From 2ff6a34b17e9602d20324653ad0a85336c889deb Mon Sep 17 00:00:00 2001 From: Antonia Martinovich Date: Thu, 27 Feb 2025 17:41:07 +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..820355b --- /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 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 deploy DeepSeek [AI](http://chichichichichi.top:9000)'s first-generation frontier design, DeepSeek-R1, in addition to the distilled versions varying from 1.5 to 70 billion parameters to develop, experiment, and properly scale your generative [AI](https://plamosoku.com) concepts 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 comparable actions to release the distilled versions of the models as well.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a big language design (LLM) established by DeepSeek [AI](https://git.on58.com) that [utilizes reinforcement](https://mychampionssport.jubelio.store) finding out to enhance reasoning capabilities through a multi-stage training process from a DeepSeek-V3-Base structure. An essential distinguishing feature is its support learning (RL) step, which was used to improve the model's reactions beyond the basic pre-training and fine-tuning process. By including RL, DeepSeek-R1 can adjust more successfully to user feedback and objectives, ultimately boosting both importance and clarity. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) technique, suggesting it's equipped to break down intricate inquiries and factor through them in a detailed way. This assisted thinking process permits the model to produce more precise, transparent, [wiki.snooze-hotelsoftware.de](https://wiki.snooze-hotelsoftware.de/index.php?title=Benutzer:BrigetteBastyan) and detailed answers. This design integrates RL-based [fine-tuning](https://okoskalyha.hu) with CoT capabilities, aiming to generate structured reactions while concentrating on interpretability and user interaction. With its extensive capabilities DeepSeek-R1 has actually caught the market's attention as a flexible text-generation model that can be incorporated into various workflows such as representatives, logical reasoning and data interpretation jobs.
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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, making it possible for efficient inference by routing inquiries to the most pertinent expert "clusters." This technique enables the design to specialize in different issue domains while maintaining overall [effectiveness](http://83.151.205.893000). 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 model. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.
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DeepSeek-R1 distilled models bring the [thinking capabilities](https://social.netverseventures.com) of the main R1 model to more effective architectures based on 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, more effective designs to mimic the habits and reasoning patterns of the bigger DeepSeek-R1 design, using it as a teacher design.
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You can release DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11860868) we recommend releasing this design with guardrails in location. In this blog, we will use Amazon Bedrock Guardrails to present safeguards, prevent harmful material, and evaluate models against essential safety requirements. At the time of composing this blog site, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can produce several guardrails tailored to various use cases and use them to the DeepSeek-R1 model, enhancing user experiences and standardizing security controls throughout your generative [AI](https://sound.descreated.com) applications.
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Prerequisites
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To deploy the DeepSeek-R1 design, [archmageriseswiki.com](http://archmageriseswiki.com/index.php/User:KristenSwartz09) you require 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 validate you're utilizing ml.p5e.48 xlarge for [endpoint usage](http://worldwidefoodsupplyinc.com). Make certain that you have at least one ml.P5e.48 xlarge instance in the AWS Region you are deploying. To ask for a [limitation](https://git.itk.academy) increase, [trademarketclassifieds.com](https://trademarketclassifieds.com/user/profile/2847718) create a limitation increase demand and connect to your account team.
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Because you will be [deploying](https://www.hirecybers.com) this model with Amazon Bedrock Guardrails, make certain you have the correct [AWS Identity](https://www.kmginseng.com) and Gain Access To Management (IAM) consents to [utilize Amazon](https://eduberkah.disdikkalteng.id) Bedrock Guardrails. For guidelines, see Set up approvals to use guardrails for material filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails allows you to present safeguards, prevent hazardous material, and examine models against essential safety requirements. You can execute precaution for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to examine user inputs and [design actions](http://soho.ooi.kr) released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a guardrail using the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo.
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The basic flow includes the following actions: First, the system receives an input for the design. This input is then processed 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 final outcome. 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 happened at the input or output stage. The [examples](http://www.cl1024.online) showcased in the following areas show 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 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, [pipewiki.org](https://pipewiki.org/wiki/index.php/User:EveretteButters) select Model brochure under Foundation models in the navigation pane. +At the time of composing this post, you can use the InvokeModel API to conjure up the model. It doesn't support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a provider and pick the DeepSeek-R1 design.
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The model detail page offers necessary details about the model's abilities, prices structure, and application guidelines. You can discover detailed use guidelines, consisting of sample API calls and code snippets for combination. The design supports different text generation tasks, consisting of material creation, code generation, and concern answering, using its support discovering optimization and [CoT thinking](https://git.mikecoles.us) capabilities. +The page also consists of implementation options and licensing details to help you get going with DeepSeek-R1 in your applications. +3. To start utilizing DeepSeek-R1, pick 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, get in an endpoint name (in between 1-50 alphanumeric characters). +5. For Number of instances, get in a number of circumstances (in between 1-100). +6. For Instance type, select your circumstances type. For ideal efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is suggested. +Optionally, you can set up innovative security and infrastructure settings, including virtual private cloud (VPC) networking, service function consents, and file encryption settings. For a lot of utilize cases, the default settings will work well. However, for production deployments, you may desire to review these settings to align with your company's security and compliance requirements. +7. Choose Deploy to start utilizing the design.
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When the deployment is total, you can check DeepSeek-R1's abilities straight in the Amazon Bedrock playground. +8. Choose Open in play area to access an interactive user interface where you can explore various triggers and adjust design parameters like temperature and optimum length. +When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for ideal outcomes. For instance, content for reasoning.
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This is an outstanding way to explore the design's thinking and text generation capabilities before incorporating it into your applications. The playground supplies immediate feedback, helping you understand how the model responds to different inputs and letting you fine-tune your prompts for optimal results.
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You can rapidly test the model in the play area through the UI. However, to invoke the released model programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
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Run inference utilizing guardrails with the [released](https://jobs1.unifze.com) DeepSeek-R1 endpoint
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The following code example shows how to perform inference using 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](https://apyarx.com) or the API. For the example code to develop the guardrail, see the GitHub repo. After you have developed the guardrail, use the following code to execute guardrails. The script initializes the bedrock_runtime customer, sets up inference criteria, and sends a request to produce text based on a user timely.
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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 services that you can release with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your information, and deploy them into production using either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart offers two practical methods: using the instinctive SageMaker JumpStart UI or carrying out [programmatically](http://www.vpsguards.co) through the SageMaker Python SDK. Let's check out both methods to assist you select the method that best matches 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 utilizing 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 produce a domain. +3. On the SageMaker Studio console, pick JumpStart in the navigation pane.
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The design internet browser displays available designs, with details like the supplier name and design capabilities.
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4. Search for DeepSeek-R1 to view the DeepSeek-R1 design card. +Each model card reveals crucial details, consisting of:
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- Model name +- Provider name +- Task category (for instance, Text Generation). +Bedrock Ready badge (if appropriate), suggesting that this design can be signed up with Amazon Bedrock, permitting you to use Amazon Bedrock APIs to invoke the model
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5. Choose the model card to view the design details page.
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The design details page includes the following details:
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- The design name and [service provider](https://betalk.in.th) details. +Deploy button to deploy the design. +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](https://heartbeatdigital.cn). +- License details. +- Technical specs. +- Usage guidelines
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Before you release the model, [hb9lc.org](https://www.hb9lc.org/wiki/index.php/User:DerrickBlackburn) it's recommended to review the design details and license terms to validate compatibility with your use case.
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6. Choose Deploy to [proceed](https://pioneerayurvedic.ac.in) with implementation.
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7. For Endpoint name, use the immediately created name or produce a customized one. +8. For example type ΒΈ choose an instance type (default: ml.p5e.48 xlarge). +9. For Initial instance count, enter the variety of circumstances (default: 1). +Selecting appropriate circumstances types and counts is vital for cost and efficiency optimization. Monitor your implementation to adjust these settings as needed.Under Inference type, Real-time inference is picked by default. This is enhanced for sustained traffic and low latency. +10. Review all configurations for accuracy. For this design, we strongly suggest sticking to SageMaker JumpStart default settings and making certain that network isolation remains in location. +11. Choose Deploy to deploy the model.
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The [release procedure](https://liveyard.tech4443) can take numerous minutes to complete.
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When implementation is complete, your endpoint status will alter to [InService](https://charmyajob.com). At this point, the design is ready to accept reasoning demands through the endpoint. You can monitor the implementation progress on the SageMaker console Endpoints page, which will display relevant metrics and status details. When the deployment is total, you can conjure up the design utilizing a [SageMaker](https://gitlab.innive.com) runtime client and integrate 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 utilizing the SageMaker Python SDK, you will need 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 demonstrates how to release and utilize DeepSeek-R1 for inference programmatically. The code for deploying the model is supplied in the Github here. You can clone the note pad 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 produce a guardrail using 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 prevent undesirable charges, finish the steps in this section to clean up your resources.
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Delete the Amazon Bedrock Marketplace deployment
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If you deployed the design using Amazon Bedrock Marketplace, total the following actions:
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1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, pick Marketplace deployments. +2. In the Managed releases area, locate the [endpoint](http://47.107.80.2363000) you want to delete. +3. Select the endpoint, and on the Actions menu, select Delete. +4. Verify the endpoint details to make certain you're erasing the appropriate release: 1. [Endpoint](http://git.storkhealthcare.cn) 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 expenses if you leave it running. Use the following code to erase the endpoint if you wish 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](https://edujobs.itpcrm.net) how you can access and [higgledy-piggledy.xyz](https://higgledy-piggledy.xyz/index.php/User:DamianWren8429) release the DeepSeek-R1 design using 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 models, 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 helps emerging generative [AI](https://gitlab.liangzhicn.com) business develop innovative options utilizing AWS services and sped up calculate. Currently, he is concentrated on developing methods for fine-tuning and enhancing the reasoning efficiency of large language designs. In his complimentary time, Vivek enjoys hiking, watching motion pictures, and trying various cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://jobsekerz.com) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His location of focus is AWS [AI](http://charge-gateway.com) 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 working on [generative](http://113.105.183.1903000) [AI](https://chumcity.xyz) with the Third-Party Model Science group at AWS.
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Banu Nagasundaram leads product, engineering, and strategic partnerships for JumpStart, SageMaker's artificial intelligence and generative [AI](https://surreycreepcatchers.ca) hub. She is passionate about constructing solutions that assist customers accelerate their [AI](https://myjobasia.com) journey and unlock organization worth.
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