From a1d3dcebb3eb36283ad5bebe5bdb607b7b9a9262 Mon Sep 17 00:00:00 2001 From: lashondapermew Date: Mon, 2 Jun 2025 10:58:19 +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..208050d --- /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 and Qwen models are available through Amazon Bedrock Marketplace and Amazon SageMaker [JumpStart](https://www.srapo.com). With this launch, [demo.qkseo.in](http://demo.qkseo.in/profile.php?id=1092580) you can now [release DeepSeek](https://www.eruptz.com) [AI](http://84.247.150.84:3000)'s [first-generation frontier](https://medicalrecruitersusa.com) model, DeepSeek-R1, along with the distilled variations ranging from 1.5 to 70 billion parameters to build, experiment, [wiki.dulovic.tech](https://wiki.dulovic.tech/index.php/User:EsperanzaMccalli) and properly scale your generative [AI](http://118.190.145.217:3000) ideas on AWS.
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In this post, we show how to start with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar actions to deploy the distilled versions of the models also.
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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://gitea.ochoaprojects.com) that uses reinforcement finding out to enhance thinking capabilities through a multi-stage training process from a DeepSeek-V3-Base structure. A crucial distinguishing function is its support knowing (RL) step, [systemcheck-wiki.de](https://systemcheck-wiki.de/index.php?title=Benutzer:LoriBrumbaugh40) which was utilized to fine-tune the model's responses beyond the basic pre-training and fine-tuning procedure. By including RL, DeepSeek-R1 can adapt more successfully to user feedback and objectives, eventually enhancing both relevance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) approach, suggesting it's geared up to break down intricate questions and reason through them in a detailed manner. This directed thinking process permits the design to produce more accurate, transparent, and detailed answers. This design combines RL-based fine-tuning with CoT abilities, aiming to generate structured actions while concentrating on interpretability and user interaction. With its comprehensive abilities DeepSeek-R1 has actually captured the market's attention as a flexible text-generation design that can be integrated into numerous workflows such as representatives, logical thinking and information [analysis tasks](https://job-maniak.com).
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DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion criteria in size. The [MoE architecture](https://alapcari.com) allows [activation](http://blueroses.top8888) of 37 billion specifications, enabling effective reasoning by routing queries to the most relevant specialist "clusters." This technique enables the model to specialize in various problem domains while maintaining overall efficiency. DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for reasoning. In this post, we will use an ml.p5e.48 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 capabilities of the main R1 model to more effective architectures 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 sized, more [efficient models](https://www.nas-store.com) to mimic the habits and reasoning patterns of the bigger DeepSeek-R1 model, 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 advise deploying this model with guardrails in place. In this blog site, we will utilize Amazon Bedrock Guardrails to [introduce](http://219.150.88.23433000) safeguards, avoid hazardous content, and examine models against essential safety criteria. At the time of composing this blog, for [yewiki.org](https://www.yewiki.org/User:ChanelEliott) DeepSeek-R1 releases on [SageMaker JumpStart](https://forum.alwehdaclub.sa) and Bedrock Marketplace, Bedrock Guardrails supports only the [ApplyGuardrail API](https://weldersfabricators.com). You can develop several guardrails tailored to different use cases and use them to the DeepSeek-R1 model, improving user experiences and standardizing security controls across your generative [AI](http://8.138.140.94:3000) applications.
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Prerequisites
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To deploy 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 verify 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 ask for a limitation boost, develop a limit increase demand and reach out to your account group.
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Because you will be deploying this model with Amazon Bedrock Guardrails, make certain you have the proper AWS Identity and Gain Access To Management (IAM) authorizations to utilize Amazon Bedrock Guardrails. For guidelines, see Establish authorizations to use guardrails for [material filtering](https://thevesti.com).
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails permits you to present safeguards, avoid damaging content, and evaluate models against crucial security requirements. You can implement precaution for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This [permits](https://www.ieo-worktravel.com) you to apply guardrails to evaluate user inputs and model actions released 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 develop the guardrail, see the GitHub repo.
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The basic circulation includes the following steps: First, the system receives an input for the model. This input is then processed through the [ApplyGuardrail API](http://170.187.182.1213000). If the input passes the guardrail check, it's sent out to the model for inference. After receiving the design's output, another guardrail check is used. If the output passes this last check, it's returned as the result. 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 occurred at the input or output stage. The examples showcased in the following areas demonstrate inference utilizing this API.
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Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
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Amazon Bedrock Marketplace gives you access to over 100 popular, emerging, and specialized foundation designs (FMs) through Amazon Bedrock. To [gain access](https://www.yohaig.ng) to DeepSeek-R1 in Amazon Bedrock, total the following actions:
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1. On the Amazon Bedrock console, pick [Model catalog](https://solegeekz.com) under Foundation models in the navigation pane. +At the time of writing this post, you can use the InvokeModel API to invoke the design. It does not support Converse APIs and other [Amazon Bedrock](https://git.jackbondpreston.me) tooling. +2. Filter for DeepSeek as a provider and pick the DeepSeek-R1 model.
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The design detail page offers vital details about the model's abilities, pricing structure, and application standards. You can discover detailed use instructions, including sample API calls and code snippets for combination. The model supports numerous text generation tasks, consisting of content creation, code generation, and question answering, using its support discovering optimization and CoT thinking abilities. +The page likewise consists of release options and licensing details to help you begin with DeepSeek-R1 in your applications. +3. To begin utilizing DeepSeek-R1, select Deploy.
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You will be prompted to set up the deployment details for DeepSeek-R1. The model ID will be pre-populated. +4. For Endpoint name, go into an endpoint name (between 1-50 alphanumeric characters). +5. For Variety of circumstances, get in a variety of instances (between 1-100). +6. For example type, select your circumstances type. For [ideal performance](https://www.naukrinfo.pk) with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised. +Optionally, you can set up [innovative security](https://newnormalnetwork.me) and facilities settings, consisting of virtual personal cloud (VPC) networking, service role authorizations, and file encryption settings. For a lot of utilize cases, the default settings will work well. However, for production deployments, you may desire to evaluate these settings to line up with your company's security and compliance requirements. +7. Choose Deploy to [start utilizing](https://git.uucloud.top) the model.
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When the release is complete, you can evaluate DeepSeek-R1's abilities straight in the Amazon Bedrock play ground. +8. Choose Open in play ground to access an interactive interface where you can try out different prompts and adjust model specifications like temperature level and optimum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for optimal results. For instance, material for inference.
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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 area provides immediate feedback, assisting you understand how the model reacts to various inputs and letting you tweak your prompts for ideal results.
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You can quickly evaluate the design in the play area through the UI. However, to invoke the [deployed design](https://tiwarempireprivatelimited.com) programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
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Run inference using guardrails with the released DeepSeek-R1 endpoint
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The following code example shows how to perform reasoning utilizing a deployed DeepSeek-R1 design through Amazon Bedrock using the invoke_model and [ApplyGuardrail API](http://121.37.208.1923000). You can create a guardrail utilizing the Amazon Bedrock [console](https://www.activeline.com.au) or [wiki.myamens.com](http://wiki.myamens.com/index.php/User:SheliaHercus8) the API. For the example code to develop the guardrail, see the GitHub repo. After you have actually created the guardrail, utilize the following code to carry out guardrails. The script initializes the bedrock_runtime customer, sets up inference specifications, and sends out a demand to create text based upon a user prompt.
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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 services that you can deploy with simply a few clicks. With SageMaker JumpStart, you can [tailor pre-trained](https://git.we-zone.com) models to your use case, with your information, and deploy them into [production utilizing](https://truthbook.social) either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart provides 2 hassle-free techniques: utilizing the [user-friendly SageMaker](https://git.torrents-csv.com) JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's check out both techniques to assist you choose the approach 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 utilizing SageMaker JumpStart:
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1. On the SageMaker console, select Studio in the navigation pane. +2. First-time users will be triggered to [produce](https://20.112.29.181) a domain. +3. On the SageMaker Studio console, pick JumpStart in the navigation pane.
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The model web browser shows available models, with details like the provider name and model abilities.
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4. Look for DeepSeek-R1 to see the DeepSeek-R1 model card. +Each design card shows crucial details, consisting of:
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- Model name +- Provider name +- Task classification (for example, Text Generation). +[Bedrock Ready](http://git.520hx.vip3000) badge (if appropriate), suggesting that this model can be signed up with Amazon Bedrock, permitting you to utilize Amazon [Bedrock APIs](http://sbstaffing4all.com) to [conjure](https://gogs.fytlun.com) up the design
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5. Choose the design card to view the design details page.
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The design details page consists of the following details:
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- The model 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 consists of crucial 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 recommended 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, utilize the automatically produced name or create a custom one. +8. For Instance type ΒΈ select a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, get in the variety of instances (default: 1). +Selecting suitable circumstances types and counts is important for expense and efficiency optimization. Monitor your release to change these settings as needed.Under Inference type, Real-time reasoning is chosen by default. This is optimized for sustained traffic and [low latency](https://noxxxx.com). +10. Review all setups for precision. For this design, we strongly recommend sticking to SageMaker JumpStart default settings and making certain that network seclusion 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 change to InService. At this moment, the model is prepared to accept reasoning requests through the endpoint. You can keep an eye on the deployment development on the SageMaker console Endpoints page, which will show pertinent metrics and status details. When the [implementation](https://git.jiewen.run) is total, you can conjure up the design utilizing 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 start 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 approvals and environment setup. The following is a detailed code example that demonstrates how to deploy and utilize DeepSeek-R1 for [reasoning programmatically](https://kcshk.com). The code for 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 extra 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 likewise utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can create a [guardrail utilizing](https://askcongress.org) the Amazon Bedrock console or the API, and [execute](https://git.lab.evangoo.de) it as displayed 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 implementation
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If you released the model utilizing Amazon Bedrock Marketplace, complete the following actions:
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1. On the Amazon Bedrock console, under Foundation designs in the [navigation](http://www.xyais.cn) pane, select Marketplace releases. +2. In the Managed deployments section, locate the endpoint you want 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 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 model you deployed will sustain costs if you leave it running. Use the following code to erase 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 JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get begun. 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](https://crmthebespoke.a1professionals.net) 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](http://encocns.com:30001) business develop ingenious options using AWS services and sped up compute. Currently, he is concentrated on developing methods for fine-tuning and optimizing the reasoning efficiency of big language designs. In his [leisure](http://gitlab.ileadgame.net) time, Vivek takes pleasure in treking, seeing films, and trying different cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://yaseen.tv) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His area of focus is AWS [AI](http://120.201.125.140:3000) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.
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Jonathan Evans is a Professional Solutions Architect dealing with generative [AI](https://sosyalanne.com) 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](https://videofrica.com) intelligence and generative [AI](https://droomjobs.nl) hub. She is enthusiastic about constructing solutions that assist customers accelerate their [AI](https://askcongress.org) journey and unlock company value.
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