From 77e06f41508bfd3e985876c9492ec47874593216 Mon Sep 17 00:00:00 2001 From: estebanatlas43 Date: Mon, 17 Feb 2025 13:35:05 +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..f5dc7ad --- /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 designs are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](https://gitea.robertops.com)'s first-generation frontier model, DeepSeek-R1, along with the [distilled variations](http://gitlabhwy.kmlckj.com) ranging from 1.5 to 70 billion specifications to construct, experiment, and responsibly scale your generative [AI](http://b-ways.sakura.ne.jp) concepts 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 actions to deploy the distilled variations of the models as well.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a big language model (LLM) developed by DeepSeek [AI](https://git.augustogunsch.com) that utilizes reinforcement learning to enhance thinking abilities through a multi-stage training process from a DeepSeek-V3-Base structure. An essential distinguishing feature is its reinforcement learning (RL) step, which was utilized to refine the model's actions beyond the standard pre-training and fine-tuning process. By including RL, DeepSeek-R1 can adapt better to user feedback and objectives, ultimately boosting both relevance and clarity. In addition, DeepSeek-R1 [employs](https://livy.biz) a chain-of-thought (CoT) technique, indicating it's equipped to break down complex questions and [forum.altaycoins.com](http://forum.altaycoins.com/profile.php?id=1092690) reason through them in a detailed manner. This [directed](http://git.sanshuiqing.cn) thinking process enables the model to produce more precise, transparent, and detailed answers. This model integrates RL-based [fine-tuning](https://195.216.35.156) with CoT capabilities, aiming to [generate structured](http://59.110.125.1643062) responses while concentrating on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has actually recorded the market's attention as a flexible text-generation design that can be incorporated into different workflows such as agents, sensible reasoning and information analysis tasks.
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DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture permits activation of 37 billion specifications, allowing effective reasoning by routing questions to the most appropriate professional "clusters." This method enables the model to specialize in different problem domains while maintaining overall performance. DeepSeek-R1 needs at least 800 GB of [HBM memory](https://malidiaspora.org) 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 comes with 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 efficient architectures based upon popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a process of training smaller, more efficient designs to imitate the behavior and reasoning patterns of the larger DeepSeek-R1 design, using it as a teacher model.
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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 deploying this model with guardrails in location. In this blog, we will use Amazon Bedrock Guardrails to present safeguards, prevent damaging content, and evaluate designs against essential security criteria. 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 develop several guardrails tailored to different usage cases and apply them to the DeepSeek-R1 design, enhancing user experiences and standardizing safety controls throughout your generative [AI](http://8.137.58.20:3000) applications.
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Prerequisites
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To deploy the DeepSeek-R1 model, you require access to an ml.p5e instance. To check if you have quotas for P5e, open the Service Quotas [console](http://www.hxgc-tech.com3000) and under AWS Services, select Amazon 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 instance in the AWS Region you are deploying. To request a limitation increase, produce a limit boost demand and reach out to your account team.
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Because you will be [deploying](https://www.eruptz.com) this design with Amazon Bedrock Guardrails, make certain you have the appropriate AWS Identity and Gain Access To Management (IAM) approvals to use Amazon Bedrock Guardrails. For directions, see Set up consents to use guardrails for content filtering.
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Implementing guardrails with the [ApplyGuardrail](http://makerjia.cn3000) API
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Amazon Bedrock Guardrails allows you to present safeguards, avoid harmful material, and assess designs against essential security requirements. You can carry out security measures for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to apply guardrails to evaluate user inputs and model responses 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 general flow involves the following actions: First, the system receives an input for the design. This input is then [processed](https://zidra.ru) through the ApplyGuardrail API. If the input passes the guardrail check, it's sent out to the design for inference. After getting the model's output, another guardrail check is used. If the output passes this last check, it's returned as the last outcome. 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 took place at the input or output stage. The examples showcased in the following areas demonstrate inference using this API.
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Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
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Amazon [Bedrock Marketplace](http://www.ipbl.co.kr) provides you access to over 100 popular, 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, choose Model catalog under Foundation designs in the navigation pane. +At the time of writing this post, you can utilize the [InvokeModel API](http://39.98.253.1923000) to conjure up the model. It doesn't support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a provider and choose the DeepSeek-R1 model.
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The model detail page offers necessary details about the design's capabilities, prices structure, and implementation guidelines. You can find detailed use directions, including sample API calls and code bits for combination. The design supports various text generation tasks, including content production, [surgiteams.com](https://surgiteams.com/index.php/User:CathleenMadison) code generation, and question answering, utilizing its support discovering optimization and CoT thinking capabilities. +The page likewise includes implementation options and licensing details to help you get started with DeepSeek-R1 in your [applications](https://www.calogis.com). +3. To start using DeepSeek-R1, select Deploy.
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You will be triggered to configure the release details for DeepSeek-R1. The design ID will be pre-populated. +4. For Endpoint name, go into an endpoint name (between 1-50 alphanumeric characters). +5. For Variety of instances, go into a variety of [circumstances](https://dhivideo.com) (in between 1-100). +6. For example type, choose your instance type. For ideal efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is suggested. +Optionally, you can set up advanced security and facilities settings, consisting of virtual personal cloud (VPC) networking, consents, and file encryption settings. For a lot of utilize cases, the default settings will work well. However, for [production](https://video.emcd.ro) deployments, you may desire to examine these settings to line up with your organization's security and [compliance requirements](http://git.wh-ips.com). +7. Choose Deploy to start using the design.
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When the implementation is complete, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock playground. +8. Choose Open in play area to access an interactive user interface where you can explore various prompts and adjust design criteria like temperature level and optimum length. +When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for ideal results. For instance, material for reasoning.
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This is an outstanding method to check out the model's thinking and text generation capabilities before integrating it into your applications. The play ground supplies immediate feedback, assisting you comprehend how the model reacts to various inputs and letting you fine-tune your triggers for optimum outcomes.
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You can [rapidly](https://www.ssecretcoslab.com) check the model in the play area through the UI. However, [wavedream.wiki](https://wavedream.wiki/index.php/User:MerryBauman) to conjure up the deployed model programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
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Run reasoning utilizing guardrails with the [released](https://sublimejobs.co.za) DeepSeek-R1 endpoint
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The following code example [demonstrates](https://23.23.66.84) how to carry out reasoning utilizing a deployed DeepSeek-R1 design through Amazon Bedrock using 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 the guardrail, see the GitHub repo. After you have created the guardrail, use the following code to implement guardrails. The script initializes the bedrock_runtime customer, configures inference specifications, and sends a request to generate text based on a user prompt.
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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](http://parasite.kicks-ass.org3000) with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your use case, with your information, and deploy them into [production](https://gitea.bone6.com) using either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart uses 2 hassle-free techniques: using the user-friendly SageMaker JumpStart UI or implementing programmatically through the [SageMaker](http://www.grainfather.co.nz) Python SDK. Let's check out both [techniques](https://sebagai.com) to help you select the [technique](https://ofebo.com) that best 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 utilizing SageMaker JumpStart:
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1. On the SageMaker console, select Studio in the navigation pane. +2. First-time users will be [prompted](https://git.fracturedcode.net) to produce a domain. +3. On the SageMaker Studio console, pick JumpStart in the navigation pane.
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The design browser displays available models, with details like the supplier name and design capabilities.
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4. Look for DeepSeek-R1 to see the DeepSeek-R1 model card. +Each design card shows key details, including:
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- Model name +- Provider name +- Task category (for example, Text Generation). +Bedrock Ready badge (if applicable), indicating that this model can be signed up with Amazon Bedrock, 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 model details page consists of the following details:
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- The design name and company details. +Deploy button to release the model. +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 specs. +[- Usage](http://39.101.160.118099) guidelines
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Before you release the model, it's suggested to examine the model details and license terms to verify compatibility with your use case.
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6. Choose Deploy to proceed with deployment.
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7. For Endpoint name, use the immediately created name or create a custom-made one. +8. For Instance type ΒΈ select an instance type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, enter the number of circumstances (default: 1). +Selecting proper instance types and counts is vital for expense and performance optimization. Monitor your release to change these settings as needed.Under Inference type, [Real-time reasoning](https://www.lightchen.info) is selected by default. This is enhanced for sustained traffic and low latency. +10. Review all setups for precision. For this design, [larsaluarna.se](http://www.larsaluarna.se/index.php/User:JanessaDamron28) we highly advise sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in location. +11. Choose Deploy to release the design.
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The implementation process can take numerous minutes to finish.
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When deployment is complete, your endpoint status will alter to InService. At this point, the model is prepared to accept inference requests through the endpoint. You can keep track of the implementation progress on the SageMaker console Endpoints page, which will show pertinent metrics and status details. When the implementation is complete, you can conjure up the model 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 begin with DeepSeek-R1 using the SageMaker Python SDK, you will [require](http://gitea.infomagus.hu) to set up the SageMaker Python SDK and make certain you have the essential AWS consents and environment setup. The following is a detailed code example that demonstrates how to deploy and utilize DeepSeek-R1 for inference programmatically. The code for deploying the design is supplied 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 reasoning 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 create a guardrail utilizing the Amazon Bedrock console or the API, and execute it as shown in the following code:
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Clean up
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To avoid unwanted charges, complete the steps in this area to clean up your [resources](https://www.punajuaj.com).
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Delete the Amazon Bedrock Marketplace release
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If you released the design using Amazon Bedrock Marketplace, complete the following actions:
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1. On the Amazon Bedrock console, under [Foundation designs](https://baescout.com) in the navigation pane, select Marketplace implementations. +2. In the Managed implementations area, locate the endpoint you want 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 release: 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 deployed will sustain costs if you leave it running. Use the following code to delete the endpoint if you desire to stop sustaining charges. For more details, [higgledy-piggledy.xyz](https://higgledy-piggledy.xyz/index.php/User:LawerenceIsenber) 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 model utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to begin. For more details, refer to Use Amazon Bedrock [tooling](https://wavedream.wiki) with Amazon SageMaker JumpStart designs, SageMaker JumpStart [pretrained](https://lovematch.vip) designs, 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](http://koreaeducation.co.kr) companies build innovative solutions using AWS services and accelerated calculate. Currently, he is concentrated on establishing strategies for fine-tuning and optimizing the reasoning performance of big language models. In his spare time, Vivek takes pleasure in hiking, enjoying movies, and trying different foods.
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Niithiyn Vijeaswaran is a Generative [AI](http://121.28.134.38:2039) [Specialist Solutions](https://precise.co.za) Architect with the Third-Party Model Science team at AWS. His location of focus is AWS [AI](https://baripedia.org) [accelerators](http://82.19.55.40443) (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
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Jonathan Evans is an Expert Solutions Architect dealing with generative [AI](https://startuptube.xyz) with the Third-Party Model Science team at AWS.
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[Banu Nagasundaram](https://www.ignitionadvertising.com) leads item, engineering, and strategic partnerships for [wavedream.wiki](https://wavedream.wiki/index.php/User:ClaireSparling1) Amazon SageMaker JumpStart, SageMaker's artificial intelligence and [generative](https://git.unicom.studio) [AI](http://66.112.209.2:3000) hub. She is passionate about building solutions that help clients accelerate their [AI](https://namesdev.com) journey and unlock business value.
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