用 Amazon SageMaker JumpStart 部署与评估模型
原标题：Deploy and evaluate models with Amazon SageMaker JumpStart
AI 摘要
AWS 官方博客介绍如何使用 Amazon SageMaker JumpStart 部署和评估预训练模型。文章给出可复用的 Python 脚本流程：从 JumpStart 目录发现并部署模型、用示例提示词测量延迟与生成速率、测试完成后删除端点。2026 年 JumpStart 新增 Model Hub 浏览体验、面向低成本/低延迟/通用场景的预配置端点部署选项，以及与 Hugging Face 的一键深度链接集成。
正文节选
Deploy and evaluate models with Amazon SageMaker JumpStart Choosing the right model for a specific application requires a practical way to test it with representative inputs. Amazon SageMaker JumpStart provides access to a broad catalog of already pretrained models. In this post, we show how to deploy an available model from SageMaker JumpStart, run a lightweight and repeatable performance test using the SageMaker Python SDK, and delete the endpoint when testing is complete. For supported models