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Gradient Informed, GPU Accelerated Lens modelling (GIGA-Lens) is a package for fast and rigorous Bayesian inference on strong gravitational lenses, with support for multi-device and multi-node GPU acceleration for large-scale inference workloads. For details, please see our original paper and our latest paper. Documentation is provided here.

Note: Documentation follows our Github release, which will be updated pending paper acceptance

Installation

GIGA-Lens can be installed via pip:

pip install gigalens[cuda]

If you wish to test the installation, tests can be run simply by running tox in the root directory.

If you don’t have access to institutional GPUs, one easy way is to use GPU on Google Colab. Please remember the very first cell should have !pip install gigalens[cuda].

If you do have access to institutional GPUs, you can set up a notebook to run on GPU. For example, at NESRC, for running GIGA-Lens on a single GPU node, you can choose the kernel tensorflow-2.6.0, and include in the first cell: !pip install gigalens[cuda].

Demo Notebooks

The notebooks below demonstrate some examples of lens modeling with JAX and Tensorflow respectively. For the quickstart notebooks, you can run them directly using Google Colab.

Requirements

Python Version >= 3.12

The following packages are requirements for GIGA-Lens. However, we recommend using pip to avoid issues with subpackage dependencies.

  • jax==0.6.2

  • tensorflow-probability==0.25.0

  • lenstronomy>=1.13.2,<2.0.0

  • optax>=0.2.6,<0.3.0

  • objax>=1.8.0,<2.0.0

  • numpy>=2.0.2

  • tqdm>=4.67.1,<5.0.0

Authors

GIGA-Lens was written by Andi Gu in 2021, and is developed by:

Cite:

Latest arXiv release
@misc{huang2026gigalens20stronglensmodeling,
    title={GIGA-Lens 2.0: Strong-Lens Modeling on Multiple GPU Nodes},
    author={Xiaosheng Huang and Linus Upson and Nicolas Ratier-Werbin and Harry Lu and Sean Xu and Elden Yap and Evan Odell and Ansel Parke and Harsh Ambardekar and Saul Baltasar and Nestor Demeure and Bradley Richardson and Andi Gu and Yuan-Ming Hsu and Junyi Liu},
    year={2026},
    eprint={2606.30633},
    archivePrefix={arXiv},
    primaryClass={astro-ph.CO},
    url={https://arxiv.org/abs/2606.30633},
}

Metadata

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