From 5a1251bbc48a900d97b55410ae5686ae20de8c33 Mon Sep 17 00:00:00 2001 From: terifranks3925 Date: Sun, 6 Apr 2025 14:36:52 +0000 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..a29674e --- /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 designs are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](https://aggm.bz)'s first-generation frontier design, DeepSeek-R1, together with the distilled versions varying from 1.5 to 70 billion specifications to construct, experiment, and properly scale your generative [AI](http://60.250.156.230:3000) ideas 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 similar steps to release the distilled variations of the designs as well.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a large language model (LLM) developed by DeepSeek [AI](https://git.rell.ru) that utilizes support finding out to enhance thinking capabilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. A crucial differentiating function is its [support](https://usa.life) [knowing](https://www.grandtribunal.org) (RL) action, which was utilized to refine the design's reactions beyond the standard pre-training and [fine-tuning procedure](https://www.menacopt.com). By incorporating RL, DeepSeek-R1 can adapt more efficiently to user feedback and objectives, ultimately boosting both significance and clarity. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) technique, indicating it's equipped to break down complicated inquiries and factor through them in a detailed way. This assisted thinking procedure enables the design to produce more precise, transparent, [kousokuwiki.org](http://kousokuwiki.org/wiki/%E5%88%A9%E7%94%A8%E8%80%85:KelleySpowers1) and [detailed answers](https://git.eisenwiener.com). This model integrates RL-based fine-tuning with CoT capabilities, aiming to produce structured actions while focusing on interpretability and user interaction. With its comprehensive abilities DeepSeek-R1 has caught the industry's attention as a versatile text-generation model that can be incorporated into numerous workflows such as representatives, logical thinking and information analysis jobs.
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DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture permits activation of 37 billion specifications, making it possible for effective reasoning by routing queries to the most relevant expert "clusters." This approach permits the model to focus on various issue domains while maintaining general performance. DeepSeek-R1 needs at least 800 GB of [HBM memory](https://sudanre.com) in FP8 format for inference. In this post, we will utilize an ml.p5e.48 xlarge circumstances to deploy 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 reasoning capabilities of the main R1 design to more [efficient architectures](https://social.stssconstruction.com) based on popular open [designs](https://gitlab.ucc.asn.au) like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of training smaller sized, more efficient designs to mimic the habits and thinking patterns of the bigger DeepSeek-R1 design, utilizing it as a teacher design.
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You can deploy DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we recommend deploying this design with guardrails in location. In this blog, we will utilize Amazon Bedrock Guardrails to introduce safeguards, avoid damaging content, and examine models against [essential](http://121.40.194.1233000) security criteria. At the time of writing this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create numerous guardrails tailored to different usage cases and apply them to the DeepSeek-R1 design, enhancing user [experiences](https://droomjobs.nl) and standardizing security controls throughout your generative [AI](http://120.36.2.217:9095) 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, select Amazon SageMaker, and confirm you're using 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 ask for a limitation boost, develop a limit increase demand and connect to your account team.
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Because you will be releasing this model with Amazon Bedrock Guardrails, make certain you have the right AWS Identity and Gain Access To Management (IAM) approvals to use Amazon Bedrock Guardrails. For guidelines, see Establish permissions to utilize guardrails for material filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails allows you to introduce safeguards, avoid damaging content, and examine designs against crucial safety requirements. You can execute precaution for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to evaluate user inputs and design reactions 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 develop 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 model. 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 final check, it's returned as the result. However, if either the input or output is intervened by the guardrail, a message is returned indicating the nature of the intervention and whether it occurred at the input or output stage. The examples showcased in the following sections demonstrate reasoning 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 structure](https://gl.b3ta.pl) models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following steps:
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1. On the Amazon Bedrock console, pick Model catalog under Foundation designs in the navigation pane. +At the time of writing this post, you can use the InvokeModel API to invoke the design. It doesn't support Converse APIs and other Amazon Bedrock tooling. +2. Filter for [hb9lc.org](https://www.hb9lc.org/wiki/index.php/User:LonDorron09442) DeepSeek as a company and select the DeepSeek-R1 design.
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The design detail page offers necessary details about the design's capabilities, pricing structure, and application standards. You can discover detailed use directions, consisting of sample API calls and code snippets for integration. The model supports different text generation jobs, consisting of content creation, code generation, and question answering, using its [support learning](https://medea.medianet.cs.kent.edu) optimization and CoT reasoning capabilities. +The page also includes release choices and licensing details to help you begin with DeepSeek-R1 in your applications. +3. To start using DeepSeek-R1, choose Deploy.
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You will be prompted to set up the deployment 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 circumstances, get in a variety of instances (in between 1-100). +6. For example type, select your circumstances type. For ideal efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is recommended. +Optionally, [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:GenieFaulding06) you can set up advanced security and [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11860868) facilities settings, consisting of 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 wish to evaluate these settings to line up with your organization's security and compliance requirements. +7. Choose Deploy to start utilizing the model.
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When the release is complete, you can check DeepSeek-R1's abilities straight in the Amazon Bedrock play ground. +8. Choose Open in play ground to access an interactive user interface where you can try out different prompts and change model specifications like temperature level and maximum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for optimum outcomes. For example, content for inference.
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This is an excellent method to check out the model's thinking and text generation capabilities before incorporating it into your applications. The playground supplies instant feedback, helping you comprehend how the [model reacts](https://lr-mediconsult.de) to numerous inputs and letting you fine-tune your triggers for optimal outcomes.
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You can quickly check the model in the playground through the UI. However, to conjure up the released model programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
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Run reasoning utilizing guardrails with the deployed DeepSeek-R1 endpoint
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The following code example demonstrates how to perform reasoning using a released DeepSeek-R1 design through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can produce a guardrail utilizing the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo. After you have created the guardrail, use the following code to carry out guardrails. The script initializes the bedrock_runtime customer, configures reasoning criteria, and sends out a request to generate 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 options that you can release with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your use case, with your information, and release them into production utilizing either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart offers 2 convenient techniques: [utilizing](https://www.ojohome.listatto.ca) the user-friendly SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's check out 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, choose 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.
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The design internet browser displays available models, with details like the provider name and model capabilities.
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4. Look for DeepSeek-R1 to view the DeepSeek-R1 model card. +Each design card reveals crucial details, including:
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- Model name +- [Provider](https://camtalking.com) name +- Task classification (for example, Text Generation). +Bedrock Ready badge (if suitable), indicating that this design can be signed up with Amazon Bedrock, enabling you to utilize Amazon Bedrock APIs to conjure up the model
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5. Choose the model card to see the model details page.
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The design details page consists of the following details:
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- The design name and service provider details. +Deploy button to release the model. +About and Notebooks tabs with detailed details
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The About tab includes [crucial](https://woowsent.com) details, such as:
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- Model description. +- License details. +- Technical specifications. +- Usage standards
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Before you release the design, it's advised to review 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, utilize the automatically produced name or create a customized one. +8. For example type ΒΈ select a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial instance count, go into the number of instances (default: 1). +Selecting appropriate circumstances types and counts is vital for cost and performance optimization. Monitor your release to adjust these settings as needed.Under Inference type, Real-time inference is chosen by default. This is enhanced for [sustained traffic](https://vybz.live) and low latency. +10. Review all configurations for precision. For this design, we highly suggest sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in location. +11. Choose Deploy to deploy the model.
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The implementation procedure can take a number of minutes to complete.
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When release is total, your endpoint status will change to InService. At this point, the model is all set to accept inference requests through the endpoint. You can keep track of the deployment development on the SageMaker console Endpoints page, which will display pertinent metrics and status details. When the release 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 using the SageMaker Python SDK
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To get going with DeepSeek-R1 using the SageMaker Python SDK, you will require to install the SageMaker Python SDK and make certain you have the necessary AWS approvals 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 model is supplied in the Github here. You can clone the note pad 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](http://121.43.99.1283000) with your [SageMaker](https://fcschalke04fansclub.com) 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 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 actions in this section to clean up your resources.
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Delete the Amazon Bedrock Marketplace deployment
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If you 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, pick Marketplace releases. +2. In the Managed releases section, locate the [endpoint](https://goodprice-tv.com) you wish to erase. +3. Select the endpoint, and on the [Actions](https://git.gilesmunn.com) menu, pick Delete. +4. Verify the endpoint details to make certain you're erasing the correct 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](https://familytrip.kr) JumpStart model you released 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 checked out how you can access and deploy the DeepSeek-R1 [design utilizing](https://116.203.22.201) Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker [JumpStart](http://121.40.194.1233000) in SageMaker Studio or Amazon Bedrock Marketplace now to get going. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, SageMaker JumpStart pretrained models, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting going 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://groupeudson.com) companies construct innovative solutions using AWS services and accelerated calculate. Currently, he is focused on developing techniques for fine-tuning and optimizing the reasoning performance of big language designs. In his downtime, Vivek delights in treking, viewing movies, and attempting various foods.
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Niithiyn Vijeaswaran is a Generative [AI](https://git.bwnetwork.us) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His location of focus is AWS [AI](http://152.136.232.113:3000) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and [Bioinformatics](https://git.coalitionofinvisiblecolleges.org).
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Jonathan Evans is an Expert [Solutions Architect](https://travel-friends.net) dealing with generative [AI](http://125.ps-lessons.ru) with the Third-Party Model Science team 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://emplealista.com) hub. She is enthusiastic about building options that help consumers accelerate their [AI](https://git.weingardt.dev) journey and unlock company worth.
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