伦敦医学成像与人工智能中心利用 Run:ai 加速研究
公司规模
Large Corporate
地区
- Europe
国家
- United Kingdom
产品
- Run:ai's Platform
技术栈
- AI Hardware
- Deep Learning Training Models
- GPU Compute
实施规模
- Enterprise-wide Deployment
影响指标
- Productivity Improvements
- Innovation Output
技术
- 分析与建模 - 机器学习
- 应用基础设施与中间件 - API 集成与管理
适用行业
- 医疗保健和医院
适用功能
- 产品研发
用例
- 预测性维护
- 计算机视觉
服务
- 数据科学服务
- 系统集成
关于客户
伦敦医学影像与人工智能价值型医疗保健中心是一个由学术、医疗和行业合作伙伴组成的联盟，由伦敦国王学院牵头，总部位于圣托马斯医院。该中心利用英国国家医疗服务体系 (NHS) 持有的医学图像和电子医疗数据来训练复杂的深度学习算法，用于计算机视觉和自然语言处理。这些算法用于创建新的工具，以实现有效筛查、更快诊断和个性化治疗，从而改善患者的健康状况。
挑战
伦敦医学影像与人工智能中心（价值型医疗保健）的人工智能硬件面临多项挑战。尽管研究人员有需求，但 GPU 总利用率低于 30%，部分 GPU 仍有大量空闲时间。系统多次超载，需要运行作业的 GPU 数量超过可用数量。可见性和调度不佳导致延迟和浪费，需要大量 GPU 的大型实验有时无法开始，因为仅使用少量 GPU 的小型作业无法满足其资源需求。
解决方案
人工智能中心实施了 Run:ai 平台来应对这些挑战。该平台将 GPU 利用率提高了 110%，从而提高了实验速度。研究人员在 40 天内进行了 300 多次实验，而没有 Run:ai 的相同环境模拟中仅进行了 162 次实验。通过动态地将池化 GPU 分配给工作负载，硬件资源共享效率更高。该平台还通过高级监控和集群管理工具提高了可见性，使数据科学家能够查看哪些 GPU 资源未被使用，并动态调整其作业的大小以利用可用容量运行。该平台还实现了公平调度和保证资源，允许大型持续工作负载在低需求时段使用最佳数量的 GPU，并自动允许较短、优先级较高的工作负载同时运行。
运营影响
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