From 7acde50c636229c47b1c0e09dc5ed3e6fc1b128e Mon Sep 17 00:00:00 2001 From: carleybussey50 Date: Sun, 9 Feb 2025 09:17:10 +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..3b9ee62 --- /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 R1 [distilled Llama](https://git2.nas.zggsong.cn5001) and Qwen models are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](https://15.164.25.185)'s first-generation frontier design, DeepSeek-R1, together with the distilled variations varying from 1.5 to 70 billion specifications to build, experiment, [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:JulianaCobbett7) and properly scale your generative [AI](https://ayjmultiservices.com) ideas on AWS.
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In this post, we demonstrate how to begin with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar actions to deploy the distilled variations of the designs too.
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[Overview](https://right-fit.co.uk) of DeepSeek-R1
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DeepSeek-R1 is a large language design (LLM) established by DeepSeek [AI](http://35.207.205.18:3000) that uses reinforcement discovering to improve reasoning abilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A crucial differentiating feature is its support learning (RL) action, which was used to fine-tune the design's reactions beyond the basic pre-training and tweak procedure. By [including](https://autogenie.co.uk) RL, DeepSeek-R1 can adapt more effectively to user feedback and objectives, ultimately improving both importance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) method, indicating it's geared up to break down intricate inquiries and factor through them in a detailed manner. This guided thinking procedure enables the model to produce more precise, transparent, and [detailed responses](https://scm.fornaxian.tech). This design combines RL-based fine-tuning with CoT abilities, aiming to generate structured actions while [concentrating](https://git.progamma.com.ua) on interpretability and user interaction. With its [extensive abilities](https://systemcheck-wiki.de) DeepSeek-R1 has actually captured the industry's attention as a flexible text-generation design that can be incorporated into numerous workflows such as agents, rational thinking and information interpretation jobs.
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DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture allows activation of 37 billion specifications, allowing effective reasoning by routing queries to the most relevant professional "clusters." This technique enables the model to focus on various 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 use an ml.p5e.48 xlarge circumstances to release the design. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.
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DeepSeek-R1 distilled designs bring the [reasoning abilities](http://47.93.234.49) of the main R1 design to more effective architectures based upon popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a process of training smaller sized, more effective models to simulate the behavior and thinking patterns of the larger DeepSeek-R1 design, utilizing it as a teacher design.
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You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we suggest releasing this design with guardrails in location. In this blog site, we will utilize Amazon Bedrock Guardrails to present safeguards, avoid damaging material, and assess models against essential safety requirements. At the time of writing this blog, for DeepSeek-R1 deployments on [SageMaker JumpStart](http://globalnursingcareers.com) and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can produce numerous guardrails tailored to different usage cases and use them to the DeepSeek-R1 design, enhancing user experiences and standardizing security controls across your generative [AI](https://clearcreek.a2hosted.com) [applications](https://git.parat.swiss).
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Prerequisites
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To deploy the DeepSeek-R1 design, you require access to an ml.p5e [circumstances](http://121.4.70.43000). To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick Amazon SageMaker, and verify you're utilizing ml.p5e.48 xlarge for endpoint usage. Make certain that you have at least one ml.P5e.48 xlarge instance in the AWS Region you are releasing. To request a limitation increase, develop a limit boost demand and connect to your account team.
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Because you will be deploying this model with Amazon Bedrock Guardrails, make certain you have the right AWS Identity and Gain Access To Management (IAM) authorizations to use Amazon Bedrock Guardrails. For guidelines, see [Establish authorizations](http://101.200.181.61) to utilize guardrails for content filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails allows you to introduce safeguards, avoid hazardous material, and assess models against essential security criteria. You can execute precaution for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to [apply guardrails](https://tjoobloom.com) to assess user inputs and [model actions](https://dhivideo.com) deployed on Amazon Bedrock Marketplace and [SageMaker JumpStart](http://47.116.130.49). You can [develop](http://www5f.biglobe.ne.jp) a guardrail using 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 actions: First, the system receives an input for the design. 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 design's output, another guardrail check is used. If the output passes this last check, it's returned as the [outcome](https://git.molokoin.ru). However, if either the input or output is stepped in by the guardrail, a message is returned showing the nature of the intervention and whether it occurred at the input or output phase. The examples showcased in the following sections show reasoning utilizing 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, [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:KathrinSabella) emerging, and specialized structure 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, pick Model catalog under Foundation models in the navigation pane. +At the time of composing this post, you can utilize the InvokeModel API to conjure up the design. It doesn't support Converse APIs and other [Amazon Bedrock](https://twoo.tr) tooling. +2. Filter for DeepSeek as a supplier and pick the DeepSeek-R1 design.
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The model detail page provides vital details about the model's abilities, pricing structure, and [execution standards](http://bluemobile010.com). You can discover detailed usage directions, including sample API calls and code bits for combination. The model supports different text generation tasks, consisting of material creation, code generation, and concern answering, utilizing its reinforcement discovering optimization and CoT reasoning capabilities. +The page likewise consists of implementation choices and licensing details to assist you begin with DeepSeek-R1 in your applications. +3. To begin using DeepSeek-R1, select 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, get in an endpoint name (in between 1-50 alphanumeric characters). +5. For Variety of instances, go into a variety of instances (between 1-100). +6. For example type, select your circumstances type. For ideal 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, consisting of virtual private cloud (VPC) networking, service role consents, and file encryption settings. For the majority of utilize cases, the default settings will work well. However, for production deployments, you might want to evaluate these settings to line up with your company's security and [compliance](http://jialcheerful.club3000) requirements. +7. Choose Deploy to begin using the model.
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When the release is total, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock playground. +8. Choose Open in play ground to access an interactive user interface where you can explore different prompts and adjust design parameters like temperature and optimum length. +When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for ideal results. For instance, material for reasoning.
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This is an exceptional way to check out the design's thinking and text generation abilities before integrating it into your applications. The play ground offers immediate feedback, helping you comprehend how the model reacts to different inputs and letting you fine-tune your prompts for ideal outcomes.
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You can rapidly test the design in the play area through the UI. However, to invoke the released design programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
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Run inference utilizing guardrails with the released DeepSeek-R1 endpoint
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The following code example demonstrates how to carry out [inference utilizing](http://60.204.229.15120080) a deployed DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can develop a guardrail utilizing the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo. After you have actually developed the guardrail, utilize the following code to carry out guardrails. The script initializes the bedrock_runtime client, configures inference parameters, and sends out a request to generate 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) center with FMs, integrated algorithms, and prebuilt ML options that you can deploy with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained [designs](http://ods.ranker.pub) 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 design through SageMaker JumpStart uses two hassle-free approaches: utilizing the user-friendly SageMaker JumpStart UI or [genbecle.com](https://www.genbecle.com/index.php?title=Utilisateur:RosarioHairston) implementing programmatically through the SageMaker Python SDK. Let's check out both approaches to help you choose the technique that best suits your needs.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following steps to release DeepSeek-R1 utilizing SageMaker JumpStart:
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1. On the [SageMaker](https://vitricongty.com) console, select Studio in the navigation pane. +2. First-time users will be triggered to create a domain. +3. On the [SageMaker Studio](http://jialcheerful.club3000) console, pick JumpStart in the navigation pane.
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The model internet [browser](https://warleaks.net) shows available models, with details like the supplier name and [design capabilities](https://git.flandre.net).
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4. Look for DeepSeek-R1 to view the DeepSeek-R1 model 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), suggesting that this design can be registered with Amazon Bedrock, allowing you to use Amazon Bedrock APIs to conjure up the design
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5. Choose the model 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 release the design. +About and Notebooks tabs with detailed details
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The About tab includes crucial details, such as:
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- Model description. +- License details. +- Technical specifications. +- Usage guidelines
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Before you release the model, [garagesale.es](https://www.garagesale.es/author/toshahammon/) it's recommended to review the model details and license terms to confirm compatibility with your use case.
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6. [Choose Deploy](https://git.randomstar.io) to continue with release.
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7. For [wiki.rolandradio.net](https://wiki.rolandradio.net/index.php?title=User:LeonoraGresham4) Endpoint name, utilize the automatically generated name or produce a custom-made one. +8. For example [type ΒΈ](https://scm.fornaxian.tech) select an instance type (default: ml.p5e.48 xlarge). +9. For [Initial](http://httelecom.com.cn3000) circumstances count, get in the variety of circumstances (default: 1). +Selecting proper circumstances types and counts is vital for cost and performance optimization. Monitor your release 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 model, we strongly suggest adhering to [SageMaker JumpStart](http://globalnursingcareers.com) default settings and making certain that network isolation remains in location. +11. Choose Deploy to deploy the design.
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The [deployment process](https://git.zzxxxc.com) can take numerous minutes to finish.
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When deployment is total, your [endpoint status](https://git2.nas.zggsong.cn5001) will alter to InService. At this point, the model is prepared to accept inference requests through the endpoint. You can keep an eye on the implementation progress on the SageMaker console Endpoints page, which will show relevant metrics and status details. When the [release](https://git.noisolation.com) is complete, you can conjure up the design utilizing a SageMaker runtime customer 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 require 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 demonstrates how to release and utilize DeepSeek-R1 for inference programmatically. The code for [wiki.whenparked.com](https://wiki.whenparked.com/User:LetaX2026348693) releasing the design is provided in the Github here. You can clone the note pad and run from SageMaker Studio.
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You can run additional requests 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 develop a guardrail utilizing the Amazon Bedrock console or the API, and implement it as displayed in the following code:
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Tidy up
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To prevent unwanted charges, finish 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 model utilizing Amazon Bedrock Marketplace, total the following steps:
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1. On the Amazon Bedrock console, under Foundation models in the navigation pane, select Marketplace deployments. +2. In the Managed releases section, find the endpoint you desire to delete. +3. Select the endpoint, and on the Actions menu, select Delete. +4. Verify the endpoint details to make certain you're deleting the appropriate deployment: 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 you deployed 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 checked out 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 get going. 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 Getting going with Amazon SageMaker JumpStart.
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About the Authors
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Vivek Gangasani is a Lead Specialist for Inference at AWS. He assists emerging generative [AI](http://www.colegio-sanandres.cl) companies construct ingenious services utilizing AWS services and sped up calculate. Currently, he is focused on establishing strategies for fine-tuning and enhancing the inference performance of big language designs. In his downtime, Vivek enjoys hiking, enjoying motion pictures, [wiki.myamens.com](http://wiki.myamens.com/index.php/User:TawnyaWhitley87) and attempting various cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://www.pakgovtnaukri.pk) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His area of focus is AWS [AI](https://gogs.2dz.fi) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.
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Jonathan Evans is a Specialist Solutions Architect dealing with generative [AI](https://gitea.adminakademia.pl) with the Third-Party Model Science team at AWS.
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Banu Nagasundaram leads item, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](http://193.123.80.202:3000) hub. She is passionate about building options that assist clients accelerate their [AI](https://akinsemployment.ca) journey and unlock business worth.
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