一个可复现的黑盒优化测试框架，解决依赖冲突问题。
Bencher: Simple and Reproducible Benchmarking for Black-Box Optimization
- 将每个测试用例隔离在独立环境，通过统一接口调用。
- 支持80个跨连续、分类、二值域的基准测试。
- 轻量客户端，本地或集群部署皆宜，适合实证研究者。
我们提出Bencher，一种模块化的黑盒优化基准测试框架，从根本上将基准执行与优化逻辑解耦。不同于以往将多个基准集成于单一项目的做法，Bencher引入清晰的抽象边界：每个基准独立运行于自己的虚拟Python环境中，并通过统一、版本无关的远程过程调用（RPC）接口访问。该设计消除了依赖冲突，简化了复杂且互斥软件需求的真实世界基准集成。Bencher可通过Docker本地部署或通过Singularity在高性能计算（HPC）集群上远程部署，为任意基准提供容器化、可复现的运行时。其轻量级客户端设置简单，支持对80个基准的即插即用评估，覆盖连续、类别和二值领域。
原文摘要 · Abstract (English)
We present Bencher, a modular benchmarking framework for black-box optimization that fundamentally decouples benchmark execution from optimization logic. Unlike prior suites that focus on combining many benchmarks in a single project, Bencher introduces a clean abstraction boundary: each benchmark is isolated in its own virtual Python environment and accessed via a unified, version-agnostic remote procedure call (RPC) interface. This design eliminates dependency conflicts and simplifies the integration of diverse, real-world benchmarks, which often have complex and conflicting software requirements. Bencher can be deployed locally or remotely via Docker or on high-performance computing (HPC) clusters via Singularity, providing a containerized, reproducible runtime for any benchmark. Its lightweight client requires minimal setup and supports drop-in evaluation of 80 benchmarks across continuous, categorical, and binary domains.
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