From a06e843562aaa53e0774100a4a44762a367dd666 Mon Sep 17 00:00:00 2001 From: napoleoncarrei Date: Mon, 7 Apr 2025 00:47:13 +0300 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..450ab52 --- /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 deploy DeepSeek [AI](https://gogs.artapp.cn)'s first-generation frontier model, DeepSeek-R1, in addition to the distilled versions varying from 1.5 to 70 billion criteria to build, experiment, and properly scale your [generative](https://4kwavemedia.com) [AI](https://www.meetgr.com) ideas on AWS.
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In this post, we demonstrate how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar actions to deploy the distilled variations of the designs too.
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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://git.info666.com) that utilizes reinforcement discovering to improve reasoning abilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. An essential distinguishing feature is its reinforcement learning (RL) step, which was utilized to improve the model's reactions beyond the basic pre-training and fine-tuning procedure. By [including](http://www.scitqn.cn3000) RL, DeepSeek-R1 can adapt better to user feedback and goals, eventually boosting both importance and clarity. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) approach, suggesting it's geared up to break down complex inquiries and factor through them in a detailed manner. This assisted reasoning procedure allows the model to produce more precise, transparent, and detailed answers. This design combines RL-based fine-tuning with CoT capabilities, aiming to generate structured reactions while concentrating on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has actually captured the industry's attention as a flexible text-generation design that can be incorporated into different workflows such as representatives, rational thinking and data analysis tasks.
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DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture allows activation of 37 billion criteria, enabling effective inference by routing questions to the most pertinent specialist "clusters." This technique permits the design to focus on different issue domains while maintaining overall effectiveness. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for inference. In this post, we will utilize an ml.p5e.48 [xlarge circumstances](https://git.youxiner.com) to deploy the model. ml.p5e.48 xlarge comes with 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 effective 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 models to mimic the habits and reasoning patterns of the bigger DeepSeek-R1 model, using it as an instructor design.
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You can deploy DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we advise deploying this model with [guardrails](http://dnd.achoo.jp) in place. In this blog site, we will use Amazon Bedrock Guardrails to present safeguards, prevent harmful content, and assess models against crucial security requirements. At the time of composing this blog site, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can produce several guardrails tailored to different usage cases and use them to the DeepSeek-R1 design, improving user experiences and standardizing safety controls across your generative [AI](http://git.e365-cloud.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](https://www.thehappyservicecompany.com). To examine if you have quotas for P5e, open the Service Quotas [console](https://git.brodin.rocks) and under AWS Services, choose Amazon SageMaker, and validate you're utilizing ml.p5e.48 xlarge for endpoint usage. 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 boost demand and connect to your account team.
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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 utilize Amazon Bedrock Guardrails. For guidelines, see Establish consents to use guardrails for material filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails enables you to present safeguards, avoid hazardous material, and assess designs against crucial security requirements. You can implement safety steps for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to apply guardrails to assess user inputs and model reactions 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 develop the guardrail, see the GitHub repo.
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The general circulation includes 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 out to the model for reasoning. After getting the model'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 indicating the nature of the intervention and whether it at the input or output stage. The examples showcased in the following areas show [inference](http://cjma.kr) 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 models (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 composing this post, you can utilize the [InvokeModel API](http://47.92.218.2153000) to invoke the model. It does not 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 necessary details about the design's capabilities, pricing structure, and application standards. You can discover detailed usage instructions, including sample API calls and code snippets for integration. The model supports different text generation tasks, consisting of content development, code generation, and concern answering, utilizing its reinforcement learning optimization and CoT thinking capabilities. +The page also consists of deployment options and licensing details to help you start with DeepSeek-R1 in your applications. +3. To begin using DeepSeek-R1, choose Deploy.
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You will be triggered to set up the implementation details for DeepSeek-R1. The design ID will be pre-populated. +4. For Endpoint name, get in an endpoint name (between 1-50 alphanumeric characters). +5. For Number of circumstances, go into a variety of circumstances (between 1-100). +6. For example type, pick your instance type. For optimum performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is suggested. +Optionally, you can configure advanced security and facilities settings, including virtual personal cloud (VPC) networking, service role approvals, and file encryption settings. For many use cases, the default settings will work well. However, for production implementations, you may want to evaluate these settings to align with your organization's security and compliance requirements. +7. Choose Deploy to start utilizing the design.
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When the release is complete, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock play area. +8. Choose Open in play ground to access an interactive user interface where you can experiment with different triggers and adjust design criteria like temperature level and maximum length. +When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for optimal outcomes. For example, content for inference.
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This is an excellent way to check out the design's reasoning and text generation capabilities before incorporating it into your applications. The play area supplies immediate feedback, helping you understand how the design responds to different inputs and letting you fine-tune your prompts for optimum results.
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You can rapidly evaluate the model in the play ground through the UI. However, to conjure up the deployed design programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
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Run reasoning utilizing 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 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can create a guardrail utilizing 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, use the following code to execute guardrails. The script initializes the bedrock_runtime client, sets up [reasoning](http://183.238.195.7710081) specifications, and sends out a demand to generate text 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) hub with FMs, integrated algorithms, and [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:Natisha96L) prebuilt ML solutions that you can deploy with just a couple of clicks. With [SageMaker](http://101.200.241.63000) JumpStart, you can tailor pre-trained models to your usage case, with your information, and deploy them into production utilizing either the UI or SDK.
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Deploying DeepSeek-R1 design through SageMaker JumpStart provides 2 convenient techniques: utilizing the intuitive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's [explore](http://175.6.40.688081) both approaches to help you choose the approach that [finest fits](https://redebuck.com.br) your [requirements](http://gamebizdev.ru).
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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 JumpStart:
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1. On the SageMaker console, pick Studio in the navigation pane. +2. First-time users will be triggered to develop a domain. +3. On the SageMaker Studio console, pick JumpStart in the navigation pane.
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The model web browser shows available designs, with details like the provider name and design capabilities.
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4. Look for DeepSeek-R1 to view the DeepSeek-R1 design card. +Each design card reveals key details, consisting of:
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- Model name +- Provider name +- Task category (for instance, Text Generation). +Bedrock Ready badge (if suitable), indicating that this model can be registered with Amazon Bedrock, permitting you to use Amazon Bedrock APIs to invoke the design
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5. Choose the model card to see the model details page.
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The design details page consists of the following details:
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- The design name and service provider details. +Deploy button to deploy 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 release the model, it's suggested to review the model details and license terms to verify compatibility with your usage case.
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6. Choose Deploy to proceed with implementation.
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7. For Endpoint name, use the immediately produced name or create a custom one. +8. For Instance type ΒΈ choose a circumstances type (default: ml.p5e.48 xlarge). +9. For [Initial](https://jobs.constructionproject360.com) [instance](https://www.jccer.com2223) count, go into the variety of instances (default: 1). +Selecting proper [instance](https://workonit.co) types and counts is vital for cost and performance optimization. Monitor your release to adjust 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 precision. For this model, we strongly recommend adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in place. +11. Choose Deploy to deploy the design.
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The release procedure can take [numerous](http://121.36.37.7015501) minutes to finish.
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When release is complete, your endpoint status will change to InService. At this point, the design is prepared to accept inference demands through the endpoint. You can keep an eye on the release development on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the release is total, you can invoke the design utilizing a [SageMaker runtime](http://118.89.58.193000) 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 [utilizing](https://sudanre.com) the SageMaker Python SDK, you will need to install the [SageMaker Python](http://westec-immo.com) SDK and make certain you have the necessary AWS consents and environment setup. The following is a detailed code example that shows how to deploy and use DeepSeek-R1 for inference programmatically. The code for deploying the model is supplied in the Github here. You can clone the note pad 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 predictor
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Similar to Amazon Bedrock, you can likewise utilize the ApplyGuardrail API with your [SageMaker JumpStart](https://git.visualartists.ru) predictor. You can produce a guardrail utilizing 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](http://47.113.125.2033000) charges, finish the steps in this section to tidy up your resources.
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Delete the Amazon Bedrock Marketplace deployment
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If you released the model using 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 implementations. +2. In the Managed deployments area, locate the endpoint you wish to delete. +3. Select the endpoint, and on the Actions menu, choose Delete. +4. Verify the endpoint details to make certain you're deleting the correct deployment: 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](https://nse.ai) model you deployed will sustain expenses if you leave it [running](https://git.daoyoucloud.com). 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 design utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to begin. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, Amazon [Bedrock](https://jmusic.me) 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 [helps emerging](http://www.vmeste-so-vsemi.ru) generative [AI](https://ipmanage.sumedangkab.go.id) companies build innovative solutions utilizing AWS services and sped up calculate. Currently, he is focused on developing strategies for [fine-tuning](https://aji.ghar.ku.jaldi.nai.aana.ba.tume.dont.tach.me) and enhancing the reasoning performance of big language designs. In his [leisure](https://nepalijob.com) time, Vivek enjoys hiking, enjoying movies, and trying various cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](http://49.232.207.113:3000) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His location of focus is AWS [AI](https://nakshetra.com.np) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
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Jonathan Evans is a Specialist Solutions Architect dealing with generative [AI](http://gitea.ucarmesin.de) 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 [AI](https://gitea.phywyj.dynv6.net) center. She is enthusiastic about developing options that assist clients accelerate their [AI](https://yourfoodcareer.com) [journey](https://www.boatcareer.com) and unlock service worth.
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