From 4217d80cd862e6c1eaaa7035b3280d563207152e Mon Sep 17 00:00:00 2001 From: chanelmcalroy Date: Fri, 28 Feb 2025 16:12:39 +0800 Subject: [PATCH] Add '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..7c32002 --- /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 deploy DeepSeek [AI](http://git.foxinet.ru)'s first-generation frontier design, DeepSeek-R1, in addition to the distilled variations ranging from 1.5 to 70 billion criteria to build, experiment, and responsibly scale your generative [AI](https://careerworksource.org) concepts on AWS.
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In this post, we show how to begin with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar actions to release the distilled versions of the models too.
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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://surgiteams.com) that uses reinforcement learning to improve reasoning abilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. An essential differentiating feature is its support knowing (RL) step, which was utilized to fine-tune the design's reactions beyond the basic pre-training and tweak process. By including RL, DeepSeek-R1 can adjust more effectively to user feedback and goals, eventually boosting both significance and clearness. In addition, DeepSeek-R1 uses a [chain-of-thought](https://maram.marketing) (CoT) method, meaning it's geared up to break down intricate inquiries and factor through them in a detailed way. This assisted thinking process permits the model to produce more accurate, transparent, and [systemcheck-wiki.de](https://systemcheck-wiki.de/index.php?title=Benutzer:StuartLowman4) detailed answers. This design integrates RL-based fine-tuning with CoT capabilities, aiming to generate structured actions while focusing on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has captured the industry's attention as a versatile text-generation model that can be integrated into different workflows such as representatives, sensible thinking and data interpretation tasks.
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DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture permits activation of 37 billion parameters, making it possible for efficient reasoning by routing inquiries to the most pertinent professional "clusters." This method allows the design to concentrate on various problem domains while maintaining overall [performance](https://git.christophhagen.de). DeepSeek-R1 needs 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 release the design. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.
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DeepSeek-R1 distilled designs bring the thinking abilities of the main R1 design to more [effective architectures](https://git.xiaoya360.com) based upon popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a process of training smaller sized, more effective models to simulate the habits and of the bigger DeepSeek-R1 model, using it as a teacher model.
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You can deploy DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an [emerging](http://8.140.229.2103000) model, we recommend releasing this design with guardrails in location. In this blog, we will use Amazon Bedrock Guardrails to present safeguards, avoid hazardous content, and examine models against essential security requirements. At the time of writing this blog, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can produce multiple guardrails tailored to various usage cases and use them to the DeepSeek-R1 design, improving user experiences and standardizing safety controls throughout your generative [AI](https://jobsingulf.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 check if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick Amazon SageMaker, and confirm you're utilizing ml.p5e.48 xlarge for endpoint usage. Make certain that you have at least one ml.P5e.48 xlarge circumstances in the AWS Region you are deploying. To request a limit boost, develop a limit boost request and reach out to your [account](http://101.42.41.2543000) 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 [forum.pinoo.com.tr](http://forum.pinoo.com.tr/profile.php?id=1333679) Gain Access To Management (IAM) permissions to use Amazon Bedrock Guardrails. For directions, see Establish authorizations to use guardrails for content 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 material, and assess models against essential safety criteria. You can carry out precaution for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to assess user inputs and design responses deployed 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 produce the guardrail, see the GitHub repo.
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The general circulation includes 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, [yewiki.org](https://www.yewiki.org/User:TommyCulbert459) it's sent to the design for inference. After getting the model's output, another guardrail check is applied. If the [output passes](https://cannabisjobs.solutions) this final 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](http://125.43.68.2263001) showing the nature of the intervention and whether it happened at the input or output stage. The examples showcased in the following sections show inference 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, 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](https://www.weben.online) models in the navigation pane. +At the time of composing this post, you can utilize 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 service provider and select the DeepSeek-R1 model.
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The design detail page provides necessary details about the design's capabilities, prices structure, and implementation guidelines. You can find detailed use guidelines, including sample API calls and code snippets for combination. The design supports numerous text generation tasks, [consisting](http://lstelecom.co.kr) of content development, code generation, and concern answering, using its support finding out optimization and CoT reasoning abilities. +The page likewise consists of deployment options and licensing details to help you begin with DeepSeek-R1 in your applications. +3. To start utilizing DeepSeek-R1, pick Deploy.
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You will be prompted to set up 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 Number of circumstances, enter a variety of circumstances (between 1-100). +6. For example type, pick your circumstances type. For ideal performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is recommended. +Optionally, you can configure advanced security and infrastructure settings, consisting of virtual personal cloud (VPC) networking, service function permissions, and encryption settings. For most utilize cases, the default settings will work well. However, for production deployments, you may want to review these settings to line up with your company's security and compliance requirements. +7. Choose Deploy to begin utilizing the model.
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When the release is complete, you can test DeepSeek-R1's abilities straight in the Amazon Bedrock playground. +8. Choose Open in playground to access an interactive interface where you can experiment with different triggers and adjust model criteria like temperature level and optimum length. +When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for optimal results. For instance, content for inference.
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This is an exceptional way to explore the model's reasoning and [text generation](http://www.thekaca.org) capabilities before incorporating it into your applications. The play area supplies immediate feedback, assisting you understand how the [design reacts](http://travelandfood.ru) to different inputs and [letting](http://47.105.162.154) you tweak your triggers for ideal results.
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You can quickly evaluate the model in the play ground 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 reasoning utilizing guardrails with the released DeepSeek-R1 endpoint
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The following code example shows how to carry out reasoning using a deployed DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can develop a guardrail utilizing the Amazon Bedrock [console](https://contractoe.com) or the API. For the example code to develop the guardrail, see the GitHub repo. After you have developed the guardrail, [utilize](https://www.jobassembly.com) the following code to [implement guardrails](http://vts-maritime.com). The script initializes the bedrock_runtime client, sets up reasoning specifications, 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) center with FMs, integrated algorithms, and prebuilt ML options that you can deploy with just a few clicks. With SageMaker JumpStart, you can tailor [yewiki.org](https://www.yewiki.org/User:ScottyMcIlvain) pre-trained designs to your usage case, with your data, and release them into production utilizing either the UI or SDK.
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[Deploying](https://bandbtextile.de) DeepSeek-R1 model through SageMaker JumpStart offers two practical approaches: utilizing the user-friendly SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's explore both methods to help you select the approach that best matches your requirements.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following steps to release DeepSeek-R1 utilizing SageMaker JumpStart:
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1. On the SageMaker console, choose Studio in the [navigation](http://107.182.30.1906000) pane. +2. First-time users will be prompted to develop a domain. +3. On the SageMaker Studio console, pick JumpStart in the navigation pane.
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The model browser displays available models, with details like the service provider name and design abilities.
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4. Search for DeepSeek-R1 to view the DeepSeek-R1 [model card](https://church.ibible.hk). +Each model card reveals key details, including:
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- Model name +- Provider name +- Task category (for instance, Text Generation). +Bedrock Ready badge (if suitable), [indicating](https://vidhiveapp.com) that this design can be registered with Amazon Bedrock, allowing you to utilize Amazon Bedrock APIs to conjure up the model
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5. Choose the model card to see the design [details](https://daystalkers.us) page.
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The model details page includes the following details:
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- The design name and service provider details. +[Deploy button](https://www.ojohome.listatto.ca) to deploy 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 requirements. +- Usage standards
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Before you release the design, it's recommended to evaluate the design details and license terms to confirm compatibility with your usage case.
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6. Choose Deploy to continue with [release](https://git.mbyte.dev).
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7. For Endpoint name, utilize the automatically produced name or develop a [customized](https://job4thai.com) one. +8. For example type ΒΈ choose an instance type (default: ml.p5e.48 xlarge). +9. For Initial instance count, enter the number of circumstances (default: 1). +Selecting suitable circumstances types and counts is crucial for [wiki.myamens.com](http://wiki.myamens.com/index.php/User:OpalHenn8730) cost and efficiency 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 configurations for precision. For this model, we strongly advise adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in location. +11. [Choose Deploy](https://wiki.rolandradio.net) to deploy the design.
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The deployment procedure can take a number of minutes to finish.
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When release is total, your endpoint status will change to InService. At this point, the model is prepared to accept inference demands through the endpoint. You can keep an eye on the implementation development 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 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 start 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 approvals and environment setup. The following is a detailed code example that demonstrates how to deploy and use DeepSeek-R1 for inference programmatically. The code for deploying the model is provided in the Github here. You can clone the note pad 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 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. You can [develop](https://whotube.great-site.net) a guardrail using the Amazon Bedrock console or the API, and implement it as shown in the following code:
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Tidy up
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To prevent undesirable charges, complete the steps in this section to clean up your resources.
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Delete the Amazon Bedrock Marketplace release
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If you released the design utilizing Amazon Bedrock Marketplace, total the following steps:
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1. On the Amazon Bedrock console, under Foundation models in the navigation pane, pick Marketplace implementations. +2. In the Managed releases section, locate the [endpoint](http://sgvalley.co.kr) you wish to erase. +3. Select the endpoint, and on the Actions menu, select Delete. +4. Verify the endpoint details to make certain you're deleting 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 model you released will sustain costs if you leave it running. Use the following code to delete 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 how you can access and deploy the DeepSeek-R1 design using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to begin. For more details, describe 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](https://paksarkarijob.com) generative [AI](https://www.nc-healthcare.co.uk) companies develop ingenious services using AWS services and sped up [calculate](https://careers.ecocashholdings.co.zw). Currently, he is concentrated on establishing techniques for fine-tuning and optimizing the inference efficiency of large language models. In his leisure time, Vivek delights in treking, seeing films, and attempting various cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://git.xaviermaso.com) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His location of focus is AWS [AI](http://git.gonstack.com) 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 working on generative [AI](https://gitea.gumirov.xyz) with the Third-Party Model Science group at AWS.
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Banu Nagasundaram leads item, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://webloadedsolutions.com) hub. She is passionate about developing solutions that assist clients accelerate their [AI](http://archmageriseswiki.com) journey and unlock business value.
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