![]() ![]() We strongly suggest you to create a Python environment via Anaconda: conda create -n openbox3.7 python = 3.7 Python >= 3.6 (Python 3.7 is recommended!).OpenBox Capabilities in a Glance Build-in Optimization Components Pasca, which adopts Openbox to support neural architecture search functionality, won the Best Student Paper Award at WWW'22.OpenBox team won the Top Prize (special prize) in the open-source innovation competition at 2021 CCF ChinaSoft conference.OpenBox based solutions achieved the First Place of ACM CIKM 2021 AnalyticCup (Track - Automated Hyperparameter Optimization of Recommendation System).Fault tolerance, extensibility, and data privacy protection.High efficiency: Effective use of parallel resources, system optimization with transfer-learning and multi-fidelities, etc.Scalability: Scale to dimensions on the number of input variables, objectives, tasks, trials, and parallel evaluations.Resource-aware management: Give cost-model-based advice to users, e.g., minimal workers or time-budget.Consistent performance: Host state-of-the-art optimization algorithms Choose the proper algorithm automatically.Ease of use: Minimal user effort, and user-friendly visualization for tracking and managing BBO tasks.The design of OpenBox follows the following principles: Through which users can easily track and manage the tasks. Users can access this service via REST API conveniently, and do not need to worry about other issues such as environment setup, software maintenance, programming, and optimization of the execution. We adopt the "BBO as a service" paradigm and implement OpenBox as a managed general service for black-box optimization. Users can install the released package and use it with Python. ![]() Software Artifacts Standalone Python package. ![]() OpenBox is designed and developed by the AutoML team from the DAIR Lab at Peking University, and its goal is to make blackbox optimization easier to apply both in industry and academia, and help facilitate data science. OpenBox is an efficient and generalized blackbox optimization (BBO) system, which supports the following characteristics: 1) BBO with multiple objectives and constraints, 2) BBO with transfer learning, 3) BBO with distributed parallelization, 4) BBO with multi-fidelity acceleration and 5) BBO with early stops. ![]() OpenBox Doc | OpenBox中文文档 OpenBox: Generalized and Efficient Blackbox Optimization System ![]()
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