From 74f9bc6d8b4f95bd3ba00e7bda32346446592408 Mon Sep 17 00:00:00 2001 From: ginoarriola427 Date: Mon, 2 Jun 2025 13:35:25 +0800 Subject: [PATCH] Add DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart --- ...tplace 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..3ea20eb --- /dev/null +++ b/DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart.-.md @@ -0,0 +1,93 @@ +
Today, we are thrilled 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 release DeepSeek [AI](https://disgaeawiki.info)'s first-generation frontier model, DeepSeek-R1, along with the distilled versions varying from 1.5 to 70 billion specifications to build, experiment, and responsibly scale your [generative](https://www.outletrelogios.com.br) [AI](http://81.70.25.144:3000) ideas on AWS.
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In this post, we show how to begin with DeepSeek-R1 on Amazon Bedrock [Marketplace](http://82.156.24.19310098) and SageMaker JumpStart. You can follow comparable actions to release the [distilled versions](http://fangding.picp.vip6060) of the designs too.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a big language model (LLM) developed by DeepSeek [AI](https://foris.gr) that uses reinforcement discovering to enhance thinking capabilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. An essential distinguishing function is its reinforcement knowing (RL) step, which was utilized to refine the model's reactions beyond the standard pre-training and fine-tuning procedure. By [incorporating](https://www.trabahopilipinas.com) RL, DeepSeek-R1 can adapt more effectively to user feedback and goals, ultimately improving both [significance](https://code.linkown.com) and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) method, meaning it's geared up to break down intricate inquiries and reason through them in a detailed manner. This guided [thinking process](http://118.190.175.1083000) allows the design to produce more accurate, transparent, and detailed responses. This model combines RL-based fine-tuning with CoT capabilities, aiming to generate structured reactions while concentrating on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has captured the industry's attention as a flexible text-generation design that can be integrated into various workflows such as representatives, sensible thinking and information analysis tasks.
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DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture allows activation of 37 billion parameters, enabling effective inference by routing questions to the most appropriate specialist "clusters." This method allows the design to focus on different [issue domains](http://engineerring.net) while maintaining general effectiveness. DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for reasoning. In this post, we will utilize an ml.p5e.48 xlarge instance to release the model. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.
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DeepSeek-R1 distilled [designs](http://121.37.166.03000) bring the thinking abilities of the main R1 design to more efficient architectures based on popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a process of training smaller, more effective models to imitate the habits and reasoning patterns of the larger DeepSeek-R1 design, utilizing it as an instructor design.
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You can release DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we suggest deploying this model with guardrails in place. In this blog site, we will use Amazon Bedrock Guardrails to introduce safeguards, avoid damaging material, and evaluate designs against essential safety requirements. At the time of composing this blog, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can develop numerous guardrails tailored to various usage cases and use them to the DeepSeek-R1 model, improving user experiences and standardizing safety controls across your generative [AI](https://careers.jabenefits.com) applications.
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Prerequisites
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To deploy the DeepSeek-R1 design, you need access to an ml.p5e circumstances. To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, select Amazon SageMaker, and verify you're utilizing 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 limit increase, produce a limitation boost demand and connect to your account team.
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Because you will be releasing this design with Amazon Bedrock Guardrails, make certain you have the right AWS Identity and Gain Access To Management (IAM) consents to use Amazon Bedrock Guardrails. For directions, see Set up approvals to use guardrails for content filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails allows you to introduce safeguards, prevent harmful material, and assess models against key safety [criteria](https://familytrip.kr). You can carry out precaution for the DeepSeek-R1 [design utilizing](https://yooobu.com) the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to assess user inputs and model actions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can develop a guardrail using the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo.
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The basic circulation [involves](https://code.linkown.com) the following actions: First, the system gets an input for the design. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it's sent out to the design for reasoning. After getting the design's output, another guardrail check is applied. If the output passes this last check, [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11860868) it's returned as the outcome. However, if either the input or output is stepped in by the guardrail, a message is returned showing 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 gives you access to over 100 popular, emerging, and specialized foundation models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following steps:
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1. On the Amazon Bedrock console, choose Model brochure under Foundation models in the navigation pane. +At the time of composing this post, you can utilize the InvokeModel API to conjure up the design. It doesn't support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a supplier and select the DeepSeek-R1 design.
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The model detail page [supplies essential](https://redsocial.cl) details about the model's abilities, rates structure, and implementation standards. You can discover detailed usage directions, consisting of sample API calls and code bits for combination. The design supports numerous text generation jobs, including material development, code generation, and [question](https://video.etowns.ir) answering, utilizing its support learning optimization and CoT thinking capabilities. +The page also consists of deployment options and licensing details to help you get started with DeepSeek-R1 in your applications. +3. To start utilizing DeepSeek-R1, pick Deploy.
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You will be prompted to configure the release details for DeepSeek-R1. The design ID will be pre-populated. +4. For Endpoint name, enter an endpoint name (between 1-50 alphanumeric characters). +5. For Variety of circumstances, get in a variety of instances (between 1-100). +6. For Instance type, select your circumstances type. For [optimal performance](http://www.todak.co.kr) with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is [recommended](http://8.137.12.293000). +Optionally, you can configure innovative security and facilities settings, consisting of virtual private cloud (VPC) networking, service function consents, and file encryption settings. For a lot of utilize cases, the default settings will work well. However, for production deployments, you might wish to examine these settings to align with your company's security and compliance requirements. +7. Choose Deploy to begin utilizing the model.
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When the release is total, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock play area. +8. Choose Open in play area to access an interactive user interface where you can explore various triggers and adjust design criteria like temperature level and optimum length. +When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimal outcomes. For instance, content for reasoning.
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This is an excellent way to check out the design's reasoning and text generation capabilities before integrating it into your applications. The playground provides immediate feedback, assisting you understand how the model responds to numerous inputs and letting you fine-tune your prompts for optimal outcomes.
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You can rapidly evaluate the model in the play area 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 utilizing guardrails with the released DeepSeek-R1 endpoint
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The following code example shows how to carry out inference utilizing a deployed DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can create a guardrail [utilizing](https://awaz.cc) the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo. After you have developed the guardrail, use the following code to execute guardrails. The script initializes the bedrock_runtime customer, configures inference specifications, and sends out a demand to produce text based upon a user timely.
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Deploy DeepSeek-R1 with SageMaker JumpStart
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SageMaker JumpStart is an artificial intelligence (ML) hub with FMs, built-in algorithms, and prebuilt ML services that you can release with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage case, with your information, and release them into production using either the UI or SDK.
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[Deploying](http://www.colegio-sanandres.cl) DeepSeek-R1 model through SageMaker JumpStart offers 2 practical techniques: utilizing the user-friendly SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's explore both methods to assist you pick the approach that finest suits your needs.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following steps to deploy DeepSeek-R1 utilizing SageMaker JumpStart:
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1. On the SageMaker console, select Studio in the navigation pane. +2. First-time users will be triggered to create a domain. +3. On the SageMaker Studio console, select JumpStart in the navigation pane.
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The design internet browser displays available models, with details like the provider name and design abilities.
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4. Search for DeepSeek-R1 to view the DeepSeek-R1 model card. +Each design card reveals essential details, including:
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- Model name +- Provider name +- Task classification (for example, Text Generation). +Bedrock Ready badge (if suitable), indicating that this design can be signed up with Amazon Bedrock, enabling you to utilize Amazon Bedrock APIs to [conjure](http://8.137.12.293000) up the model
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5. Choose the model card to view the model details page.
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The design details page includes the following details:
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- The design name and service provider details. +Deploy button to deploy the model. +About and Notebooks tabs with detailed details
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The About tab consists of important details, such as:
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- Model description. +- License details. +- Technical specs. +- Usage guidelines
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Before you deploy the design, it's recommended to review the model details and license terms to confirm compatibility with your usage case.
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6. Choose Deploy to [continue](https://sportsprojobs.net) with release.
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7. For Endpoint name, [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:Windy79138129) use the immediately [produced](https://ipen.com.hk) name or create a custom-made one. +8. For [disgaeawiki.info](https://disgaeawiki.info/index.php/User:AngelinaVanRaalt) Instance type ΒΈ choose an instance type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, enter the variety of circumstances (default: 1). +Selecting proper circumstances types and counts is important for cost and performance optimization. Monitor your deployment to change these settings as needed.Under Inference type, Real-time reasoning is picked by default. This is optimized for sustained traffic and low latency. +10. Review all configurations for precision. For this model, we strongly suggest adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in place. +11. Choose Deploy to deploy the model.
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The deployment process can take a number of minutes to finish.
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When deployment is total, your endpoint status will change to InService. At this moment, the design is ready to accept inference demands through the endpoint. You can keep an eye on the deployment development on the SageMaker console Endpoints page, which will display appropriate metrics and [status details](https://gitlab.tncet.com). When the release is total, you can invoke the design using a SageMaker runtime client and [incorporate](https://familytrip.kr) it with your applications.
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Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
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To begin with DeepSeek-R1 utilizing the SageMaker Python SDK, you will need to set up the SageMaker Python SDK and make certain you have the necessary AWS consents and environment setup. The following is a detailed code example that shows how to deploy and utilize DeepSeek-R1 for inference programmatically. The code for deploying the design is provided in the Github here. You can clone the note pad and range from SageMaker Studio.
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You can run additional requests against the predictor:
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Implement guardrails and run reasoning with your SageMaker JumpStart predictor
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Similar to Amazon Bedrock, you can likewise utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a guardrail utilizing the Amazon Bedrock console or the API, and implement it as [displayed](http://www.withsafety.net) in the following code:
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Clean up
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To prevent unwanted charges, finish the steps in this section to tidy up your resources.
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Delete the Amazon Bedrock Marketplace release
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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 implementations. +2. In the Managed implementations area, find the endpoint you wish to erase. +3. Select the endpoint, and on the Actions menu, pick Delete. +4. Verify the endpoint details to make certain you're erasing the proper deployment: 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 deployed will sustain expenses if you leave it running. Use the following code to delete the endpoint if you wish 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 design utilizing Bedrock [Marketplace](http://xn---atd-9u7qh18ebmihlipsd.com) and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to start. For more details, refer to Use Amazon Bedrock [tooling](http://406.gotele.net) with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained models, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting going with Amazon SageMaker .
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About the Authors
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Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He assists emerging generative [AI](https://www.onlywam.tv) business build ingenious services utilizing AWS services and sped up calculate. Currently, he is concentrated on establishing methods for fine-tuning and enhancing the inference performance of large [language](http://fridayad.in) designs. In his leisure time, [trademarketclassifieds.com](https://trademarketclassifieds.com/user/profile/2701513) Vivek takes pleasure in hiking, seeing films, and attempting various foods.
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Niithiyn Vijeaswaran is a Generative [AI](http://rernd.com) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His area of focus is AWS [AI](http://git.jishutao.com) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.
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Jonathan Evans is an Expert Solutions Architect working on generative [AI](https://woowsent.com) with the Third-Party Model Science group at AWS.
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Banu Nagasundaram leads item, engineering, and [tactical collaborations](http://git.twopiz.com8888) for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://harborhousejeju.kr) center. She is passionate about developing solutions that assist consumers accelerate their [AI](http://154.209.4.10:3001) journey and unlock company value.
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