From 0c8034671cccd4c6d3efdcc00db0393eb28aa424 Mon Sep 17 00:00:00 2001 From: thaliajudkins Date: Thu, 10 Apr 2025 02:45:22 +0000 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..922bada --- /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 announce that [DeepSeek](http://jobjungle.co.za) 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](http://124.220.187.142:3000)'s first-generation frontier design, DeepSeek-R1, in addition to the distilled variations varying from 1.5 to 70 billion criteria to develop, experiment, and properly scale your generative [AI](https://sb.mangird.com) concepts on AWS.
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In this post, we show how to begin with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable steps 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 model (LLM) developed by DeepSeek [AI](https://socipops.com) that utilizes reinforcement finding out to enhance thinking abilities through a multi-stage training procedure from a DeepSeek-V3[-Base structure](http://xiaomu-student.xuetangx.com). A key differentiating function is its reinforcement knowing (RL) action, which was [utilized](https://sttimothysignal.org) to improve the design's responses beyond the basic pre-training and tweak procedure. By [incorporating](https://gitlab.healthcare-inc.com) RL, DeepSeek-R1 can adapt better to user feedback and objectives, eventually improving both significance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) method, meaning it's geared up to break down complex questions and reason through them in a detailed way. This directed thinking process enables the model to produce more precise, transparent, and detailed answers. This design integrates RL-based fine-tuning with CoT abilities, aiming to create [structured responses](https://jp.harmonymart.in) while [concentrating](https://jamboz.com) on interpretability and user interaction. With its comprehensive abilities DeepSeek-R1 has caught the industry's attention as a flexible text-generation design that can be incorporated into [numerous workflows](https://gogs.jublot.com) such as agents, rational reasoning and information interpretation jobs.
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DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture enables activation of 37 billion parameters, enabling effective reasoning by [routing questions](https://equijob.de) to the most appropriate expert "clusters." This technique permits the design to concentrate on various issue domains while maintaining general effectiveness. DeepSeek-R1 requires a minimum of 800 GB of HBM memory in FP8 format for inference. In this post, we will use an ml.p5e.48 xlarge instance to deploy the model. ml.p5e.48 [xlarge features](https://www.homebasework.net) 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.
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DeepSeek-R1 distilled models 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 process of training smaller sized, more effective models to simulate the [behavior](https://zeroth.one) and reasoning patterns of the bigger DeepSeek-R1 model, using it as an instructor [pediascape.science](https://pediascape.science/wiki/User:Mazie58C75) 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 recommend deploying this design with guardrails in [location](https://oliszerver.hu8010). In this blog site, we will use Amazon Bedrock Guardrails to introduce safeguards, avoid damaging material, and [examine models](https://myafritube.com) against key security requirements. At the time of writing this blog, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can produce several guardrails tailored to various usage cases and use them to the DeepSeek-R1 model, enhancing user experiences and standardizing safety controls across your generative [AI](http://gitlab.pakgon.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. To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and verify you're using ml.p5e.48 xlarge for endpoint use. Make certain that you have at least one ml.P5e.48 xlarge instance in the AWS Region you are releasing. To ask for a limitation boost, develop a limit boost request and reach out to your account team.
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Because you will be releasing this design with Amazon Bedrock Guardrails, make certain you have the proper AWS Identity and Gain Access To Management (IAM) consents to use Amazon Bedrock Guardrails. For directions, see Establish authorizations 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, avoid hazardous material, and evaluate models against key security requirements. You can implement precaution for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to evaluate user inputs and design responses deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can develop a [guardrail utilizing](http://120.48.7.2503000) the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo.
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The general flow involves 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 getting the model's output, another guardrail check is used. If the output passes this final check, it's returned as the outcome. However, if either the input or output is stepped in by the guardrail, a message is returned indicating the nature of the intervention and whether it happened at the input or output phase. The examples showcased in the following areas show inference 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 foundation designs (FMs) through Amazon Bedrock. 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 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 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 model.
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The design detail page supplies vital details about the design's abilities, rates structure, and execution standards. You can discover detailed usage guidelines, including sample API calls and code snippets for combination. The design supports different text generation tasks, including material production, code generation, and concern answering, utilizing its reinforcement discovering optimization and CoT thinking abilities. +The page also [consists](https://www.jobassembly.com) of implementation options and licensing details to help you begin with DeepSeek-R1 in your applications. +3. To start using DeepSeek-R1, pick Deploy.
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You will be prompted to configure the deployment details for DeepSeek-R1. The model ID will be pre-populated. +4. For Endpoint name, enter an endpoint name (between 1-50 alphanumeric characters). +5. For Number of circumstances, get in a variety of circumstances (in between 1-100). +6. For Instance type, select your [circumstances type](http://gitlab.gomoretech.com). For optimal performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is recommended. +Optionally, you can configure innovative security and facilities settings, including virtual personal cloud (VPC) networking, service function authorizations, and file encryption settings. For most use cases, the default settings will work well. However, for production releases, you might wish to review these settings to align with your company's security and compliance requirements. +7. Choose Deploy to start utilizing the model.
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When the deployment is complete, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock playground. +8. Choose Open in playground to access an interactive user interface where you can explore various prompts and change design parameters like temperature and optimum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for optimum results. For instance, material for reasoning.
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This is an excellent way to explore the model's reasoning and text generation capabilities before integrating it into your applications. The playground offers instant feedback, helping you comprehend how the design reacts to numerous inputs and letting you tweak your prompts for optimal outcomes.
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You can quickly evaluate the design in the playground through the UI. However, to invoke the deployed design programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
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Run inference using guardrails with the deployed DeepSeek-R1 endpoint
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The following code example demonstrates how to perform reasoning utilizing a deployed DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can produce a guardrail using the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo. After you have [developed](http://git.jzcure.com3000) the guardrail, use the following code to carry out [guardrails](http://secretour.xyz). The script initializes the bedrock_runtime client, configures reasoning parameters, and sends out a request to [produce text](https://viraltry.com) 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 solutions that you can release with simply a couple of clicks. With SageMaker JumpStart, you can [tailor pre-trained](http://geoje-badapension.com) models to your use case, with your information, and deploy them into production using either the UI or SDK.
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Deploying DeepSeek-R1 design through SageMaker JumpStart provides 2 practical approaches: using the intuitive SageMaker JumpStart UI or carrying out programmatically through the [SageMaker Python](https://git.mintmuse.com) SDK. Let's check out both [techniques](http://ncdsource.kanghehealth.com) to help you pick the approach that best matches your needs.
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Deploy DeepSeek-R1 through [SageMaker JumpStart](https://vlabs.synology.me45) UI
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Complete the following steps to deploy DeepSeek-R1 utilizing SageMaker JumpStart:
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1. On the SageMaker console, select Studio in the [navigation](https://vishwakarmacommunity.org) 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 design web [browser](https://epcblind.org) displays 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](http://111.160.87.828004). +Each design card shows key details, including:
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- Model name +- Provider name +- Task category (for example, Text Generation). +Bedrock Ready badge (if appropriate), showing that this design can be registered with Amazon Bedrock, permitting you to utilize Amazon Bedrock APIs to conjure up the design
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5. Choose the design card to view the design details page.
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The [model details](https://social.japrime.id) 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 important details, such as:
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- Model [description](https://www.themart.co.kr). +- License details. +- Technical [specifications](https://git.elferos.keenetic.pro). +- Usage standards
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Before you release the design, it's suggested to evaluate the design details and license terms to validate compatibility with your use case.
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6. Choose Deploy to [continue](https://wiki.team-glisto.com) with release.
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7. For Endpoint name, utilize 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, enter the variety of circumstances (default: 1). +Selecting appropriate circumstances types and counts is important for cost and efficiency optimization. [Monitor](https://src.enesda.com) your implementation to change these settings as needed.Under Inference type, Real-time inference is picked by default. This is enhanced for sustained traffic and low latency. +10. Review all setups for accuracy. For this design, we highly advise adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in location. +11. Choose Deploy to release the design.
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The implementation process can take several minutes to complete.
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When deployment is total, your endpoint status will change to InService. At this moment, the model is all set to accept inference requests through the endpoint. You can keep track of the implementation progress on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the [implementation](http://www.stes.tyc.edu.tw) is total, you can [conjure](https://b52cum.com) 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 get started with DeepSeek-R1 [utilizing](https://pioneercampus.ac.in) the SageMaker Python SDK, you will need to install the SageMaker Python SDK and make certain you have the required AWS consents and environment setup. The following is a detailed code example that shows how to release and use DeepSeek-R1 for reasoning 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 additional 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 also use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can create a guardrail using the Amazon Bedrock console or the API, and execute it as shown in the following code:
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Tidy up
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To avoid unwanted charges, complete the steps in this area to clean up your resources.
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Delete the Amazon Bedrock Marketplace deployment
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If you released the design 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 [releases](https://git.pandaminer.com). +2. In the Managed releases section, find 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 release: 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 want to stop sustaining charges. For more details, see Delete Endpoints and [Resources](http://178.44.118.232).
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Conclusion
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In this post, we explored how you can access and release the DeepSeek-R1 model utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon now to get started. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained models, 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://storymaps.nhmc.uoc.gr) business construct ingenious solutions utilizing AWS services and accelerated compute. Currently, he is concentrated on developing methods for fine-tuning and enhancing the inference efficiency of large language designs. In his downtime, Vivek delights in treking, viewing films, and attempting different cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](http://wiki-tb-service.com) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His location of focus is AWS [AI](http://famedoot.in) [accelerators](http://bluemobile010.com) (AWS Neuron). He holds a Bachelor's degree in Computer [Science](https://coolroomchannel.com) and Bioinformatics.
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Jonathan Evans is a Specialist Solutions Architect dealing with generative [AI](https://meetpit.com) with the Third-Party Model Science team at AWS.
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Banu Nagasundaram leads item, engineering, and strategic collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://ezworkers.com) hub. She is enthusiastic about building services that assist clients accelerate their [AI](https://www.lakarjobbisverige.se) journey and unlock company worth.
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