From bd127212a5244d938d77c4eb6b9552a24a49e224 Mon Sep 17 00:00:00 2001 From: jadakaufmann7 Date: Sun, 9 Feb 2025 22:01:07 +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..d42f408 --- /dev/null +++ b/DeepSeek-R1-Model-now-Available-in-Amazon-Bedrock-Marketplace-And-Amazon-SageMaker-JumpStart.md @@ -0,0 +1,93 @@ +
Today, we are delighted 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](http://8.142.36.79:3000)'s first-generation frontier design, DeepSeek-R1, in addition to the distilled versions varying from 1.5 to 70 billion criteria to develop, experiment, and responsibly scale your generative [AI](https://nextcode.store) ideas on AWS.
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In this post, we demonstrate how to start with DeepSeek-R1 on Amazon Bedrock [Marketplace](http://49.235.130.76) and SageMaker JumpStart. You can follow comparable steps to deploy the distilled variations of the designs too.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a large language design (LLM) established by DeepSeek [AI](http://101.33.234.216:3000) that utilizes reinforcement discovering to boost reasoning through a multi-stage training procedure from a DeepSeek-V3-Base structure. An essential differentiating feature is its support knowing (RL) step, which was used to fine-tune the [design's responses](http://116.62.145.604000) beyond the basic pre-training and [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:FlorenceGuillen) tweak process. By incorporating RL, [larsaluarna.se](http://www.larsaluarna.se/index.php/User:HelenTennyson48) DeepSeek-R1 can adapt more efficiently to user feedback and objectives, eventually improving both importance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) method, meaning it's geared up to break down complicated inquiries and factor through them in a detailed way. This guided reasoning procedure allows the model to produce more accurate, transparent, and detailed answers. This model combines RL-based fine-tuning with CoT abilities, aiming to [produce structured](http://lesstagiaires.com) responses while concentrating on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has actually recorded the industry's attention as a versatile text-generation model that can be integrated into various workflows such as representatives, rational thinking and information analysis tasks.
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DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion [parameters](http://photorum.eclat-mauve.fr) in size. The MoE architecture permits activation of 37 billion specifications, making it possible for effective reasoning by routing questions to the most pertinent specialist "clusters." This approach permits the design to focus on different problem domains while maintaining general effectiveness. DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for inference. In this post, we will use an ml.p5e.48 [xlarge circumstances](http://47.119.27.838003) to deploy the design. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.
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DeepSeek-R1 distilled designs bring the thinking abilities of the main R1 model to more efficient architectures based on popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of training smaller, more efficient designs to imitate the behavior and thinking patterns of the larger DeepSeek-R1 design, utilizing it as an instructor model.
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You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we recommend deploying this design with guardrails in [location](https://www.iratechsolutions.com). In this blog site, we will use Amazon Bedrock Guardrails to present safeguards, avoid harmful material, and [evaluate models](https://www.gritalent.com) against crucial safety 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](https://horizonsmaroc.com). You can develop several guardrails tailored to different usage cases and use them to the DeepSeek-R1 design, [enhancing](http://www.forwardmotiontx.com) user experiences and standardizing safety controls throughout your generative [AI](https://git.andy.lgbt) applications.
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Prerequisites
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To deploy the DeepSeek-R1 model, you need access to an ml.p5e circumstances. To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, [select Amazon](https://git.k8sutv.it.ntnu.no) SageMaker, and validate 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 releasing. To request a limitation boost, create a limitation boost request and reach out to your account team.
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Because you will be releasing this design with Amazon Bedrock Guardrails, make certain you have the right AWS Identity and Gain Access To [Management](https://livy.biz) (IAM) permissions to utilize Amazon Bedrock Guardrails. For directions, see Set up authorizations to use guardrails for content filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails allows you to introduce safeguards, avoid harmful material, and assess designs against crucial security criteria. You can execute precaution for the DeepSeek-R1 design utilizing the Amazon Bedrock [ApplyGuardrail](http://175.27.215.923000) API. This allows you to use guardrails to assess user inputs and model reactions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can develop a [guardrail utilizing](http://40th.jiuzhai.com) 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](https://youtubegratis.com) includes the following actions: First, the system gets an input for the model. This input is then processed through the [ApplyGuardrail API](https://git.dsvision.net). If the input passes the guardrail check, it's sent to the model for inference. After getting the design's output, another guardrail check is applied. 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 indicating the nature of the intervention and whether it occurred 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 offers you access to over 100 popular, emerging, and specialized structure designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11857434) total the following actions:
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1. On the Amazon Bedrock console, choose Model brochure under Foundation models in the navigation pane. +At the time of composing this post, you can use the InvokeModel API to [conjure](http://git.fmode.cn3000) up the model. It doesn't [support Converse](https://pl.velo.wiki) APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a [provider](http://47.93.16.2223000) and pick the DeepSeek-R1 design.
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The model detail page offers necessary details about the design's capabilities, prices structure, and execution standards. You can find detailed usage instructions, including sample API calls and code bits for combination. The design supports various text generation jobs, including material creation, code generation, and concern answering, using its support discovering optimization and CoT thinking abilities. +The page also includes implementation choices and [licensing details](http://repo.bpo.technology) to help you begin with DeepSeek-R1 in your applications. +3. To begin utilizing DeepSeek-R1, [select Deploy](https://network.janenk.com).
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You will be prompted to set up the implementation details for DeepSeek-R1. The design ID will be pre-populated. +4. For Endpoint name, enter an endpoint name (in between 1-50 alphanumeric characters). +5. For Number of instances, get in a variety of circumstances (in between 1-100). +6. For example type, choose your instance type. For ideal efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised. +Optionally, you can [configure innovative](https://www.mpowerplacement.com) [security](https://www.loupanvideos.com) and [facilities](https://gitea.sb17.space) settings, including virtual personal cloud (VPC) networking, service function permissions, and encryption settings. For the majority of utilize cases, the default settings will work well. However, for production implementations, you might want to review these settings to line up with your organization's security and compliance requirements. +7. Choose Deploy to begin using the design.
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When the deployment is total, you can test DeepSeek-R1's abilities straight in the Amazon Bedrock playground. +8. Choose Open in play area to access an interactive interface where you can try out various prompts and adjust design 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 example, material for inference.
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This is an [exceptional](https://sjee.online) way to check out the model's reasoning and text generation abilities before incorporating it into your applications. The play ground supplies instant feedback, assisting you understand how the model reacts to different inputs and letting you fine-tune your prompts for optimum results.
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You can rapidly test the model in the play area through the UI. However, to conjure up the deployed model programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
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Run inference using guardrails with the released DeepSeek-R1 endpoint
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The following code example demonstrates how to perform inference utilizing a deployed DeepSeek-R1 model through Amazon Bedrock using the invoke_model and [ApplyGuardrail API](https://wiki.uqm.stack.nl). You can create a guardrail using the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo. After you have actually developed the guardrail, utilize the following code to execute guardrails. The script initializes the bedrock_runtime customer, [configures reasoning](https://happylife1004.co.kr) specifications, and sends out a request to create 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) center with FMs, integrated algorithms, and prebuilt ML options that you can deploy with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your data, and release them into production using either the UI or SDK.
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Deploying DeepSeek-R1 design through SageMaker JumpStart provides two convenient techniques: utilizing the user-friendly SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's check out both methods to help you choose the technique that best fits your requirements.
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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, select JumpStart in the navigation pane.
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The design internet browser displays available designs, with details like the service provider name and model capabilities.
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4. Look for DeepSeek-R1 to see the DeepSeek-R1 design card. +Each model card shows essential details, including:
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- Model name +- Provider name +- Task classification (for example, Text Generation). +Bedrock Ready badge (if appropriate), showing that this model can be signed up with Amazon Bedrock, permitting you to use Amazon Bedrock APIs to invoke the design
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5. Choose the design card to view the design 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 deploy the model. +About and Notebooks tabs with [detailed](http://47.114.187.1113000) details
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The About tab consists of important details, such as:
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- Model [description](https://yourfoodcareer.com). +- License [details](https://ahlamhospitalityjobs.com). +- Technical specifications. +- Usage standards
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Before you release the design, it's advised to review the design details and license terms to validate compatibility with your use case.
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6. Choose Deploy to [continue](https://www.pakgovtnaukri.pk) with [release](http://gitlab.kci-global.com.tw).
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7. For Endpoint name, utilize the instantly generated name or develop a custom-made one. +8. For Instance type ΒΈ select an instance type (default: ml.p5e.48 xlarge). +9. For Initial instance count, get in the variety of instances (default: 1). +Selecting suitable instance types and counts is essential for cost and performance optimization. Monitor your implementation to adjust these [settings](https://wiki.armello.com) as needed.Under Inference type, [gratisafhalen.be](https://gratisafhalen.be/author/redajosephs/) Real-time inference is chosen by default. This is enhanced for sustained traffic and low latency. +10. Review all setups for precision. For this model, we strongly recommend adhering to SageMaker JumpStart default settings and [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:NannetteOdell3) making certain that network seclusion remains in place. +11. Choose Deploy to release the model.
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The release procedure can take numerous minutes to finish.
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When implementation is complete, your [endpoint status](https://git.pt.byspectra.com) will alter to InService. At this moment, the model is ready to accept reasoning demands through the endpoint. You can keep track of the implementation development on the SageMaker console Endpoints page, which will show pertinent metrics and status details. When the release is total, you can invoke the model utilizing a SageMaker runtime customer 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 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 release and utilize DeepSeek-R1 for reasoning programmatically. The code for deploying the design is [offered](http://dkjournal.co.kr) in the Github here. You can clone the notebook 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 also utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can create a guardrail using the Amazon Bedrock console or the API, and implement it as revealed in the following code:
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Tidy up
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To prevent unwanted charges, complete the actions in this area to tidy 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, select Marketplace deployments. +2. In the Managed releases section, locate the endpoint you wish to erase. +3. Select the endpoint, and on the Actions menu, pick Delete. +4. Verify the endpoint details to make certain you're erasing the right 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 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 checked out how you can access and release the DeepSeek-R1 model utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get going. 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 Starting 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](http://gitfrieds.nackenbox.xyz) companies build [innovative](https://git.andrewnw.xyz) solutions utilizing AWS services and accelerated compute. Currently, he is focused on establishing methods for fine-tuning and optimizing the reasoning efficiency of large language models. In his free time, Vivek enjoys treking, seeing movies, and attempting different foods.
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Niithiyn Vijeaswaran is a Generative [AI](http://git.cxhy.cn) Specialist Solutions Architect with the Third-Party Model [Science](https://southernsoulatlfm.com) group at AWS. His location of focus is AWS [AI](https://octomo.co.uk) 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](http://123.60.103.97:3000) with the Third-Party Model Science group at AWS.
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Banu Nagasundaram leads product, engineering, and [tactical partnerships](http://repo.bpo.technology) for Amazon SageMaker JumpStart, [garagesale.es](https://www.garagesale.es/author/roseannanas/) SageMaker's artificial intelligence and generative [AI](http://gogs.black-art.cn) hub. She is enthusiastic about constructing options that help consumers accelerate their [AI](https://tylerwesleywilliamson.us) journey and unlock service worth.
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