From Hugging Face to Amazon SageMaker Studio in one click

查看原文 / View Original 编辑 / Edit

From Hugging Face to Amazon SageMaker Studio in one click

从 Hugging Face 到 Amazon SageMaker Studio：一键直达

Today, we’re excited to announce a deep-link integration between Hugging Face and Amazon SageMaker AI. Developers can now go from model discovery to hands-on experimentation in SageMaker Studio with a single selection. Whether you fine-tune a foundation model (FM) from Amazon SageMaker JumpStart or deploy it to an Amazon SageMaker Inference endpoint, you can now land directly inside the relevant SageMaker Studio workflow. Your selected model is pre-loaded, and the environment is fully configured and ready to go.

今天，我们很高兴地宣布 Hugging Face 与 Amazon SageMaker AI 之间实现了深度链接集成。开发者现在只需一次选择，即可从模型发现直接进入 SageMaker Studio 进行实操实验。无论您是微调来自 Amazon SageMaker JumpStart 的基础模型 (FM)，还是将其部署到 Amazon SageMaker 推理端点，现在都可以直接进入相关的 SageMaker Studio 工作流。您选择的模型会被预加载，环境也已完全配置完毕，随时可以使用。

Previously, getting started on SageMaker Studio after discovering a model on Hugging Face required navigating multiple steps between opening Amazon SageMaker AI in the AWS Console, creating a domain, configuring IAM permissions, and sometimes requesting GPU quota. For developers who want to iterate quickly, this friction slows down the path from inspiration to experimentation. The integration creates a more direct path from discovery to enterprise deployment.

此前，在 Hugging Face 上发现模型后，若想在 SageMaker Studio 中开始使用，需要经历多个繁琐步骤：在 AWS 控制台中打开 Amazon SageMaker AI、创建域、配置 IAM 权限，有时还需要申请 GPU 配额。对于追求快速迭代的开发者而言，这种阻力减缓了从灵感迸发到动手实验的过程。此次集成创造了一条从发现到企业级部署的更直接路径。

“At Arcee, we build open models so developers and enterprises can actually own what they run: inspect the weights, post-train on their own data, and deploy on their own terms. This integration takes that promise the last mile. Going from an open model on Hugging Face straight into SageMaker Studio in a single click, then fine-tuning or deploying it inside your own AWS environment with nothing to wire up, is the kind of experience open models have been missing. Open weights you own, running in the cloud you control. That is exactly the combination our customers have been asking for.” — Mark McQuade, Founder and CEO, Arcee AI

“在 Arcee，我们构建开放模型，旨在让开发者和企业能够真正掌控他们运行的内容：检查权重、使用自己的数据进行后训练，并按自己的条款进行部署。此次集成让这一承诺迈出了最后一步。从 Hugging Face 上的开放模型一键直达 SageMaker Studio，然后在您自己的 AWS 环境中进行微调或部署，且无需任何繁琐配置，这正是开放模型一直所缺失的体验。拥有属于自己的开放权重，并在您掌控的云端运行。这正是我们的客户一直所追求的组合。”—— Mark McQuade，Arcee AI 创始人兼首席执行官

With the launch of a one-click Studio landing experience, choosing Customize on SageMaker AI or Deploy on SageMaker AI on a supported Hugging Face model page takes you directly to the console. SageMaker AI then automatically provisions a new domain with pre-configured permissions in seconds and carries the model context through.

随着一键式 Studio 登录体验的推出，在支持的 Hugging Face 模型页面上选择“Customize on SageMaker AI”（在 SageMaker AI 上自定义）或“Deploy on SageMaker AI”（在 SageMaker AI 上部署），即可直接跳转至控制台。随后，SageMaker AI 会在几秒钟内自动配置好具有预设权限的新域，并自动携带模型上下文信息。

What’s new

新功能亮点

This launch introduces three capabilities that shorten the path from a Hugging Face model to a working SageMaker Studio workflow.

此次发布引入了三项功能，缩短了从 Hugging Face 模型到可运行的 SageMaker Studio 工作流的路径。

Deep links from Hugging Face into SageMaker Studio When you browse models on Hugging Face, you’ll now see action buttons alongside supported models that map directly to SageMaker Studio workflows:

  • Customize on SageMaker AI opens the Model Customization page in Studio with the selected model pre-loaded, ready to fine-tune.
  • Deploy on SageMaker AI opens the Deployment page in Studio with the model pre-configured for endpoint deployment. Each entry point preserves the context, meaning you don’t need to search for the model again once inside Studio.

从 Hugging Face 到 SageMaker Studio 的深度链接 当您在 Hugging Face 上浏览模型时，现在会在支持的模型旁边看到直接映射到 SageMaker Studio 工作流的操作按钮：

  • “Customize on SageMaker AI”：打开 Studio 中的模型自定义页面，并预加载所选模型，随时准备进行微调。
  • “Deploy on SageMaker AI”：打开 Studio 中的部署页面，并为端点部署预配置好模型。 每个入口点都会保留上下文，这意味着一旦进入 Studio，您无需再次搜索该模型。

Pre-configured permissions New Studio environments created through this flow come with permissions already configured for the full range of SageMaker AI capabilities, including model customization, training jobs, notebook experimentation, and endpoint deployment. A new managed policy, AmazonSageMakerModelCustomizationCoreAccess, is created and attached for you. It provides permissions for serverless model customization jobs using supervised fine-tuning (SFT), direct preference optimization (DPO), reinforcement learning with verifiable rewards (RLVR), and reinforcement learning from AI feedback (RLAIF), with supported deployment to SageMaker AI or Amazon Bedrock endpoints. This alleviates the need to manually create and configure AWS Identity and Access Management (IAM) roles and policies before you can start experimenting. For existing Studio environments, actionable messages with direct links to documentation guide you through adding these permissions.

预配置权限 通过此流程创建的新 Studio 环境已预先配置了使用全套 SageMaker AI 功能所需的权限，包括模型自定义、训练作业、Notebook 实验和端点部署。系统会为您创建并附加一项新的托管策略 AmazonSageMakerModelCustomizationCoreAccess。它为使用监督微调 (SFT)、直接偏好优化 (DPO)、带可验证奖励的强化学习 (RLVR) 以及来自 AI 反馈的强化学习 (RLAIF) 的无服务器模型自定义作业提供权限，并支持部署到 SageMaker AI 或 Amazon Bedrock 端点。这免去了在开始实验前手动创建和配置 AWS Identity and Access Management (IAM) 角色和策略的麻烦。对于现有的 Studio 环境，系统会提供带有文档直接链接的可操作消息，引导您完成这些权限的添加。

GPU quota visibility When selecting instance types for deployment or training, the Studio UI now surfaces quota availability directly in the instance selection list. You can immediately see which GPU instance types (G5, G6) are available under your account’s current limits. You don’t need to navigate separately to Service Quotas. If you still need to request a limit increase, you’re redirected directly to the Service Quotas page for the respective instance type.

GPU 配额可见性 在选择部署或训练的实例类型时，Studio UI 现在会直接在实例选择列表中显示配额可用性。您可以立即查看在您账户当前限制下哪些 GPU 实例类型（G5、G6）可用，无需单独跳转到“服务配额”(Service Quotas) 页面。如果您仍需申请增加限额，系统会直接将您重定向到相应实例类型的“服务配额”页面。

Walkthrough: Deep-linking from Hugging Face to SageMaker Studio

操作指南：从 Hugging Face 深度链接至 SageMaker Studio

Let’s walk through the experience of customizing or deploying a model starting from Hugging Face. 让我们通过一个示例，了解从 Hugging Face 开始自定义或部署模型的体验。

Step 1: Discover and select On the Hugging Face model page, click on “Deploy” and select “Amazon SageMaker AI”. If the model is supported, you will see two buttons, “Deploy on SageMaker AI” and “Customize on SageMaker AI”. Then select “Customize on SageMaker AI” for a supported model.

第一步：发现并选择 在 Hugging Face 模型页面上，点击“Deploy”并选择“Amazon SageMaker AI”。如果该模型受支持，您将看到“Deploy on SageMaker AI”和“Customize on SageMaker AI”两个按钮。选择“Customize on SageMaker AI”即可。

Step 2: Sign in You’re prompted to sign in to AWS using your existing credentials. If you already have an active console session, this step is skipped automatically.

第二步：登录 系统会提示您使用现有的凭证登录 AWS。如果您已经有处于活动状态的控制台会话，此步骤将自动跳过。

Step 3: Land in Studio You arrive directly on the Model Customization page inside SageMaker Studio with your model pre-selected. Next, configure your fine-tuning parameters such as training data, hyperparameters, and instance type, then submit the customization job. Alternatively, selecting Deploy on SageMaker AI opens the endpoint deployment page in Studio with the model pre-configured. Select your instance type (quota visibility included), review the settings, and deploy.

第三步：进入 Studio 您将直接到达 SageMaker Studio 内的模型自定义页面，且模型已被预先选中。接下来，配置您的微调参数（如训练数据、超参数和实例类型），然后提交自定义作业。或者，选择“Deploy on SageMaker AI”将打开 Studio 中的端点部署页面，模型已预先配置好。选择您的实例类型（包含配额可见性）、检查设置并进行部署。

Step 4: Test your endpoint After you deploy your endpoint, test inference directly from Studio’s endpoint testing interface.

第四步：测试端点 部署端点后，直接从 Studio 的端点测试界面进行推理测试。

Getting started

开始使用

You can try this experience today:

  • Browse models on Hugging Face.
  • Look for the Customize on SageMaker AI or Deploy on SageMaker AI buttons on supported models.
  • Select and follow the streamlined sign-in flow.
  • Start building in a fully configured SageMaker Studio environment.

您现在就可以尝试此体验：

  • 浏览 Hugging Face 上的模型。
  • 在支持的模型上查找“Customize on SageMaker AI”或“Deploy on SageMaker AI”按钮。
  • 选择并按照简化的登录流程操作。
  • 在配置完善的 SageMaker Studio 环境中开始构建。

Conclusion

总结

The launch of a one-click Studio landing experience minimizes the friction between discovering a model and experimenting with it. By connecting Hugging Face directly to the SageMaker…

一键式 Studio 登录体验的推出，最大限度地减少了从发现模型到进行实验之间的阻力。通过将 Hugging Face 直接连接到 SageMaker…

← 返回新闻列表 / Back to news list