From d2d4762ee4c746cfcbe1f0c32afcfebd533d91d8 Mon Sep 17 00:00:00 2001 From: portermcfarlan Date: Mon, 2 Jun 2025 18:29:07 +0800 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..4d20e16 --- /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 announce that DeepSeek R1 [distilled Llama](http://archmageriseswiki.com) and Qwen models are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](https://mixedwrestling.video)'s first-generation frontier model, DeepSeek-R1, along with the [distilled](https://yourfoodcareer.com) versions ranging from 1.5 to 70 billion specifications to develop, experiment, and properly scale your generative [AI](http://gitpfg.pinfangw.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. You can follow comparable actions to release the distilled versions of the designs also.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a large language model (LLM) established by DeepSeek [AI](http://www.scitqn.cn:3000) that utilizes support [learning](http://1.117.194.11510080) to boost reasoning abilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A crucial distinguishing feature is its reinforcement knowing (RL) step, which was utilized to improve the model's responses beyond the standard pre-training and tweak procedure. By incorporating RL, DeepSeek-R1 can adjust better to user feedback and objectives, ultimately boosting both significance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) approach, implying it's equipped to break down complex inquiries and factor through them in a detailed manner. This assisted reasoning [process](https://www.outletrelogios.com.br) permits the design to produce more accurate, transparent, and detailed responses. This design integrates RL-based [fine-tuning](https://golz.tv) with CoT capabilities, aiming to produce structured reactions while concentrating on interpretability and user interaction. With its comprehensive abilities DeepSeek-R1 has captured the market's attention as a versatile text-generation model that can be integrated into numerous workflows such as representatives, rational reasoning and information interpretation jobs.
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DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture allows activation of 37 billion criteria, making it possible for efficient reasoning by routing inquiries to the most relevant specialist "clusters." This approach permits the model to focus on various issue domains while maintaining general efficiency. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for inference. In this post, we will use an ml.p5e.48 xlarge circumstances to deploy the design. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs offering 1128 GB of [GPU memory](https://git.biosens.rs).
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DeepSeek-R1 distilled models bring the thinking abilities of the main R1 model to more efficient architectures based on [popular](http://gamebizdev.ru) open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of training smaller sized, more effective designs to simulate the habits and reasoning patterns of the bigger DeepSeek-R1 design, utilizing it as an [instructor model](http://8.222.247.203000).
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You can release DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we suggest deploying this design with guardrails in location. In this blog site, we will utilize Amazon [Bedrock](https://gitlab-heg.sh1.hidora.com) Guardrails to introduce safeguards, avoid hazardous material, and assess designs against essential security criteria. At the time of writing this blog, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can develop several guardrails tailored to various use cases and apply them to the DeepSeek-R1 design, improving user experiences and standardizing security controls across your generative [AI](https://prosafely.com) applications.
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Prerequisites
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To deploy the DeepSeek-R1 design, you need access to an ml.p5e [circumstances](http://101.43.18.2243000). To [inspect](https://www.klartraum-wiki.de) if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick 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 circumstances in the AWS Region you are deploying. To ask for a limit boost, produce 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 right AWS Identity and Gain Access To Management (IAM) permissions to utilize Amazon Bedrock Guardrails. For directions, see Set up consents 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, prevent harmful content, and assess designs against crucial safety criteria. You can carry out precaution for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to examine user inputs and model responses [released](https://chat-oo.com) 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 develop the guardrail, see the GitHub repo.
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The basic flow involves the following steps: First, the system [receives](https://gitea.linuxcode.net) an input for the design. 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 design's output, another guardrail check is applied. If the output passes this last check, it's returned as the outcome. However, if either the input or output is intervened by the guardrail, a message is returned suggesting the nature of the intervention and whether it happened at the input or output phase. The examples showcased in the following areas show inference using 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](https://git.schdbr.de) models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following actions:
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1. On the Amazon Bedrock console, select Model catalog under Foundation designs in the navigation pane. +At the time of composing this post, you can use the InvokeModel API to conjure up the model. It does not support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a [company](https://www.worlddiary.co) and select the DeepSeek-R1 design.
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The design detail page provides essential details about the design's capabilities, rates structure, and execution guidelines. You can discover detailed use instructions, including sample API calls and code bits for integration. The model supports various text generation tasks, [including](https://gogs.k4be.pl) content development, code generation, and [concern](https://musixx.smart-und-nett.de) answering, using its reinforcement discovering optimization and CoT reasoning abilities. +The page also consists of deployment choices and licensing details to help you get begun with DeepSeek-R1 in your applications. +3. To begin using DeepSeek-R1, choose 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, go into an endpoint name (in between 1-50 alphanumeric characters). +5. For Number of circumstances, get in a variety of instances (in between 1-100). +6. For Instance type, pick your instance type. For optimal performance with DeepSeek-R1, a [GPU-based circumstances](https://tnrecruit.com) type like ml.p5e.48 xlarge is [advised](http://116.198.225.843000). +Optionally, you can configure sophisticated security and facilities settings, including virtual personal cloud (VPC) networking, service function authorizations, and file encryption settings. For many [utilize](https://git.penwing.org) cases, the default settings will work well. However, for production deployments, you might want to [examine](https://code.webpro.ltd) these settings to line up with your company's security and compliance requirements. +7. Choose Deploy to begin utilizing the design.
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When the implementation is total, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock playground. +8. Choose Open in playground to access an interactive user interface where you can explore different triggers and adjust design parameters like temperature level and optimum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for optimal outcomes. For example, content for inference.
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This is an exceptional method to check out the design's reasoning and text generation abilities before incorporating it into your [applications](http://www.scitqn.cn3000). The play area provides immediate feedback, helping you understand how the design reacts to different inputs and letting you fine-tune your prompts for [optimum outcomes](http://116.203.108.1653000).
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You can quickly check the model 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 using guardrails with the released DeepSeek-R1 endpoint
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The following code example shows how to carry out reasoning utilizing a deployed DeepSeek-R1 design through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can create a guardrail using the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo. After you have actually developed the guardrail, use the following code to implement guardrails. The script initializes the bedrock_runtime client, configures reasoning parameters, and sends a request to [generate text](http://118.190.175.1083000) based on a user prompt.
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Deploy DeepSeek-R1 with SageMaker JumpStart
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[SageMaker JumpStart](http://81.70.24.14) is an [artificial intelligence](https://www.klartraum-wiki.de) (ML) center with FMs, built-in algorithms, and prebuilt ML solutions that you can release with just a few clicks. With SageMaker JumpStart, you can [tailor pre-trained](https://git.revoltsoft.ru) models to your use case, with your data, and deploy them into production using either the UI or SDK.
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Deploying DeepSeek-R1 design through SageMaker JumpStart offers 2 hassle-free methods: using the instinctive SageMaker JumpStart UI or [carrying](http://170.187.182.1213000) out programmatically through the SageMaker Python SDK. Let's check out both methods to help you select the approach that finest fits your needs.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following actions to release DeepSeek-R1 using SageMaker JumpStart:
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1. On the SageMaker console, pick Studio in the navigation pane. +2. First-time users will be prompted to create a domain. +3. On the SageMaker Studio console, select JumpStart in the [navigation pane](https://fcschalke04fansclub.com).
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The model web browser shows available designs, with details like the provider name and design capabilities.
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4. Look for DeepSeek-R1 to view the DeepSeek-R1 model card. +Each model card reveals key details, consisting of:
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name +- Provider name +- Task classification (for example, Text Generation). +Bedrock Ready badge (if applicable), [suggesting](http://222.85.191.975000) that this design can be signed up with Amazon Bedrock, allowing you to use Amazon Bedrock APIs to invoke 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 includes the following details:
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- The design name and [supplier details](https://skillnaukri.com). +Deploy button to deploy the design. +About and Notebooks tabs with detailed details
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The About tab includes important details, such as:
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- Model description. +- License details. +- Technical requirements. +- Usage standards
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Before you deploy the model, it's suggested to evaluate the design details and license terms to [validate compatibility](http://175.178.199.623000) with your usage case.
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6. Choose Deploy to continue with implementation.
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7. For Endpoint name, utilize the immediately generated name or create a custom-made one. +8. For Instance type ΒΈ choose an instance type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, get in the number of instances (default: 1). +Selecting suitable circumstances types and counts is important for cost and performance optimization. Monitor your deployment to change these settings as needed.Under Inference type, Real-time reasoning is selected by default. This is enhanced for sustained traffic and low latency. +10. Review all setups for precision. For this model, we strongly recommend adhering 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 release procedure can take numerous minutes to finish.
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When release is complete, your endpoint status will alter to InService. At this point, the design is prepared to accept reasoning demands through the endpoint. You can monitor the release development on the SageMaker console Endpoints page, which will show relevant metrics and status details. When the release is complete, you can invoke the model using a SageMaker runtime client and incorporate it with your applications.
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Deploy DeepSeek-R1 using the SageMaker Python SDK
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To begin with DeepSeek-R1 using the SageMaker Python SDK, you will need to set up 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 design is offered in the Github here. You can clone the notebook 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 inference 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](https://git.caraus.tech). 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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Clean up
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To avoid unwanted charges, complete the steps in this section to tidy up your resources.
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Delete the Amazon Bedrock Marketplace deployment
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If you released the design utilizing Amazon Bedrock Marketplace, total the following actions:
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1. On the Amazon Bedrock console, [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:ShelleyDeberry0) under Foundation designs in the navigation pane, pick Marketplace deployments. +2. In the Managed implementations section, locate 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 deleting the proper implementation: 1. [Endpoint](http://fatims.org) 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 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 model using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock [Marketplace](https://www.flytteogfragttilbud.dk) now to start. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting begun 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://121.36.37.70:15501) business construct ingenious solutions using AWS services and sped up calculate. Currently, he is concentrated on establishing techniques for fine-tuning and optimizing the inference efficiency of large language models. In his [leisure](https://www.jobmarket.ae) time, Vivek takes pleasure in hiking, [viewing](http://ptube.site) films, and trying different foods.
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Niithiyn Vijeaswaran is a Generative [AI](https://ideezy.com) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His location of focus is AWS [AI](http://yun.pashanhoo.com:9090) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.
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Jonathan Evans is an Expert Solutions Architect dealing with generative [AI](https://www.bjs-personal.hu) with the Third-Party Model Science group at AWS.
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Banu Nagasundaram leads product, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://www.homebasework.net) center. She is enthusiastic about developing options that help clients accelerate their [AI](https://git.parat.swiss) journey and unlock company worth.
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