From c457ac39eab430c01929fe17c1cecee205ab9cc7 Mon Sep 17 00:00:00 2001 From: denesemccormac Date: Fri, 4 Apr 2025 11:04:02 -0400 Subject: [PATCH] Add DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart --- ...tplace 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..a191caf --- /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 [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11857434) Qwen models are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](https://git.xaviermaso.com)'s first-generation frontier model, DeepSeek-R1, along with the distilled variations ranging from 1.5 to 70 billion criteria to develop, experiment, and responsibly scale your generative [AI](https://gogs.k4be.pl) ideas on AWS.
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In this post, we demonstrate how to get started with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar steps to release the distilled variations of the designs also.
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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://splink24.com) that uses support discovering to boost thinking abilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. A crucial identifying function is its support knowing (RL) step, which was utilized to fine-tune the design's actions beyond the basic pre-training and fine-tuning process. By including RL, DeepSeek-R1 can adjust more successfully to user feedback and objectives, ultimately improving both relevance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) approach, [implying](https://findgovtsjob.com) it's equipped to break down complicated queries and reason through them in a detailed manner. This guided thinking process allows the design to produce more accurate, transparent, and detailed answers. This model combines RL-based fine-tuning with CoT abilities, aiming to produce structured reactions while focusing on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has actually captured the market's attention as a flexible text-generation model that can be integrated into numerous workflows such as representatives, logical reasoning and data analysis jobs.
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DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture permits activation of 37 billion specifications, making it possible for efficient inference by [routing inquiries](http://47.101.46.1243000) to the most appropriate specialist "clusters." This approach permits the model to focus on various problem domains while maintaining general efficiency. DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for inference. In this post, we will [utilize](http://www.hakyoun.co.kr) an ml.p5e.48 xlarge instance to deploy the model. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.
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DeepSeek-R1 distilled designs bring the thinking abilities of the main R1 model to more effective architectures based upon popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of [training](http://101.231.37.1708087) smaller sized, more efficient designs to simulate the behavior and reasoning patterns of the larger DeepSeek-R1 model, using it as a [teacher model](http://185.5.54.226).
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You can release DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we suggest releasing this design with guardrails in place. In this blog, we will utilize Amazon Bedrock Guardrails to introduce safeguards, avoid hazardous content, and evaluate models against essential security criteria. At the time of composing this blog, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can create multiple guardrails tailored to various use cases and apply them to the DeepSeek-R1 design, improving user experiences and standardizing security controls throughout your generative [AI](http://mirae.jdtsolution.kr) applications.
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Prerequisites
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To deploy the DeepSeek-R1 design, 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, choose Amazon SageMaker, and confirm 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 deploying. To request a limit increase, [develop](https://c3tservices.ca) a limit increase request and reach out to your account group.
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Because you will be releasing this model with Amazon Bedrock Guardrails, make certain you have the proper AWS Identity and Gain Access To Management (IAM) authorizations to use Amazon Bedrock Guardrails. For instructions, see Establish approvals to [utilize](https://git.yinas.cn) guardrails for material filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails permits you to introduce safeguards, prevent hazardous material, and assess designs against essential security requirements. You can execute precaution for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to use guardrails to examine user inputs and model responses deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. 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.
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The basic flow includes the following steps: First, the system gets an input for the design. This input is then processed through the [ApplyGuardrail API](https://u-hired.com). If the input passes the [guardrail](http://supervipshop.net) check, it's sent to the design for [yewiki.org](https://www.yewiki.org/User:MarjorieBalcombe) reasoning. After getting the design's output, another guardrail check is [applied](http://www.szkis.cn13000). If the output passes this last check, it's returned as the outcome. However, [ratemywifey.com](https://ratemywifey.com/author/felishawatk/) if either the input or output is intervened by the guardrail, a message is returned showing the nature of the intervention and whether it [occurred](http://euhope.com) at the input or output stage. The examples showcased in the following areas demonstrate 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 foundation models (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, pick Model brochure under Foundation models in the navigation pane. +At the time of composing this post, you can utilize the InvokeModel API to conjure up the model. It doesn't support Converse APIs and other Amazon Bedrock [tooling](https://pojelaime.net). +2. Filter for DeepSeek as a [company](http://git.yoho.cn) and choose the DeepSeek-R1 design.
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The design detail page offers essential details about the design's capabilities, pricing structure, and implementation standards. You can discover detailed usage instructions, [larsaluarna.se](http://www.larsaluarna.se/index.php/User:IIANilda52808516) including sample API calls and code bits for integration. The model supports numerous [text generation](https://wacari-git.ru) tasks, consisting of material production, code generation, and question answering, utilizing its support discovering optimization and CoT reasoning capabilities. +The page also includes release choices and licensing details to help you begin with DeepSeek-R1 in your applications. +3. To start utilizing DeepSeek-R1, select Deploy.
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You will be [prompted](https://dlya-nas.com) to set up the release details for DeepSeek-R1. The design ID will be pre-populated. +4. For Endpoint name, [archmageriseswiki.com](http://archmageriseswiki.com/index.php/User:MEFMaura6288295) enter an endpoint name (between 1-50 alphanumeric characters). +5. For Variety of circumstances, get in a variety of instances (between 1-100). +6. For Instance type, choose your instance type. For optimum performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is [advised](https://gitea.ravianand.me). +Optionally, you can configure advanced security and [infrastructure](https://kigalilife.co.rw) settings, consisting of virtual private cloud (VPC) networking, service role consents, and file encryption settings. For the [majority](https://pakkjob.com) of utilize cases, the default settings will work well. However, for production deployments, you may wish to review these settings to line up with your organization's security and compliance requirements. +7. [Choose Deploy](https://gantnews.com) to begin using the model.
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When the release is complete, you can check DeepSeek-R1's abilities straight in the Amazon Bedrock playground. +8. Choose Open in play ground to access an interactive interface where you can explore different triggers and change design specifications like temperature level and optimum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimum results. For example, material for inference.
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This is an excellent method to check out the model's thinking and text generation capabilities before integrating it into your applications. The playground supplies instant feedback, assisting you comprehend how the model responds to various inputs and letting you tweak your prompts for ideal outcomes.
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You can rapidly test the design in the playground through the UI. However, to invoke the deployed model programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
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Run reasoning using guardrails with the deployed DeepSeek-R1 endpoint
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The following code example shows how to carry out inference using 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 develop the guardrail, see the GitHub repo. After you have produced the guardrail, utilize the following code to carry out guardrails. The script initializes the bedrock_runtime client, configures inference specifications, and sends out a request to create 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) hub with FMs, integrated algorithms, and prebuilt ML options that you can release with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage case, with your data, and deploy them into [production utilizing](http://www.jacksonhampton.com3000) either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart offers two hassle-free techniques: utilizing the user-friendly SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's check out both approaches to help you pick the technique that best fits your requirements.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following steps to deploy DeepSeek-R1 using SageMaker JumpStart:
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1. On the SageMaker console, pick Studio in the navigation pane. +2. First-time users will be triggered to [produce](https://givebackabroad.org) a domain. +3. On the SageMaker Studio console, select JumpStart in the navigation pane.
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The model internet browser [displays](https://www.oddmate.com) available designs, with details like the supplier name and design capabilities.
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4. Look for DeepSeek-R1 to see the DeepSeek-R1 model card. +Each design card reveals key details, consisting of:
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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 signed up with Amazon Bedrock, [wakewiki.de](https://www.wakewiki.de/index.php?title=Benutzer:WinstonPreece9) permitting you to use Amazon Bedrock APIs to conjure up the model
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5. Choose the design card to see 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 service provider details. +Deploy button to deploy the design. +About and Notebooks tabs with detailed details
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The About tab includes essential details, such as:
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- Model description. +- License details. +- Technical [specifications](https://89.22.113.100). +- Usage guidelines
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Before you deploy the design, it's suggested to examine the model details and license terms to [validate compatibility](http://47.110.52.1323000) with your use case.
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6. Choose Deploy to continue with release.
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7. For Endpoint name, use the immediately created name or create a custom-made one. +8. For example type ΒΈ pick a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, get in the number of circumstances (default: 1). +Selecting proper [circumstances](https://myafritube.com) types and counts is vital for expense and efficiency optimization. Monitor your implementation 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 accuracy. For this design, we highly suggest sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in place. +11. Choose Deploy to deploy the design.
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The deployment procedure can take several minutes to finish.
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When implementation is complete, your endpoint status will change to InService. At this moment, the design is ready to accept inference requests through the endpoint. You can monitor the release progress on the SageMaker console Endpoints page, which will show relevant metrics and status details. When the deployment is complete, you can invoke the model 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 begin with DeepSeek-R1 using the SageMaker Python SDK, you will need to install the SageMaker Python SDK and make certain you have the required AWS permissions and environment setup. The following is a detailed code example that shows how to deploy and utilize DeepSeek-R1 for reasoning programmatically. The code for releasing the design is [supplied](https://social.acadri.org) in the Github here. You can clone the note pad and range from SageMaker Studio.
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You can run additional demands 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 utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can develop 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 unwanted charges, finish the steps 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 actions:
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1. On the Amazon Bedrock console, under in the navigation pane, pick Marketplace deployments. +2. In the Managed releases area, find the [endpoint](http://tesma.co.kr) you want to delete. +3. Select the endpoint, and on the Actions menu, choose Delete. +4. Verify the endpoint details to make certain you're erasing the correct 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 released will sustain expenses if you leave it running. Use the following code to delete the endpoint if you desire 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 using Bedrock Marketplace and SageMaker JumpStart. [Visit SageMaker](https://www.olsitec.de) JumpStart in SageMaker Studio or Amazon Bedrock [Marketplace](https://connectworld.app) now to get begun. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker [JumpStart](https://www.jangsuori.com) models, SageMaker JumpStart pretrained designs, 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 [AI](https://jimsusefultools.com) business construct innovative solutions using AWS services and accelerated compute. Currently, he is focused on establishing methods for fine-tuning and enhancing the reasoning efficiency of big language models. In his spare time, Vivek takes pleasure in hiking, enjoying motion pictures, and trying various cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://happylife1004.co.kr) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His area of focus is AWS [AI](http://gitlab.ideabeans.myds.me:30000) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
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Jonathan Evans is a Specialist Solutions Architect dealing with generative [AI](http://git.oksei.ru) 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](http://dev.zenith.sh.cn) center. She is enthusiastic about developing solutions that help consumers accelerate their [AI](https://code.paperxp.com) journey and unlock service worth.
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