SigOpt - Open Source SigOpt: Intelligent Experimentation and Hyperparameter Optimization
SigOpt offers open source versions for intelligent experimentation and hyperparameter optimization, allowing users to access advanced computational tooling and manage self-hosted SigOpt servers for private data usage.
SigOpt Product Information
What is SigOpt?
SigOpt provides a platform for exploring, organizing, and visualizing experiment progress, integrating seamless XGBoost support, and enabling lightweight installations for computational tooling. The open source release empowers users to leverage SigOpt's advanced math and technology for accelerating the design of manufacturing processes, iterating quickly in machine learning and physical sciences, and efficiently exploring black-box objective functions.
How to Use SigOpt?
To access SigOpt's open source version, clone the repository from https://github.com/sigopt/sigopt-server.git. Additionally, the lightweight installation can be obtained by running 'pip install sigopt[lite]' to use SigOpt's computational tooling with the computation running locally.
Core Features of SigOpt
- Intelligent Experimentation
- Hyperparameter Optimization
- XGBoost Integration
- Self-hosted Server
- Visualization and Organization
Use Cases of SigOpt
- Machine Learning Experiments
- Physical Sciences Research
- Manufacturing Process Design
- Black-box Objective Function Exploration
FAQ about SigOpt
What is SigOpt?
SigOpt is an open source platform for intelligent experimentation, hyperparameter optimization, and advanced computational tooling.
How can I access the open source version of SigOpt?
The open source version of SigOpt can be accessed by cloning the repository from https://github.com/sigopt/sigopt-server.git or by installing the lightweight version with 'pip install sigopt[lite]'.
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