From ea5cb62627197502e4986fb73ec3556cfdc560ee Mon Sep 17 00:00:00 2001 From: allenparenteau Date: Fri, 7 Feb 2025 05:33:59 +0000 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..9ff784d --- /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 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://5.34.202.199:3000)'s first-generation frontier model, DeepSeek-R1, together with the distilled variations varying from 1.5 to 70 billion parameters to construct, experiment, and responsibly scale your generative [AI](https://datemyfamily.tv) [concepts](https://bestwork.id) on AWS.
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In this post, we show how to begin with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable actions to deploy the distilled variations of the designs 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](https://sujansadhu.com) that uses reinforcement discovering to [improve](https://gitlab.ccc.org.co) [reasoning abilities](https://jamesrodriguezclub.com) through a multi-stage training process from a DeepSeek-V3-Base foundation. An essential identifying function is its support learning (RL) step, which was utilized to refine the model's reactions beyond the basic pre-training and tweak process. By integrating RL, DeepSeek-R1 can adapt better to user feedback and goals, [fishtanklive.wiki](https://fishtanklive.wiki/User:GiuseppeXve) eventually boosting both relevance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) approach, indicating it's [equipped](http://gpra.jpn.org) to break down complicated questions and reason through them in a detailed manner. This assisted reasoning procedure enables the design to produce more accurate, transparent, and detailed responses. This design integrates RL-based fine-tuning with CoT capabilities, aiming to generate structured responses while focusing on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has caught the industry's attention as a versatile text-generation design that can be incorporated into different workflows such as representatives, logical reasoning and information interpretation tasks.
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DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion [specifications](https://gitea.offends.cn) in size. The MoE architecture enables activation of 37 billion specifications, enabling effective inference by routing questions to the most relevant specialist "clusters." This technique permits the design to focus on various problem domains while maintaining total performance. DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for [gratisafhalen.be](https://gratisafhalen.be/author/aidasneed47/) inference. In this post, we will utilize an ml.p5e.48 xlarge circumstances to deploy the model. ml.p5e.48 xlarge features 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 model to more efficient architectures based on popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a procedure of training smaller sized, more efficient designs to imitate the habits and reasoning patterns of the bigger 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 model, [wiki.vst.hs-furtwangen.de](https://wiki.vst.hs-furtwangen.de/wiki/User:LindaIsenberg91) we recommend releasing this model with guardrails in place. In this blog, we will use [Amazon Bedrock](http://hmkjgit.huamar.com) Guardrails to introduce safeguards, avoid damaging material, and evaluate designs against key security requirements. At the time of [writing](http://hoteltechnovalley.com) this blog site, 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 use them to the DeepSeek-R1 model, enhancing user experiences and standardizing security controls throughout your generative [AI](https://nepaxxtube.com) applications.
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Prerequisites
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To deploy the DeepSeek-R1 model, you require 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 deploying. To request a limitation boost, produce a limit increase request and reach out to your [account](https://oliszerver.hu8010) group.
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Because you will be releasing this design with Amazon Bedrock Guardrails, make certain you have the right AWS Identity and Gain Access To Management (IAM) consents to use Amazon Bedrock Guardrails. For guidelines, see Establish authorizations to utilize guardrails for content filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails enables you to introduce safeguards, prevent harmful content, and [examine](https://wiki.idealirc.org) models against key security requirements. You can execute security steps for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This permits you to use guardrails to examine user inputs and design actions released on Amazon Bedrock Marketplace and SageMaker JumpStart. 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.
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The basic flow includes the following actions: First, the system receives an input for the design. This input is then processed through the ApplyGuardrail API. If the [input passes](https://jobboat.co.uk) the guardrail check, it's sent out to the model for inference. After getting the design's output, another [guardrail check](http://git.magic-beans.cn3000) is applied. If the output passes this last check, it's returned as the result. However, if either the input or output is stepped in by the guardrail, a message is returned showing the nature of the intervention and whether it occurred at the input or output stage. The examples showcased in the following areas demonstrate reasoning utilizing this API.
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Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
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Amazon Bedrock Marketplace gives you access to over 100 popular, emerging, and [specialized structure](https://tiktack.socialkhaleel.com) models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following steps:
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1. On the Amazon Bedrock console, pick Model catalog under Foundation models in the navigation pane. +At the time of writing this post, you can use the InvokeModel API to conjure up the design. It does not [support Converse](http://jobs.freightbrokerbootcamp.com) APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a provider and choose the DeepSeek-R1 design.
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The design detail page offers essential details about the model's abilities, pricing structure, and execution guidelines. You can discover detailed usage directions, including sample API calls and code snippets for combination. The design supports different text generation jobs, including content creation, code generation, and question answering, using its reinforcement finding out optimization and CoT reasoning capabilities. +The page also consists of release alternatives and licensing details to help you begin with DeepSeek-R1 in your applications. +3. To begin utilizing DeepSeek-R1, select Deploy.
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You will be triggered to set up the deployment details for DeepSeek-R1. The model ID will be pre-populated. +4. For Endpoint name, go into an endpoint name (between 1-50 alphanumeric characters). +5. For Variety of circumstances, get in a variety of instances (between 1-100). +6. For example type, pick your circumstances type. For ideal efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is recommended. +Optionally, you can set up sophisticated security and facilities settings, consisting of virtual personal cloud (VPC) networking, service function approvals, and encryption settings. For the majority of utilize cases, the default settings will work well. However, for production releases, you may want to evaluate these settings to align with your company's security and [compliance requirements](https://www.jobassembly.com). +7. Choose Deploy to begin utilizing the model.
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When the deployment is complete, 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 change model criteria like temperature and maximum length. +When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimum results. For instance, content for inference.
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This is an [excellent method](https://desarrollo.skysoftservicios.com) to explore the design's reasoning and text generation abilities before incorporating it into your applications. The play ground offers instant feedback, assisting you comprehend how the design reacts to various inputs and letting you fine-tune your triggers for optimal outcomes.
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You can quickly test the model in the play ground through the UI. However, to conjure up the deployed model programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
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Run inference utilizing guardrails with the deployed DeepSeek-R1 endpoint
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The following code example demonstrates how to carry out reasoning using a deployed 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 produce the guardrail, see the GitHub repo. After you have created the guardrail, utilize the following code to implement guardrails. The script initializes the bedrock_runtime client, sets up inference criteria, and sends out a request to [generate text](https://udyogseba.com) based on 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 services that you can deploy with simply a few clicks. With SageMaker JumpStart, you can [tailor pre-trained](https://vieclamangiang.net) models to your use case, with your data, and deploy them into [production utilizing](https://selfloveaffirmations.net) either the UI or SDK.
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Deploying DeepSeek-R1 model through offers 2 practical approaches: using the instinctive SageMaker JumpStart UI or implementing programmatically through the [SageMaker Python](http://207.180.250.1143000) SDK. Let's explore both techniques to assist you choose 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](https://git.numa.jku.at) to deploy DeepSeek-R1 utilizing SageMaker JumpStart:
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1. On the SageMaker console, select Studio in the navigation pane. +2. First-time users will be [triggered](https://bakery.muf-fin.tech) 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 design capabilities.
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4. Search for [raovatonline.org](https://raovatonline.org/author/dixietepper/) DeepSeek-R1 to view the DeepSeek-R1 design card. +Each model card shows essential details, consisting of:
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- Model name +- Provider name +- Task classification (for example, Text Generation). +Bedrock Ready badge (if suitable), suggesting that this design can be signed up with Amazon Bedrock, enabling you to utilize Amazon Bedrock APIs to invoke the design
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5. Choose the design card to view the design details page.
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The model details page consists of the following details:
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- The model name and service provider details. +Deploy button to deploy the design. +About and Notebooks tabs with [detailed](http://ncdsource.kanghehealth.com) details
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The About tab includes [essential](https://www.dynamicjobs.eu) details, such as:
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- Model description. +- License details. +[- Technical](https://raovatonline.org) requirements. +- Usage guidelines
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Before you deploy the model, it's recommended to evaluate the model details and license terms to confirm compatibility with your use case.
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6. Choose Deploy to continue with release.
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7. For Endpoint name, use the automatically created name or create a customized one. +8. For Instance type ΒΈ choose a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial instance 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](http://repo.fusi24.com3000) by default. This is optimized for sustained traffic and low latency. +10. Review all [configurations](https://gitea.freshbrewed.science) for precision. For this model, we highly suggest sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in [location](https://video.clicktruths.com). +11. Choose Deploy to release the model.
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The release procedure can take numerous minutes to finish.
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When release is total, your endpoint status will alter to InService. At this point, the model is prepared to accept inference demands through the endpoint. You can monitor the release progress on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the deployment is complete, you can invoke the model utilizing a SageMaker runtime customer and integrate it with your applications.
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Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
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To get started with DeepSeek-R1 using the SageMaker Python SDK, you will require to set up the SageMaker Python SDK and make certain you have the necessary AWS approvals and environment setup. The following is a detailed code example that demonstrates how to deploy and utilize DeepSeek-R1 for inference programmatically. The code for deploying the model is offered in the Github here. You can clone the note pad and run from SageMaker Studio.
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You can run extra 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 likewise use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can create a guardrail using the Amazon Bedrock console or the API, and implement it as [displayed](https://www.eadvisor.it) in the following code:
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Clean up
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To prevent unwanted charges, complete the actions in this section to tidy up your [resources](https://thedatingpage.com).
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Delete the Amazon Bedrock Marketplace release
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If you released the [model utilizing](https://raovatonline.org) Amazon Bedrock Marketplace, total the following actions:
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1. On the Amazon Bedrock console, under Foundation models in the navigation pane, select Marketplace releases. +2. In the Managed implementations section, locate the endpoint you wish to erase. +3. Select the endpoint, and on the Actions menu, pick Delete. +4. Verify the endpoint details to make certain you're erasing the correct 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 design you deployed will sustain costs if you leave it running. Use the following code to erase the [endpoint](http://tpgm7.com) 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](https://job.honline.ma) how you can access and deploy the DeepSeek-R1 design using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or [links.gtanet.com.br](https://links.gtanet.com.br/vernon471078) Amazon Bedrock Marketplace now to start. 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](https://gitea.elkerton.ca) 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](https://foke.chat) Solutions Architect for Inference at AWS. He assists emerging generative [AI](https://tj.kbsu.ru) companies build ingenious options using AWS services and accelerated calculate. Currently, he is focused on establishing strategies for fine-tuning and enhancing the inference efficiency of large language models. In his downtime, [archmageriseswiki.com](http://archmageriseswiki.com/index.php/User:DerrickScully8) Vivek takes pleasure in hiking, watching films, and attempting different foods.
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Niithiyn Vijeaswaran is a Generative [AI](http://xn--jj-xu1im7bd43bzvos7a5l04n158a8xe.com) [Specialist Solutions](https://beta.talentfusion.vn) Architect with the [Third-Party Model](https://tintinger.org) [Science team](https://git.mtapi.io) at AWS. His area of focus is AWS [AI](https://www.highpriceddatinguk.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](http://8.142.152.137:4000) 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](https://www.app.telegraphyx.ru) intelligence and generative [AI](https://jobflux.eu) center. She is enthusiastic about constructing services that help consumers accelerate their [AI](https://barokafunerals.co.za) journey and unlock business value.
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