commit 803992bda2213bf41ac196dd74b291709b08d2fa Author: chauharry28750 Date: Thu Apr 3 15:10:28 2025 +0800 Add DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart 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..244fe87 --- /dev/null +++ b/DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart.-.md @@ -0,0 +1,93 @@ +
Today, we are excited 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](http://120.77.221.199:3000)'s first-generation frontier model, DeepSeek-R1, together with the distilled versions ranging from 1.5 to 70 billion parameters to build, experiment, and properly scale your generative [AI](https://git.mitsea.com) ideas on AWS.
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In this post, we show how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar steps to release the distilled versions of the designs as well.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a large language design (LLM) established by DeepSeek [AI](https://git.alexavr.ru) that uses reinforcement finding out to boost reasoning abilities through a multi-stage training process from a DeepSeek-V3-Base structure. A key differentiating function is its support learning (RL) action, which was used to fine-tune the design's reactions beyond the standard pre-training and fine-tuning procedure. By incorporating RL, DeepSeek-R1 can adapt more successfully to user feedback and goals, ultimately enhancing both relevance and clarity. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) method, implying it's equipped to break down complicated queries and reason through them in a detailed way. This guided reasoning procedure enables the model to produce more accurate, transparent, and detailed answers. This design combines RL-based fine-tuning with CoT capabilities, aiming to produce structured actions while concentrating on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has recorded the industry's attention as a flexible text-generation design that can be integrated into numerous workflows such as representatives, sensible reasoning and data interpretation tasks.
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DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture allows activation of 37 billion specifications, allowing effective inference by routing questions to the most appropriate expert "clusters." This approach allows the design to concentrate on various problem domains while maintaining overall effectiveness. DeepSeek-R1 needs at least 800 GB of [HBM memory](http://git.jaxc.cn) in FP8 format for inference. In this post, we will use an ml.p5e.48 xlarge circumstances to release the model. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.
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DeepSeek-R1 distilled designs bring the reasoning abilities of the main R1 model to more efficient architectures based on popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a procedure of training smaller sized, more effective models to mimic the habits and reasoning patterns of the bigger DeepSeek-R1 design, using 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 model, we recommend deploying this design with guardrails in place. In this blog site, we will use Amazon Bedrock Guardrails to introduce safeguards, avoid harmful material, and evaluate models against crucial safety criteria. At the time of composing this blog, for DeepSeek-R1 implementations on [SageMaker JumpStart](https://www.beyoncetube.com) and Bedrock Marketplace, Bedrock Guardrails supports only the [ApplyGuardrail API](http://112.124.19.388080). You can produce numerous guardrails [tailored](http://www.dahengsi.com30002) to different use cases and use them to the DeepSeek-R1 design, improving user experiences and standardizing safety controls throughout your generative [AI](https://vagas.grupooportunityrh.com.br) 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 inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick Amazon SageMaker, and verify you're using 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 limit boost, produce a limit boost request 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 appropriate AWS Identity and [Gain Access](https://tweecampus.com) To Management (IAM) authorizations to use Amazon Bedrock Guardrails. For instructions, see Set up permissions to utilize guardrails for material filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails enables you to present safeguards, prevent hazardous material, and assess designs against key safety requirements. You can carry out precaution for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to assess user inputs and model actions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can develop a guardrail utilizing the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo.
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The general circulation involves the following steps: First, the system receives 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 used. If the output passes this last check, it's returned as the outcome. However, if either the input or output is intervened by the guardrail, a message is returned showing the nature of the intervention and whether it happened at the input or output phase. The examples showcased in the following areas demonstrate inference 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, pick Model catalog under Foundation models in the navigation pane. +At the time of composing this post, you can utilize the InvokeModel API to invoke the model. It does not 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 design detail page offers important details about the model's capabilities, pricing structure, and application guidelines. You can discover detailed usage directions, including sample API calls and code bits for combination. The [design supports](https://kaamdekho.co.in) different text generation tasks, consisting of material development, code generation, and [trademarketclassifieds.com](https://trademarketclassifieds.com/user/profile/2746667) concern answering, utilizing its support discovering optimization and CoT reasoning abilities. +The page likewise includes release options and licensing details to assist you get going 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 [implementation details](http://47.97.159.1443000) for DeepSeek-R1. The design ID will be pre-populated. +4. For Endpoint name, go into an endpoint name (in between 1-50 alphanumeric characters). +5. For Variety of instances, enter a number of circumstances (in between 1-100). +6. For Instance type, choose your instance type. For optimal efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is recommended. +Optionally, you can set up advanced security and facilities settings, consisting of virtual personal cloud (VPC) networking, service role approvals, [trademarketclassifieds.com](https://trademarketclassifieds.com/user/profile/2672496) and file encryption settings. For a lot of utilize cases, the default settings will work well. However, for production implementations, you may want to examine these settings to align with your organization's security and compliance requirements. +7. Choose Deploy to start utilizing the design.
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When the release is complete, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock playground. +8. Choose Open in play ground to access an interactive interface where you can try out different prompts and adjust design criteria like temperature and maximum length. +When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for ideal results. For instance, material for reasoning.
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This is an exceptional way to explore the model's thinking and text generation abilities before incorporating it into your applications. The playground provides instant feedback, assisting you comprehend how the model responds to different inputs and letting you fine-tune your triggers for ideal outcomes.
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You can rapidly evaluate the design in the playground through the UI. However, to conjure up the released design programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
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Run reasoning utilizing guardrails with the deployed DeepSeek-R1 endpoint
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The following code example shows how to carry out [inference](https://trulymet.com) using a deployed DeepSeek-R1 design through Amazon Bedrock using the invoke_model and [ApplyGuardrail API](https://git.bluestoneapps.com). You can produce a guardrail utilizing the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo. After you have actually produced the guardrail, use the following code to execute guardrails. The script initializes the bedrock_runtime customer, sets up inference parameters, and sends a demand to generate 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) hub with FMs, integrated algorithms, and prebuilt ML services that you can release with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage case, with your information, and deploy them into production utilizing either the UI or SDK.
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Deploying DeepSeek-R1 design through SageMaker JumpStart offers 2 convenient methods: using the instinctive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's check out both methods to help you select the method that finest matches your requirements.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following steps to [release](http://daeasecurity.com) DeepSeek-R1 utilizing 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 create a domain. +3. On the SageMaker Studio console, pick JumpStart in the navigation pane.
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The design web browser shows available models, with details like the company 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 category (for instance, Text Generation). +[Bedrock Ready](https://app.deepsoul.es) badge (if suitable), indicating that this model can be signed up with Amazon Bedrock, allowing you to utilize Amazon Bedrock APIs to invoke the model
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5. Choose the model card to see the [design details](https://gitea.sb17.space) page.
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The model details page consists of the following details:
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- The design name and supplier details. +Deploy button to release the model. +About and Notebooks tabs with [detailed](https://wiki.cemu.info) details
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The About tab includes essential details, such as:
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- Model description. +- License details. +- Technical specs. +[- Usage](http://39.99.224.279022) guidelines
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Before you release the model, it's advised to review the model details and license terms to confirm compatibility with your usage case.
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6. Choose Deploy to continue with .
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7. For Endpoint name, use the automatically produced name or produce a customized one. +8. For example type ΒΈ select an instance type (default: ml.p5e.48 xlarge). +9. For Initial instance count, get in the [variety](https://ttaf.kr) of instances (default: 1). +Selecting appropriate circumstances types and counts is essential for cost and efficiency optimization. Monitor your release to change these settings as needed.Under Inference type, Real-time inference is [selected](http://grainfather.asia) by default. This is enhanced for sustained traffic and low latency. +10. Review all setups for precision. For this design, we highly recommend sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in place. +11. Choose Deploy to deploy the design.
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The [release procedure](https://tricityfriends.com) can take a number of minutes to complete.
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When release is total, your [endpoint status](http://begild.top8418) will alter to InService. At this moment, the design is prepared to accept inference requests through the endpoint. You can monitor the release progress on the SageMaker console Endpoints page, which will display relevant metrics and status details. When the [deployment](https://actu-info.fr) is complete, you can conjure up the model utilizing a SageMaker runtime customer and incorporate it with your applications.
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Deploy DeepSeek-R1 using the SageMaker Python SDK
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To begin with DeepSeek-R1 utilizing the SageMaker Python SDK, you will require to install the SageMaker Python SDK and make certain you have the required AWS approvals and environment setup. The following is a [detailed](https://git.marcopacs.com) code example that demonstrates how to release and use DeepSeek-R1 for reasoning programmatically. The code for releasing the model is provided in the Github here. You can clone the notebook and run from SageMaker Studio.
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You can run additional requests 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 utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a [guardrail utilizing](http://expand-digitalcommerce.com) the Amazon Bedrock console or the API, and execute it as displayed 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 tidy up your resources.
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Delete the Amazon Bedrock Marketplace release
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If you deployed the design utilizing Amazon Bedrock Marketplace, complete the following actions:
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1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, pick Marketplace implementations. +2. In the Managed deployments section, locate the endpoint you desire to erase. +3. Select the endpoint, and on the Actions menu, select Delete. +4. Verify the endpoint details to make certain you're erasing the right 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 costs if you leave it running. Use the following code to delete the [endpoint](http://47.101.46.1243000) 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 and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to begin. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, 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](http://code.istudy.wang) is a Lead Specialist Solutions Architect for Inference at AWS. He assists emerging generative [AI](https://weworkworldwide.com) companies construct ingenious services using AWS services and accelerated compute. Currently, he is concentrated on developing methods for fine-tuning and [it-viking.ch](http://it-viking.ch/index.php/User:TawannaSancho) enhancing the inference performance of large language models. In his leisure time, Vivek takes pleasure in treking, watching films, and attempting different cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://www.jobcreator.no) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His area of focus is AWS [AI](https://wiki.cemu.info) accelerators (AWS Neuron). He holds a [Bachelor's degree](http://124.222.6.973000) in Computer Science and Bioinformatics.
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Jonathan Evans is a Professional Solutions Architect working on generative [AI](http://40.73.118.158) with the Third-Party Model Science team at AWS.
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Banu Nagasundaram leads product, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial [intelligence](https://www.social.united-tuesday.org) and generative [AI](https://www.yohaig.ng) center. She is passionate about developing options that assist clients [accelerate](https://ayjmultiservices.com) their [AI](http://211.119.124.110:3000) journey and unlock business worth.
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