From c2b78e11b17d5648b53ed8eef6cc74476966419d Mon Sep 17 00:00:00 2001 From: maryannelasley Date: Sun, 1 Jun 2025 14:04:57 +0800 Subject: [PATCH] Add 'DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart' --- ...ketplace-And-Amazon-SageMaker-JumpStart.md | 93 +++++++++++++++++++ 1 file changed, 93 insertions(+) create mode 100644 DeepSeek-R1-Model-now-Available-in-Amazon-Bedrock-Marketplace-And-Amazon-SageMaker-JumpStart.md diff --git a/DeepSeek-R1-Model-now-Available-in-Amazon-Bedrock-Marketplace-And-Amazon-SageMaker-JumpStart.md b/DeepSeek-R1-Model-now-Available-in-Amazon-Bedrock-Marketplace-And-Amazon-SageMaker-JumpStart.md new file mode 100644 index 0000000..d6c1a4a --- /dev/null +++ b/DeepSeek-R1-Model-now-Available-in-Amazon-Bedrock-Marketplace-And-Amazon-SageMaker-JumpStart.md @@ -0,0 +1,93 @@ +
Today, we are delighted to reveal that DeepSeek R1 distilled Llama and Qwen designs are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](https://tenacrebooks.com)'s first-generation frontier model, DeepSeek-R1, along with the distilled variations varying from 1.5 to 70 billion specifications to construct, experiment, and properly scale your generative [AI](https://calciojob.com) concepts on AWS.
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In this post, we show how to start with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar actions to deploy the distilled versions of the models too.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a big [language design](https://micircle.in) (LLM) developed by DeepSeek [AI](http://testyourcharger.com) that uses support finding out to boost reasoning abilities through a [multi-stage training](http://192.241.211.111) process from a DeepSeek-V3-Base foundation. A crucial differentiating function is its reinforcement knowing (RL) action, which was utilized to refine the model's reactions beyond the basic pre-training and fine-tuning process. By incorporating RL, [larsaluarna.se](http://www.larsaluarna.se/index.php/User:MyraNeudorf4) DeepSeek-R1 can adjust better to user feedback and goals, eventually enhancing both importance and [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11857434) clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) method, implying it's equipped to break down intricate questions and factor through them in a detailed way. This assisted thinking process enables the design to produce more precise, transparent, and detailed responses. This model integrates RL-based fine-tuning with CoT capabilities, aiming to generate structured reactions while focusing on interpretability and user interaction. With its [comprehensive abilities](https://satitmattayom.nrru.ac.th) DeepSeek-R1 has actually captured the industry's attention as a versatile text-generation design that can be integrated into various workflows such as agents, sensible reasoning and data analysis jobs.
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DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture enables activation of 37 billion specifications, making it possible for efficient reasoning by routing inquiries to the most appropriate expert "clusters." This [approach enables](http://kuma.wisilicon.com4000) the model to focus on different problem domains while maintaining total efficiency. DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for reasoning. In this post, we will use an ml.p5e.48 xlarge instance to deploy the model. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.
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DeepSeek-R1 distilled designs bring the thinking capabilities of the main R1 model to more efficient architectures based upon popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of training smaller sized, [pipewiki.org](https://pipewiki.org/wiki/index.php/User:Ralph407818) more efficient models to imitate the habits and thinking patterns of the bigger DeepSeek-R1 design, utilizing it as an instructor model.
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You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we advise releasing this model with guardrails in place. In this blog, we will use Amazon Bedrock Guardrails to introduce safeguards, prevent hazardous content, and evaluate models against crucial safety criteria. At the time of composing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, [Bedrock Guardrails](https://becalm.life) supports just the ApplyGuardrail API. You can produce multiple guardrails tailored to different use cases and apply them to the DeepSeek-R1 model, improving user experiences and standardizing safety [controls](https://laboryes.com) across your generative [AI](https://www.complete-jobs.com) applications.
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Prerequisites
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To release the DeepSeek-R1 design, you require access to an ml.p5e instance. To examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, select Amazon SageMaker, and validate you're using ml.p5e.48 xlarge for endpoint usage. Make certain that you have at least one ml.P5e.48 xlarge circumstances in the AWS Region you are deploying. To ask for a limitation boost, produce a limitation boost request and connect to your account group.
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Because you will be releasing this model with Amazon Bedrock Guardrails, make certain you have the correct AWS Identity and Gain Access To Management (IAM) authorizations to utilize Amazon [Bedrock](https://elit.press) Guardrails. For directions, see Set up consents to use guardrails for material filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails allows you to introduce safeguards, avoid damaging material, and assess designs against crucial security criteria. You can execute precaution for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to examine user inputs and model responses deployed on Amazon Bedrock Marketplace and [SageMaker JumpStart](https://gitea.aventin.com). You can create a guardrail using the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo.
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The basic flow involves the following actions: First, the system receives an input for the design. This input is then processed through the ApplyGuardrail API. If the input passes the [guardrail](https://globalhospitalitycareer.com) check, it's sent to the design for reasoning. After getting the model's output, another guardrail check is applied. If the output passes this final check, it's returned as the result. However, if either the input or output is stepped in by the guardrail, a message is returned suggesting the nature of the intervention and whether it happened at the input or output stage. The examples showcased in the following sections demonstrate reasoning using this API.
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Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
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Amazon Bedrock Marketplace offers you access to over 100 popular, emerging, and specialized structure designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following actions:
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1. On the Amazon Bedrock console, [choose Model](http://git.pancake2021.work) catalog under Foundation designs in the navigation pane. +At the time of writing this post, you can utilize the InvokeModel API to invoke the design. It doesn't support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a [service provider](https://gogs.xinziying.com) and [systemcheck-wiki.de](https://systemcheck-wiki.de/index.php?title=Benutzer:VernitaWestmacot) select the DeepSeek-R1 design.
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The model detail page supplies necessary details about the design's capabilities, pricing structure, and execution guidelines. You can find detailed use guidelines, consisting of sample API calls and code bits for integration. The model supports various text generation jobs, consisting of content development, code generation, and concern answering, [pipewiki.org](https://pipewiki.org/wiki/index.php/User:JonnaCanipe) utilizing its reinforcement discovering optimization and CoT reasoning abilities. +The page also consists of implementation choices and licensing details to assist you get started with DeepSeek-R1 in your applications. +3. To begin using DeepSeek-R1, pick Deploy.
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You will be triggered to set up the deployment details for DeepSeek-R1. The design ID will be pre-populated. +4. For Endpoint name, get in an endpoint name (between 1-50 alphanumeric characters). +5. For Number of instances, enter a number of circumstances (between 1-100). +6. For Instance type, choose your instance type. For optimum performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised. +Optionally, you can set up sophisticated security and facilities settings, including virtual [personal](http://101.43.112.1073000) cloud (VPC) networking, service role permissions, and encryption settings. For most utilize cases, the default settings will work well. However, for production releases, you may wish to evaluate these settings to line up with your organization's security and compliance requirements. +7. Choose Deploy to begin utilizing the design.
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When the deployment is total, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock play area. +8. Choose Open in play ground to access an interactive user interface where you can try out various prompts and change model specifications like temperature and maximum length. +When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for optimal results. For example, content for inference.
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This is an outstanding method to explore the model's reasoning and text generation abilities before integrating it into your applications. The playground offers instant feedback, assisting you comprehend how the model reacts to numerous inputs and [letting](https://www.bakicicepte.com) you fine-tune your triggers for ideal results.
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You can rapidly test the design in the play ground through the UI. However, to invoke the released model programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
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Run inference using guardrails with the released DeepSeek-R1 endpoint
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The following code example demonstrates how to carry out inference using a deployed DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can produce a guardrail utilizing the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo. After you have produced the guardrail, utilize the following code to [implement guardrails](https://www.klaverjob.com). The script initializes the bedrock_runtime client, sets up reasoning specifications, and sends out a request to produce text based upon a user prompt.
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Deploy DeepSeek-R1 with SageMaker JumpStart
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SageMaker JumpStart is an artificial intelligence (ML) center with FMs, integrated algorithms, [archmageriseswiki.com](http://archmageriseswiki.com/index.php/User:SashaGuidi) and prebuilt ML solutions that you can release with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage case, with your data, and release them into production using either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart offers two convenient techniques: using the user-friendly SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both techniques to help you choose the technique that best fits your needs.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following steps to release DeepSeek-R1 using SageMaker JumpStart:
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1. On the SageMaker console, pick Studio in the navigation pane. +2. First-time users will be triggered to develop a domain. +3. On the SageMaker Studio console, select JumpStart in the navigation pane.
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The design browser displays available models, with details like the provider name and design abilities.
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4. Look for DeepSeek-R1 to view the DeepSeek-R1 model card. +Each [model card](https://git.ivran.ru) reveals key details, consisting of:
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- Model name +- Provider name +- [Task category](https://ayjmultiservices.com) (for example, Text Generation). +[Bedrock Ready](https://career.abuissa.com) badge (if applicable), [suggesting](https://www.fionapremium.com) that this design can be signed up with Amazon Bedrock, permitting you to utilize Amazon [Bedrock APIs](http://gitea.zyimm.com) to invoke the model
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5. Choose the model card to view the design details page.
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The design details page consists of the following details:
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- The model name and company details. +Deploy button to release the model. +About and Notebooks tabs with detailed details
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The About tab includes essential details, such as:
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- Model description. +- License details. +[- Technical](https://viraltry.com) specifications. +- Usage guidelines
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Before you release the model, it's [advised](https://gitlab.edebe.com.br) to examine the model details and license terms to validate compatibility with your usage case.
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6. Choose Deploy to continue with release.
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7. For Endpoint name, use the immediately created name or create a custom-made one. +8. For example type ΒΈ choose a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial instance count, enter the variety of instances (default: 1). +Selecting appropriate [circumstances types](https://music.elpaso.world) and counts is essential for cost and performance optimization. Monitor [wiki.lafabriquedelalogistique.fr](https://wiki.lafabriquedelalogistique.fr/Utilisateur:GregSho817) your implementation to change these settings as needed.Under Inference type, Real-time inference is chosen by default. This is optimized for sustained traffic and low latency. +10. Review all configurations for precision. For this design, we highly recommend sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in location. +11. Choose Deploy to release the design.
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The deployment procedure can take several minutes to complete.
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When release is total, your endpoint status will alter to InService. At this point, the model is all set to accept inference demands through the [endpoint](http://66.112.209.23000). You can keep an eye on the deployment progress on the SageMaker console Endpoints page, which will display appropriate metrics and status details. When the release is total, you can conjure up the design using a SageMaker runtime client and incorporate it with your [applications](https://www.trabahopilipinas.com).
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Deploy DeepSeek-R1 using the SageMaker Python SDK
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To get going with DeepSeek-R1 utilizing the SageMaker Python SDK, you will require to install the [SageMaker Python](https://git.elferos.keenetic.pro) SDK and make certain you have the necessary AWS authorizations and environment setup. The following is a detailed code example that shows how to deploy and use DeepSeek-R1 for inference programmatically. The code for deploying the design is supplied in the Github here. You can clone the [notebook](https://git.torrents-csv.com) and range from SageMaker Studio.
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You can run additional against the predictor:
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Implement guardrails and run inference with your SageMaker JumpStart predictor
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Similar to Amazon Bedrock, you can also use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can create a guardrail utilizing the Amazon Bedrock console or the API, and execute it as [displayed](https://www.indianhighcaste.com) in the following code:
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Tidy up
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To avoid unwanted charges, complete the steps in this area to clean up your resources.
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Delete the Amazon Bedrock Marketplace implementation
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If you released the model utilizing Amazon Bedrock Marketplace, total the following steps:
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1. On the Amazon Bedrock console, under Foundation models in the navigation pane, select Marketplace releases. +2. In the Managed implementations area, locate the [endpoint](https://tiktokbeans.com) you wish to delete. +3. Select the endpoint, and on the Actions menu, choose Delete. +4. Verify the endpoint details to make certain you're [deleting](https://medicalstaffinghub.com) the proper release: 1. Endpoint name. +2. Model name. +3. Endpoint status
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Delete the SageMaker JumpStart predictor
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The SageMaker JumpStart model you released will sustain expenses if you leave it running. Use the following code to delete the endpoint if you want to stop sustaining charges. For more details, see Delete Endpoints and Resources.
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Conclusion
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In this post, we checked out how you can access and release the DeepSeek-R1 model using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to begin. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting going with Amazon SageMaker JumpStart.
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About the Authors
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Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He helps emerging generative [AI](https://picturegram.app) companies develop innovative solutions using AWS services and accelerated compute. Currently, he is focused on establishing techniques for fine-tuning and enhancing the reasoning efficiency of large language designs. In his leisure time, Vivek enjoys hiking, enjoying motion pictures, and attempting various cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://git.thomasballantine.com) Specialist Solutions Architect with the [Third-Party Model](https://47.100.42.7510443) Science group at AWS. His location of focus is AWS [AI](https://jobsfevr.com) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
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Jonathan Evans is an Expert Solutions Architect dealing with generative [AI](https://vsbg.info) with the Third-Party Model Science group at AWS.
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Banu Nagasundaram leads product, engineering, and tactical collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](http://121.37.138.2) hub. She is passionate about building solutions that assist clients accelerate their [AI](https://starleta.xyz) journey and unlock business worth.
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