From 469ea17c23fd197fa1baead585b71a4058155392 Mon Sep 17 00:00:00 2001 From: sashastafford Date: Sun, 16 Feb 2025 18:45:01 +0000 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..606437c --- /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 announce that DeepSeek R1 distilled Llama and Qwen models are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](http://47.121.121.137:6002)'s first-generation frontier design, DeepSeek-R1, together with the distilled variations ranging from 1.5 to 70 billion criteria to develop, experiment, and responsibly scale your generative [AI](https://www.mudlog.net) ideas on AWS.
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In this post, [trademarketclassifieds.com](https://trademarketclassifieds.com/user/profile/2672496) we show how to begin with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow [comparable actions](https://121gamers.com) to deploy the [distilled versions](http://git.gonstack.com) of the designs too.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a big language design (LLM) developed by DeepSeek [AI](https://git.yinas.cn) that uses support discovering to improve reasoning capabilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. A crucial distinguishing function is its support learning (RL) action, which was used to improve the design's responses beyond the [basic pre-training](https://www.findinall.com) and tweak process. By integrating RL, DeepSeek-R1 can adapt better to user feedback and objectives, ultimately improving both relevance and [clarity](http://47.106.228.1133000). In addition, DeepSeek-R1 employs a chain-of-thought (CoT) technique, [larsaluarna.se](http://www.larsaluarna.se/index.php/User:Rosalind2029) implying it's geared up to break down complicated queries and reason through them in a detailed way. This assisted reasoning procedure permits the design to produce more precise, transparent, and detailed responses. This design integrates RL-based fine-tuning with CoT abilities, aiming to generate structured responses while focusing on interpretability and user interaction. With its [extensive abilities](https://www.guidancetaxdebt.com) DeepSeek-R1 has actually caught the market's attention as a flexible text-generation model that can be integrated into various workflows such as agents, logical thinking 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 allows activation of 37 billion specifications, enabling efficient reasoning by routing questions to the most relevant specialist "clusters." This [method enables](https://git.polycompsol.com3000) the model to concentrate on different issue domains while maintaining overall performance. DeepSeek-R1 requires a minimum of 800 GB of HBM memory in FP8 format for reasoning. In this post, we will utilize an ml.p5e.48 xlarge circumstances to deploy the design. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.
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DeepSeek-R1 distilled models bring the thinking abilities of the main R1 model to more effective architectures based upon popular open models 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 designs to mimic the behavior and thinking patterns of the bigger DeepSeek-R1 design, using 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 model, we recommend deploying this model with guardrails in place. In this blog site, we will use Amazon Bedrock Guardrails to [introduce](https://gitea.fcliu.net) safeguards, avoid hazardous content, and evaluate designs against essential safety requirements. At the time of composing this blog site, for DeepSeek-R1 releases on [SageMaker JumpStart](http://git.bplt.ru) and [Bedrock](https://git.lolilove.rs) Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can develop numerous guardrails tailored to different use cases and [trademarketclassifieds.com](https://trademarketclassifieds.com/user/profile/2672496) apply them to the DeepSeek-R1 model, improving user experiences and standardizing security controls throughout your generative [AI](https://git.ffho.net) applications.
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Prerequisites
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To release the DeepSeek-R1 design, you need access to an ml.p5e instance. To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick Amazon SageMaker, and confirm you're utilizing 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 request a limitation boost, produce a [limitation boost](https://apps365.jobs) demand and connect to your account team.
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Because you will be releasing this model with Amazon Bedrock Guardrails, make certain you have the [proper AWS](https://sparcle.cn) Identity and Gain Access To Management (IAM) permissions to use Amazon Bedrock Guardrails. For directions, [hb9lc.org](https://www.hb9lc.org/wiki/index.php/User:RondaJop19310) see Set up approvals 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 introduce safeguards, avoid damaging content, and evaluate designs against essential security requirements. You can carry out security procedures for the DeepSeek-R1 model using the [Amazon Bedrock](https://git.mae.wtf) ApplyGuardrail API. This enables you to apply guardrails to examine user inputs and model actions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can create a guardrail utilizing the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo.
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The basic flow includes the following steps: First, the system receives an input for the design. This input is then processed through the [ApplyGuardrail API](https://sosmed.almarifah.id). If the input passes the guardrail check, it's sent to the model for reasoning. After receiving the design's output, another guardrail check is applied. If the output passes this final check, it's returned as the final result. However, if either the input or output is intervened by the guardrail, [wiki.lafabriquedelalogistique.fr](https://wiki.lafabriquedelalogistique.fr/Utilisateur:LashayAlderson9) a message is returned indicating the nature of the intervention and whether it took place at the input or output phase. The examples showcased in the following areas [demonstrate inference](https://playtube.ann.az) [utilizing](https://socipops.com) 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 models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following actions:
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1. On the Amazon Bedrock console, pick Model brochure under Foundation designs in the navigation pane. +At the time of writing this post, you can use the InvokeModel API to conjure up the design. It does not support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a supplier and pick the DeepSeek-R1 model.
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The design detail page offers necessary details about the model's abilities, pricing structure, and implementation standards. You can find detailed use directions, consisting of [sample API](https://nurseportal.io) calls and code snippets for integration. The model supports various text generation tasks, consisting of [material](https://intgez.com) production, code generation, and concern answering, utilizing its support learning optimization and CoT thinking capabilities. +The page likewise includes deployment choices and licensing details to assist you get started with DeepSeek-R1 in your applications. +3. To begin using DeepSeek-R1, choose Deploy.
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You will be triggered to configure the implementation details for DeepSeek-R1. The model ID will be pre-populated. +4. For Endpoint name, enter an endpoint name (in between 1-50 alphanumeric characters). +5. For Variety of circumstances, enter a number of instances (in between 1-100). +6. For example type, pick your [circumstances type](http://betterlifenija.org.ng). For [optimum](https://git.eisenwiener.com) efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is recommended. +Optionally, you can set up innovative security and facilities settings, including virtual personal cloud (VPC) networking, service role authorizations, and encryption settings. For most utilize cases, the default settings will work well. However, for production releases, you might wish to examine these to align with your company's security and [compliance](https://feniciaett.com) requirements. +7. Choose Deploy to begin utilizing the model.
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When the implementation is total, you can check DeepSeek-R1's abilities straight in the Amazon Bedrock play area. +8. Choose Open in playground to access an interactive user interface where you can try out various triggers and change design parameters like temperature and maximum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for optimum results. For example, content for reasoning.
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This is an outstanding method to check out the model's reasoning and text generation capabilities before incorporating it into your applications. The play ground provides instant feedback, helping you [comprehend](https://www.jr-it-services.de3000) how the design responds to various inputs and letting you tweak your triggers for optimum outcomes.
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You can rapidly [evaluate](https://derivsocial.org) the design in the [playground](http://betterlifenija.org.ng) 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 inference utilizing guardrails with the [deployed](https://www.workinternational-df.com) DeepSeek-R1 endpoint
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The following code example shows how to perform reasoning using a released DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can produce a guardrail utilizing the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo. After you have actually developed the guardrail, [utilize](http://soho.ooi.kr) the following code to carry out guardrails. The script initializes the bedrock_runtime client, [configures inference](https://skytechenterprisesolutions.net) parameters, and sends out a demand to produce 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) hub with FMs, integrated algorithms, and prebuilt ML solutions 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 release them into production using either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart uses 2 hassle-free techniques: utilizing the intuitive SageMaker JumpStart UI or [implementing programmatically](https://allcollars.com) through the SageMaker Python SDK. Let's check out both techniques to assist you select the technique that finest matches your needs.
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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 develop a domain. +3. On the SageMaker Studio console, pick JumpStart in the navigation pane.
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The model internet browser displays available designs, with details like the provider name and design capabilities.
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4. Search for DeepSeek-R1 to view the DeepSeek-R1 design card. +Each model card reveals key details, including:
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- Model name +- Provider name +- Task classification (for instance, Text Generation). +Bedrock Ready badge (if relevant), indicating that this model can be registered 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 see the design details page.
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The model details page consists of the following details:
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- The design 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 important details, such as:
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- Model description. +- License details. +- Technical specifications. +- Usage standards
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Before you release the model, it's recommended to evaluate the model details and license terms to verify compatibility with your usage case.
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6. Choose Deploy to proceed with release.
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7. For Endpoint name, use the instantly generated name or create a custom one. +8. For example type ΒΈ pick a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial instance count, get in the number of circumstances (default: 1). +Selecting proper circumstances types and counts is crucial for expense and performance optimization. [Monitor](https://www.p3r.app) your implementation to change these settings as needed.Under Inference type, Real-time reasoning is chosen by default. This is enhanced for [sustained traffic](https://faraapp.com) and low latency. +10. Review all setups for precision. For this model, we strongly [recommend sticking](http://8.134.237.707999) to SageMaker JumpStart default settings and making certain that network seclusion remains in place. +11. Choose Deploy to release the model.
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The [deployment process](https://www.eadvisor.it) can take numerous minutes to complete.
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When release 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 keep an eye on the release progress on the SageMaker console Endpoints page, which will display pertinent metrics and status details. When the deployment 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 utilizing the SageMaker Python SDK
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To get started with DeepSeek-R1 utilizing the SageMaker Python SDK, you will require to set up the SageMaker Python SDK and make certain you have the required AWS authorizations 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 [deploying](https://nextjobnepal.com) the model is provided 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 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 [revealed](http://park7.wakwak.com) in the following code:
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Tidy up
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To prevent undesirable charges, complete the steps in this section to clean 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, complete the following steps:
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1. On the Amazon Bedrock console, under Foundation models in the navigation pane, [select Marketplace](https://mediawiki1334.00web.net) implementations. +2. In the Managed deployments area, locate the [endpoint](http://secdc.org.cn) you desire to erase. +3. Select the endpoint, and on the Actions menu, choose Delete. +4. Verify the endpoint details to make certain you're deleting the appropriate deployment: 1. Endpoint name. +2. Model name. +3. [Endpoint](https://www.youtoonet.com) 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 explored 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 get begun. For more details, refer to Use Amazon Bedrock tooling with [Amazon SageMaker](https://www.wakewiki.de) JumpStart designs, SageMaker JumpStart pretrained models, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Beginning 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://git.kraft-werk.si) companies build innovative options utilizing AWS services and accelerated compute. Currently, he is concentrated on developing methods for fine-tuning and optimizing the reasoning performance of large language models. In his free time, Vivek takes pleasure in treking, viewing motion pictures, and trying various cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://git.augustogunsch.com) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His area of focus is AWS [AI](https://career.finixia.in) 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 working on generative [AI](https://gitea.evo-labs.org) with the Third-Party Model Science group at AWS.
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Banu Nagasundaram leads product, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://ouptel.com) hub. She is enthusiastic about developing solutions that assist consumers accelerate their [AI](https://git2.ujin.tech) journey and [unlock business](https://git.polycompsol.com3000) value.
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