From 4301524b244d2c92ba0957452fbe512d0cb3dfca Mon Sep 17 00:00:00 2001 From: kalaportus7574 Date: Thu, 3 Apr 2025 01:55:36 +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..b0977be --- /dev/null +++ b/DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart.-.md @@ -0,0 +1,93 @@ +
Today, we are excited 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 release DeepSeek [AI](http://xn--o39aoby1e85nw4rx0fwvcmubsl71ekzf4w4a.kr)'s [first-generation frontier](http://47.242.77.180) model, DeepSeek-R1, together with the distilled versions varying from 1.5 to 70 billion criteria to develop, experiment, and responsibly scale your generative [AI](https://www.soundofrecovery.org) ideas on AWS.
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In this post, we demonstrate how to get started with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable 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 large language model (LLM) developed by DeepSeek [AI](http://120.46.37.243:3000) that uses support learning to improve reasoning capabilities through a [multi-stage training](https://dreamcorpsllc.com) procedure from a DeepSeek-V3-Base structure. A key differentiating function is its reinforcement knowing (RL) action, which was used to fine-tune the design's reactions beyond the basic pre-training and fine-tuning procedure. By incorporating RL, DeepSeek-R1 can adjust better to user feedback and goals, ultimately boosting both importance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) method, indicating it's equipped to break down complex inquiries and reason through them in a detailed way. This guided thinking process enables the design to produce more accurate, transparent, and detailed responses. This model integrates RL-based fine-tuning with CoT capabilities, aiming to produce structured actions while concentrating on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has caught the market's attention as a flexible text-generation design that can be integrated into various workflows such as representatives, logical thinking and information interpretation tasks.
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DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion [criteria](http://boiler.ttoslinux.org8888) in size. The MoE architecture allows activation of 37 billion specifications, enabling effective inference by routing queries to the most relevant expert "clusters." This approach permits the model to concentrate on various issue domains while maintaining total effectiveness. DeepSeek-R1 requires a minimum of 800 GB of HBM memory 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 comes with 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 model to more effective architectures based on [popular](http://www.yasunli.co.id) open models like Qwen (1.5 B, 7B, [higgledy-piggledy.xyz](https://higgledy-piggledy.xyz/index.php/User:JeniferHorton1) 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of training smaller, more [effective designs](http://www.grainfather.de) to mimic the habits and thinking patterns of the bigger DeepSeek-R1 model, utilizing it as a teacher design.
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You can release DeepSeek-R1 model either through SageMaker JumpStart or [Bedrock](https://ari-sound.aurumai.io) Marketplace. Because DeepSeek-R1 is an emerging model, we suggest deploying this model with guardrails in place. In this blog site, we will utilize Amazon Bedrock Guardrails to present safeguards, avoid [damaging](http://dev.shopraves.com) material, and [forum.pinoo.com.tr](http://forum.pinoo.com.tr/profile.php?id=1335129) assess designs against key safety requirements. At the time of composing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can produce several guardrails tailored to different use cases and use them to the DeepSeek-R1 design, enhancing user experiences and standardizing safety controls throughout your generative [AI](https://gitea.itskp-odense.dk) applications.
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Prerequisites
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To deploy the DeepSeek-R1 design, you need access to an ml.p5e instance. To examine 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 usage. Make certain that you have at least one ml.P5e.48 xlarge circumstances in the AWS Region you are deploying. To request a limitation increase, [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:CatalinaHoffnung) produce a limit increase request and reach out to your account group.
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Because you will be deploying this design with Amazon Bedrock Guardrails, make certain you have the appropriate AWS Identity and Gain Access To Management (IAM) consents to use Amazon Bedrock Guardrails. For directions, see Set up permissions to use [guardrails](http://39.98.194.763000) 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 hazardous content, and examine designs against essential security criteria. You can execute precaution for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This enables you to use guardrails to [evaluate](https://vazeefa.com) user inputs and design responses released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can develop a guardrail utilizing 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 involves](https://www.happylove.it) 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, it's sent to the model for reasoning. After getting the model's output, another guardrail check is used. If the output passes this last check, it's returned as the last result. However, [higgledy-piggledy.xyz](https://higgledy-piggledy.xyz/index.php/User:MilesFellows9) if either the input or output is intervened by the guardrail, a message is returned indicating the nature of the intervention and whether it took place at the input or output phase. The examples in the following sections demonstrate 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, pick Model catalog under Foundation models in the navigation pane. +At the time of writing this post, you can use the InvokeModel API to invoke the design. It does not support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a service provider and choose the DeepSeek-R1 design.
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The model detail page offers necessary details about the model's capabilities, prices structure, and application standards. You can find detailed usage guidelines, including [sample API](http://pplanb.co.kr) calls and code bits for combination. The model supports different text generation tasks, including material production, code generation, and question answering, using its reinforcement discovering optimization and CoT reasoning abilities. +The page also includes deployment alternatives and licensing details to assist you begin with DeepSeek-R1 in your applications. +3. To start utilizing DeepSeek-R1, choose Deploy.
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You will be prompted to configure 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 Variety of instances, go into a variety of instances (in between 1-100). +6. For example type, choose 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 private cloud (VPC) networking, [service role](http://120.46.37.2433000) approvals, and file encryption settings. For [surgiteams.com](https://surgiteams.com/index.php/User:TracyHiller54) most utilize cases, the default settings will work well. However, for production releases, you might wish to examine these settings to align with your company's security and compliance requirements. +7. Choose Deploy to begin [utilizing](https://bgzashtita.es) the model.
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When the release is total, you can test DeepSeek-R1's abilities straight in the Amazon Bedrock play area. +8. Choose Open in play area to access an interactive interface where you can explore various prompts and adjust model criteria like temperature and maximum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for ideal outcomes. For example, content for inference.
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This is an [excellent](https://wiki.lspace.org) way to check out the model's thinking and text generation capabilities before incorporating it into your applications. The play ground supplies immediate feedback, assisting you understand how the design responds to numerous inputs and letting you tweak your prompts for optimum outcomes.
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You can rapidly test the design in the play ground through the UI. However, to conjure up the deployed model [programmatically](https://kahkaham.net) 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 reasoning utilizing 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 or [engel-und-waisen.de](http://www.engel-und-waisen.de/index.php/Benutzer:ReedDugger) 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 [execute guardrails](https://eukariyer.net). The script initializes the bedrock_runtime customer, sets up inference specifications, and sends a request to produce text based upon 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, 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 use case, with your data, and deploy them into production utilizing either the UI or SDK.
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Deploying DeepSeek-R1 design through SageMaker JumpStart provides 2 convenient approaches: using the intuitive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's explore both approaches to help you select the approach that best matches your needs.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following steps to release 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 develop 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 company name and design abilities.
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4. Look for DeepSeek-R1 to see the DeepSeek-R1 design card. +Each design card shows crucial details, consisting of:
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- Model name +- Provider name +- Task classification (for example, Text Generation). +Bedrock Ready badge (if applicable), suggesting that this design can be signed up with Amazon Bedrock, [allowing](http://appleacademy.kr) you to use Amazon Bedrock APIs to conjure up the model
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5. Choose the model card to see the design details page.
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The design details page includes the following details:
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- The model name and supplier details. +Deploy button to release the design. +About and Notebooks tabs with detailed details
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The About tab consists of essential details, such as:
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- Model [description](https://www.top5stockbroker.com). +- License details. +- Technical specs. +- Usage guidelines
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Before you release the model, it's suggested to examine the design details and license terms to verify compatibility with your use case.
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6. Choose Deploy to proceed with release.
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7. For Endpoint name, use the instantly produced name or [develop](https://git.songyuchao.cn) a customized one. +8. For example type ΒΈ select an instance type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, go into the variety of instances (default: 1). +Selecting proper instance types and counts is [essential](https://sc.e-path.cn) for cost and efficiency optimization. [Monitor](https://nurseportal.io) your [implementation](https://www.ayuujk.com) to adjust these settings as needed.Under Inference type, [Real-time inference](https://socials.chiragnahata.is-a.dev) is picked by default. This is optimized for sustained traffic and low latency. +10. Review all configurations for precision. For this model, we [highly advise](https://www.50seconds.com) adhering to SageMaker JumpStart [default settings](https://10-4truckrecruiting.com) and making certain that network seclusion remains in place. +11. Choose Deploy to release the model.
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The implementation procedure can take several minutes to complete.
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When release is complete, your endpoint status will change to InService. At this point, the design is ready to accept reasoning requests through the endpoint. You can keep an eye on the implementation progress on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the implementation is complete, [systemcheck-wiki.de](https://systemcheck-wiki.de/index.php?title=Benutzer:HallieBoothe4) you can invoke the model using a SageMaker runtime client and integrate it with your applications.
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Deploy DeepSeek-R1 utilizing 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 essential AWS approvals and environment setup. The following is a detailed code example that shows how to deploy and use DeepSeek-R1 for inference programmatically. The code for releasing the design is provided in the Github here. You can clone the note pad and range from SageMaker Studio.
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You can run extra 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 create a guardrail utilizing the Amazon Bedrock console or the API, and execute it as displayed in the following code:
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Clean up
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To avoid [unwanted](http://8.211.134.2499000) charges, finish the steps in this area to tidy up your resources.
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Delete the Amazon Bedrock Marketplace implementation
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If you deployed the model using Amazon Bedrock Marketplace, complete the following steps:
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1. On the Amazon Bedrock console, under Foundation models in the navigation pane, select Marketplace releases. +2. In the Managed deployments section, find the endpoint you want 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 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 [JumpStart design](https://bewerbermaschine.de) you released will sustain expenses if you leave it running. Use the following code to erase the endpoint if you want 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](http://git.fmode.cn3000) and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get started. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained designs, Amazon SageMaker [JumpStart Foundation](https://whoosgram.com) 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://littlebigempire.com) [companies develop](https://www.zapztv.com) ingenious options utilizing AWS [services](https://www.speedrunwiki.com) and sped up compute. Currently, he is focused on establishing strategies for fine-tuning and optimizing the inference efficiency of big language designs. In his downtime, Vivek takes pleasure in treking, viewing films, and trying different foods.
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Niithiyn Vijeaswaran is a [Generative](http://114.55.2.296010) [AI](https://www.ayuujk.com) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His location of focus is AWS [AI](https://lovelynarratives.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 [AI](https://gitea.blubeacon.com) with the Third-Party Model Science team at AWS.
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Banu Nagasundaram leads item, engineering, and tactical collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://git.purplepanda.cc) hub. She is passionate about developing solutions that assist consumers accelerate their [AI](https://followmylive.com) journey and unlock company worth.
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