From 685fd6d7fd7c85175f18c754cf6797222ea04b02 Mon Sep 17 00:00:00 2001 From: shadq448263074 Date: Thu, 27 Feb 2025 03:15:45 +0100 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..547b4c1 --- /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](http://185.87.111.463000) Marketplace and Amazon SageMaker JumpStart. With this launch, you can now [release DeepSeek](https://interlinkms.lk) [AI](http://maitri.adaptiveit.net)'s first-generation frontier design, DeepSeek-R1, along with the distilled variations varying from 1.5 to 70 billion specifications to build, experiment, and responsibly scale your generative [AI](https://watch-wiki.org) concepts on AWS.
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In this post, we demonstrate how to start with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar steps to release the distilled variations of the models as well.
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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://spotlessmusic.com) that uses reinforcement finding out to improve reasoning abilities through a multi-stage training procedure from a DeepSeek-V3[-Base foundation](https://sb.mangird.com). A key identifying feature is its reinforcement knowing (RL) step, which was used to refine the design's responses beyond the basic pre-training and tweak process. By incorporating RL, DeepSeek-R1 can adapt better to user feedback and goals, ultimately boosting both importance and [higgledy-piggledy.xyz](https://higgledy-piggledy.xyz/index.php/User:JonahRiddick43) clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) approach, meaning it's geared up to break down intricate queries and factor through them in a detailed manner. This directed thinking procedure enables the model to produce more precise, transparent, and detailed answers. This design combines RL-based [fine-tuning](https://www.jooner.com) with CoT capabilities, aiming to generate structured [actions](http://git.jcode.net) while concentrating on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has recorded the market's attention as a flexible text-generation design that can be incorporated into various workflows such as agents, sensible reasoning and data interpretation jobs.
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DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture permits activation of 37 billion parameters, [genbecle.com](https://www.genbecle.com/index.php?title=Utilisateur:MozelleNorthcutt) enabling efficient reasoning by routing inquiries to the most appropriate specialist "clusters." This technique allows the model to concentrate on various problem domains while maintaining general efficiency. DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for reasoning. In this post, we will use an ml.p5e.48 [xlarge circumstances](https://site4people.com) to release the design. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.
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DeepSeek-R1 distilled models bring the thinking abilities of the main R1 model to more efficient architectures based upon popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a process of training smaller sized, more efficient models to mimic the behavior and thinking patterns of the bigger DeepSeek-R1 design, using it as an instructor design.
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You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we suggest releasing this model with guardrails in place. In this blog site, we will use Amazon Bedrock Guardrails to introduce safeguards, prevent harmful material, and evaluate models against key safety requirements. At the time of writing this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can produce numerous guardrails tailored to different use cases and use them to the DeepSeek-R1 design, [improving](https://www.designxri.com) user experiences and standardizing security controls across your [generative](http://www.andreagorini.it) [AI](https://dyipniflix.com) applications.
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Prerequisites
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To deploy 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, choose Amazon SageMaker, and verify you're utilizing ml.p5e.48 xlarge for endpoint usage. Make certain that you have at least one ml.P5e.48 xlarge instance in the AWS Region you are releasing. To ask for a limit increase, develop a limit boost demand and reach out to your account group.
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Because you will be [releasing](https://gitlab.healthcare-inc.com) this design with Amazon Bedrock Guardrails, make certain you have the right AWS Identity and Gain Access To Management (IAM) approvals to utilize Amazon [Bedrock](https://travel-friends.net) Guardrails. For directions, 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 Guardrails permits you to present safeguards, prevent hazardous material, and evaluate models against essential security criteria. You can implement security steps for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to use guardrails to assess user inputs and model reactions released 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 produce the guardrail, see the GitHub repo.
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The general flow includes the following actions: First, the system gets 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, [archmageriseswiki.com](http://archmageriseswiki.com/index.php/User:DessieLundstrom) another guardrail check is used. If the output passes this final check, it's returned as the result. However, if either the input or output is stepped in 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 inference using this API.
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Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
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Amazon Bedrock [Marketplace](http://git.liuhung.com) 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, select Model catalog under Foundation designs in the navigation pane. +At the time of composing this post, [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:NannetteOdell3) you can utilize the InvokeModel API to conjure up the design. It does not support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a service provider and pick the DeepSeek-R1 design.
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The model detail page provides essential details about the design's capabilities, prices structure, and application guidelines. You can discover detailed use instructions, including sample API calls and code snippets for combination. The model supports numerous text generation tasks, including content development, code generation, and question answering, using its support learning optimization and CoT reasoning abilities. +The page likewise consists of release choices and licensing details to help you start with DeepSeek-R1 in your applications. +3. To start utilizing DeepSeek-R1, select Deploy.
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You will be prompted to configure the implementation details for DeepSeek-R1. The design ID will be pre-populated. +4. For Endpoint name, go into an endpoint name (in between 1-50 alphanumeric characters). +5. For Number of instances, go into a variety of instances (between 1-100). +6. For Instance type, select your circumstances type. For ideal 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, consisting of [virtual personal](https://speeddating.co.il) cloud (VPC) networking, service role authorizations, and file encryption settings. For most use cases, the default settings will work well. However, for production deployments, you might wish to review these settings to align with your company's security and compliance requirements. +7. Choose Deploy to begin using the design.
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When the release is total, you can evaluate DeepSeek-R1's abilities straight in the Amazon Bedrock playground. +8. Choose Open in play ground to access an interactive user interface where you can experiment with different triggers and change design parameters like temperature level and maximum length. +When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for optimal results. For example, content for reasoning.
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This is an exceptional method to check out the model's reasoning and text generation abilities before integrating it into your applications. The play ground provides immediate feedback, helping you understand how the model reacts to various inputs and letting you fine-tune your triggers for optimal results.
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You can rapidly test the design in the play ground through the UI. However, to invoke the deployed model programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
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Run inference utilizing guardrails with the released 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 [pipewiki.org](https://pipewiki.org/wiki/index.php/User:ArlethaReis) ApplyGuardrail API. You can produce a guardrail utilizing the Amazon [Bedrock console](https://gajaphil.com) or the API. For the example code to produce the guardrail, see the GitHub repo. After you have developed the guardrail, use the following code to [execute guardrails](https://demo.theme-sky.com). The script initializes the bedrock_runtime client, configures reasoning parameters, and sends out a demand to generate 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 services that you can deploy with just a couple of clicks. With SageMaker JumpStart, you can tailor [pipewiki.org](https://pipewiki.org/wiki/index.php/User:MelaineHartz5) pre-trained models to your use case, with your data, and release them into production utilizing either the UI or SDK.
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Deploying DeepSeek-R1 design through SageMaker JumpStart offers two practical techniques: utilizing the user-friendly SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's explore both approaches to help you pick the technique that finest suits your needs.
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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, pick Studio in the navigation pane. +2. First-time users will be triggered to create a domain. +3. On the SageMaker Studio console, select JumpStart in the navigation pane.
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The model internet browser displays available designs, with details like the supplier name and model capabilities.
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4. Look for DeepSeek-R1 to view the DeepSeek-R1 design card. +Each model card reveals key details, consisting of:
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- Model name +- [Provider](https://mssc.ltd) name +- Task category (for example, Text Generation). +Bedrock Ready badge (if appropriate), showing that this model can be registered with Amazon Bedrock, allowing you to use Amazon Bedrock APIs to invoke the model
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5. Choose the model 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 supplier details. +Deploy button to release the design. +About and Notebooks tabs with detailed details
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The About tab consists of important details, such as:
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- Model description. +- License details. +- Technical specs. +- Usage guidelines
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Before you deploy the model, it's recommended to examine the [design details](https://vishwakarmacommunity.org) and license terms to confirm compatibility with your usage case.
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6. Choose Deploy to continue with implementation.
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7. For Endpoint name, utilize the automatically generated name or develop a customized one. +8. For Instance type ΒΈ pick a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial [instance](https://jimsusefultools.com) count, enter the number of instances (default: 1). +Selecting suitable instance types and counts is important for cost and efficiency optimization. Monitor your implementation to adjust these settings as needed.Under Inference type, Real-time inference is selected by default. This is enhanced for sustained traffic and low latency. +10. Review all setups for accuracy. For this model, we highly recommend adhering to SageMaker JumpStart default settings and making certain that network isolation remains in location. +11. Choose Deploy to deploy the design.
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The release procedure can take a number of minutes to finish.
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When deployment is complete, your endpoint status will change to InService. At this moment, the model is ready to accept reasoning demands through the endpoint. You can monitor the deployment progress on the SageMaker console Endpoints page, which will show relevant metrics and status details. When the release is complete, you can invoke the design utilizing a [SageMaker runtime](https://git.marcopacs.com) client and integrate it with your applications.
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Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
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To get going 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 shows how to deploy and use DeepSeek-R1 for reasoning programmatically. The code for releasing the design is provided in the Github here. You can clone the notebook and range from SageMaker Studio.
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You can run extra requests 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](http://secdc.org.cn). You can create a guardrail [utilizing](https://gitea.rodaw.net) the Amazon Bedrock console or the API, and execute it as displayed in the following code:
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Tidy up
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To avoid 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 [deployed](https://yeetube.com) the design using Amazon Bedrock Marketplace, complete the following steps:
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1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, choose Marketplace releases. +2. In the Managed implementations section, find the [endpoint](http://47.108.161.783000) you wish 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 correct implementation: 1. Endpoint name. +2. Model name. +3. status
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Delete the SageMaker JumpStart predictor
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The SageMaker JumpStart model you released will [sustain costs](https://freelancejobsbd.com) 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 [release](http://famedoot.in) the DeepSeek-R1 model 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 designs, [SageMaker](https://findgovtsjob.com) 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](https://teachersconsultancy.com) companies develop ingenious services utilizing AWS services and sped up compute. Currently, he is focused on developing strategies for [fine-tuning](http://git.suxiniot.com) and optimizing the reasoning performance of large language designs. In his spare time, Vivek takes pleasure in treking, viewing films, and attempting various foods.
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Niithiyn Vijeaswaran is a Generative [AI](https://gps-hunter.ru) Specialist Solutions Architect with the Third-Party Model [Science](http://kiwoori.com) team at AWS. His location of focus is AWS [AI](https://drshirvany.ir) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.
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Jonathan Evans is a Specialist Solutions Architect dealing with generative [AI](https://flexychat.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](http://81.70.93.203:3000) center. She is passionate about constructing solutions that assist consumers accelerate their [AI](http://123.111.146.235:9070) journey and [unlock company](https://git.uucloud.top) value.
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