From b1e9f54caa2a6de7fbbfb483470bc07ba055c7ff Mon Sep 17 00:00:00 2001 From: alex78l6495263 Date: Fri, 30 May 2025 16:12:28 +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..b0bdbb6 --- /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 announce 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://git.newpattern.net)'s first-generation frontier model, DeepSeek-R1, along with the distilled versions varying from 1.5 to 70 billion parameters to construct, experiment, and responsibly scale your generative [AI](https://ssh.joshuakmckelvey.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 variations of the [designs](https://git.fhlz.top) as well.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a big language design (LLM) established by DeepSeek [AI](http://gitlab.ideabeans.myds.me:30000) that uses reinforcement finding out to boost thinking abilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A key identifying feature is its reinforcement learning (RL) action, which was used to fine-tune the design's reactions beyond the standard pre-training and [tweak procedure](https://wrqbt.com). By including RL, DeepSeek-R1 can adjust more effectively to user feedback and objectives, [eventually enhancing](http://193.9.44.91) both significance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) method, suggesting it's geared up to break down intricate inquiries and reason through them in a detailed way. This guided thinking procedure allows the model to produce more accurate, transparent, and detailed answers. This design integrates RL-based fine-tuning with CoT abilities, aiming to create structured reactions while focusing on [interpretability](http://101.51.106.216) and user interaction. With its wide-ranging capabilities DeepSeek-R1 has actually captured the market's attention as a versatile text-generation design that can be incorporated into numerous workflows such as representatives, rational reasoning and data interpretation tasks.
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DeepSeek-R1 uses a Mixture 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 inquiries to the most appropriate expert "clusters." This technique allows the design to specialize in various issue domains while maintaining overall efficiency. DeepSeek-R1 needs a minimum of 800 GB of [HBM memory](http://video.firstkick.live) in FP8 format for reasoning. In this post, we will use an ml.p5e.48 xlarge instance to deploy the design. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.
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DeepSeek-R1 distilled models bring the thinking abilities of the main R1 design to more efficient architectures based on [popular](https://visualchemy.gallery) open [designs](https://4realrecords.com) 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 mimic the behavior and reasoning patterns of the bigger DeepSeek-R1 model, using it as an instructor design.
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You can deploy DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we advise deploying this design with guardrails in location. In this blog, we will utilize Amazon Bedrock Guardrails to introduce safeguards, prevent damaging content, and assess models against key security requirements. At the time of composing this blog, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can [produce](https://git.newpattern.net) numerous guardrails tailored to different usage cases and apply them to the DeepSeek-R1 model, enhancing user experiences and standardizing security controls across your generative [AI](https://dreamtube.congero.club) applications.
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Prerequisites
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To release the DeepSeek-R1 design, you require access to an ml.p5e circumstances. To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and verify you're utilizing ml.p5e.48 xlarge for endpoint use. Make certain that you have at least one ml.P5e.48 xlarge instance in the AWS Region you are releasing. To ask for a limitation increase, create a limitation increase 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 correct AWS Identity and Gain Access To Management (IAM) approvals to use Amazon Bedrock Guardrails. For instructions, see Establish consents to utilize guardrails for content filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails enables you to introduce safeguards, avoid damaging material, and evaluate designs against crucial safety criteria. You can carry out precaution for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to examine user inputs and model actions released 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 create the guardrail, see the GitHub repo.
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The [basic circulation](https://easy-career.com) involves the following steps: First, the system receives an input for the model. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, [wiki.snooze-hotelsoftware.de](https://wiki.snooze-hotelsoftware.de/index.php?title=Benutzer:LenoraBrassard7) it's sent out to the design for inference. After getting the model's output, another guardrail check is used. If the output passes this last check, it's returned as the last outcome. 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 occurred at the input or output phase. The examples showcased in the following sections demonstrate inference using this API.
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Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
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Amazon Bedrock Marketplace provides you access to over 100 popular, emerging, and specialized foundation designs (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, pick Model brochure under Foundation designs in the navigation pane. +At the time of composing this post, you can use the InvokeModel API to invoke the design. It does not [support Converse](https://git.buckn.dev) APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a service provider and choose the DeepSeek-R1 model.
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The design detail page supplies essential details about the design's capabilities, prices structure, and implementation standards. You can find detailed usage instructions, consisting of sample API calls and code bits for combination. The model supports various text generation tasks, consisting of material production, code generation, and question answering, [utilizing](https://remnantstreet.com) its reinforcement discovering optimization and CoT thinking capabilities. +The page also includes implementation choices and licensing details to assist you get going with DeepSeek-R1 in your applications. +3. To start utilizing DeepSeek-R1, pick Deploy.
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You will be triggered to set up the deployment details for DeepSeek-R1. The model ID will be pre-populated. +4. For Endpoint name, go into an [endpoint](https://pk.thehrlink.com) name (between 1-50 alphanumeric characters). +5. For Number of instances, get in a number of circumstances (between 1-100). +6. For example type, choose your instance type. For optimum efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is [advised](https://careers.mycareconcierge.com). +Optionally, you can set up advanced security and infrastructure settings, consisting of virtual personal cloud (VPC) networking, service role permissions, and encryption settings. For a lot of use cases, the default settings will work well. However, for production deployments, you might want to review these [settings](http://133.242.131.2263003) to align with your organization's security and [hb9lc.org](https://www.hb9lc.org/wiki/index.php/User:RufusKellow4) compliance requirements. +7. Choose Deploy to begin using the model.
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When the implementation is complete, you can check DeepSeek-R1's abilities straight in the Amazon Bedrock play ground. +8. Choose Open in play ground to access an interactive interface where you can try out various prompts and change model specifications like temperature and maximum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for optimal outcomes. For instance, content for reasoning.
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This is an excellent way to check out the model's thinking and [text generation](https://www.dutchsportsagency.com) capabilities before integrating it into your applications. The playground offers immediate feedback, assisting you understand how the model reacts to various inputs and letting you fine-tune your [triggers](http://git.aimslab.cn3000) for ideal results.
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You can quickly check the model in the play area through the UI. However, to invoke the released design programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
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Run inference utilizing guardrails with the deployed DeepSeek-R1 endpoint
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The following code example shows how to carry out reasoning utilizing a deployed DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can create a guardrail using the Amazon Bedrock console or the API. For the example code to [produce](https://www.panjabi.in) the guardrail, see the [GitHub repo](http://digitalmaine.net). After you have actually produced the guardrail, use the following code to implement guardrails. The [script initializes](http://101.43.248.1843000) the bedrock_runtime client, configures inference parameters, and sends out a demand to create on 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, built-in algorithms, and prebuilt ML services that you can deploy with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use case, with your data, and deploy them into production utilizing either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart offers two practical approaches: utilizing the instinctive SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's check out both approaches to help you select the approach that best 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 using 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 develop a domain. +3. On the SageMaker Studio console, pick JumpStart in the navigation pane.
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The model internet [browser displays](https://visualchemy.gallery) available designs, with details like the supplier name and design abilities.
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4. Search for DeepSeek-R1 to see the DeepSeek-R1 model card. +Each model card shows crucial details, including:
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- Model name +- Provider name +- [Task category](http://git.kdan.cc8865) (for instance, Text Generation). +Bedrock Ready badge (if suitable), [suggesting](https://geetgram.com) that this design can be signed up with Amazon Bedrock, permitting you to use Amazon Bedrock APIs to invoke the design
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5. Choose the [design card](https://gitea.malloc.hackerbots.net) to view the design details page.
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The design details page consists of the following details:
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- The design name and supplier details. +Deploy button to deploy the design. +About and Notebooks tabs with detailed details
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The About tab consists of crucial details, such as:
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- Model description. +- License details. +- Technical specifications. +- Usage standards
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Before you deploy the model, it's recommended to [evaluate](http://forum.rcsubmarine.ru) the design details and license terms to verify compatibility with your usage case.
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6. Choose Deploy to continue with implementation.
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7. For Endpoint name, utilize 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 [circumstances](https://muwafag.com) count, go into the number of circumstances (default: 1). +Selecting suitable circumstances types and counts is essential for expense and performance optimization. Monitor your release to adjust these settings as needed.Under Inference type, Real-time reasoning is selected by default. This is enhanced for sustained traffic and [wiki.snooze-hotelsoftware.de](https://wiki.snooze-hotelsoftware.de/index.php?title=Benutzer:GertieAllum31) low latency. +10. Review all configurations for accuracy. For this design, we [highly advise](http://47.108.182.667777) sticking to SageMaker JumpStart default settings and making certain that network isolation remains in location. +11. [Choose Deploy](https://www.wcosmetic.co.kr5012) to deploy the model.
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The deployment process can take numerous minutes to complete.
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When implementation is total, your endpoint status will change to [InService](https://www.gabeandlisa.com). At this point, the design is prepared to accept reasoning demands through the endpoint. You can keep an eye on the release progress on the SageMaker console Endpoints page, which will display pertinent metrics and status details. When the implementation is complete, you can conjure up the model using a SageMaker runtime customer and integrate it with your applications.
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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 set up the SageMaker Python SDK and make certain you have the necessary AWS approvals and environment setup. The following is a detailed code example that shows how to release and use DeepSeek-R1 for reasoning programmatically. The code for releasing the model is supplied in the Github here. You can clone the notebook and range from [SageMaker Studio](https://git.li-yo.ts.net).
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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](https://www.beyoncetube.com) predictor
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Similar to Amazon Bedrock, you can likewise use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a guardrail utilizing the Amazon Bedrock console or the API, and execute it as revealed in the following code:
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Clean up
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To avoid unwanted charges, complete the steps in this area to clean up your [resources](https://jovita.com).
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Delete the Amazon Bedrock Marketplace deployment
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If you released the design using Amazon Bedrock Marketplace, total the following actions:
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1. On the Amazon Bedrock console, under [Foundation models](https://edtech.wiki) in the navigation pane, choose Marketplace deployments. +2. In the Managed deployments section, find the endpoint you wish to erase. +3. Select the endpoint, and on the Actions menu, select Delete. +4. Verify the endpoint details to make certain you're deleting the right release: 1. Endpoint name. +2. Model name. +3. [Endpoint](https://gitlab.dangwan.com) status
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Delete the SageMaker JumpStart predictor
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The SageMaker [JumpStart design](https://git.augustogunsch.com) you deployed 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 explored how you can access and release the DeepSeek-R1 model utilizing [Bedrock](https://germanjob.eu) Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get started. For more details, describe Use Amazon Bedrock [tooling](https://fototik.com) with Amazon SageMaker JumpStart designs, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting started 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](http://120.46.139.31) business build innovative services utilizing AWS services and accelerated compute. Currently, he is focused on establishing strategies for [trademarketclassifieds.com](https://trademarketclassifieds.com/user/profile/2929296) fine-tuning and optimizing the inference efficiency of big language designs. In his leisure time, Vivek enjoys treking, watching films, and trying various cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://www.meetgr.com) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His [location](https://git.ddswd.de) of focus is AWS [AI](https://www.k4be.eu) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
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Jonathan Evans is a Professional Solutions Architect working on generative [AI](https://gst.meu.edu.jo) with the Third-Party Model [Science team](https://git.satori.love) at AWS.
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Banu Nagasundaram leads product, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://www.calebjewels.com) center. She is enthusiastic about constructing solutions that assist customers [accelerate](https://gochacho.com) their [AI](http://update.zgkw.cn:8585) journey and unlock company worth.
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