FlexServe：将PyTorch模型部署为灵活的REST端点
FlexServe: Deployment of PyTorch Models as Flexible REST Endpoints
摘要：
通过使用基于云的机器学习服务和代表性状态转移架构设计，将人工智能功能集成到现代软件系统中的过程越来越简单。然而，有关基础模型起源的信息不足和缺乏对模型演化的控制，阻碍了在许多具有严格安全要求的操作环境中更广泛地采用这些服务。此外，TensorFlow Serving等工具允许将模型部署为RESTful端点，但需要对PyTorch模型进行易于出错的转换，因为这些动态计算图。这与TensorFlow的静态计算图形成对比。为了实现PyTorch模型的快速部署而无需中间转换，我们开发了FlexServe，这是一个简单的库，可以通过灵活的配置部署多模型集成。
英文摘要：
The integration of artificial intelligence capabilities into modern software systems is increasingly being simplified through the use of cloud-based machine learning services and representational state transfer architecture design. However, insufficient information regarding underlying model provenance and the lack of control over model evolution serve as an impediment to the more widespread adoption of these services in many operational environments which have strict security requirements. Furthermore, tools such as TensorFlow Serving allow models to be deployed as RESTful endpoints, but require error-prone transformations for PyTorch models as these dynamic computational graphs. This is in contrast to the static computational graphs of TensorFlow. To enable rapid deployments of PyTorch models without intermediate transformations we have developed FlexServe, a simple library to deploy multi-model ensembles with flexible batching.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
