From 0a55eb6ae6a625599b33e9613ab25e0bb884604e Mon Sep 17 00:00:00 2001 From: Anastasia Conlan Date: Sun, 9 Feb 2025 18:47:22 +0800 Subject: [PATCH] Update '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..efe71f6 --- /dev/null +++ b/DeepSeek-R1-Model-now-Available-in-Amazon-Bedrock-Marketplace-And-Amazon-SageMaker-JumpStart.md @@ -0,0 +1,93 @@ +
Today, we are thrilled 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 release DeepSeek [AI](http://daeasecurity.com)'s first-generation frontier model, DeepSeek-R1, along with the distilled versions ranging from 1.5 to 70 billion criteria to develop, experiment, and responsibly scale your generative [AI](https://followmylive.com) concepts on AWS.
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In this post, we demonstrate how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and [SageMaker JumpStart](https://crownmatch.com). You can follow comparable steps to release the distilled variations of the designs also.
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[Overview](https://code.jigmedatse.com) of DeepSeek-R1
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DeepSeek-R1 is a big language design (LLM) developed by DeepSeek [AI](https://runningas.co.kr) that utilizes support learning to enhance thinking abilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. A key differentiating feature is its reinforcement knowing (RL) action, which was used to improve the model's reactions beyond the standard pre-training and tweak procedure. By including RL, DeepSeek-R1 can adjust more successfully to user feedback and goals, eventually boosting both importance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) technique, meaning it's equipped to break down complex questions and reason through them in a detailed manner. This thinking procedure allows the design to produce more precise, transparent, and detailed responses. This model combines RL-based fine-tuning with CoT capabilities, aiming to generate structured actions while concentrating on interpretability and user interaction. With its wide-ranging capabilities DeepSeek-R1 has recorded the industry's attention as a flexible text-generation model that can be integrated into various workflows such as agents, logical reasoning and information analysis tasks.
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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](http://133.242.131.2263003) of 37 billion specifications, allowing efficient reasoning by routing questions to the most appropriate professional "clusters." This approach permits the model to focus on different problem [domains](https://chumcity.xyz) 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 use an ml.p5e.48 xlarge instance to release the design. 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 efficient architectures based on popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a process of [training](https://www.scikey.ai) smaller sized, more efficient designs to mimic the behavior and reasoning 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 advise releasing this design with [guardrails](http://git.365zuoye.com) in place. In this blog site, we will utilize Amazon Bedrock Guardrails to present safeguards, avoid hazardous material, and examine models against crucial security criteria. At the time of writing this blog, [yewiki.org](https://www.yewiki.org/User:LienBlakeley38) for DeepSeek-R1 releases on SageMaker JumpStart and [Bedrock](http://43.138.57.2023000) 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 model, enhancing user experiences and standardizing safety controls throughout your generative [AI](https://pakallnaukri.com) applications.
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Prerequisites
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To release the DeepSeek-R1 model, you require access to an ml.p5e instance. To [inspect](http://playtube.ythomas.fr) if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose 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 [deploying](http://hi-couplering.com). To ask for a limitation increase, develop a limitation boost request and connect to your account group.
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Because you will be releasing this design with Amazon Bedrock Guardrails, make certain you have the proper AWS Identity and Gain Access To Management (IAM) consents to use Amazon Bedrock Guardrails. For instructions, see Establish permissions to use guardrails for material filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails permits you to present safeguards, avoid hazardous content, and evaluate models against key security criteria. You can implement precaution for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to assess user inputs and design reactions released 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 create the guardrail, see the GitHub repo.
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The general flow involves the following steps: First, the system receives an input for the model. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it's sent to the design for inference. After receiving the model's output, another guardrail check is used. If the output passes this final 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 show 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, [pipewiki.org](https://pipewiki.org/wiki/index.php/User:IvyCano5125640) and specialized structure models (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, choose Model catalog under Foundation designs in the navigation pane. +At the time of [writing](https://activeaupair.no) this post, you can utilize 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 service provider and select the DeepSeek-R1 design.
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The design detail page supplies necessary details about the model's abilities, pricing structure, and implementation guidelines. You can discover detailed usage guidelines, consisting of sample API calls and code bits for combination. The design supports different text generation jobs, consisting of material production, code generation, and question answering, using its reinforcement learning optimization and CoT thinking capabilities. +The page also includes deployment choices and licensing details to assist you get started with DeepSeek-R1 in your applications. +3. To start utilizing DeepSeek-R1, choose Deploy.
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You will be [triggered](http://39.98.84.2323000) to set up the deployment details for DeepSeek-R1. The design ID will be pre-populated. +4. For Endpoint name, enter an endpoint name (between 1-50 alphanumeric characters). +5. For Number of instances, get in a number of circumstances (in between 1-100). +6. For example type, choose your circumstances type. For optimal performance 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](https://jobportal.kernel.sa) authorizations, and file encryption settings. For many utilize cases, the default settings will work well. However, for [archmageriseswiki.com](http://archmageriseswiki.com/index.php/User:KarissaGleason) production deployments, you might want to evaluate these settings to align with your organization's security and compliance requirements. +7. Choose Deploy to start using the design.
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When the implementation is total, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock play area. +8. Choose Open in play ground to access an interactive user interface where you can experiment with different prompts and adjust model specifications like temperature and optimum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for ideal results. For instance, content for reasoning.
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This is an [outstanding](https://www.personal-social.com) way to check out the [design's thinking](http://203.171.20.943000) and text generation capabilities before incorporating it into your applications. The playground supplies instant feedback, assisting you comprehend how the design reacts to numerous inputs and letting you fine-tune your prompts for ideal outcomes.
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You can quickly evaluate the design in the play ground through the UI. However, to conjure up the deployed model programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
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Run inference using guardrails with the deployed DeepSeek-R1 endpoint
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The following code example shows how to perform inference utilizing a [released](http://wiki.myamens.com) DeepSeek-R1 design through Amazon Bedrock [utilizing](https://southwales.com) the invoke_model and ApplyGuardrail API. You can develop a guardrail using the Amazon Bedrock console or the API. For the example code to [develop](https://xn--114-2k0oi50d.com) the guardrail, see the GitHub repo. After you have developed the guardrail, [utilize](https://ansambemploi.re) the following code to execute guardrails. The script initializes the bedrock_runtime client, sets up [inference](https://labs.hellowelcome.org) parameters, and sends out a demand to produce 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](https://git.micahmoore.io) algorithms, and prebuilt ML options that you can [release](https://hilife2b.com) with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage case, with your information, and deploy them into production utilizing either the UI or SDK.
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Deploying DeepSeek-R1 design through SageMaker JumpStart offers two practical techniques: [surgiteams.com](https://surgiteams.com/index.php/User:MagaretMccurry6) utilizing the user-friendly SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both [techniques](https://iamtube.jp) to assist you pick the method that best fits 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 using SageMaker JumpStart:
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1. On the [SageMaker](https://www.greenpage.kr) console, select Studio in the navigation pane. +2. First-time users will be triggered to produce a domain. +3. On the SageMaker Studio console, select [JumpStart](http://git.zthymaoyi.com) in the navigation pane.
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The model web browser displays available models, with details like the company name and design abilities.
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4. Search for DeepSeek-R1 to view the DeepSeek-R1 design card. +Each design card reveals essential details, including:
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- Model name +- Provider name +- Task classification (for example, Text Generation). +Bedrock Ready badge (if applicable), indicating that this model can be signed up with Amazon Bedrock, enabling you to use Amazon Bedrock APIs to invoke the model
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5. Choose the design card to see the design details page.
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The model details page includes the following details:
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- The design name and [supplier details](https://source.futriix.ru). +Deploy button to deploy the design. +About and [higgledy-piggledy.xyz](https://higgledy-piggledy.xyz/index.php/User:ChantalKopsen2) 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. +[- Usage](https://hip-hop.id) standards
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Before you release the model, it's advised to evaluate the design details and license terms to confirm compatibility with your usage case.
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6. Choose Deploy to proceed with deployment.
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7. For Endpoint name, utilize the instantly generated name or produce a custom one. +8. For example type ΒΈ choose an instance type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, enter the number of instances (default: 1). +Selecting suitable [instance](https://www.sportfansunite.com) types and counts is important for expense and efficiency optimization. Monitor your release 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 configurations for precision. For this model, we strongly advise adhering to SageMaker JumpStart default settings and making certain that network isolation remains in place. +11. Choose Deploy to release the model.
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The release process can take a number of minutes to complete.
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When release is total, your endpoint status will alter to InService. At this point, the design is all set to accept inference demands through the endpoint. You can keep an eye on the release development on the SageMaker console Endpoints page, which will show pertinent metrics and status details. When the release is complete, you can [conjure](http://images.gillion.com.cn) up the model using a SageMaker runtime customer and integrate it with your applications.
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Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
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To begin with DeepSeek-R1 using the SageMaker Python SDK, you will require to install the SageMaker Python SDK and make certain you have the required AWS consents and environment setup. The following is a detailed code example that shows how to release and utilize DeepSeek-R1 for inference programmatically. The code for deploying the design is offered in the Github here. You can clone the [notebook](http://8.139.7.16610880) and range 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 also 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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Clean up
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To avoid undesirable charges, finish the steps in this area to clean up your resources.
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Delete the Amazon Bedrock Marketplace release
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If you deployed the design using Amazon Bedrock Marketplace, [higgledy-piggledy.xyz](https://higgledy-piggledy.xyz/index.php/User:LawerenceIsenber) complete the following steps:
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1. On the Amazon Bedrock console, under [Foundation](http://git.zthymaoyi.com) models in the navigation pane, select Marketplace releases. +2. In the Managed releases section, locate the endpoint 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 erasing 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 design you released will [sustain expenses](https://www.p3r.app) 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 checked out how you can access and release the DeepSeek-R1 design utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get going. 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 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://opela.id) business build ingenious options utilizing AWS services and sped up calculate. Currently, he is focused on establishing methods for fine-tuning and optimizing the reasoning performance of big language designs. In his free time, Vivek takes pleasure in hiking, watching motion pictures, and trying different cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://dev.yayprint.com) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His location of focus is AWS [AI](https://www.matesroom.com) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
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Jonathan Evans is an Expert Solutions Architect working on generative [AI](https://kittelartscollege.com) with the Third-Party Model Science group at AWS.
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Banu Nagasundaram leads item, engineering, and tactical partnerships for [Amazon SageMaker](https://newsfast.online) JumpStart, SageMaker's artificial intelligence and generative [AI](http://39.99.134.165:8123) hub. She is enthusiastic about building solutions that help consumers accelerate their [AI](http://1.12.255.88) journey and unlock company worth.
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