From 7c0354817c2431e714e60b9135f48fe047bd9e72 Mon Sep 17 00:00:00 2001 From: Alethea Thaxton Date: Sat, 1 Mar 2025 07:24:37 +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..913195d --- /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](http://180.76.133.25316300) JumpStart. With this launch, you can now release DeepSeek [AI](https://git.howdoicomputer.lol)'s first-generation frontier design, DeepSeek-R1, together with the distilled versions [ranging](https://menfucks.com) from 1.5 to 70 billion criteria to develop, experiment, and properly scale your generative [AI](https://git.opskube.com) 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 comparable actions to deploy the distilled variations of the models as well.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a large language design (LLM) developed by DeepSeek [AI](https://oros-git.regione.puglia.it) that [utilizes reinforcement](https://tiktokbeans.com) finding out to boost reasoning abilities through a multi-stage training process from a DeepSeek-V3-Base foundation. An essential distinguishing feature is its support knowing (RL) step, which was used to refine the model's responses beyond the basic pre-training and tweak process. By integrating RL, DeepSeek-R1 can adapt more [efficiently](https://git.rungyun.cn) to user feedback and goals, ultimately enhancing both importance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) method, [meaning](https://fmstaffingsource.com) it's geared up to break down complicated questions and factor through them in a detailed way. This assisted thinking process enables the design to produce more accurate, transparent, and detailed answers. This model integrates RL-based fine-tuning with CoT abilities, aiming to [generate structured](https://rassi.tv) responses while concentrating on interpretability and user interaction. With its comprehensive abilities DeepSeek-R1 has caught the market's attention as a flexible text-generation model that can be integrated into different workflows such as agents, logical reasoning and information analysis tasks.
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DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture permits activation of 37 billion criteria, allowing efficient inference by routing queries to the most appropriate professional "clusters." This technique allows the design to concentrate on different issue domains while maintaining total effectiveness. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will use an ml.p5e.48 xlarge circumstances 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 reasoning abilities of the main R1 design 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 procedure of [training](https://gulfjobwork.com) smaller, more efficient designs to imitate the behavior and thinking patterns of the larger DeepSeek-R1 model, using it as an instructor design.
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You can release DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we suggest deploying this design with guardrails in location. In this blog site, we will use Amazon Bedrock Guardrails to introduce safeguards, prevent hazardous content, and evaluate designs against crucial safety criteria. At the time of composing this blog, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can develop numerous guardrails tailored to different use cases and [forum.pinoo.com.tr](http://forum.pinoo.com.tr/profile.php?id=1344971) use them to the DeepSeek-R1 model, enhancing user experiences and standardizing security controls across your generative [AI](https://maibuzz.com) applications.
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Prerequisites
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To deploy the DeepSeek-R1 design, you need access to an ml.p5e circumstances. To examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick Amazon SageMaker, and validate 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](https://rami-vcard.site) a limit boost, produce a [limitation boost](https://www.telix.pl) demand and reach out to your account team.
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Because you will be releasing this model with [Amazon Bedrock](https://supremecarelink.com) Guardrails, make certain you have the right AWS Identity and Gain Access To Management (IAM) authorizations to use Amazon Bedrock Guardrails. For instructions, see Set up approvals to use guardrails 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 damaging content, and assess designs against essential safety requirements. You can carry out security measures for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to examine user inputs and design responses released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can create a guardrail using the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo.
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The basic circulation involves the following actions: First, the system gets an input for the design. This input is then processed through the [ApplyGuardrail API](https://parejas.teyolia.mx). 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](https://lovematch.vip) as the 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 occurred at the input or [genbecle.com](https://www.genbecle.com/index.php?title=Utilisateur:AnnaBoxer61535) output stage. The examples showcased in the following areas show reasoning 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 models (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, choose Model brochure under Foundation models in the navigation pane. +At the time of composing this post, you can utilize the InvokeModel API to conjure up the model. It doesn't support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a supplier and choose the DeepSeek-R1 model.
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The model detail page offers essential details about the design's capabilities, rates structure, and execution guidelines. You can find detailed use instructions, including sample API calls and code bits for combination. The model supports numerous text generation tasks, including content production, code generation, and question answering, utilizing its reinforcement learning optimization and CoT reasoning abilities. +The page also includes release choices and licensing details to assist you start with DeepSeek-R1 in your applications. +3. To start using DeepSeek-R1, choose Deploy.
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You will be prompted to configure the deployment 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 Number of circumstances, go into a number of instances (between 1-100). +6. For example type, choose your instance type. For optimum efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is [advised](https://lokilocker.com). +Optionally, you can set up advanced security and infrastructure settings, consisting of virtual personal cloud (VPC) networking, service function approvals, and file encryption settings. For the majority of utilize cases, the default settings will work well. However, for production deployments, you might want to evaluate these settings to align with your company's security and compliance requirements. +7. Choose Deploy to start using the model.
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When the implementation 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 explore different prompts and adjust model parameters like temperature and maximum length. +When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for optimal outcomes. For instance, content for inference.
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This is an exceptional way to explore the design's thinking and text generation [abilities](http://gitlab.digital-work.cn) before integrating it into your applications. The playground provides instant feedback, [helping](http://18.178.52.993000) you understand how the design reacts to numerous inputs and letting you tweak your triggers for optimum outcomes.
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You can rapidly evaluate the design in the play area through the UI. However, to conjure up the deployed model programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
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Run reasoning using guardrails with the released DeepSeek-R1 endpoint
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The following code example shows how to carry out reasoning using a released DeepSeek-R1 model through Amazon Bedrock [utilizing](https://geetgram.com) the invoke_model and ApplyGuardrail API. You can produce a guardrail utilizing the Amazon Bedrock [console](https://gitea.fcliu.net) or the API. For the example code to produce the guardrail, see the GitHub repo. After you have actually developed the guardrail, use the following code to execute guardrails. The script initializes the bedrock_runtime customer, configures inference criteria, and sends a demand to [generate text](http://106.15.41.156) 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](http://git.520hx.vip3000) algorithms, and prebuilt ML [options](https://gajaphil.com) that you can deploy 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 using either the UI or SDK.
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Deploying DeepSeek-R1 design through SageMaker JumpStart uses 2 practical methods: using the user-friendly SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's explore both approaches to assist you select the approach that finest matches your requirements.
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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](http://106.15.48.1323880) console, choose Studio in the navigation pane. +2. First-time users will be triggered to develop a domain. +3. On the SageMaker Studio console, select JumpStart in the [navigation pane](http://woorichat.com).
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The design internet browser displays available designs, with details like the company name and model abilities.
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4. Search for DeepSeek-R1 to view the DeepSeek-R1 design card. +Each model card shows key details, including:
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- Model name +- Provider name +- Task classification (for example, Text Generation). +Bedrock Ready badge (if appropriate), [indicating](https://embargo.energy) that this design can be registered with Amazon Bedrock, enabling 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 model details page includes the following details:
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- The model name and provider details. +Deploy button to release the model. +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. +- License details. +- Technical specifications. +- Usage guidelines
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Before you deploy the design, it's suggested to review the design details and license terms to confirm compatibility with your usage case.
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6. Choose Deploy to proceed with deployment.
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7. For Endpoint name, utilize the automatically created name or produce a custom-made one. +8. For example type ΒΈ pick an instance type (default: ml.p5e.48 xlarge). +9. For Initial instance count, enter the number of instances (default: 1). +Selecting suitable instance types and counts is important for expense and performance optimization. Monitor your release to adjust these settings as needed.Under Inference type, is picked by default. This is optimized for sustained traffic and low latency. +10. Review all configurations for precision. For this design, we strongly recommend sticking 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 process 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 design is ready to accept reasoning requests through the endpoint. You can keep an eye on the deployment progress on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the release is complete, you can conjure up the design using a SageMaker runtime client and incorporate it with your [applications](http://svn.ouj.com).
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Deploy DeepSeek-R1 [utilizing](http://recruitmentfromnepal.com) the SageMaker Python SDK
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To get begun with DeepSeek-R1 utilizing the SageMaker Python SDK, you will need to set up the [SageMaker Python](https://git.foxarmy.org) SDK and make certain you have the necessary AWS permissions and environment setup. The following is a detailed code example that shows how to deploy and utilize DeepSeek-R1 for inference programmatically. The code for releasing the design is [supplied](https://vtuvimo.com) in the Github here. You can clone the note pad and range from [SageMaker Studio](https://www.hb9lc.org).
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You can run additional 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 utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce 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 prevent unwanted charges, complete the actions in this section to clean up your resources.
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Delete the Amazon Bedrock Marketplace release
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If you released the model utilizing Amazon Bedrock Marketplace, total the following actions:
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1. On the Amazon Bedrock console, under [Foundation](https://event.genie-go.com) designs in the navigation pane, select Marketplace implementations. +2. In the Managed releases section, find the endpoint you desire 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 implementation: 1. Endpoint name. +2. Model name. +3. Endpoint status
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Delete the [SageMaker JumpStart](https://code.miraclezhb.com) predictor
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The SageMaker JumpStart model you released will sustain expenses if you leave it running. Use the following code to delete 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 [explored](http://120.237.152.2188888) how you can access and release the DeepSeek-R1 design utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in [SageMaker Studio](http://suvenir51.ru) 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 begun 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://wacari-git.ru) [business build](https://gitr.pro) ingenious solutions utilizing AWS services and sped up compute. Currently, he is concentrated on establishing methods for fine-tuning and enhancing the reasoning performance of big language models. In his downtime, Vivek takes pleasure in hiking, seeing movies, [surgiteams.com](https://surgiteams.com/index.php/User:BertHowden) and trying different cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://www.miptrucking.net) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His area of focus is AWS [AI](http://154.64.253.77:3000) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
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Jonathan Evans is a Professional Solutions Architect dealing with generative [AI](https://dev.ncot.uk) with the Third-Party Model Science team at AWS.
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Banu Nagasundaram leads item, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://recruitment.transportknockout.com) center. She is enthusiastic about [building options](http://89.251.156.112) that assist clients accelerate their [AI](https://gitea.ecommercetools.com.br) journey and unlock business value.
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