Gurobi OptiMods is an open-source Python repository of implemented optimization use cases using Gurobi, each with clear and informative documentation that explains how to use it and the mathematical model behind it.

The package is a collection of independent ‘Mods’. Each Mod is intended to be immediately applicable to real use cases. However, we expect that for many practical applications, users will need to understand and extend the implementation of a Mod to tailor it to their use case. Read the Usage section first for an overview of the design and use case for the OptiMods.

Check out The OptiMods Gallery for a quick overview of the current set of implemented Mods. We welcome contributions of new Mods based on use cases you are interested in, as well as fixes and improvements to existing Mods. See Contributing to OptiMods and Adding a new Mod for more information on how to get involved in the project.




Learning Resources

Gurobi OptiMods Webinar

In this webinar, we present Gurobi OptiMods: an open-source Python repository of optimization use cases implemented using gurobipy. OptiMods allow users to quickly apply optimization to solve a specific problem in their field of interest via intuitive, data-driven APIs.

We provide an overview of the goals and design of the project and demonstrate how several of the current set of Mods can be used. Finally, we will outline how the community can contribute additional use-cases or extensions to the project in future.

Watch the webinar, “Gurobi OptiMods: Simple APIs for Common Optimization Tasks.”

Gurobi Opti201 Training Video – What’s Next: The Gurobi ML Package & OptiMods Example

Join us in this training session to learn about our machine learning open-source tools and our brand-new Gurobi OptiMods.

Watch the training session, “Gurobi Opti201 Training Video – What’s Next: The Gurobi ML Package & OptiMods Example.”



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