A fast solver for Lasso-like problems#

celer is a Python package that solves Lasso-like problems and provides estimators that follow the scikit-learn API. Thanks to a tailored implementation, celer provides a fast solver that tackles large-scale datasets with millions of features up to 100 times faster than scikit-learn.

Currently, the package handles the following problems:

The supported lasso-like problems#


Support of weights

Native cross-validation



Group Lasso

Multitask Lasso

Sparse Logistic regression

Why celer?#

celer is specially designed to handle Lasso-like problems which enable it to solve them quickly. celer comes particularly with

  • automated parallel cross-validation

  • support of sparse and dense data

  • optional feature centering and normalization

  • unpenalized intercept fitting

celer also provides easy-to-use estimators as it is designed under the scikit-learn API.

Install celer#

celer can be easily installed through the Python package manager pip. To get the laster version of the package, run:

$ pip install -U celer

Head directly to the Get started page to get a hands-on example of how to use celer.


celer is an open source package licensed under the BSD 3-Clause License. Hence, you are free to use it. And if you do so, do not forget to cite:

  title     = {Celer: a Fast Solver for the Lasso with Dual Extrapolation},
  author    = {Massias, Mathurin and Gramfort, Alexandre and Salmon, Joseph},
  booktitle = {Proceedings of the 35th International Conference on Machine Learning},
  pages     = {3321--3330},
  year      = {2018},
  volume    = {80},
  author  = {Mathurin Massias and Samuel Vaiter and Alexandre Gramfort and Joseph Salmon},
  title   = {Dual Extrapolation for Sparse GLMs},
  journal = {Journal of Machine Learning Research},
  year    = {2020},
  volume  = {21},
  number  = {234},
  pages   = {1-33},
  url     = {http://jmlr.org/papers/v21/19-587.html}

celer is a outcome of perseverant research. Here are the links to the original papers:

Explore the documentation#