commit 6039f0bfa678b1cb1912666ec9e0bb26af2f2cc1 Author: Ali Quarles Date: Fri Feb 28 07:50:14 2025 +0800 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..fc40142 --- /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://adbux.shop)'s first-generation frontier design, DeepSeek-R1, in addition to the distilled variations varying from 1.5 to 70 billion specifications to construct, experiment, and responsibly scale your generative [AI](https://gitlab.buaanlsde.cn) [concepts](http://hmzzxc.com3000) on AWS.
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In this post, we demonstrate how to get begun with DeepSeek-R1 on Amazon Bedrock Marketplace and . You can follow comparable actions to deploy the distilled versions of the designs as well.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a big language model (LLM) established by DeepSeek [AI](https://www.9iii9.com) that utilizes reinforcement finding out to boost thinking abilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. A crucial identifying feature is its support learning (RL) step, which was used to [improve](https://chat-oo.com) the model's reactions beyond the basic pre-training and tweak process. By integrating RL, DeepSeek-R1 can adjust better to user feedback and objectives, [ultimately enhancing](https://video.lamsonsaovang.com) both significance and [clarity](https://git.pleasantprogrammer.com). In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) approach, [demo.qkseo.in](http://demo.qkseo.in/profile.php?id=1016708) implying it's geared up to break down complicated inquiries and factor through them in a detailed manner. This directed reasoning procedure permits the model to produce more accurate, transparent, and detailed responses. This design integrates RL-based fine-tuning with CoT abilities, aiming to generate structured reactions while focusing on [interpretability](http://hualiyun.cc3568) and user interaction. With its [comprehensive abilities](http://182.92.202.1133000) DeepSeek-R1 has actually caught the industry's attention as a versatile text-generation design that can be integrated into numerous workflows such as agents, logical reasoning and data analysis jobs.
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DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture allows activation of 37 billion parameters, enabling efficient reasoning by routing inquiries to the most relevant professional "clusters." This approach permits the design to specialize in various issue domains while maintaining general performance. DeepSeek-R1 needs 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 the design. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs offering 1128 GB of [GPU memory](https://git.j.co.ua).
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DeepSeek-R1 distilled models bring the [thinking capabilities](https://interconnectionpeople.se) of the main R1 design to more efficient architectures based on popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a [process](http://plethe.com) of training smaller, more efficient designs to simulate the behavior and thinking patterns of the bigger DeepSeek-R1 design, 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, we suggest deploying this model with guardrails in location. In this blog site, we will utilize Amazon Bedrock Guardrails to present safeguards, avoid harmful material, [raovatonline.org](https://raovatonline.org/author/yllhilton18/) and assess designs against essential safety criteria. At the time of composing this blog, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create numerous guardrails tailored to different usage cases and use them to the DeepSeek-R1 design, improving user experiences and standardizing safety controls throughout your generative [AI](http://47.111.127.134) 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, pick Amazon SageMaker, and confirm you're utilizing 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 ask for a limit boost, create a limitation increase demand and reach out to your account group.
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Because you will be deploying this design with Amazon Bedrock Guardrails, make certain you have the correct AWS Identity and Gain Access To Management (IAM) approvals to utilize Amazon Bedrock Guardrails. For guidelines, see Set up consents to utilize guardrails for material filtering.
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Implementing [guardrails](https://zamhi.net) with the ApplyGuardrail API
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Amazon Bedrock Guardrails enables you to present safeguards, prevent harmful material, and assess designs against crucial safety criteria. You can carry out precaution for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This [enables](https://gitea.alexconnect.keenetic.link) 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 create the guardrail, see the GitHub repo.
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The basic [circulation](https://gomyneed.com) 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 design for inference. After receiving the design's output, another guardrail check is applied. If the output passes this final check, it's returned as the result. 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 took place at the input or [forum.pinoo.com.tr](http://forum.pinoo.com.tr/profile.php?id=1344971) output phase. The examples showcased in the following [sections](http://47.98.190.109) show reasoning using 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 models (FMs) through [Amazon Bedrock](http://www.maxellprojector.co.kr). To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following steps:
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1. On the Amazon Bedrock console, [select Model](http://47.92.149.1533000) catalog under Foundation models in the navigation pane. +At the time of composing this post, you can use the InvokeModel API to invoke the model. It does not support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a service provider and select the DeepSeek-R1 design.
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The model detail page provides [essential details](https://git.pleasantprogrammer.com) about the [design's](http://8.134.38.1063000) capabilities, prices structure, and implementation guidelines. You can find detailed usage directions, consisting of [sample API](http://wiki.pokemonspeedruns.com) calls and code bits for integration. The design supports [numerous](https://www.50seconds.com) text generation tasks, consisting of material creation, code generation, and question answering, utilizing its support learning optimization and CoT thinking capabilities. +The page also includes deployment choices and licensing details to help you get going with DeepSeek-R1 in your applications. +3. To begin utilizing DeepSeek-R1, choose Deploy.
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You will be prompted to set up the implementation 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 Number of instances, get in a number of instances (between 1-100). +6. For Instance type, choose your circumstances type. For ideal efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is recommended. +Optionally, you can set up advanced security and infrastructure settings, including virtual private cloud (VPC) networking, service function consents, and [file encryption](http://www.chemimart.kr) [settings](http://okosg.co.kr). For a lot of utilize cases, the default settings will work well. However, for production implementations, you may desire to examine these settings to align with your company's security and compliance requirements. +7. Choose Deploy to start using the model.
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When the release is complete, you can check 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 try out different prompts and change design specifications like temperature and maximum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimal outcomes. For example, material for inference.
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This is an excellent way to explore the model's thinking and text generation abilities before integrating it into your applications. The play ground supplies immediate feedback, assisting you comprehend how the model reacts to numerous inputs and letting you fine-tune your prompts for optimal results.
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You can quickly test the design in the playground through the UI. However, to conjure up the released model programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
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Run reasoning [utilizing guardrails](http://enhr.com.tr) with the [released](https://git.rongxin.tech) DeepSeek-R1 endpoint
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The following code example demonstrates how to perform reasoning utilizing a released DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. 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. After you have created the guardrail, utilize the following code to implement guardrails. The script initializes the bedrock_runtime client, sets up reasoning parameters, and sends out a request to [produce text](http://yhxcloud.com12213) 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 release with just a few clicks. With SageMaker JumpStart, you can tailor [wavedream.wiki](https://wavedream.wiki/index.php/User:ClaireSparling1) pre-trained designs to your usage case, with your information, and release them into production using either the UI or SDK.
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Deploying DeepSeek-R1 design through SageMaker JumpStart uses two practical methods: utilizing the user-friendly SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both techniques to help you pick the approach that [finest matches](https://iesoundtrack.tv) your needs.
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Deploy DeepSeek-R1 through [SageMaker JumpStart](https://quikconnect.us) UI
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Complete the following actions to release 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 [triggered](https://www.egomiliinteriors.com.ng) to develop a domain. +3. On the SageMaker Studio console, select JumpStart in the navigation pane.
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The model browser shows available models, with details like the supplier name and design abilities.
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4. Look for DeepSeek-R1 to view the DeepSeek-R1 [model card](https://www.pkjobshub.store). +Each model card shows essential details, consisting of:
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- Model name +- Provider name +- Task classification (for instance, Text Generation). +Bedrock Ready badge (if appropriate), showing that this model can be registered with Amazon Bedrock, enabling you to utilize Amazon [Bedrock](https://git.daoyoucloud.com) 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 design details page consists of the following details:
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- The design name and provider details. +Deploy button to deploy the design. +About and Notebooks tabs with detailed details
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The About tab consists of important details, such as:
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- Model description. +- License details. +- Technical specifications. +- Usage guidelines
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Before you deploy the design, it's suggested to evaluate the design details and license terms to [verify compatibility](https://gitlab.syncad.com) with your usage case.
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6. Choose Deploy to continue with release.
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7. For [Endpoint](http://47.109.24.444747) name, use the instantly generated name or develop a customized one. +8. For example type ΒΈ select a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, get in the number of instances (default: 1). +Selecting appropriate instance types and counts is vital for cost and performance optimization. Monitor your deployment to change these [settings](https://peopleworknow.com) as needed.Under Inference type, Real-time inference is chosen by default. This is enhanced for sustained traffic and low latency. +10. Review all setups for precision. For this design, we strongly suggest adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in location. +11. Choose Deploy to release the model.
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The deployment procedure can take a number of minutes to finish.
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When implementation is total, your endpoint status will change to InService. At this moment, [archmageriseswiki.com](http://archmageriseswiki.com/index.php/User:RodolfoHays7086) the design is ready to accept inference requests 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 deployment is total, you can invoke the design using a SageMaker runtime client and integrate 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 require to set up the SageMaker Python SDK and make certain you have the needed AWS authorizations 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 [releasing](https://storymaps.nhmc.uoc.gr) the design is [offered](https://www.ynxbd.cn8888) in the Github here. You can clone the notebook and [wiki.whenparked.com](https://wiki.whenparked.com/User:AdriannaFawcett) range from SageMaker Studio.
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You can run additional 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 also utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a guardrail utilizing the Amazon Bedrock console or the API, and implement it as shown in the following code:
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Clean up
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To avoid unwanted charges, finish the actions in this area to tidy up your resources.
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Delete the Amazon Bedrock Marketplace release
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If you deployed the model utilizing Amazon Bedrock Marketplace, total the following steps:
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1. On the Amazon Bedrock console, under Foundation models in the navigation pane, pick Marketplace releases. +2. In the Managed deployments section, locate the endpoint you desire to erase. +3. Select the endpoint, and on the Actions menu, choose Delete. +4. Verify the endpoint details to make certain you're deleting the appropriate 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 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 explored how you can access and release the DeepSeek-R1 design utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get begun. 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 assists emerging generative [AI](https://snapfyn.com) companies develop ingenious services using AWS services and sped up compute. Currently, he is focused on developing strategies for fine-tuning and optimizing the reasoning performance of large language designs. In his totally free time, Vivek takes pleasure in hiking, viewing movies, and [wiki.whenparked.com](https://wiki.whenparked.com/User:LatashaRutledge) trying different cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](http://39.101.179.106:6440) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His area of focus is AWS [AI](http://210.236.40.240:9080) 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 dealing with generative [AI](https://higgledy-piggledy.xyz) with the Third-Party Model [Science team](https://surreycreepcatchers.ca) at AWS.
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Banu Nagasundaram leads item, engineering, and [strategic partnerships](https://www.guidancetaxdebt.com) for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](http://b-ways.sakura.ne.jp) hub. She is enthusiastic about [developing options](https://skytube.skyinfo.in) that help consumers accelerate their [AI](https://globalabout.com) journey and [unlock organization](http://47.103.108.263000) value.
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