

Using an existing benchmark

Replicating an existing benchmark is simple. Here is how to do so for the L2-logistic Regression benchmark.

  1. Clone the benchmark repository and cd to it
  1. Install the desired solvers automatically with benchopt
  1. Run the benchmark to get the figure below

These steps illustrate how to reproduce the L2-logistic Regression benchmark. Find the complete list of the Available benchmarks. Also, refer to the documentation to learn more about benchopt CLI and its features. You can also easily extend this benchmark by adding a dataset, solver or metric. Learn that and more in the Benchmark workflow.

Creating a benchmark

The section Write a benchmark of the documentation provides a tutorial for creating a benchmark. The benchopt community also maintains a template benchmark to quickly and easily start a new benchmark.

Finding help

Join benchopt discord server and get in touch with the community! Feel free to drop us a message to get help with running/constructing benchmarks or (why not) discuss new features to be added and future development directions that benchopt should take.

Citing Benchopt

Benchopt is a continuous effort to make reproducible and transparent ML and optimization benchmarks. Join us in this endeavor! If you use benchopt in a scientific publication, please cite

Available benchmarks

Problem Results Build Status
Ordinary Least Squares (OLS) Results Build Status OLS
Non-Negative Least Squares (NNLS) Results Build Status NNLS
LASSO: L1-Regularized Least Squares Results Build Status Lasso
LASSO Path Results Build Status Lasso Path
Elastic Net Build Status ElasticNet
MCP Results Build Status MCP
L2-Regularized Logistic Regression Results Build Status LogRegL2
L1-Regularized Logistic Regression Results Build Status LogRegL1
L2-regularized Huber regression Build Status HuberL2
L1-Regularized Quantile Regression Results Build Status QuantileRegL1
Linear SVM for Binary Classification Build Status LinearSVM
Linear ICA Build Status LinearICA
Approximate Joint Diagonalization (AJD) Build Status JointDiag
1D Total Variation Denoising Build Status TV1D
2D Total Variation Denoising Build Status TV2D
ResNet Classification Results Build Status ResNetClassif
Bilevel Optimization Results Build Status Bilevel