From 237b35648e6e53a12ed4c1318cebb4318ded9981 Mon Sep 17 00:00:00 2001 From: cruzmacias5457 Date: Mon, 7 Apr 2025 10:19:04 +0800 Subject: [PATCH] Update 'DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart' --- ...k-Marketplace-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..2b097bb --- /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 announce that DeepSeek R1 distilled Llama and Qwen models are available through Amazon Bedrock Marketplace and Amazon SageMaker [JumpStart](http://e-kou.jp). With this launch, you can now release DeepSeek [AI](http://47.108.92.88:3000)'s first-generation frontier model, DeepSeek-R1, in addition to the distilled versions varying from 1.5 to 70 billion criteria to develop, experiment, and responsibly scale your generative [AI](https://sebeke.website) ideas on AWS.
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In this post, we demonstrate how to start with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar steps to deploy the [distilled versions](http://47.104.65.21419206) of the models 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://190.117.85.58:8095) that uses support learning to improve reasoning capabilities through a multi-stage training process from a DeepSeek-V3-Base structure. A crucial distinguishing feature is its reinforcement learning (RL) step, which was used to improve the model's reactions beyond the basic pre-training and tweak [procedure](https://barbersconnection.com). By integrating RL, DeepSeek-R1 can adapt better to user feedback and objectives, eventually boosting both importance and clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) method, meaning it's geared up to break down complicated inquiries and factor through them in a detailed manner. This guided thinking process enables the model to produce more accurate, transparent, and detailed responses. This model integrates RL-based fine-tuning with CoT capabilities, aiming to create structured reactions 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 model that can be incorporated into various workflows such as representatives, rational thinking and [christianpedia.com](http://christianpedia.com/index.php?title=User:Noble11U12) data analysis jobs.
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DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture enables activation of 37 billion criteria, enabling effective inference by routing queries to the most relevant professional "clusters." This method permits the model to concentrate on different problem domains while maintaining overall effectiveness. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will use an ml.p5e.48 xlarge instance to release the design. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.
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DeepSeek-R1 distilled designs bring the reasoning abilities of the main R1 design to more effective architectures based on popular open designs 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 simulate the habits and reasoning patterns of the larger DeepSeek-R1 design, utilizing it as a [teacher model](http://gkpjobs.com).
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You can [release](http://47.120.70.168000) DeepSeek-R1 model either through SageMaker JumpStart or Bedrock [Marketplace](https://git.zyhhb.net). Because DeepSeek-R1 is an emerging model, we advise deploying this design with [guardrails](https://dronio24.com) in place. In this blog site, we will use Amazon Bedrock Guardrails to present safeguards, avoid harmful material, and evaluate designs against crucial safety requirements. At the time of writing this blog, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can produce multiple guardrails tailored to various use cases and apply them to the DeepSeek-R1 model, improving user experiences and standardizing security [controls](https://git.xantxo-coquillard.fr) throughout your generative [AI](http://123.56.193.182:3000) applications.
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Prerequisites
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To deploy the DeepSeek-R1 model, 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, 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 circumstances](https://talento50zaragoza.com) in the AWS Region you are deploying. To ask for a limitation increase, create a limit boost demand and reach out to your account group.
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Because you will be deploying this model with Amazon Bedrock Guardrails, make certain you have the right AWS Identity and Gain Access To (IAM) consents to use Amazon Bedrock [Guardrails](https://ivebo.co.uk). For instructions, see Establish approvals to use [guardrails](http://yanghaoran.space6003) for content filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails allows you to present safeguards, prevent damaging material, and examine models against essential security requirements. You can execute safety steps for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to apply guardrails to examine user inputs and design responses 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 general flow involves the following actions: 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 model for inference. After receiving the design's output, another guardrail check is used. If the output passes this last check, it's returned as the last result. 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 stage. 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 provides you access to over 100 popular, emerging, [89u89.com](https://www.89u89.com/author/darrellgron/) 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, choose Model brochure under Foundation designs in the navigation pane. +At the time of composing this post, you can [utilize](https://sadegitweb.pegasus.com.mx) the InvokeModel API to conjure up the design. It does not support Converse APIs and other Amazon Bedrock [tooling](http://www.forwardmotiontx.com). +2. Filter for DeepSeek as a supplier and pick the DeepSeek-R1 model.
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The design detail page offers essential details about the model's capabilities, prices structure, and implementation standards. You can discover detailed usage instructions, [wiki.myamens.com](http://wiki.myamens.com/index.php/User:NathanielRehfisc) including sample API calls and code snippets for combination. The design supports numerous text generation tasks, consisting of material production, code generation, and concern answering, using its support finding out optimization and CoT reasoning [capabilities](https://www.chinami.com). +The page likewise consists of deployment alternatives and licensing details to assist you get going with DeepSeek-R1 in your applications. +3. To start using DeepSeek-R1, choose Deploy.
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You will be prompted to configure the deployment details for DeepSeek-R1. The design ID will be pre-populated. +4. For Endpoint name, get in an endpoint name (in between 1-50 alphanumeric characters). +5. For Variety of instances, enter a variety of [circumstances](https://arthurwiki.com) (in between 1-100). +6. For example type, pick your circumstances type. For optimum performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is suggested. +Optionally, you can set up sophisticated security and [infrastructure](http://bolling-afb.rackons.com) settings, including virtual personal cloud (VPC) networking, service role authorizations, and encryption settings. For many utilize cases, the default settings will work well. However, for production implementations, you might wish to evaluate these settings to line up 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 check DeepSeek-R1's abilities straight in the Amazon Bedrock playground. +8. Choose Open in playground to access an interactive interface where you can experiment with different prompts and change model criteria like temperature level and optimum length. +When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for optimal results. For instance, content for reasoning.
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This is an outstanding way to explore the design's thinking and text generation capabilities before incorporating it into your applications. The [playground supplies](https://gitea.daysofourlives.cn11443) immediate feedback, assisting you comprehend how the model reacts to different inputs and [letting](https://owangee.com) you tweak your triggers for ideal results.
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You can quickly check the design in the play area through the UI. However, to conjure up the [deployed design](https://zenithgrs.com) programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
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Run reasoning using guardrails with the released DeepSeek-R1 endpoint
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The following code example shows how to carry out reasoning utilizing a [deployed](https://git.olivierboeren.nl) DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can produce a guardrail utilizing 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 carry out guardrails. The script initializes the bedrock_[runtime](https://medifore.co.jp) client, sets up inference specifications, [trademarketclassifieds.com](https://trademarketclassifieds.com/user/profile/2672496) and sends a demand to create text based upon a user timely.
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Deploy DeepSeek-R1 with SageMaker JumpStart
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[SageMaker JumpStart](http://66.85.76.1223000) is an artificial intelligence (ML) hub with FMs, built-in algorithms, and prebuilt ML solutions that you can deploy with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use case, with your information, and deploy them into production using either the UI or SDK.
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Deploying DeepSeek-R1 design through SageMaker JumpStart provides 2 practical methods: using the user-friendly SageMaker [JumpStart](https://wiki.lafabriquedelalogistique.fr) UI or implementing programmatically through the SageMaker Python SDK. Let's explore both [methods](https://git.runsimon.com) to assist you select the method 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, [systemcheck-wiki.de](https://systemcheck-wiki.de/index.php?title=Benutzer:SusieChipman) choose Studio in the navigation pane. +2. First-time users will be triggered to create a domain. +3. On the SageMaker Studio console, choose JumpStart in the navigation pane.
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The model web browser displays available models, with details like the provider name and model abilities.
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4. Look for DeepSeek-R1 to view the DeepSeek-R1 model card. +Each design card shows key details, including:
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- Model name +- Provider name +- Task category (for example, Text Generation). +Bedrock Ready badge (if suitable), showing that this design can be registered with Amazon Bedrock, allowing you to utilize Amazon Bedrock APIs to conjure up the model
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5. Choose the model card to see the design details page.
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The design details page includes the following details:
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- The model name and service provider details. +Deploy button to [release](http://autogangnam.dothome.co.kr) 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 specs. +- Usage guidelines
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Before you release the design, it's suggested to evaluate the model details and license terms to verify compatibility with your use case.
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6. Choose Deploy to proceed with deployment.
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7. For Endpoint name, use the automatically created name or create a custom one. +8. For example type ΒΈ select a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, get in the variety of instances (default: 1). +Selecting appropriate circumstances types and counts is essential for expense and efficiency optimization. Monitor your release to change these settings as needed.Under Inference type, Real-time inference is selected by default. This is optimized for sustained traffic and low [latency](http://47.108.69.3310888). +10. Review all setups for accuracy. For this design, we highly recommend adhering to SageMaker JumpStart default settings and making certain that network isolation remains in location. +11. Choose Deploy to deploy the design.
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The release procedure can take several minutes to finish.
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When implementation is total, your endpoint status will alter to InService. At this point, the model is ready to [accept reasoning](http://111.229.9.193000) requests through the endpoint. You can keep an eye on the release progress on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the deployment is total, you can conjure up the design using 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 set up the [SageMaker Python](http://139.224.253.313000) 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 utilize DeepSeek-R1 for reasoning programmatically. The code for deploying the design is provided 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 develop a guardrail utilizing the Amazon Bedrock console or the API, and execute it as [displayed](http://betterlifenija.org.ng) in the following code:
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Tidy up
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To prevent 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](https://git.xjtustei.nteren.net) console, under Foundation models in the navigation pane, pick Marketplace deployments. +2. In the Managed releases section, locate the endpoint you wish to delete. +3. Select the endpoint, and on the Actions menu, [wiki-tb-service.com](http://wiki-tb-service.com/index.php?title=Benutzer:Dwight6450) choose 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 released 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 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 start. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, SageMaker JumpStart pretrained designs, Amazon SageMaker [JumpStart](https://sugardaddyschile.cl) Foundation Models, [Amazon Bedrock](https://git.tea-assets.com) Marketplace, and Beginning 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 assists emerging generative [AI](http://1.15.187.67) business develop ingenious options utilizing AWS services and sped up compute. Currently, he is concentrated on developing methods for fine-tuning and optimizing the reasoning performance of large language designs. In his downtime, Vivek delights in hiking, enjoying films, and trying various foods.
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Niithiyn Vijeaswaran is a Generative [AI](http://www.visiontape.com) [Specialist Solutions](https://rapid.tube) Architect with the Third-Party Model Science group at AWS. His location of focus is AWS [AI](http://web.joang.com:8088) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
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Jonathan Evans is an Expert Solutions Architect working on generative [AI](https://starleta.xyz) with the [Third-Party Model](http://www.grainfather.global) Science group at AWS.
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Banu Nagasundaram leads product, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://prazskypantheon.cz) hub. She is passionate about building solutions that assist clients accelerate their [AI](http://111.53.130.194:3000) journey and unlock business value.
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