This is a docs for test!!! #

OpenBox is an efficient open-source system designed for solving generalized black-box optimization (BBO) problems, such as automatic hyper-parameter tuning, automatic A/B testing, experimental design, database knob tuning, processor architecture and circuit design, resource allocation, automatic chemical design, etc.

The design of OpenBox follows the philosophy of providing “BBO as a service” - we opt to implement OpenBox as a distributed, fault-tolerant, scalable, and efficient service, with a wide range of application scope, stable performance across problems and advantages such as ease of use, portability, and zero maintenance.

There are two ways to use OpenBox: Standalone python package and Online BBO service.

OpenBox GitHub: https://github.com/PKU-DAIR/open-box

News#

  • a test news

Who should consider using OpenBox#

  • Those who want to tune hyper-parameters for their ML tasks automatically.

  • Those who want to find the optimal configuration for their configuration search tasks (e.g., database knob tuning).

  • Data platform owners who want to provide BBO service in their platform.

  • Researchers and data scientists who want to solve generalized BBO problems easily.

OpenBox capabilities#

OpenBox has a wide range of functionality scope, which includes:

  1. BBO with multiple objectives and constraints.

  2. BBO with transfer learning.

  3. BBO with distributed parallelization.

  4. BBO with multi-fidelity acceleration.

  5. BBO with early stops.

In the following, we provide a taxonomy of existing BBO systems:

System/Package

Multi-obj.

FIOC

Constraint

History

Distributed

Hyperopt

×

√

×

×

√

Spearmint

×

×

√

×

×

SMAC3

×

√

×

×

×

BoTorch

√

×

√

×

×

GPflowOPT

√

×

√

×

×

Vizier

×

√

×

△

√

HyperMapper

√

√

√

×

×

HpBandSter

×

√

×

×

√

OpenBox

√

√

√

√

√

  • FIOC: Support different input variable types, including Float, Integer, Ordinal and Categorical.

  • Multi-obj.: Support optimizing multiple objectives.

  • Constraint: Support inequality constraints.

  • History: Support injecting prior knowledge from previous tasks into the current search. (△ means the system cannot support it for general cases)

  • Distributed: Support parallel evaluations in a distributed environment.

This is a docs for TEST!!!