From 7aeddb37f48dda0a8c0aae824e8f6ab4ea32197d Mon Sep 17 00:00:00 2001 From: jorjalenehan59 Date: Fri, 4 Apr 2025 20:56:40 +0000 Subject: [PATCH] Update '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..2319fac --- /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 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](http://carpediem.so:30000)'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://nursingguru.in) ideas on AWS.
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In this post, we demonstrate how to start with DeepSeek-R1 on Amazon Bedrock [Marketplace](http://git.liuhung.com) and SageMaker JumpStart. You can follow comparable steps to release the distilled versions of the designs also.
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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://git.progamma.com.ua) that utilizes support discovering to improve reasoning capabilities through a multi-stage training process from a DeepSeek-V3-Base structure. An essential distinguishing feature is its support learning (RL) step, which was used to fine-tune the model's responses beyond the standard and tweak procedure. By integrating RL, DeepSeek-R1 can adapt more effectively to user feedback and goals, ultimately enhancing both importance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) technique, indicating it's equipped to break down complicated queries and factor through them in a detailed manner. This guided reasoning procedure allows the model to produce more accurate, transparent, and detailed responses. This design [combines RL-based](https://hireblitz.com) [fine-tuning](http://www.tuzh.top3000) with CoT abilities, aiming to produce structured actions while concentrating on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has 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 specifications in size. The MoE architecture allows activation of 37 billion criteria, allowing efficient reasoning by routing inquiries to the most pertinent expert "clusters." This approach enables the design to focus on different problem domains while maintaining overall efficiency. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will utilize an ml.p5e.48 xlarge instance to deploy the design. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.
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DeepSeek-R1 distilled designs bring the [reasoning capabilities](http://47.98.190.109) of the main R1 model to more efficient architectures based upon 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, more [efficient models](https://goodinfriends.com) to imitate the habits and reasoning patterns of the bigger DeepSeek-R1 design, utilizing it as an instructor model.
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You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we suggest releasing this model with guardrails in [location](http://forum.pinoo.com.tr). In this blog site, we will utilize Amazon [Bedrock Guardrails](https://hireblitz.com) to introduce safeguards, prevent damaging content, and assess designs against key safety requirements. At the time of writing this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can develop several guardrails tailored to various use cases and use them to the DeepSeek-R1 model, improving user experiences and standardizing security controls throughout your generative [AI](https://git.hitchhiker-linux.org) 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 check if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and confirm you're using 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 boost demand and connect to your account group.
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Because you will be releasing this design with Amazon Bedrock Guardrails, make certain you have the proper [AWS Identity](https://git.xxb.lttc.cn) and Gain Access To Management (IAM) permissions to utilize 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 present safeguards, prevent harmful content, and assess designs against [essential security](http://busforsale.ae) [criteria](http://81.68.246.1736680). You can carry out [safety measures](https://git.juxiong.net) for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This enables you to use guardrails to examine user inputs and model actions released on Amazon Bedrock [Marketplace](https://remoterecruit.com.au) and SageMaker JumpStart. You can produce 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 basic flow 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 out to the design for reasoning. After receiving the model's output, another guardrail check is used. 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 suggesting](http://repo.sprinta.com.br3000) the nature of the intervention and whether it occurred at the input or output phase. The examples showcased in the following sections show inference 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 foundation models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following actions:
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1. On the Amazon Bedrock console, choose Model brochure under Foundation designs in the navigation pane. +At the time of composing this post, you can use the InvokeModel API to conjure up the model. It does not [support Converse](https://www.kukustream.com) APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a [provider](https://forum.alwehdaclub.sa) and choose the DeepSeek-R1 design.
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The model detail page provides essential details about the design's capabilities, rates structure, and execution guidelines. You can find detailed usage instructions, consisting of [sample API](https://estekhdam.in) calls and code bits for integration. The design supports various text generation tasks, [including material](https://asromafansclub.com) creation, code generation, and question answering, using its support learning optimization and CoT reasoning abilities. +The page likewise includes deployment options and licensing [details](https://git.gday.express) to assist you get begun with DeepSeek-R1 in your applications. +3. To start utilizing DeepSeek-R1, pick Deploy.
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You will be triggered to [configure](https://zeroth.one) the release 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 (between 1-100). +6. For Instance type, select your [circumstances type](http://37.187.2.253000). For optimum efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is recommended. +Optionally, you can [configure innovative](https://tnrecruit.com) security and [disgaeawiki.info](https://disgaeawiki.info/index.php/User:AnthonyDuell) infrastructure settings, including virtual private cloud (VPC) networking, service function authorizations, and encryption settings. For many use cases, [wavedream.wiki](https://wavedream.wiki/index.php/User:MorrisVerge81) the default settings will work well. However, for production deployments, you might wish to evaluate these settings to line up with your organization's security and compliance requirements. +7. Choose Deploy to start utilizing the design.
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When the release is total, you can evaluate DeepSeek-R1's abilities straight in the Amazon Bedrock play area. +8. Choose Open in play area to access an interactive user interface where you can try out various triggers and change model parameters like temperature 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 reasoning.
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This is an outstanding method to check out the model's reasoning and text generation abilities before integrating it into your applications. The playground offers immediate feedback, assisting you comprehend how the [design reacts](https://gitlab.damage.run) to various inputs and [letting](https://git.logicloop.io) you tweak your triggers for ideal results.
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You can quickly test the model in the play ground 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 [inference](https://justhired.co.in) using guardrails with the deployed DeepSeek-R1 endpoint
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The following code example demonstrates how to carry out inference utilizing a deployed DeepSeek-R1 design through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can create a guardrail using the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo. After you have actually [developed](http://47.103.108.263000) the guardrail, use the following code to implement guardrails. The script initializes the bedrock_runtime client, configures reasoning criteria, and sends a demand to produce 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) center with FMs, integrated algorithms, and prebuilt ML options that you can deploy with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained designs 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 uses two hassle-free methods: [utilizing](https://git.progamma.com.ua) the instinctive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's explore both methods to help you select the technique that best matches your needs.
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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, choose Studio in the navigation pane. +2. First-time users will be prompted to produce a domain. +3. On the SageMaker Studio console, choose JumpStart in the navigation pane.
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The design web browser shows available designs, with details like the provider name and model capabilities.
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4. Look for DeepSeek-R1 to see the DeepSeek-R1 design card. +Each model card [reveals crucial](https://www.askmeclassifieds.com) details, including:
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- Model name +- Provider name +- Task classification (for instance, Text Generation). +Bedrock Ready badge (if suitable), showing that this design can be signed up with Amazon Bedrock, permitting you to utilize Amazon Bedrock APIs to invoke the design
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5. Choose the design card to see the model details page.
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The model details page includes 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 details
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The About tab consists of essential details, such as:
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- Model description. +- License details. +- Technical requirements. +[- Usage](https://gitlab.syncad.com) standards
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Before you release the design, it's suggested to review the [model details](https://smartcampus-seskoal.id) and license terms to confirm compatibility with your use case.
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6. Choose Deploy to proceed with implementation.
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7. For Endpoint name, use the instantly created name or produce a customized one. +8. For example type ΒΈ choose a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, go into the variety of instances (default: 1). +Selecting suitable circumstances types and counts is essential for expense and performance optimization. Monitor your implementation to adjust these settings as needed.Under Inference type, Real-time inference is picked by default. This is optimized for sustained traffic and low latency. +10. Review all configurations for accuracy. For this model, we strongly recommend 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 implementation procedure can take a number of minutes to finish.
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When release is complete, your endpoint status will alter to InService. At this moment, the design is all set to accept inference requests through the endpoint. You can keep track of the deployment development on the SageMaker console Endpoints page, which will display pertinent metrics and status details. When the release is total, [archmageriseswiki.com](http://archmageriseswiki.com/index.php/User:RMYCelsa2545331) you can conjure up the design utilizing a SageMaker runtime customer and incorporate 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 utilizing the SageMaker Python SDK, you will need to install 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 reasoning programmatically. The code for deploying the design is [offered](https://jobboat.co.uk) in the Github here. You can clone the note pad and range from SageMaker Studio.
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You can run additional 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 also utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a guardrail utilizing the Amazon Bedrock console or the API, and execute it as shown in the following code:
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Clean up
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To prevent undesirable charges, complete the steps in this area to tidy up your resources.
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Delete the Amazon Bedrock Marketplace deployment
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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 designs in the navigation pane, choose Marketplace deployments. +2. In the Managed deployments area, find the endpoint you wish to erase. +3. Select the endpoint, and on the Actions menu, choose Delete. +4. Verify the endpoint details to make certain you're erasing the proper 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 delete 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 checked out how you can access and deploy the DeepSeek-R1 model 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 designs, SageMaker JumpStart pretrained models, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting started 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://gitea.b54.co) [AI](https://3srecruitment.com.au) business construct ingenious solutions using AWS services and sped up calculate. Currently, he is concentrated on developing strategies for fine-tuning and [enhancing](https://git.ivran.ru) the inference performance of large language designs. In his leisure time, Vivek takes pleasure in treking, watching motion pictures, [wiki.eqoarevival.com](https://wiki.eqoarevival.com/index.php/User:BFRJesenia) and attempting different cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://kanjob.de) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His location of focus is AWS [AI](https://bug-bounty.firwal.com) 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://git.trov.ar) with the Third-Party Model Science group at AWS.
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Banu Nagasundaram leads product, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://git.logicp.ca) center. She is enthusiastic about building solutions that assist customers accelerate their [AI](http://116.62.118.242) journey and [engel-und-waisen.de](http://www.engel-und-waisen.de/index.php/Benutzer:EwanInnes84) unlock business worth.
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