Access the open source version of SigOpt for intelligent experimentation
SigOpt explores multiple competing metrics, including search-style experiments with many constraints.
SigOpt’s self-hosted server gives users the ability to organize, visualize and share their experiment progress.
XGBoost users can more easily and efficiently learn hyperparameters for their models using SigOpt’s integration.
SigOpt has always been designed with customer privacy in mind. With the release of our self-hosted server solution, SigOpt users can run SigOpt in whatever environment they want, and without any data leaving their servers.
>> git clone https://github.com/sigopt/sigopt-server.git
In addition to our self-hosted SigOpt server, we also have released a version of SigOpt’s core module that can be run in-memory. Running
>> pip install 'sigopt[lite]'
lets you use the standard SigOpt connection to power a SigOpt experiment with SigOpt’s computation running locally.
University of Pittsburgh
SigOpt has been a valued tool and SigOpt researchers have been valued partners for our additive and nanomanufacturing projects for many years. The SigOpt platform gives us access to advanced math and technology that accelerates the design of manufacturing processes.
Massachusetts Institute of Technology
Machine learning and physical sciences interplay to invent new materials, and such novel situations require tools like SigOpt to iterate quickly and surpass human intuition. I encourage everyone in my group to use SigOpt.
University of Arizona
When we came across SigOpt, we knew it would be useful in materials research. Indeed, our observations are truly expensive: be it lab experiments or physics-based simulations. SigOpt helps us with optimal designs and efficient exploration of black-box objective functions.