Hugging Face SageMaker 部署规划

原名：hf-cloud-sagemaker-deployment-planner

规划 Hugging Face 模型在 Amazon SageMaker 上的实例、镜像、端点与发布步骤。

中文 Skills 技能说明

适合正式创建端点前比较模型大小、推理框架、实例、费用和伸缩方案；本技能只形成有证据的部署计划，不默认创建收费资源。开源工具可以本地使用；访问 Hugging Face Hub、Spaces、ZeroGPU、AWS 或托管服务时，用户需自行确认中国大陆网络、账户、区域、费用、模型与数据许可证及跨境要求。执行前应读取项目现状和官方文档，输出目标、依据、影响、验证结果与回退方式；没有真实证据时明确说明未知，不编造配置、数据或成功状态。代码、日志、令牌、连接串、模型、数据集和企业资料按敏感内容处理，只读取任务所需范围，上传或外发前完成授权、脱敏和最小化。

上游能力依据

上游原始适用说明：Plan and coordinate the deployment of a model to Amazon SageMaker AI. Use this skill whenever the user wants to deploy, host, serve, or expose a model on SageMaker or AWS — including phrases like "deploy a model", "host this LLM on AWS", "serve this embedding model", "deploy a reranker", "deploy a text-to-image / diffusion model", "host this for async inference", "create an endpoint", "serve my fine-tuned model", or any request that involves making a model available for inference on AWS. Use this even when the user is vague (e.g. "I just want to get this running on AWS, you figure it out"). Works for text-generation LLMs, embedding models, rerankers, classifiers, text-to-image / diffusion models — picks the right serving stack and chooses between real-time and async inference. This is the entry-point skill for SageMaker deployment work — it asks clarifying questions, picks a deployment pathway, and coordinates the other deployment skills.

上游 SKILL.md 主要章节（保留原文标题）：

Workflow phases
Discovery: ask only what you need
Pathway selection
Instance selection: check quota before recommending
Style

使用边界

先核对运行环境、账号、区域、依赖和最小权限。创建资源、改配置、发布服务或产生费用前必须让使用者确认。

作者、翻译与许可证

原作者: Hugging Face 与 huggingface/skills contributors
中文翻译: CEOFans翻译
许可证: Apache-2.0
上游来源: https://github.com/huggingface/skills/tree/cead19e10754e773bad24fecef83cb64be24094e/skills/hf-cloud-sagemaker-deployment-planner

适用范围

平台：linux、macos、windows；标签：云架构治理、Hugging Face SageMaker 部署规划

安全提示

基础静态扫描不等于绝对安全。技能可能调用命令、浏览器、云服务或本地文件，请在最小权限环境中使用，高风险操作必须人工确认。

如发现侵权、许可证或安全问题，可在本页前台提交投诉，管理员复核后可立即下架。