commit 3337812a0177c658639f28487ff9a90849838937 Author: Yvette Nowakowski Date: Fri Feb 7 10:26:20 2025 +0900 Add DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart 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..e7e73d7 --- /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 reveal 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](http://gitlab.awcls.com)'s first-generation frontier model, DeepSeek-R1, along with the distilled variations ranging from 1.5 to 70 billion specifications to construct, experiment, and responsibly scale your generative [AI](http://xn--ok0bw7u60ff7e69dmyw.com) ideas on AWS.
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In this post, we show how to get begun with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar actions to release 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) developed by DeepSeek [AI](https://nextcode.store) that uses support learning to improve thinking abilities through a multi-stage training process from a DeepSeek-V3-Base structure. A crucial distinguishing function is its support learning (RL) step, which was utilized to improve the model's responses beyond the [standard](http://8.140.229.2103000) pre-training and fine-tuning process. By integrating RL, DeepSeek-R1 can adjust more effectively to user feedback and objectives, ultimately boosting both importance and clarity. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) method, implying it's geared up to break down intricate questions and reason through them in a detailed manner. This guided reasoning process enables the model to produce more precise, transparent, and detailed answers. This model combines RL-based fine-tuning with CoT capabilities, aiming to generate structured reactions while focusing on interpretability and user interaction. With its wide-ranging capabilities DeepSeek-R1 has caught the industry's attention as a flexible text-generation model that can be integrated into different workflows such as representatives, sensible reasoning and information analysis tasks.
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DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture permits activation of 37 billion parameters, enabling effective inference by routing queries to the most pertinent expert "clusters." This technique permits the design to [specialize](https://exajob.com) in different issue domains while maintaining total effectiveness. DeepSeek-R1 needs at least 800 GB of [HBM memory](http://122.51.46.213) in FP8 format for reasoning. In this post, we will use an ml.p5e.48 xlarge instance to deploy the model. ml.p5e.48 xlarge features 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 upon open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). [Distillation refers](https://dev.worldluxuryhousesitting.com) to a process of training smaller, more effective designs to simulate the behavior and reasoning patterns of the larger DeepSeek-R1 design, utilizing it as a teacher model.
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You can deploy DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we suggest deploying this model with guardrails in place. In this blog, we will use Amazon Bedrock Guardrails to introduce safeguards, [prevent damaging](https://gitea.lelespace.top) material, and assess models against essential security criteria. At the time of composing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can produce numerous guardrails tailored to various use cases and use them to the DeepSeek-R1 design, improving user experiences and standardizing security [controls](http://8.138.173.1953000) across your generative [AI](http://bolsatrabajo.cusur.udg.mx) applications.
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Prerequisites
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To release the DeepSeek-R1 design, you require access to an ml.p5e instance. To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and [wiki.myamens.com](http://wiki.myamens.com/index.php/User:LoreenErtel66) verify you're utilizing ml.p5e.48 xlarge for endpoint use. Make certain that you have at least one ml.P5e.48 [xlarge circumstances](http://gitlab.abovestratus.com) in the AWS Region you are deploying. To request a limit boost, [produce](http://154.8.183.929080) a limit boost demand and connect to your account group.
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Because you will be releasing this model with Amazon Bedrock Guardrails, make certain you have the appropriate AWS Identity and [Gain Access](https://ifin.gov.so) To Management (IAM) authorizations to utilize Amazon Bedrock Guardrails. For instructions, 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 enables you to introduce safeguards, prevent hazardous material, and examine designs against essential security requirements. You can carry out security steps for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to apply guardrails to evaluate user inputs and design reactions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can develop a guardrail using the Amazon Bedrock [console](https://dessinateurs-projeteurs.com) or the API. For the example code to develop the guardrail, see the GitHub repo.
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The basic flow involves the following steps: First, the system receives an input for the design. This input is then processed through the [ApplyGuardrail API](https://archie2429263902267.bloggersdelight.dk). If the input passes the guardrail check, it's sent out to the design 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](http://stockzero.net). However, if either the input or output is intervened by the guardrail, a message is returned suggesting the nature of the intervention and whether it occurred at the input or output phase. The examples showcased in the following areas show inference utilizing 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, 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, select Model catalog under Foundation designs in the navigation pane. +At the time of composing this post, you can utilize the InvokeModel API to invoke the model. It doesn't support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a company and choose the DeepSeek-R1 model.
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The design detail page supplies essential details about the design's capabilities, prices structure, and [execution guidelines](http://www.jedge.top3000). You can discover detailed use directions, including sample API calls and code bits for integration. The design supports different text generation tasks, consisting of content production, code generation, and question answering, using its reinforcement finding out optimization and CoT reasoning [abilities](http://tobang-bangsu.co.kr). +The page also includes implementation choices and licensing details to assist you get begun 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 design ID will be pre-populated. +4. For Endpoint name, enter an endpoint name (between 1-50 alphanumeric characters). +5. For Variety of instances, enter a number of instances (in between 1-100). +6. For Instance type, select your instance type. For optimum performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised. +Optionally, you can configure sophisticated security and infrastructure settings, including virtual [private cloud](http://106.15.235.242) (VPC) networking, service function consents, and encryption settings. For the majority of utilize cases, the default settings will work well. However, for production releases, you might wish to evaluate these settings to align with your company's security and compliance requirements. +7. [Choose Deploy](https://gitee.mmote.ru) 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 ground. +8. Choose Open in play ground to access an interactive user interface where you can experiment with different triggers and adjust model parameters like temperature and optimum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimal results. For example, material for reasoning.
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This is an excellent way to explore the model's reasoning and text generation capabilities before incorporating it into your applications. The play ground supplies immediate feedback, [assisting](https://huconnect.org) you comprehend how the model responds to numerous inputs and letting you fine-tune your prompts for optimum outcomes.
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You can quickly test the design in the play area through the UI. However, to invoke the released model programmatically with any Amazon Bedrock APIs, you require 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](http://thinking.zicp.io3000) how to carry out reasoning utilizing a deployed DeepSeek-R1 model 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 actually created the guardrail, [utilize](https://kition.mhl.tuc.gr) the following code to carry out guardrails. The script initializes the bedrock_runtime client, configures inference parameters, and sends a demand to [produce text](https://croart.net) 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, built-in algorithms, and prebuilt ML solutions that you can deploy with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained [designs](http://git.gonstack.com) to your usage case, with your data, and release them into production utilizing either the UI or SDK.
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[Deploying](http://41.111.206.1753000) DeepSeek-R1 model through SageMaker JumpStart uses two hassle-free methods: using the intuitive SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's check out both techniques to help you select the technique that finest suits your [requirements](https://social.engagepure.com).
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following actions to deploy DeepSeek-R1 [utilizing SageMaker](http://www.mitt-slide.com) JumpStart:
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1. On the SageMaker console, choose Studio in the navigation pane. +2. First-time users will be triggered to develop a domain. +3. On the SageMaker Studio console, select JumpStart in the navigation pane.
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The model web browser shows available designs, with details like the supplier name and model abilities.
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4. Search for DeepSeek-R1 to see the DeepSeek-R1 design card. +Each [design card](https://www.youtoonet.com) shows key details, including:
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- Model name +- Provider name +- Task category (for instance, Text Generation). +Bedrock Ready badge (if relevant), indicating that this design can be registered with Amazon Bedrock, enabling you to use Amazon Bedrock APIs to invoke the model
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5. Choose the model card to view the model details page.
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The model details page consists of the following details:
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- The design name and service provider details. +Deploy button to deploy the design. +About and Notebooks tabs with detailed details
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The About tab consists of crucial details, such as:
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- Model description. +- License details. +- Technical requirements. +- Usage guidelines
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Before you release the design, it's recommended to examine the design details and license terms to confirm compatibility with your usage 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 produce a customized one. +8. For Instance type ΒΈ select an [instance type](https://eelam.tv) (default: ml.p5e.48 xlarge). +9. For Initial instance count, get in the number of circumstances (default: 1). +Selecting suitable instance types and counts is vital for cost and performance optimization. Monitor your release to change these settings as needed.Under Inference type, Real-time inference is chosen by default. This is optimized for sustained traffic and low latency. +10. Review all setups for [precision](http://autogangnam.dothome.co.kr). For this model, we highly advise adhering to SageMaker JumpStart default settings and making certain that network isolation remains in place. +11. Choose Deploy to deploy the design.
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The deployment procedure can take several minutes to complete.
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When implementation is complete, your [endpoint status](https://zenithgrs.com) will alter to InService. At this point, the design is ready to accept reasoning demands through the endpoint. You can keep an eye on the implementation development on the SageMaker console Endpoints page, which will display relevant metrics and status details. When the release is complete, 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 begin with DeepSeek-R1 using the SageMaker Python SDK, you will require to set up the SageMaker Python SDK and make certain you have the needed AWS consents and environment setup. The following is a detailed code example that shows how to release 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 range from SageMaker Studio.
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You can run extra requests 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 [predictor](https://wiki.aipt.group). You can produce a guardrail utilizing the Amazon Bedrock console or the API, and execute it as revealed in the following code:
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Tidy up
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To prevent undesirable charges, [pipewiki.org](https://pipewiki.org/wiki/index.php/User:PaulineMcLaurin) complete the steps in this area to tidy 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, complete the following steps:
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1. On the Amazon Bedrock console, under Foundation models in the [navigation](http://slfood.co.kr) pane, [select Marketplace](http://ecoreal.kr) implementations. +2. In the Managed deployments area, locate the endpoint you wish to erase. +3. Select the endpoint, and on the Actions menu, select Delete. +4. Verify the endpoint details to make certain you're erasing the right 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 model you released will sustain expenses if you leave it running. Use the following code to erase 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 checked out how you can access and deploy the DeepSeek-R1 design using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get started. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting going 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](http://ep210.co.kr) at AWS. He assists emerging generative [AI](http://git.youkehulian.cn) business develop innovative solutions utilizing AWS services and accelerated calculate. Currently, he is focused on developing strategies for fine-tuning and optimizing the inference efficiency of large language models. In his downtime, Vivek takes pleasure in hiking, enjoying motion pictures, and attempting different cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://rabota.newrba.ru) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His location of focus is AWS [AI](https://gogs.k4be.pl) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.
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Jonathan Evans is a Professional Solutions Architect dealing with generative [AI](https://rami-vcard.site) 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://lensez.info) center. She is enthusiastic about building services that help consumers accelerate their [AI](https://myclassictv.com) journey and [wiki.lafabriquedelalogistique.fr](https://wiki.lafabriquedelalogistique.fr/Utilisateur:GiuseppeGlenelg) unlock organization worth.
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