commit 1e4d6f952877f6d499757da0481ef22703c9f5a6 Author: angelicamattoc Date: Fri Feb 7 14:27:07 2025 +0000 Add 'DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart' 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..0b453bc --- /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](http://63.32.145.226) and Qwen designs are available through Amazon [Bedrock Marketplace](https://121.36.226.23) and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](https://wiki.snooze-hotelsoftware.de)'s first-generation frontier design, DeepSeek-R1, along with the distilled variations varying from 1.5 to 70 billion criteria to develop, experiment, and properly scale your [generative](https://asw.alma.cl) [AI](http://kacm.co.kr) ideas 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 comparable actions to deploy the distilled versions of the designs also.
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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://c-hireepersonnel.com) that utilizes reinforcement learning to improve reasoning abilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A key differentiating function is its support learning (RL) action, which was used to fine-tune the model's actions beyond the basic pre-training and fine-tuning process. By integrating RL, DeepSeek-R1 can adjust better to user feedback and objectives, ultimately enhancing both significance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) technique, implying it's equipped to break down [intricate queries](https://talentup.asia) and factor through them in a detailed way. This guided reasoning process enables the design to produce more accurate, transparent, and [detailed responses](https://ehrsgroup.com). This model integrates RL-based fine-tuning with CoT capabilities, aiming to generate structured responses while focusing on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has captured the market's attention as a versatile text-generation design that can be integrated into different workflows such as agents, sensible reasoning and information interpretation tasks.
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DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture permits activation of 37 billion parameters, enabling effective inference by routing queries to the most appropriate [professional](https://git.skyviewfund.com) "clusters." This approach allows the design to concentrate on different problem domains while maintaining total effectiveness. DeepSeek-R1 requires at least 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 includes 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.
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DeepSeek-R1 distilled models bring the thinking capabilities of the main R1 model to more efficient 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, [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:TAHRena195267306) more effective designs to mimic the habits and reasoning patterns of the larger DeepSeek-R1 design, using it as an [instructor design](https://lovelynarratives.com).
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You can deploy DeepSeek-R1 design either through [SageMaker JumpStart](http://photorum.eclat-mauve.fr) or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we recommend deploying this model with guardrails in location. In this blog, we will utilize Amazon Bedrock Guardrails to present safeguards, avoid harmful material, and assess designs against essential safety criteria. At the time of writing this blog site, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the [ApplyGuardrail API](http://www.zhihutech.com). You can develop several [guardrails tailored](https://melaninbook.com) to various usage cases and use them to the DeepSeek-R1 design, improving user experiences and standardizing safety controls throughout your generative [AI](https://network.janenk.com) applications.
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Prerequisites
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To release the DeepSeek-R1 design, you require access to an ml.p5e instance. To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and verify you're using 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 ask for a limit boost, develop a limitation increase request and reach out to your account group.
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Because you will be releasing this design with Amazon Bedrock Guardrails, make certain you have the correct AWS Identity and Gain Access To Management (IAM) authorizations to utilize Amazon Bedrock Guardrails. For guidelines, see Establish approvals to utilize 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 harmful material, and evaluate designs against key security criteria. You can carry out safety procedures for [trademarketclassifieds.com](https://trademarketclassifieds.com/user/profile/2672496) the DeepSeek-R1 design utilizing the [Amazon Bedrock](https://neejobs.com) ApplyGuardrail API. This allows you to use guardrails to assess user inputs and design actions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can develop 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 circulation involves the following actions: First, the system gets an input for the model. This input is then [processed](https://bibi-kai.com) through the ApplyGuardrail API. If the input passes the guardrail check, it's sent out to the model for inference. After getting the design's output, another [guardrail check](http://www.youly.top3000) is used. If the output passes this last check, it's returned as the result. However, [wakewiki.de](https://www.wakewiki.de/index.php?title=Benutzer:AndyDana123) if either the input or output is intervened by the guardrail, a [message](https://git.thewebally.com) is returned showing the nature of the [intervention](http://211.119.124.1103000) and whether it happened 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 offers 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 actions:
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1. On the Amazon Bedrock console, pick Model brochure under Foundation models in the navigation pane. +At the time of composing this post, you can utilize the InvokeModel API to invoke the model. It does not support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a [service provider](http://47.113.115.2393000) and choose the DeepSeek-R1 design.
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The model detail page supplies essential details about the model's capabilities, [pricing](http://kacm.co.kr) structure, and application standards. You can find detailed usage directions, including sample API calls and code snippets for combination. The model supports various text generation jobs, including content production, code generation, and concern answering, utilizing its support discovering optimization and CoT reasoning capabilities. +The page likewise consists of deployment options and licensing details to help you get started with DeepSeek-R1 in your applications. +3. To start using DeepSeek-R1, pick Deploy.
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You will be triggered to set up the deployment details for DeepSeek-R1. The model ID will be pre-populated. +4. For Endpoint name, get in an endpoint name (between 1-50 alphanumeric characters). +5. For Variety of instances, get in a number of circumstances (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 advised. +Optionally, you can set up advanced security and facilities settings, consisting of virtual private cloud (VPC) networking, service function authorizations, and encryption settings. For many use cases, the default settings will work well. However, for production implementations, you might wish to evaluate these settings to line up with your organization's security and compliance requirements. +7. Choose Deploy to begin utilizing the design.
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When the deployment is complete, you can evaluate DeepSeek-R1's abilities straight in the Amazon Bedrock play ground. +8. Choose Open in play area to access an interactive interface where you can experiment with different triggers and adjust design criteria like temperature and optimum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for optimum results. For example, content for reasoning.
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This is an excellent way to check out the design's thinking and text generation abilities before integrating it into your applications. The play ground provides instant feedback, helping you [understand](http://ufidahz.com.cn9015) how the design reacts to different inputs and letting you tweak your [prompts](https://nse.ai) for [35.237.164.2](https://35.237.164.2/wiki/User:BessieFitzRoy) optimum outcomes.
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You can rapidly check the model in the playground through the UI. However, to conjure up the deployed design programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
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Run inference utilizing guardrails with the deployed DeepSeek-R1 endpoint
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The following code example shows how to carry out inference utilizing a released 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 actually created the guardrail, utilize the following code to execute guardrails. The script initializes the bedrock_runtime client, [configures reasoning](https://www.indianpharmajobs.in) parameters, and sends a demand to create 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, built-in algorithms, and prebuilt ML options that you can release with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your data, and deploy them into production using either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart offers two convenient techniques: using the [intuitive SageMaker](https://kahps.org) JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's explore both techniques to assist you select the technique that finest matches your requirements.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following actions to deploy DeepSeek-R1 using SageMaker JumpStart:
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1. On the SageMaker console, pick Studio in the [navigation pane](https://awaz.cc). +2. First-time users will be triggered to produce a domain. +3. On the [SageMaker Studio](http://acs-21.com) console, pick JumpStart in the navigation pane.
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The design internet browser shows available models, [surgiteams.com](https://surgiteams.com/index.php/User:KaseyDees635) with details like the provider name and design abilities.
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4. Search for DeepSeek-R1 to see the DeepSeek-R1 design card. +Each design card shows key details, including:
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- Model name +- [Provider](http://wowonder.technologyvala.com) name +- Task category (for instance, Text Generation). +Bedrock Ready badge (if applicable), suggesting that this model can be registered with Amazon Bedrock, enabling you to utilize Amazon Bedrock APIs to conjure up the design
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5. Choose the model card to see the design [details](https://thankguard.com) page.
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The model [details](https://dev-members.writeappreviews.com) page consists of the following details:
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- The design name and supplier details. +Deploy button to deploy the design. +About and Notebooks tabs with detailed details
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The About tab consists of 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 advised to examine the model details and license terms to confirm 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 automatically created name or develop 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 variety of instances (default: 1). +[Selecting](https://realestate.kctech.com.np) appropriate circumstances types and counts is vital for cost and efficiency optimization. Monitor your deployment to change these settings as needed.Under Inference type, Real-time inference is selected by default. This is enhanced for sustained traffic and low latency. +10. Review all setups for [accuracy](https://laborando.com.mx). For this model, we strongly recommend sticking to SageMaker JumpStart default settings and making certain that network isolation remains in place. +11. Choose Deploy to release the design.
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The implementation process can take a number of minutes to complete.
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When implementation is complete, your [endpoint status](https://zidra.ru) will change to [InService](https://daeshintravel.com). At this moment, the design is ready to accept inference demands through the endpoint. You can keep track of the release development on the SageMaker console Endpoints page, which will display relevant metrics and status details. When the release is total, you can invoke the model utilizing a SageMaker runtime client and integrate it with your applications.
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Deploy DeepSeek-R1 [utilizing](http://images.gillion.com.cn) the SageMaker Python SDK
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To get begun with DeepSeek-R1 utilizing the SageMaker Python SDK, you will require to install the SageMaker Python SDK and make certain you have the essential AWS authorizations and environment setup. The following is a detailed code example that shows how to release and use DeepSeek-R1 for reasoning programmatically. The code for deploying the model is provided in the Github here. You can clone the notebook and run 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, [forum.pinoo.com.tr](http://forum.pinoo.com.tr/profile.php?id=1321148) you can also utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can create a guardrail using the Amazon Bedrock console or the API, and [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:LucasFtu80211) execute it as revealed in the following code:
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Tidy up
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To avoid undesirable charges, complete 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, total the following steps:
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1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, choose Marketplace releases. +2. In the Managed deployments area, find the endpoint you desire 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 implementation: 1. [Endpoint](http://secdc.org.cn) 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 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 get going. For more details, describe Use Amazon Bedrock tooling with [Amazon SageMaker](https://www.wotape.com) JumpStart designs, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting started 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://nytia.org) business construct innovative solutions utilizing AWS services and sped up calculate. Currently, he is focused on establishing methods for fine-tuning and enhancing the inference performance of big [language models](http://106.39.38.2421300). In his downtime, Vivek enjoys treking, watching motion pictures, and trying different cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://warleaks.net) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His of focus is AWS [AI](https://ready4hr.com) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer [technology](https://travel-friends.net) and Bioinformatics.
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Jonathan Evans is an Expert Solutions Architect working on generative [AI](http://jejuanimalnow.org) with the Third-Party Model [Science team](http://49.235.130.76) at AWS.
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Banu Nagasundaram leads item, engineering, and tactical collaborations for [Amazon SageMaker](https://aladin.social) JumpStart, SageMaker's artificial intelligence and generative [AI](http://175.6.40.68:8081) hub. She is enthusiastic about constructing services that help clients accelerate their [AI](https://www.anetastaffing.com) journey and unlock service worth.
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