From ef06a2101dc31d922352af856706a07976f60697 Mon Sep 17 00:00:00 2001 From: joieloftis6452 Date: Thu, 6 Mar 2025 01:33:47 +0700 Subject: [PATCH] Update '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..8cc6327 --- /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 designs](http://106.15.120.1273000) are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](http://code.istudy.wang)'s first-generation frontier design, DeepSeek-R1, along with the distilled versions varying from 1.5 to 70 billion parameters to develop, experiment, and responsibly scale your generative [AI](http://clipang.com) ideas on AWS.
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In this post, we show how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable actions to deploy the distilled variations of the models too.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a big language model (LLM) established by DeepSeek [AI](https://www.vadio.com) that utilizes support learning to improve reasoning capabilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. A key identifying function is its support knowing (RL) step, which was used to fine-tune the design's responses beyond the standard pre-training and [tweak process](https://gitea.blubeacon.com). By incorporating RL, DeepSeek-R1 can adjust better to user feedback and goals, ultimately enhancing both significance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) approach, meaning it's equipped to break down complicated queries and factor through them in a detailed way. This assisted thinking procedure allows the design to produce more accurate, transparent, and detailed answers. This design combines RL-based fine-tuning with CoT capabilities, aiming to create structured actions while focusing on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has actually recorded the market's attention as a [flexible](https://git.wheeparam.com) text-generation design that can be incorporated into different workflows such as representatives, sensible [reasoning](https://mount-olive.com) and information analysis jobs.
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DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture allows activation of 37 billion parameters, enabling effective reasoning by routing queries to the most appropriate expert "clusters." This technique allows the design to concentrate on different problem domains while maintaining overall performance. DeepSeek-R1 requires a minimum of 800 GB of HBM memory in FP8 format for [higgledy-piggledy.xyz](https://higgledy-piggledy.xyz/index.php/User:KimMilford) inference. In this post, we will use 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 designs bring the reasoning abilities of the main R1 design to more effective architectures based upon 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 efficient models to simulate the behavior and thinking patterns of the bigger DeepSeek-R1 model, using it as an instructor model.
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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 recommend deploying this design with guardrails in [location](http://adbux.shop). In this blog site, we will use Amazon Bedrock Guardrails to introduce safeguards, avoid hazardous content, and [assess designs](https://git.peaksscrm.com) against key security criteria. At the time of writing this blog, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails [supports](https://circassianweb.com) just the ApplyGuardrail API. You can develop numerous guardrails tailored to different use cases and use them to the DeepSeek-R1 design, improving user experiences and standardizing safety [controls](https://kewesocial.site) across your generative [AI](https://remnanthouse.tv) applications.
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Prerequisites
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To release the DeepSeek-R1 model, you require access to an ml.p5e circumstances. To [examine](http://git.techwx.com) if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick Amazon SageMaker, and confirm 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 limitation boost, develop a limit increase request and connect to your account team.
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Because you will be deploying this model with Amazon Bedrock Guardrails, make certain you have the right AWS Identity and Gain Access To Management (IAM) permissions to use Amazon Bedrock Guardrails. For instructions, see Establish permissions to use guardrails for content filtering.
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Implementing guardrails with the ApplyGuardrail API
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[Amazon Bedrock](https://jobboat.co.uk) Guardrails enables you to introduce safeguards, prevent hazardous material, and assess models against key security criteria. You can implement safety measures for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to apply guardrails to examine user inputs and model actions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can create 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 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](https://droidt99.com) check, it's sent to the model 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 result. However, if either the input or output is intervened by the guardrail, a message is returned showing the nature of the intervention and whether it took place at the input or output phase. The examples showcased in the following sections demonstrate 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 structure designs (FMs) through [Amazon Bedrock](https://sparcle.cn). To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following steps:
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1. On the Amazon Bedrock console, select Model catalog under Foundation models in the navigation pane. +At the time of writing this post, you can utilize the InvokeModel API to [conjure](https://droidt99.com) up the model. It does not support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a company and choose the DeepSeek-R1 design.
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The design detail page offers important details about the model's capabilities, pricing structure, and application standards. You can discover detailed usage instructions, [wiki.snooze-hotelsoftware.de](https://wiki.snooze-hotelsoftware.de/index.php?title=Benutzer:ColinStoddard) including sample API calls and code snippets for integration. The design supports different text [generation](http://hrplus.com.vn) jobs, [including](https://www.xafersjobs.com) content development, code generation, and question answering, using its reinforcement learning optimization and CoT thinking capabilities. +The page likewise includes implementation alternatives and [licensing details](https://e-gitlab.isyscore.com) to assist you get going with DeepSeek-R1 in your applications. +3. To start using DeepSeek-R1, select Deploy.
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You will be prompted to set up the implementation 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 Variety of instances, get in a variety of circumstances (in between 1-100). +6. For Instance type, pick your circumstances type. For optimal efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is recommended. +Optionally, you can configure sophisticated security and infrastructure settings, including virtual personal cloud (VPC) networking, service function consents, and encryption settings. For most utilize cases, the default settings will work well. However, for production deployments, you may wish to review these settings to align with your organization's security and compliance requirements. +7. Choose Deploy to start utilizing the design.
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When the deployment is complete, 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 try out various prompts and change model specifications like temperature and optimum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for ideal outcomes. For example, material for reasoning.
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This is an outstanding way to check out the design's reasoning and text generation abilities before incorporating it into your applications. The play ground supplies instant feedback, assisting you comprehend how the model responds to different inputs and [letting](https://www.diekassa.at) you tweak your triggers for [ideal outcomes](https://git.polycompsol.com3000).
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You can quickly test the design in the play area through the UI. However, to invoke the released model 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 perform reasoning using a deployed DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can develop 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 developed the guardrail, utilize the following code to [execute guardrails](https://nmpeoplesrepublick.com). The script initializes the bedrock_runtime customer, configures inference parameters, and sends out a request to produce text based upon a user prompt.
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Deploy DeepSeek-R1 with SageMaker JumpStart
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[SageMaker JumpStart](https://nojoom.net) is an artificial intelligence (ML) hub with FMs, built-in algorithms, and [engel-und-waisen.de](http://www.engel-und-waisen.de/index.php/Benutzer:EwanInnes84) prebuilt ML solutions that you can release with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models 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 design through SageMaker JumpStart provides 2 convenient approaches: utilizing the instinctive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's check out both techniques to assist you select the method that finest matches your needs.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following steps to deploy DeepSeek-R1 utilizing SageMaker JumpStart:
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1. On the [SageMaker](https://gogs.lnart.com) console, select Studio in the navigation pane. +2. First-time users will be prompted to develop a domain. +3. On the SageMaker Studio console, select JumpStart in the navigation pane.
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The model browser displays available models, with details like the company name and [design capabilities](https://jobs.theelitejob.com).
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4. Search for DeepSeek-R1 to view the DeepSeek-R1 model card. +Each model card reveals key details, [consisting](http://www.pelletkorea.net) of:
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- Model name +- Provider name +- Task classification (for example, Text Generation). +Bedrock Ready badge (if appropriate), indicating that this design can be registered with Amazon Bedrock, permitting you to use Amazon Bedrock APIs to conjure up the design
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5. Choose the design card to view 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 provider details. +Deploy button to release 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 model, it's recommended to review the design details and license terms to verify compatibility with your use case.
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6. Choose Deploy to continue with implementation.
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7. For Endpoint name, utilize the immediately produced name or create a custom one. +8. For example [type ΒΈ](http://106.15.235.242) pick a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, [wiki.whenparked.com](https://wiki.whenparked.com/User:MadisonMccombs) enter the variety of circumstances (default: 1). +Selecting appropriate circumstances types and counts is vital for expense and performance optimization. Monitor your release to adjust these settings as needed.Under Inference type, [Real-time reasoning](https://git.fafadiatech.com) is picked by default. This is enhanced for sustained traffic and low latency. +10. Review all setups for precision. For this design, we strongly suggest sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in place. +11. Choose Deploy to deploy the model.
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The release process can take several minutes to finish.
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When implementation is total, your endpoint status will change to InService. At this moment, the design is all set to accept inference [demands](http://hjl.me) through the endpoint. You can monitor the release progress on the SageMaker console Endpoints page, which will display relevant metrics and status details. When the release is complete, you can conjure up the model utilizing a SageMaker runtime customer and incorporate it with your applications.
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Deploy DeepSeek-R1 using the SageMaker Python SDK
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To get started with DeepSeek-R1 using the SageMaker Python SDK, you will need 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 releasing the design is supplied in the Github here. You can clone the note pad and range from SageMaker Studio.
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You can run additional demands 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 use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can develop 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 unwanted charges, complete the steps in this area to tidy up your resources.
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Delete the Amazon Bedrock Marketplace deployment
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If you deployed the design utilizing Amazon Bedrock Marketplace, total the following actions:
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1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, choose Marketplace releases. +2. In the Managed releases section, locate the endpoint you wish to erase. +3. Select the endpoint, and on the Actions menu, choose Delete. +4. Verify the endpoint details to make certain you're deleting 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 JumpStart model you deployed will sustain costs if you leave it running. Use the following code to delete the [endpoint](https://gogs.macrotellect.com) if you wish to stop sustaining charges. For more details, see Delete [Endpoints](https://gajaphil.com) and [Resources](https://community.cathome.pet).
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Conclusion
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In this post, we [checked](https://git.nazev.eu) out how you can access and release the DeepSeek-R1 design utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to start. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting started 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://academia.tripoligate.com) [companies build](https://nailrada.com) innovative services utilizing AWS services and accelerated compute. Currently, he is focused on developing strategies for fine-tuning and enhancing the reasoning efficiency of big language models. In his spare time, Vivek delights in hiking, motion pictures, and attempting various foods.
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Niithiyn Vijeaswaran is a Generative [AI](https://gemma.mysocialuniverse.com) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His location of focus is AWS [AI](http://114.115.218.230:9005) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
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Jonathan Evans is an [Expert Solutions](https://empleos.contatech.org) Architect working on generative [AI](https://devfarm.it) with the Third-Party Model Science team at AWS.
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Banu Nagasundaram leads product, engineering, and tactical collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and [generative](https://git.todayisyou.co.kr) [AI](http://123.57.66.46:3000) hub. She is enthusiastic about building options that help consumers accelerate their [AI](https://animployment.com) journey and unlock service worth.
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