From 47ba3ac4752ed860b2d01bd70ac4530ea3aafef4 Mon Sep 17 00:00:00 2001 From: Alba Macvitie Date: Thu, 29 May 2025 13:34:43 +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..29a9762 --- /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](https://tribetok.com) Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](https://ansambemploi.re)'s first-generation frontier model, DeepSeek-R1, along with the distilled variations varying from 1.5 to 70 billion [criteria](https://www.dynamicjobs.eu) to construct, experiment, and responsibly scale your generative [AI](https://youslade.com) ideas on AWS.
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In this post, we demonstrate how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar steps to release the distilled variations 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) developed by DeepSeek [AI](https://hireforeignworkers.ca) that [utilizes reinforcement](https://juryi.sn) finding out to enhance reasoning capabilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. A crucial differentiating feature is its reinforcement learning (RL) step, which was used to refine the design's reactions beyond the standard pre-training and [fine-tuning process](http://101.36.160.14021044). By integrating RL, [hb9lc.org](https://www.hb9lc.org/wiki/index.php/User:BiancaRosenberge) DeepSeek-R1 can adapt more efficiently to user feedback and goals, eventually improving both importance and clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) technique, [suggesting](https://realmadridperipheral.com) it's geared up to break down complex questions and reason through them in a detailed way. This guided thinking procedure allows the model to produce more accurate, transparent, and detailed responses. This model integrates RL-based fine-tuning with CoT abilities, aiming to create structured reactions while concentrating on interpretability and user interaction. With its [wide-ranging capabilities](https://naijascreen.com) DeepSeek-R1 has actually caught the [industry's attention](https://www.chinami.com) as a flexible text-generation design that can be incorporated into various workflows such as representatives, logical thinking and information interpretation tasks.
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DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture allows activation of 37 billion specifications, making it possible for efficient reasoning by routing queries to the most relevant professional "clusters." This approach enables the model to concentrate on different issue domains while maintaining total efficiency. DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for [inference](http://128.199.125.933000). 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 supplying](https://git.devinmajor.com) 1128 GB of GPU memory.
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DeepSeek-R1 distilled designs bring the reasoning capabilities of the main R1 model to more efficient architectures based upon [popular](https://157.56.180.169) open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a process of training smaller, more efficient designs to imitate the behavior and thinking patterns of the bigger DeepSeek-R1 model, using it as a teacher model.
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You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we suggest releasing this model with guardrails in place. In this blog, we will utilize Amazon Bedrock Guardrails to present safeguards, avoid harmful content, and evaluate designs against essential safety criteria. At the time of [composing](https://uptoscreen.com) this blog, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, [Bedrock Guardrails](https://955x.com) supports only the [ApplyGuardrail API](https://postyourworld.com). You can develop several guardrails tailored to different use cases and use them to the DeepSeek-R1 model, improving user experiences and standardizing security controls throughout your generative [AI](http://ratel.ng) applications.
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Prerequisites
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To deploy the DeepSeek-R1 model, you need access to an ml.p5e instance. To examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and confirm 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 deploying. To request a limit increase, produce a limitation increase request and reach out to your account group.
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Because you will be deploying this design with Amazon Bedrock Guardrails, make certain you have the appropriate AWS Identity and Gain Access To [Management](https://git.camus.cat) (IAM) consents to use Amazon Bedrock Guardrails. For directions, see Set up authorizations to use [guardrails](https://git.hackercan.dev) for content filtering.
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[Implementing guardrails](https://hyg.w-websoft.co.kr) with the ApplyGuardrail API
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Amazon Bedrock Guardrails allows you to present safeguards, avoid harmful material, and assess models against crucial safety criteria. You can carry out safety steps for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to assess user inputs and design actions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce 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 general flow involves the following steps: First, the system gets an input for the model. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it's sent to the design for reasoning. After getting the design's output, another guardrail check is used. If the output passes this final check, it's [returned](https://www.miptrucking.net) as the result. However, if either the input or output is intervened by the guardrail, a message is returned indicating the nature of the intervention and whether it occurred at the input or output phase. The examples showcased in the following areas show 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 models (FMs) through [Amazon Bedrock](https://www.indianhighcaste.com). 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 catalog under Foundation designs in the navigation pane. +At the time of composing this post, you can utilize the InvokeModel API to invoke the design. It does not [support Converse](https://hinh.com) APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a provider and select the DeepSeek-R1 model.
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The design detail page offers vital details about the design's capabilities, pricing structure, and application guidelines. You can discover detailed usage directions, consisting of sample API calls and code snippets for integration. The model supports various text generation jobs, consisting of material production, code generation, and concern answering, using its support learning optimization and CoT reasoning capabilities. +The page also consists of release options and [licensing details](http://git.1473.cn) to assist you begin with DeepSeek-R1 in your applications. +3. To begin using DeepSeek-R1, choose Deploy.
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You will be triggered to configure the implementation details for DeepSeek-R1. The model ID will be pre-populated. +4. For Endpoint name, get in an endpoint name (between 1-50 alphanumeric characters). +5. For Variety of circumstances, get in a variety of instances (in between 1-100). +6. For example type, pick 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 advanced security and infrastructure settings, including virtual private cloud (VPC) networking, service role permissions, and file encryption settings. For many use cases, the [default settings](https://globalhospitalitycareer.com) will work well. However, for production implementations, you may want to examine these settings to line up with your company's security and compliance requirements. +7. Choose Deploy to begin using the design.
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When the deployment is total, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock play ground. +8. Choose Open in play ground to access an interactive user interface where you can try out different triggers and change design criteria like temperature and optimum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for optimum outcomes. For instance, content for reasoning.
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This is an excellent method to check out the [model's thinking](https://dreamtube.congero.club) and text generation capabilities before incorporating it into your applications. The play ground provides immediate feedback, assisting you understand how the design responds to numerous inputs and letting you tweak your triggers for optimum outcomes.
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You can quickly evaluate the design in the play area through the UI. However, to invoke the [deployed design](https://paanaakgit.iran.liara.run) 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 demonstrates how to carry out inference using a deployed DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can create a guardrail using the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo. After you have produced the guardrail, use the following code to carry out guardrails. The script initializes the bedrock_runtime client, sets up inference parameters, and sends a request to [produce text](https://git.liubin.name) 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) center with FMs, integrated algorithms, and prebuilt ML options that you can release with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use case, with your information, and release them into production using either the UI or SDK.
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Deploying DeepSeek-R1 design through SageMaker JumpStart uses two convenient methods: using the instinctive SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's explore both approaches to help you pick the method that finest fits your requirements.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following actions to deploy 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 prompted to develop a domain. +3. On the SageMaker Studio console, select JumpStart in the [navigation pane](https://gitea.nafithit.com).
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The design browser displays available designs, with details like the company name and model abilities.
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4. Search for DeepSeek-R1 to see the DeepSeek-R1 design card. +Each model card reveals crucial details, including:
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- Model name +- Provider name +- Task category (for example, Text Generation). +[Bedrock Ready](http://121.42.8.15713000) badge (if suitable), showing that this model can be signed up with Amazon Bedrock, enabling you to utilize Amazon Bedrock APIs to conjure up the design
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5. Choose the model card to view the design details page.
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The model details page of the following details:
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- The design 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 requirements. +- Usage guidelines
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Before you deploy the model, it's advised to examine the design details and license terms to confirm compatibility with your usage case.
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6. Choose Deploy to proceed with implementation.
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7. For Endpoint name, utilize the immediately generated name or develop a customized one. +8. For Instance type [ΒΈ choose](https://connectworld.app) a [circumstances type](http://git.1473.cn) (default: ml.p5e.48 xlarge). +9. For [Initial circumstances](https://feleempleo.es) count, enter the variety of circumstances (default: 1). +Selecting appropriate instance types and counts is important for expense and performance optimization. Monitor your deployment to adjust these settings as needed.Under Inference type, Real-time inference is selected by default. This is enhanced for sustained traffic and low latency. +10. Review all setups for accuracy. For this model, we strongly advise adhering to SageMaker JumpStart default settings and making certain that network isolation remains in location. +11. Choose Deploy to release the design.
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The implementation process can take numerous minutes to finish.
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When implementation is complete, your [endpoint status](https://nkaebang.com) will alter to InService. At this point, the design is ready to accept inference requests through the endpoint. You can keep track of the deployment development on the SageMaker console Endpoints page, which will show pertinent metrics and status details. When the release is complete, you can conjure up the model using a [SageMaker runtime](http://gitlab.code-nav.cn) client and integrate it with your applications.
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Deploy DeepSeek-R1 using the SageMaker Python SDK
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To get started with DeepSeek-R1 using the SageMaker Python SDK, you will need to install 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 deploy and utilize DeepSeek-R1 for reasoning 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 execute it as displayed in the following code:
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Tidy up
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To avoid undesirable charges, complete the actions in this area to tidy up your resources.
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Delete the Amazon Bedrock Marketplace implementation
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If you [deployed](http://chichichichichi.top9000) the model 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, choose Marketplace releases. +2. In the Managed implementations area, find the endpoint you wish to delete. +3. Select the endpoint, and on the [Actions](https://tjoobloom.com) menu, pick Delete. +4. Verify the endpoint details to make certain you're deleting the proper implementation: 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 erase the endpoint if you desire 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 deploy the DeepSeek-R1 model using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or [Amazon Bedrock](https://prosafely.com) Marketplace now to get going. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, SageMaker JumpStart pretrained models, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting begun 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://empleos.contatech.org) companies build innovative solutions using AWS services and sped up [compute](http://140.82.32.174). Currently, he is concentrated on [developing strategies](https://funnyutube.com) for fine-tuning and enhancing the inference efficiency of big language designs. In his downtime, Vivek enjoys treking, watching films, and trying various cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](http://gitlab.qu-in.com) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His location of focus is AWS [AI](http://gitea.anomalistdesign.com) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.
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Jonathan Evans is a Specialist Solutions Architect working on generative [AI](https://vmi528339.contaboserver.net) with the Third-Party Model Science team at AWS.
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Banu Nagasundaram leads product, engineering, and tactical partnerships for [Amazon SageMaker](http://bingbinghome.top3001) JumpStart, [SageMaker's artificial](https://jobsingulf.com) intelligence and generative [AI](http://clinicanevrozov.ru) center. She is passionate about constructing services that help clients accelerate their [AI](https://git.lewd.wtf) journey and unlock business worth.
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