SigOpt API Modules

SigOpt offers two API modules: Core Module and AI Module.

Core Module

The core module is designed for general usage of sample-efficient optimization. It has a lightweight API to give the users maximum flexibility. Users have the ability to create/delete/update almost all API objects throughout the optimization process.

The core module is optimization centric. It is most suitable for simulation optimization, configuration optimization, or general blackbox optimization problems. It is supported in multiple languages: Python, Java, and Bash. In particular, Bash users can access the API without installing a client library.

All core module specific documentation can be found under the CORE MODULE API REFERENCES section.

AI Module

The AI module is design for AI/ML use cases. In particular, one of its major features is the ability to track ML training runs in details, such as learning curve, metadata, model artifacts, etc. The AI module has integration with ML libraries and the ability to orchestrate your cluster.

The AI module is only available in Python.

All AI module specific documentation can be found under the AI MODULE API REFERENCES section.

Choose a Module

In the table below, we provide a more detailed list of comparison of the two modules to help you make an informed decision.

FeaturesCore ModuleAI Module

Primary Use Cases

Simulation optimization, configuration optimization, general blackbox optimization

Machine learning (ML) hyperparameter tuning

Web Dashboard

Optimization focused

Project focused, with individual ML training run dashboard


Flexible API endpoints and objects design for general usage

API objects designed for AI/ML usage

Update/Delete all API objects

Supported at any point during an optimization experiment, via the API clients or the web dashboard

Partially supported for some objects, only through the web dashboard.

Advanced Experimentation Features

All supported

All supported

Supported Language

Python, Bash (Curl), Java, R, MATLAB

Python (including Jupyter notebook magic commands)

ML Training Run Tracking



ML Library Integration



Cluster Orchestration


SigOpt Orchestrate

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