发表机构
Dartmouth College（达特茅斯学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对云 GPU CI 成本高导致 GPU 代码测试不足的问题，提出开源 pytest 插件 pytest-gpu-proof，通过本地运行并签名凭证，实现将 GPU 测试集成到标准 CPU CI 工作流中。
AI 中文摘要
GPU 加速在机器人领域现已普遍应用，但云托管的 GPU 持续集成（CI）运行器成本高昂，导致 GPU 加速代码的测试严重不足。我们提出 pytest-gpu-proof，一个开源的 pytest 插件，提供了一种实用的折中方案。测试可在本地机器上运行，并附上精确记录运行内容及产出结果的签名凭证，随后集成到标准 CPU CI 工作流（如 GitHub Actions）中。该工具为开源项目，已发布至 PyPI，我们正积极将其整合到实验室的整个软件栈中。
英文摘要
GPU acceleration is now routine across robotics, but cloud-hosted GPU continuous integration (CI) runners are expensive, resulting in severe under-testing of GPU-accelerated code. We present pytest-gpu-proof, an open-source pytest plugin offering a practical middle ground. Tests can be run on a local machine, signed with a receipt of exactly what ran and what it produced, and integrated into standard CPU CI workflows (e.g., GitHub Actions). The tool is open source and on PyPI, and we are actively integrating it across our lab's software stack.
Comments2 pages, 1 figure
