From cc4b86f2e15660d5ca8b57da45699d8e1aeed469 Mon Sep 17 00:00:00 2001 From: wildakitterman Date: Fri, 4 Apr 2025 19:31:26 +0200 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..bf143c3 --- /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 models are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](http://124.71.134.146:3000)'s first-generation frontier design, DeepSeek-R1, along with the [distilled variations](http://110.41.19.14130000) ranging from 1.5 to 70 billion parameters to develop, experiment, and properly scale your generative [AI](http://106.15.48.132:3880) ideas on AWS.
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In this post, we demonstrate how to get begun with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar actions to deploy the distilled versions of the designs also.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a big language design (LLM) established by DeepSeek [AI](https://mychampionssport.jubelio.store) that uses support learning to enhance thinking abilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A key identifying [feature](https://git.hackercan.dev) is its support learning (RL) action, which was used to fine-tune the model's reactions beyond the basic pre-training and fine-tuning procedure. By including RL, DeepSeek-R1 can adjust better to user feedback and goals, eventually enhancing both relevance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) approach, indicating it's equipped to break down intricate inquiries and factor through them in a detailed manner. This assisted reasoning process allows the model to produce more accurate, transparent, and detailed answers. This design integrates RL-based fine-tuning with CoT capabilities, aiming to produce structured responses while concentrating on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has captured the market's attention as a flexible text-generation design that can be incorporated into numerous workflows such as representatives, logical reasoning and information analysis jobs.
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DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture permits activation of 37 billion criteria, making it possible for efficient reasoning by routing queries to the most relevant professional "clusters." This [approach](https://duniareligi.com) allows the model to focus on different problem domains while maintaining general performance. DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for inference. In this post, we will use an ml.p5e.48 xlarge instance to deploy the model. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.
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DeepSeek-R1 distilled designs bring the reasoning capabilities of the main R1 design to more efficient architectures based upon popular open designs like Qwen (1.5 B, 7B, 14B, and [kousokuwiki.org](http://kousokuwiki.org/wiki/%E5%88%A9%E7%94%A8%E8%80%85:UnaPfe0594680933) 32B) and Llama (8B and 70B). Distillation refers to a procedure of training smaller, more effective models to simulate the habits and reasoning patterns of the bigger DeepSeek-R1 model, utilizing it as a [teacher model](https://www.finceptives.com).
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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 advise deploying this design with guardrails in location. In this blog, we will use Amazon Bedrock Guardrails to introduce safeguards, avoid hazardous material, and evaluate designs against essential security criteria. At the time of [composing](https://maarifatv.ng) this blog site, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create numerous guardrails tailored to various usage cases and apply them to the DeepSeek-R1 model, improving user experiences and standardizing safety controls across your generative [AI](https://livy.biz) applications.
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Prerequisites
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To deploy the DeepSeek-R1 model, you require access to an ml.p5e instance. To [examine](https://git.jzcscw.cn) if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and validate you're using ml.p5e.48 xlarge for endpoint use. Make certain that you have at least one ml.P5e.48 xlarge circumstances in the AWS Region you are releasing. To request a limitation boost, produce a limitation boost request and reach out to your account team.
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Because you will be deploying this design with Amazon Bedrock Guardrails, make certain you have the proper [AWS Identity](https://propbuysells.com) and Gain Access To [Management](https://ezworkers.com) (IAM) authorizations to use Amazon Bedrock Guardrails. For guidelines, see Establish authorizations to utilize 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 hazardous material, and examine models against crucial safety criteria. You can implement security procedures for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This permits you to use [guardrails](https://gitee.mmote.ru) to examine user inputs and model responses 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 produce the guardrail, see the GitHub repo.
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The basic flow includes 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, it's sent to the design for reasoning. After receiving the model's output, another is applied. If the output passes this final 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 indicating the nature of the intervention and whether it happened at the input or output stage. The examples showcased in the following sections demonstrate inference using this API.
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Deploy DeepSeek-R1 in [Amazon Bedrock](https://www.iratechsolutions.com) 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, complete the following actions:
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1. On the Amazon Bedrock console, [wiki.snooze-hotelsoftware.de](https://wiki.snooze-hotelsoftware.de/index.php?title=Benutzer:GertieAllum31) choose Model catalog under Foundation models in the navigation pane. +At the time of composing this post, you can use the InvokeModel API to conjure up the model. It doesn't support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a [company](https://www.longisland.com) and choose the DeepSeek-R1 design.
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The design detail page provides necessary details about the [model's](https://git.chir.rs) abilities, prices structure, and implementation standards. You can discover detailed use instructions, including sample API calls and code snippets for integration. The model supports various text generation tasks, consisting of material creation, code generation, and question answering, utilizing its reinforcement finding out optimization and CoT reasoning abilities. +The page also [consists](https://younetwork.app) of release alternatives and licensing details to assist you get going with DeepSeek-R1 in your applications. +3. To start using DeepSeek-R1, pick Deploy.
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You will be prompted 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](https://code.jigmedatse.com) characters). +5. For Number of instances, go into a variety of instances (in between 1-100). +6. For Instance type, choose your instance type. For [larsaluarna.se](http://www.larsaluarna.se/index.php/User:ShelliSae542095) optimal performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is suggested. +Optionally, you can set up advanced security and facilities settings, including virtual private cloud (VPC) networking, service function permissions, and file encryption settings. For most utilize cases, the default settings will work well. However, for production implementations, you may wish to review these settings to align with your organization's security and compliance requirements. +7. Choose Deploy to start utilizing the model.
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When the deployment is complete, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock play area. +8. Choose Open in play area to access an interactive interface where you can explore various triggers and change design specifications like temperature level and optimum length. +When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for ideal outcomes. For example, material for inference.
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This is an exceptional method to check out the model's thinking and text generation capabilities before [integrating](http://git.suxiniot.com) it into your applications. The play area offers immediate feedback, helping you understand how the model responds to different inputs and letting you fine-tune your prompts for optimal results.
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You can quickly test the design 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 reasoning using guardrails with the deployed DeepSeek-R1 endpoint
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The following code example demonstrates how to perform inference using a released DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and [ApplyGuardrail API](https://10-4truckrecruiting.com). You can produce a guardrail using the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo. After you have created the guardrail, use the following code to implement guardrails. The script initializes the bedrock_[runtime](http://internetjo.iwinv.net) customer, 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, and prebuilt ML options that you can deploy with just 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 design through SageMaker JumpStart provides two hassle-free approaches: utilizing the instinctive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's explore both techniques to help you choose the approach that finest suits your requirements.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following steps to release DeepSeek-R1 [utilizing SageMaker](https://www.tvcommercialad.com) JumpStart:
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1. On the SageMaker console, pick Studio in the navigation pane. +2. First-time users will be triggered to produce a domain. +3. On the SageMaker Studio console, select [JumpStart](https://git.thunraz.se) in the navigation pane.
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The design internet browser shows available models, with details like the company name and design abilities.
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4. Look for DeepSeek-R1 to view the DeepSeek-R1 model card. +Each model card shows key details, including:
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- Model name +- Provider name +- Task category (for instance, Text Generation). +Bedrock Ready badge (if relevant), suggesting that this design can be registered with Amazon Bedrock, enabling you to use Amazon Bedrock APIs to invoke the model
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5. Choose the design card to see 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 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 design, it's recommended to review the model details and license terms to [confirm compatibility](https://dev.fleeped.com) with your usage case.
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6. Choose Deploy to proceed with release.
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7. For Endpoint name, use the immediately produced name or develop a custom one. +8. For Instance type ΒΈ pick a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial instance count, enter the number of circumstances (default: 1). +Selecting suitable instance types and counts is essential for cost and efficiency optimization. Monitor your implementation to change these settings as needed.Under Inference type, Real-time reasoning is chosen by default. This is enhanced for sustained traffic and low latency. +10. Review all setups for precision. For this design, we highly suggest adhering to SageMaker JumpStart [default settings](https://git.pleasantprogrammer.com) and making certain that network isolation remains in location. +11. Choose Deploy to release the model.
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The implementation process can take several minutes to finish.
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When implementation is total, your endpoint status will change to InService. At this moment, the design is prepared to accept reasoning demands through the endpoint. You can monitor the deployment progress on the SageMaker console Endpoints page, which will show appropriate [metrics](https://39.105.45.141) and status details. When the implementation is total, you can invoke the design using a [SageMaker runtime](https://job.iwok.vn) client and integrate 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 using the SageMaker Python SDK, you will [require](http://compass-framework.com3000) 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 demonstrates how to release and use DeepSeek-R1 for inference programmatically. The code for releasing the model is [offered](https://git.ivabus.dev) in the Github here. You can clone the notebook and range from SageMaker Studio.
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You can run extra demands 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 likewise utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a guardrail using the Amazon Bedrock console or the API, and execute it as displayed in the following code:
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Clean up
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To avoid undesirable charges, finish the actions in this area to clean up your resources.
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Delete the Amazon Bedrock Marketplace release
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If you released the design using Amazon Bedrock Marketplace, total the following steps:
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1. On the Amazon Bedrock console, under Foundation models in the navigation pane, choose Marketplace [releases](https://kcshk.com). +2. In the Managed deployments area, find the endpoint you want to delete. +3. Select the endpoint, and on the Actions menu, choose Delete. +4. Verify the endpoint details to make certain you're erasing the correct 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 design you released will sustain expenses if you leave it running. Use the following code to delete 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 explored 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 Marketplace now to get started. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, [SageMaker JumpStart](http://123.249.110.1285555) 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](https://alllifesciences.com) at AWS. He helps emerging generative [AI](https://www.telix.pl) [business build](http://jolgoo.cn3000) ingenious options utilizing AWS services and accelerated compute. Currently, he is concentrated on developing techniques for fine-tuning and optimizing the inference performance of large language designs. In his downtime, Vivek enjoys hiking, seeing motion pictures, and trying different foods.
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Niithiyn Vijeaswaran is a Generative [AI](https://tv.lemonsocial.com) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His location of focus is AWS [AI](http://linyijiu.cn:3000) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.
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Jonathan Evans is a Professional Solutions Architect working on generative [AI](https://photohub.b-social.co.uk) with the Third-Party Model Science team at AWS.
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Banu Nagasundaram leads item, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://musicplayer.hu) center. She is passionate about building services that help clients accelerate their [AI](http://120.79.94.122:3000) journey and unlock business value.
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