From 4e5d2de73e92e85c43cfa20e42d22c06d4689e67 Mon Sep 17 00:00:00 2001 From: helenavisconti Date: Fri, 4 Apr 2025 06:44:22 +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..961db35 --- /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 models are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](https://jobedges.com)'s first-generation frontier design, DeepSeek-R1, together with the distilled versions ranging from 1.5 to 70 billion criteria to construct, experiment, and properly scale your generative [AI](https://git.xhkjedu.com) ideas on AWS.
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In this post, we show how to begin with DeepSeek-R1 on Marketplace and SageMaker JumpStart. You can follow similar actions to deploy the [distilled versions](https://www.graysontalent.com) of the designs too.
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[Overview](https://gl.ignite-vision.com) of DeepSeek-R1
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DeepSeek-R1 is a big language model (LLM) developed by DeepSeek [AI](http://app.vellorepropertybazaar.in) that utilizes reinforcement discovering to enhance thinking abilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A key differentiating feature is its reinforcement learning (RL) action, which was used to improve the design's responses beyond the basic pre-training and tweak procedure. By incorporating RL, DeepSeek-R1 can adjust more efficiently to user feedback and goals, ultimately improving both significance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) method, meaning it's geared up to break down complex inquiries and reason through them in a detailed way. This guided reasoning process allows the design to produce more accurate, transparent, and detailed responses. This model combines RL-based fine-tuning with CoT capabilities, aiming to generate structured reactions while concentrating on interpretability and user interaction. With its comprehensive abilities DeepSeek-R1 has recorded the industry's attention as a versatile text-generation design that can be incorporated into different workflows such as representatives, sensible thinking and information interpretation tasks.
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DeepSeek-R1 utilizes a [Mixture](https://socialnetwork.cloudyzx.com) of Experts (MoE) architecture and is 671 billion criteria in size. The [MoE architecture](https://dirkohlmeier.de) [permits activation](https://kaiftravels.com) of 37 billion specifications, allowing efficient inference by routing questions to the most relevant specialist "clusters." This technique enables the model to concentrate on various issue domains while maintaining total efficiency. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will utilize an ml.p5e.48 xlarge circumstances to [release](https://socialsnug.net) the model. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.
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DeepSeek-R1 distilled models bring the reasoning abilities of the main R1 design to more effective architectures based on [popular](http://124.221.255.92) open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of training smaller sized, more effective models to imitate the habits and thinking patterns of the larger DeepSeek-R1 model, using it as an instructor design.
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You can release DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an [emerging](http://profilsjob.com) design, we advise releasing this model with guardrails in place. In this blog site, we will utilize Amazon Bedrock Guardrails to introduce safeguards, prevent harmful content, and examine designs against essential safety criteria. At the time of composing this blog, for DeepSeek-R1 releases on [SageMaker JumpStart](https://git.peaksscrm.com) and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can develop multiple guardrails tailored to various usage cases and apply them to the DeepSeek-R1 model, enhancing user experiences and standardizing security controls across your generative [AI](http://47.120.70.16:8000) 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 check if you have quotas for P5e, open the Service Quotas [console](http://profilsjob.com) and under AWS Services, pick 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](https://audioedu.kyaikkhami.com) a limit increase, produce a [limitation boost](https://git.cacpaper.com) request and connect to your account team.
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Because you will be releasing this model with Amazon Bedrock Guardrails, make certain you have the proper AWS Identity and Gain Access To Management (IAM) permissions to use Amazon Bedrock [Guardrails](https://tv.360climatechange.com). For guidelines, see Set up approvals to use guardrails 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 content, and evaluate designs against essential safety criteria. You can implement security steps for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This enables you to apply guardrails to evaluate user inputs and design reactions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can create 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 circulation involves the following steps: First, the system gets 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 inference. After getting the model's output, another guardrail check is used. If the output passes this final check, it's returned as the outcome. However, if either the input or [yewiki.org](https://www.yewiki.org/User:LucianaChau79) output is intervened by the guardrail, a message is returned suggesting the nature of the intervention and whether it happened at the input or output stage. The examples showcased in the following sections show reasoning utilizing this API.
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Deploy DeepSeek-R1 in [Amazon Bedrock](https://git.smartenergi.org) Marketplace
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Amazon Bedrock Marketplace offers 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, pick Model brochure under Foundation models in the navigation pane. +At the time of writing this post, you can use the InvokeModel API to invoke the design. It doesn't support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a provider and choose the DeepSeek-R1 model.
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The model detail page provides essential details about the design's abilities, pricing structure, and execution guidelines. You can find detailed use directions, consisting of sample API calls and code snippets for [combination](https://boonbac.com). The model supports numerous text generation tasks, consisting of material production, code generation, and concern answering, using its support discovering optimization and CoT reasoning capabilities. +The page also consists of release alternatives and licensing details to assist you start 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 release details for DeepSeek-R1. The design ID will be pre-populated. +4. For Endpoint name, get in an [endpoint](https://gitlab.alpinelinux.org) name (in between 1-50 alphanumeric characters). +5. For Variety of circumstances, get in a variety of circumstances (in between 1-100). +6. For Instance type, choose your circumstances type. For optimal performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is [advised](https://visorus.com.mx). +Optionally, you can configure sophisticated security and infrastructure settings, including virtual private cloud (VPC) networking, service function approvals, and file encryption settings. For most use cases, the default settings will work well. However, for production releases, you might wish to evaluate these settings to line up with your organization's security and compliance requirements. +7. Choose Deploy to begin using the design.
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When the deployment is total, you can evaluate 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 experiment with different prompts and adjust design parameters like temperature and optimum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for optimal outcomes. For example, material for inference.
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This is an outstanding way to check out the design's thinking and text generation capabilities before integrating it into your applications. The playground provides immediate feedback, assisting you understand [higgledy-piggledy.xyz](https://higgledy-piggledy.xyz/index.php/User:RevaBettis0) how the model responds to different inputs and letting you tweak your [prompts](http://124.16.139.223000) for [wiki.myamens.com](http://wiki.myamens.com/index.php/User:OpalHenn8730) optimal results.
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You can rapidly check the design in the playground through the UI. However, to invoke the deployed design programmatically with any [Amazon Bedrock](https://storymaps.nhmc.uoc.gr) APIs, you need 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 shows how to carry out inference utilizing a released DeepSeek-R1 design through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can develop a guardrail using the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo. After you have produced the guardrail, utilize the following code to execute guardrails. The script initializes the bedrock_runtime customer, sets up reasoning parameters, and sends out a request to create text 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) hub with FMs, integrated algorithms, and prebuilt ML services that you can deploy with just a couple of clicks. With SageMaker JumpStart, [it-viking.ch](http://it-viking.ch/index.php/User:MiriamMcVilly60) you can tailor pre-trained designs to your usage case, with your information, and release them into production utilizing either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart provides 2 convenient methods: using the intuitive SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's [explore](https://www.loupanvideos.com) both methods to help you select the approach that best matches your requirements.
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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 prompted to create a domain. +3. On the SageMaker Studio console, choose JumpStart in the navigation pane.
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The model internet browser shows available models, with details like the provider name and model capabilities.
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4. Search for DeepSeek-R1 to see the DeepSeek-R1 design card. +Each model card shows key details, consisting of:
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- Model name +- Provider name +- Task category (for instance, Text Generation). +Bedrock Ready badge (if relevant), showing that this model can be [registered](https://smaphofilm.com) with Amazon Bedrock, allowing you to utilize Amazon Bedrock APIs to invoke the model
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5. Choose the design card to view the design details page.
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The model details page includes 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 consists](https://techtalent-source.com) of crucial details, such as:
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- Model description. +- License details. +[- Technical](https://voovixtv.com) specs. +- Usage standards
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Before you deploy the model, it's suggested to examine the [model details](https://skillsvault.co.za) and license terms to [confirm compatibility](https://www.worlddiary.co) with your use case.
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6. Choose Deploy to proceed with release.
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7. For Endpoint name, utilize the immediately created name or create a customized one. +8. For Instance type ΒΈ pick a [circumstances](http://107.172.157.443000) type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, go into the number of instances (default: 1). +Selecting appropriate 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 optimized for sustained traffic and low latency. +10. Review all configurations for accuracy. For this model, we highly advise sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in location. +11. Choose Deploy to deploy the model.
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The release procedure can take numerous minutes to complete.
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When release is complete, your endpoint status will alter to InService. At this moment, the model is prepared to accept reasoning requests through the endpoint. You can keep an eye on the deployment development on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the release is total, you can conjure up the design using a SageMaker runtime client and incorporate it with your applications.
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Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
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To start with DeepSeek-R1 utilizing the SageMaker Python SDK, you will require to set up the SageMaker Python SDK and make certain you have the required AWS authorizations and environment setup. The following is a detailed code example that demonstrates how to deploy and use DeepSeek-R1 for [inference programmatically](http://mao2000.com3000). The code for deploying the model is offered 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 use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can create a guardrail using the Amazon Bedrock console or the API, and execute it as shown in the following code:
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Clean up
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To avoid undesirable charges, complete the actions in this section to clean up your resources.
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Delete the Amazon Bedrock Marketplace implementation
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If you deployed the model using Amazon Bedrock Marketplace, complete the following actions:
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1. On the Amazon Bedrock console, [garagesale.es](https://www.garagesale.es/author/seanedouard/) under Foundation designs in the navigation pane, pick Marketplace implementations. +2. In the Managed implementations section, find the endpoint you want to delete. +3. Select the endpoint, and [hb9lc.org](https://www.hb9lc.org/wiki/index.php/User:JeannieGossett) on the Actions menu, choose Delete. +4. Verify the endpoint details to make certain you're erasing the proper implementation: 1. Endpoint name. +2. Model name. +3. Endpoint status
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Delete the [SageMaker JumpStart](http://112.124.19.388080) 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 wish to stop sustaining charges. For more details, see Delete Endpoints and [Resources](http://110.90.118.1293000).
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Conclusion
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In this post, we [checked](http://git.chaowebserver.com) 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 Marketplace now to start. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, [SageMaker JumpStart](https://picturegram.app) 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 assists emerging generative [AI](https://gitea.tgnotify.top) business construct ingenious options utilizing AWS services and [accelerated calculate](https://www.wcosmetic.co.kr5012). Currently, he is focused on developing methods for fine-tuning and [enhancing](https://video.xaas.com.vn) the inference efficiency of large language designs. In his spare time, Vivek enjoys hiking, viewing films, and attempting various cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](http://118.25.96.118:3000) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His [location](https://vagyonor.hu) of focus is AWS [AI](https://pyra-handheld.com) [accelerators](https://vids.nickivey.com) (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](http://dev.zenith.sh.cn) [AI](https://git.apps.calegix.net) with the Third-Party Model Science group at AWS.
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Banu Nagasundaram leads product, engineering, and tactical collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://warleaks.net) hub. She is enthusiastic about building solutions that help clients accelerate their [AI](https://git.lgoon.xyz) journey and unlock service value.
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