From 7eb1e3b23f6ea24190c2a8d0db47cd99d2d7fad5 Mon Sep 17 00:00:00 2001 From: beckycurtsinge Date: Fri, 11 Apr 2025 10:24:43 +0800 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..f79da0e --- /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 reveal that [DeepSeek](https://cn.wejob.info) R1 distilled Llama and Qwen designs are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](https://akrs.ae)'s first-generation frontier model, DeepSeek-R1, along with the distilled versions varying from 1.5 to 70 billion parameters to build, experiment, and properly scale your generative [AI](http://www.gbape.com) concepts on AWS.
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In this post, we demonstrate how to get going with DeepSeek-R1 on [Amazon Bedrock](https://cozwo.com) Marketplace and SageMaker JumpStart. You can follow similar steps to deploy the distilled variations of the models too.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a large language model (LLM) developed by DeepSeek [AI](https://social.mirrororg.com) that uses reinforcement discovering to enhance thinking capabilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. A key differentiating feature is its [support learning](https://hankukenergy.kr) (RL) step, which was [utilized](http://xn--950bz9nf3c8tlxibsy9a.com) to refine the design's reactions beyond the standard pre-training and fine-tuning procedure. By incorporating RL, DeepSeek-R1 can adapt better to user feedback and objectives, ultimately improving both significance and clarity. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) approach, indicating it's geared up to break down intricate questions and reason through them in a detailed manner. This directed thinking process permits the model to produce more precise, transparent, and detailed responses. This design integrates RL-based fine-tuning with CoT capabilities, aiming to produce structured responses while concentrating on interpretability and user interaction. With its [extensive capabilities](https://www.frigorista.org) DeepSeek-R1 has caught the industry's attention as a flexible text-generation model that can be integrated into various workflows such as agents, logical thinking and information interpretation tasks.
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DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture enables activation of 37 billion parameters, making it possible for effective reasoning by routing inquiries to the most appropriate professional "clusters." This method allows the model to concentrate on various issue domains while maintaining total effectiveness. 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 instance to deploy the design. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.
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DeepSeek-R1 distilled designs bring the reasoning capabilities of the main R1 design to more efficient 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 effective models to simulate the behavior and thinking patterns of the larger DeepSeek-R1 design, utilizing 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 releasing this model with guardrails in location. In this blog site, we will use Amazon Bedrock Guardrails to present safeguards, avoid harmful content, and assess designs against essential safety criteria. At the time of composing this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can produce multiple guardrails tailored to different usage cases and use them to the DeepSeek-R1 design, improving user experiences and standardizing safety [controls](https://miggoo.com.br) throughout your generative [AI](http://globalnursingcareers.com) applications.
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Prerequisites
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To release the DeepSeek-R1 model, you require access to an ml.p5e circumstances. To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick 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 circumstances in the AWS Region you are releasing. To request a limitation increase, create a limit increase 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 right AWS Identity and Gain Access To Management (IAM) consents to utilize Amazon Bedrock Guardrails. For directions, see Set up consents to utilize guardrails for content filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails permits you to present safeguards, prevent [hazardous](http://94.224.160.697990) content, and examine models against essential security requirements. You can carry out safety procedures for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This enables you to apply guardrails to evaluate user inputs and model reactions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can create a guardrail using the Amazon Bedrock console or the API. For [hb9lc.org](https://www.hb9lc.org/wiki/index.php/User:HeatherBurbach6) the example code to create 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](http://106.14.65.137) through the ApplyGuardrail API. If the input passes the guardrail check, it's sent to the model for reasoning. After getting the design's output, another [guardrail check](http://8.211.134.2499000) is used. If the output passes this final check, it's returned as the last outcome. However, if either the input or output is intervened by the guardrail, a message is returned showing the nature of the intervention and whether it took place 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](http://47.104.60.1587777) Marketplace
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Amazon [Bedrock Marketplace](https://cinetaigia.com) offers you access to over 100 popular, emerging, and specialized foundation designs (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 catalog under Foundation designs in the navigation pane. +At the time of composing this post, you can utilize the InvokeModel API to invoke the design. It doesn't support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a service provider and pick the DeepSeek-R1 design.
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The design detail page offers important details about the [design's](https://findschools.worldofdentistry.org) capabilities, rates structure, and execution standards. You can [discover](http://charmjoeun.com) detailed usage directions, including sample API calls and code snippets for [combination](https://www.shopes.nl). The design supports numerous text generation jobs, including content creation, code generation, and concern answering, utilizing its reinforcement learning optimization and CoT thinking capabilities. +The page likewise consists of deployment options and licensing details to help you get started 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, go into an [endpoint](https://body-positivity.org) name (between 1-50 alphanumeric characters). +5. For Number of circumstances, go into a variety of instances (in between 1-100). +6. For example type, choose your instance type. For optimal performance with DeepSeek-R1, a [GPU-based circumstances](http://wiki.lexserve.co.ke) 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](https://bytes-the-dust.com) consents, and file encryption settings. For many utilize cases, the default settings will work well. However, for production implementations, you might wish to examine these settings to line up with your organization's security and compliance [requirements](https://goodinfriends.com). +7. Choose Deploy to start using the model.
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When the release is complete, you can evaluate DeepSeek-R1's capabilities straight in the Amazon Bedrock play area. +8. Choose Open in playground to access an interactive user interface where you can explore different triggers and adjust design criteria like temperature level and maximum length. +When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for ideal outcomes. For example, material for reasoning.
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This is an excellent way to check out the [design's reasoning](http://93.104.210.1003000) and text generation abilities before integrating it into your applications. The [playground](http://101.132.100.8) offers immediate feedback, assisting you understand how the [design reacts](https://39.98.119.14) to different inputs and letting you fine-tune your triggers for ideal outcomes.
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You can rapidly evaluate the model in the playground through the UI. However, to invoke the deployed design programmatically with any Amazon Bedrock APIs, you need to get the [endpoint](https://asesordocente.com) ARN.
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Run reasoning utilizing guardrails with the deployed DeepSeek-R1 endpoint
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The following code example demonstrates how to perform inference using a deployed DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can create a [guardrail utilizing](http://gitlab.hanhezy.com) the Amazon Bedrock [console](https://oeclub.org) or the API. For the example code to produce the guardrail, see the GitHub repo. After you have actually produced the guardrail, use the following code to carry out guardrails. The [script initializes](https://accc.rcec.sinica.edu.tw) the bedrock_runtime customer, sets up reasoning criteria, and sends a demand to generate text based upon 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 services that you can deploy with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use case, with your information, and deploy them into production utilizing either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart offers 2 practical methods: utilizing the user-friendly SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's explore both techniques to assist you choose the method that best matches your requirements.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following actions 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 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 company name and design capabilities.
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4. Look for DeepSeek-R1 to view the DeepSeek-R1 model card. +Each design card shows essential 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 design can be signed up with Amazon Bedrock, enabling you to use Amazon Bedrock APIs to conjure up the design
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5. Choose the design 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 model name and provider details. +Deploy button to deploy the model. +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 suggested to evaluate 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 instantly generated name or develop a custom-made one. +8. For Instance type ΒΈ pick a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial instance count, go into the variety of [circumstances](https://git.newpattern.net) (default: 1). +Selecting proper circumstances types and counts is essential for cost and performance optimization. Monitor your deployment to adjust these settings as needed.Under Inference type, Real-time reasoning is selected by default. This is optimized for sustained traffic and low latency. +10. Review all setups for precision. For this design, we [highly advise](https://www.ynxbd.cn8888) adhering to SageMaker JumpStart default settings and making certain that network isolation remains in location. +11. Choose Deploy to release the design.
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The release procedure can take a number of minutes to complete.
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When deployment is complete, your endpoint status will alter to InService. At this moment, the model is all set to [accept reasoning](https://repo.correlibre.org) requests through the endpoint. You can keep an eye on the implementation development on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the implementation is complete, you can conjure up 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 utilizing the SageMaker Python SDK, you will need to set up the SageMaker Python SDK and make certain you have the essential AWS [authorizations](https://esvoe.video) and environment setup. The following is a detailed code example that shows how to release and utilize DeepSeek-R1 for reasoning programmatically. The code for deploying 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 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 likewise use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a guardrail using the Amazon Bedrock console or the API, and execute it as in the following code:
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Clean up
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To prevent unwanted charges, complete the actions in this section to tidy up your resources.
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Delete the Amazon Bedrock Marketplace implementation
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If you deployed the model utilizing Amazon Bedrock Marketplace, complete the following steps:
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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 desire to delete. +3. Select the endpoint, and on the Actions menu, pick Delete. +4. Verify the endpoint details to make certain you're erasing 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 model](https://c-hireepersonnel.com) you deployed will sustain costs 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 explored 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 start. For more details, refer to 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 at AWS. He helps emerging generative [AI](https://www.beyoncetube.com) companies build innovative services using AWS services and [accelerated](https://moyatcareers.co.ke) compute. Currently, he is concentrated on developing techniques for fine-tuning and optimizing the inference efficiency of big language designs. In his downtime, Vivek enjoys treking, enjoying movies, and [attempting](https://eukariyer.net) various [cuisines](https://placementug.com).
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Niithiyn Vijeaswaran is a Generative [AI](http://111.53.130.194:3000) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His location of focus is AWS [AI](https://git.collincahill.dev) [accelerators](https://nodlik.com) (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
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Jonathan Evans is an Expert Solutions Architect dealing with generative [AI](http://durfee.mycrestron.com:3000) with the Third-Party Model Science team at AWS.
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Banu Nagasundaram leads product, engineering, and tactical partnerships for [Amazon SageMaker](http://8.211.134.2499000) JumpStart, SageMaker's artificial intelligence and generative [AI](https://testgitea.cldevops.de) center. She is passionate about [developing options](http://lohashanji.com) that help clients accelerate their [AI](http://kuzeydogu.ogo.org.tr) journey and unlock organization value.
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