commit 20f2ed2dbfc7a5b9c8f18ff50db21767bcf315ff Author: ojugemma421343 Date: Sat Apr 12 07:41:15 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..3af6ccc --- /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](https://thankguard.com) to reveal that DeepSeek R1 distilled Llama and Qwen models are available through Amazon Bedrock Marketplace and Amazon SageMaker [JumpStart](https://www.pkgovtjobz.site). With this launch, you can now deploy DeepSeek [AI](https://www.infinistation.com)'s first-generation frontier design, DeepSeek-R1, in addition to the distilled versions varying from 1.5 to 70 billion parameters to construct, experiment, and responsibly scale your generative [AI](http://g-friend.co.kr) ideas on AWS.
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In this post, we demonstrate how to start with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar steps to deploy the distilled variations of the models 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://retailjobacademy.com) that uses reinforcement discovering to improve reasoning capabilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. An essential distinguishing function is its reinforcement [learning](http://git.baige.me) (RL) action, which was utilized to [improve](https://golz.tv) the design's reactions beyond the basic pre-training and tweak procedure. By including RL, DeepSeek-R1 can adjust better to user feedback and objectives, eventually enhancing both significance and clarity. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) method, suggesting it's geared up to break down intricate queries and reason through them in a detailed manner. This guided reasoning procedure allows the design to produce more accurate, transparent, and detailed responses. This model integrates RL-based fine-tuning with CoT capabilities, aiming to produce structured actions while focusing on [interpretability](http://git.oksei.ru) and user [interaction](https://miggoo.com.br). With its wide-ranging capabilities DeepSeek-R1 has actually recorded the industry's attention as a [flexible text-generation](http://dndplacement.com) model that can be [incorporated](http://8.137.58.203000) into numerous workflows such as representatives, sensible thinking and information analysis tasks.
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DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion [parameters](https://src.strelnikov.xyz) in size. The [MoE architecture](https://cyltalentohumano.com) allows activation of 37 billion criteria, making it possible for efficient inference by routing inquiries to the most pertinent professional "clusters." This approach allows the model to specialize in different issue domains while maintaining general effectiveness. DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for inference. In this post, we will utilize an ml.p5e.48 xlarge instance to deploy the design. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs supplying 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](https://janhelp.co.in) 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, more effective designs to simulate the habits and thinking patterns of the bigger DeepSeek-R1 design, using it as a teacher design.
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You can release DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we recommend deploying this design with [guardrails](https://antoinegriezmannclub.com) in location. In this blog, we will utilize Amazon Bedrock Guardrails to introduce safeguards, prevent hazardous material, and assess models against key security requirements. At the time of writing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can produce several guardrails tailored to different use cases and apply them to the DeepSeek-R1 design, improving user experiences and standardizing safety controls across your generative [AI](http://ncdsource.kanghehealth.com) applications.
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Prerequisites
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To release the DeepSeek-R1 model, you need access to an ml.p5e circumstances. To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, [select Amazon](http://swwwwiki.coresv.net) SageMaker, and verify 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 request a limit boost, produce a limit increase demand and reach out to your [account](http://git.jcode.net) group.
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Because you will be deploying this design with Amazon Bedrock Guardrails, make certain you have the appropriate AWS Identity and Gain Access To Management (IAM) authorizations to use Amazon Bedrock Guardrails. For guidelines, see Set up approvals to use guardrails for [wiki.eqoarevival.com](https://wiki.eqoarevival.com/index.php/User:ClaudiaGartner9) material filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails allows you to introduce safeguards, avoid damaging material, and assess models against key security criteria. You can execute safety measures for the DeepSeek-R1 [model utilizing](https://vsbg.info) the Amazon Bedrock ApplyGuardrail API. This enables you to apply guardrails to assess user inputs and design actions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a guardrail using the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo.
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The general flow involves the following actions: First, the system [receives](http://124.70.58.2093000) an input for the design. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it's sent to the model for inference. After receiving the model's output, another guardrail check is applied. If the output passes this last check, it's returned as the outcome. However, if either the input or output is stepped in by the guardrail, a message is returned suggesting the nature of the intervention and whether it happened at the input or output phase. The examples showcased in the following areas demonstrate [inference utilizing](http://begild.top8418) this API.
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Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
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Amazon Bedrock Marketplace offers you access to over 100 popular, emerging, and [specialized foundation](http://sdongha.com) 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 catalog under Foundation designs in the navigation pane. +At the time of this post, you can use the InvokeModel API to conjure up the design. It doesn't support Converse APIs and other [Amazon Bedrock](https://axc.duckdns.org8091) [tooling](https://gitea.evo-labs.org). +2. Filter for DeepSeek as a [supplier](http://82.19.55.40443) and pick the DeepSeek-R1 design.
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The design detail page supplies necessary details about the model's abilities, rates structure, and implementation standards. You can discover detailed usage directions, [larsaluarna.se](http://www.larsaluarna.se/index.php/User:LonnaGeoghegan) including sample API calls and code snippets for integration. The design supports various text generation jobs, including content creation, code generation, and concern answering, utilizing its reinforcement finding out optimization and CoT thinking abilities. +The page also consists of implementation alternatives and licensing details to help you get going with DeepSeek-R1 in your applications. +3. To start using DeepSeek-R1, select Deploy.
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You will be prompted to set up the release details for DeepSeek-R1. The model ID will be pre-populated. +4. For Endpoint name, get in an endpoint name (in between 1-50 alphanumeric characters). +5. For Variety of instances, go into a variety of circumstances (in between 1-100). +6. For example type, pick your instance type. For optimum performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is recommended. +Optionally, you can set up innovative security and [infrastructure](https://yourgreendaily.com) settings, including virtual private cloud (VPC) networking, service role approvals, and encryption settings. For the majority of use cases, the default settings will work well. However, for production deployments, you might wish to examine these settings to line up with your organization's security and compliance requirements. +7. Choose Deploy to begin using the design.
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When the implementation is total, you can test DeepSeek-R1's abilities straight in the Amazon Bedrock play ground. +8. Choose Open in play ground to access an interactive user interface where you can experiment with various triggers and change model parameters like temperature and optimum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for ideal outcomes. For instance, material for inference.
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This is an exceptional way to [explore](http://202.90.141.173000) the model's thinking and text generation capabilities before incorporating it into your applications. The play area provides immediate feedback, helping you understand how the model reacts to different inputs and letting you fine-tune your triggers for optimal results.
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You can rapidly evaluate the design in the play area through the UI. However, to conjure up the released design programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
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Run reasoning utilizing guardrails with the [deployed](http://162.14.117.2343000) DeepSeek-R1 endpoint
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The following code example [demonstrates](https://zapinacz.pl) how to perform reasoning 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 produce the guardrail, see the GitHub repo. After you have actually produced the guardrail, utilize the following code to implement guardrails. The script initializes the bedrock_runtime client, sets up inference criteria, and sends out a request 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) hub with FMs, built-in algorithms, and prebuilt ML solutions that you can [release](https://gitlab.companywe.co.kr) with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your use case, with your data, and deploy them into production utilizing either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart uses 2 practical approaches: utilizing the instinctive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's check out both approaches to assist you select the method that finest matches your needs.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following actions to deploy DeepSeek-R1 using SageMaker JumpStart:
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1. On the SageMaker console, [trademarketclassifieds.com](https://trademarketclassifieds.com/user/profile/2944111) 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 [model internet](https://innovator24.com) browser shows available designs, with details like the supplier name and design capabilities.
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4. Look for DeepSeek-R1 to view the DeepSeek-R1 design 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 model can be [registered](http://194.67.86.1603100) with Amazon Bedrock, [wavedream.wiki](https://wavedream.wiki/index.php/User:MuoiQuezada) allowing you to use Amazon Bedrock APIs to conjure up the design
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5. Choose the design card to see the design details page.
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The design details page includes the following details:
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- The design name and provider details. +Deploy button to release 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. +- License details. +- Technical specifications. +- Usage guidelines
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Before you deploy the design, it's advised to review the design details and license terms to validate compatibility with your usage case.
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6. Choose Deploy to continue with release.
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7. For Endpoint name, use the automatically produced name or create a customized one. +8. For Instance type ΒΈ select a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, enter the number of circumstances (default: [surgiteams.com](https://surgiteams.com/index.php/User:CharlieTruman98) 1). +Selecting suitable instance types and counts is essential for expense and performance optimization. Monitor your [release](http://124.70.58.2093000) to adjust these settings as needed.Under Inference type, Real-time reasoning is picked by [default](http://185.5.54.226). This is enhanced for sustained traffic and low latency. +10. Review all configurations for accuracy. For this design, we strongly suggest sticking to SageMaker JumpStart default settings and making certain that network isolation remains in place. +11. Choose Deploy to release the design.
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The deployment procedure can take numerous minutes to complete.
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When implementation is total, your endpoint status will alter to InService. At this point, the design is prepared to accept inference demands through the endpoint. You can monitor the deployment development on the [SageMaker console](http://engineerring.net) Endpoints page, which will show pertinent metrics and status details. When the deployment is complete, you can conjure up the [design utilizing](http://ecoreal.kr) a SageMaker runtime client and integrate it with your applications.
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Deploy DeepSeek-R1 using the [SageMaker Python](https://git.elferos.keenetic.pro) SDK
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To get begun with DeepSeek-R1 using the SageMaker Python SDK, you will need to set up the SageMaker Python SDK and make certain you have the needed AWS approvals 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 deploying the design is offered in the Github here. You can clone the notebook and run from SageMaker Studio.
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You can run additional demands against the predictor:
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[Implement](http://59.37.167.938091) 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 develop a guardrail utilizing the Amazon Bedrock console or the API, and execute it as displayed in the following code:
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Tidy up
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To prevent unwanted charges, complete the actions in this area to tidy up your resources.
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Delete the Amazon Bedrock Marketplace implementation
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If you deployed the design utilizing Amazon Bedrock Marketplace, total the following steps:
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1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, pick Marketplace deployments. +2. In the [Managed implementations](https://gitea.qianking.xyz3443) area, locate the [endpoint](http://162.14.117.2343000) you wish to delete. +3. Select the endpoint, and on the Actions menu, pick Delete. +4. Verify the endpoint details to make certain you're deleting 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 erase 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 release the DeepSeek-R1 model utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get started. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, SageMaker JumpStart pretrained models, 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](http://revoltsoft.ru:3000) companies construct ingenious solutions utilizing AWS services and accelerated compute. Currently, he is concentrated on establishing methods for fine-tuning and optimizing the reasoning performance of big language designs. In his leisure time, Vivek delights in hiking, seeing movies, [higgledy-piggledy.xyz](https://higgledy-piggledy.xyz/index.php/User:SamualGuillen) and trying various foods.
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Niithiyn Vijeaswaran is a Generative [AI](https://localjobs.co.in) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His area of focus is AWS [AI](https://jobstoapply.com) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.
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Jonathan Evans is an Expert Solutions Architect dealing with generative [AI](https://sansaadhan.ipistisdemo.com) with the Third-Party Model Science team 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://www.shopes.nl) center. She is passionate about constructing services that assist clients accelerate their [AI](https://menfucks.com) journey and unlock service worth.
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