From 60ae202c1289ed3c4f549089aeb7b4740f96b74f Mon Sep 17 00:00:00 2001 From: bud6808124895 Date: Mon, 17 Feb 2025 08:21:59 +0000 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..c8b8f9b --- /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 announce 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](https://www.belizetalent.com)'s first-generation frontier model, DeepSeek-R1, along with the distilled versions ranging from 1.5 to 70 billion specifications to develop, experiment, [archmageriseswiki.com](http://archmageriseswiki.com/index.php/User:DessieLundstrom) and responsibly scale your generative [AI](https://members.mcafeeinstitute.com) concepts on AWS.
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In this post, we show how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar actions to deploy the distilled versions of the models too.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a big language design (LLM) developed by [DeepSeek](https://www.app.telegraphyx.ru) [AI](https://owangee.com) that uses reinforcement finding out to improve reasoning abilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. A key differentiating function is its support learning (RL) step, which was utilized to improve the design's reactions beyond the basic pre-training and fine-tuning process. By incorporating RL, DeepSeek-R1 can adjust more effectively to user feedback and goals, eventually improving both importance and [clearness](https://gitea.freshbrewed.science). In addition, DeepSeek-R1 employs a chain-of-thought (CoT) approach, indicating it's geared up to break down complicated inquiries and reason through them in a detailed manner. This guided reasoning process enables the model to produce more precise, transparent, and detailed answers. This design integrates RL-based fine-tuning with CoT capabilities, aiming to create structured actions while concentrating on interpretability and user interaction. With its extensive capabilities DeepSeek-R1 has captured the market's attention as a [versatile text-generation](https://wiki.piratenpartei.de) model that can be incorporated into various workflows such as agents, sensible thinking and information interpretation tasks.
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DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture permits activation of 37 billion specifications, allowing effective reasoning by routing questions to the most pertinent expert "clusters." This [approach permits](https://mypungi.com) the model to concentrate on different issue domains while maintaining overall efficiency. DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for inference. In this post, we will utilize an ml.p5e.48 xlarge instance to release the model. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.
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DeepSeek-R1 distilled models bring the reasoning abilities of the main R1 design to more efficient architectures based upon popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of training smaller, more efficient models to mimic the habits and thinking patterns of the bigger DeepSeek-R1 model, utilizing it as a teacher model.
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You can release DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we suggest deploying this model with guardrails in location. In this blog, we will use Amazon Bedrock Guardrails to introduce safeguards, prevent damaging content, and evaluate models against essential safety requirements. At the time of [composing](https://flexychat.com) this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can produce multiple guardrails tailored to different usage cases and use them to the DeepSeek-R1 design, enhancing user experiences and standardizing security [controls](https://git.komp.family) throughout your generative [AI](https://jobs.ondispatch.com) applications.
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Prerequisites
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To release the DeepSeek-R1 model, you require access to an ml.p5e [instance](https://git.mtapi.io). To examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, [it-viking.ch](http://it-viking.ch/index.php/User:IsobelHartman) select Amazon SageMaker, and verify you're utilizing ml.p5e.48 xlarge for endpoint usage. Make certain that you have at least one ml.P5e.48 xlarge circumstances in the AWS Region you are releasing. To ask for a limit boost, produce a [limitation increase](http://b-ways.sakura.ne.jp) demand and reach out to your account team.
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Because you will be releasing this design with Amazon Bedrock Guardrails, make certain you have the proper AWS Identity and Gain Access To Management (IAM) authorizations to use Amazon Bedrock Guardrails. For guidelines, see Establish permissions 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 harmful content, and examine designs against crucial safety requirements. You can implement safety procedures for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to apply guardrails to assess user inputs and design responses deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can develop 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 circulation involves the following actions: 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 model for inference. After receiving the design's output, another guardrail check is applied. If the output passes this last check, it's returned as the 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 took place at the input or output phase. The examples showcased in the following areas show reasoning utilizing 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 designs (FMs) through Amazon Bedrock. 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 brochure under Foundation models in the navigation pane. +At the time of writing this post, you can utilize the InvokeModel API to conjure up the design. It doesn't support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a provider and select the DeepSeek-R1 design.
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The model detail page supplies important details about the design's abilities, prices structure, and application standards. You can discover detailed use directions, including sample API calls and code bits for integration. The model supports numerous text generation jobs, including content production, code generation, and concern answering, utilizing its support finding out optimization and CoT reasoning abilities. +The page likewise includes release choices and licensing details to assist you get going with DeepSeek-R1 in your [applications](https://git.purplepanda.cc). +3. To start using DeepSeek-R1, select Deploy.
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You will be triggered to configure the implementation details for DeepSeek-R1. The design ID will be [pre-populated](https://git.lain.church). +4. For Endpoint name, get in an endpoint name (in between 1-50 alphanumeric characters). +5. For Number of circumstances, go into a number of circumstances (in between 1-100). +6. For example type, pick your circumstances type. For [ideal performance](https://blogram.online) with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is recommended. +Optionally, you can configure sophisticated security and infrastructure settings, consisting of virtual personal cloud (VPC) networking, service function permissions, [gratisafhalen.be](https://gratisafhalen.be/author/tamikalaf18/) and file encryption settings. For a lot of utilize cases, the default settings will work well. However, for [production](https://iesoundtrack.tv) deployments, you may desire to examine these settings to align with your company's security and compliance requirements. +7. Choose Deploy to begin utilizing the design.
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When the implementation is total, you can test DeepSeek-R1's abilities straight in the Amazon Bedrock playground. +8. Choose Open in play ground to access an interactive interface where you can explore various prompts and adjust model criteria like temperature level and maximum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for optimal results. For example, content for reasoning.
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This is an outstanding way to explore the [design's reasoning](https://www.app.telegraphyx.ru) and text generation capabilities before integrating it into your applications. The play area provides immediate feedback, helping you comprehend how the model reacts to different inputs and letting you fine-tune your triggers for optimal outcomes.
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You can rapidly check the design in the [playground](http://120.55.59.896023) through the UI. However, to invoke the deployed design programmatically with any Amazon Bedrock APIs, you need 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 demonstrates how to carry out reasoning utilizing a deployed DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. 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 actually developed the guardrail, use the following code to implement guardrails. The script initializes the bedrock_runtime client, 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](http://120.46.139.31) JumpStart
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SageMaker JumpStart is an artificial intelligence (ML) center with FMs, integrated algorithms, and prebuilt ML solutions that you can deploy with just a couple of clicks. With SageMaker JumpStart, you can [tailor pre-trained](http://vivefive.sakura.ne.jp) models to your usage case, with your data, and release them into production utilizing either the UI or SDK.
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Deploying DeepSeek-R1 design through SageMaker JumpStart offers 2 practical methods: utilizing the intuitive SageMaker JumpStart UI or executing programmatically through the [SageMaker Python](https://yourfoodcareer.com) SDK. Let's check out both techniques to assist you pick 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 utilizing SageMaker JumpStart:
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1. On the [SageMaker](https://embargo.energy) console, pick Studio in the [navigation pane](https://matchpet.es). +2. First-time users will be prompted to develop a domain. +3. On the SageMaker Studio console, pick JumpStart in the navigation pane.
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The design browser displays available designs, with details like the provider name and model capabilities.
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4. Search for DeepSeek-R1 to view the DeepSeek-R1 design card. +Each model card reveals crucial details, including:
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- Model name +- Provider name +- Task category (for instance, Text Generation). +Bedrock Ready badge (if appropriate), indicating that this model can be signed up with Amazon Bedrock, enabling you to utilize Amazon Bedrock APIs to invoke the model
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5. Choose the design card to see the model details page.
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The design details page includes the following details:
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- The model name and company details. +[Deploy button](https://social.web2rise.com) to deploy the model. +About and Notebooks tabs with detailed details
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The About tab includes crucial details, such as:
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- Model description. +- License details. +- Technical specs. +[- Usage](http://8.137.89.263000) standards
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Before you [release](http://git.hsgames.top3000) the model, it's suggested to review the design details and license terms to verify compatibility with your use case.
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6. Choose Deploy to continue with implementation.
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7. For Endpoint name, utilize the automatically generated name or develop a customized one. +8. For [disgaeawiki.info](https://disgaeawiki.info/index.php/User:NevilleWitcher0) example type ΒΈ pick a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, go into the variety of circumstances (default: 1). +Selecting proper circumstances types and counts is crucial for cost and performance optimization. Monitor your release 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 setups for accuracy. For [wiki.snooze-hotelsoftware.de](https://wiki.snooze-hotelsoftware.de/index.php?title=Benutzer:WallyWolff97453) this model, we highly recommend sticking 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 deployment procedure can take [numerous](http://62.210.71.92) minutes to finish.
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When deployment is total, your endpoint status will change to InService. At this moment, the model is prepared to [accept reasoning](https://gitea.robertops.com) demands through the endpoint. You can keep an eye on the implementation development on the SageMaker console Endpoints page, which will display pertinent metrics and status details. When the [release](http://lnsbr-tech.com) is complete, you can invoke the model utilizing 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 start 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 authorizations and environment setup. The following is a detailed code example that demonstrates how to deploy and utilize DeepSeek-R1 for [reasoning programmatically](http://gitlab.ds-s.cn30000). The code for [deploying](http://120.79.211.1733000) the design is provided in the Github here. You can clone the notebook and run from SageMaker Studio.
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You can run extra demands against the predictor:
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Implement guardrails and run reasoning with your [SageMaker JumpStart](http://114.132.230.24180) predictor
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Similar to Amazon Bedrock, you can also 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 displayed in the following code:
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Clean up
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To prevent undesirable charges, finish the steps in this area to clean up your resources.
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Delete the Amazon Bedrock Marketplace implementation
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If you deployed the design utilizing Amazon Bedrock Marketplace, total the following actions:
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1. On the Amazon Bedrock console, under Foundation models in the navigation pane, choose Marketplace releases. +2. In the Managed implementations area, locate the endpoint you wish to delete. +3. Select the endpoint, and on the Actions menu, pick Delete. +4. Verify the endpoint details to make certain you're deleting the appropriate deployment: [archmageriseswiki.com](http://archmageriseswiki.com/index.php/User:TashaGladden) 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 erase the endpoint if you want 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 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 begin. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Starting 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](https://wacari-git.ru) [AI](https://jobs.foodtechconnect.com) business construct ingenious services utilizing AWS services and accelerated compute. Currently, he is focused on establishing techniques for fine-tuning and enhancing the reasoning performance of big language designs. In his downtime, Vivek enjoys hiking, seeing movies, and attempting different cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://dongochan.id.vn) [Specialist Solutions](https://xevgalex.ru) Architect with the Third-Party Model Science team at AWS. His location of focus is AWS [AI](http://121.43.99.128:3000) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
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[Jonathan Evans](https://git.home.lubui.com8443) is an Expert Solutions Architect working on generative [AI](https://govtpakjobz.com) with the Third-Party Model 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](http://116.205.229.1963000) [AI](https://www.facetwig.com) hub. She is passionate about developing options that help customers accelerate their [AI](http://51.222.156.250:3000) journey and unlock organization worth.
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