From 16d07a55eda0d280b031bc54811d514e8f45725a Mon Sep 17 00:00:00 2001 From: preciousnowak4 Date: Sat, 5 Apr 2025 20:17:32 +0200 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..f81e4ab --- /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 are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](http://git.attnserver.com)'s first-generation frontier design, DeepSeek-R1, along with the distilled variations varying from 1.5 to 70 billion parameters to build, experiment, and properly scale your generative [AI](https://www.anetastaffing.com) concepts on AWS.
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In this post, we demonstrate how to begin with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar [actions](https://git.todayisyou.co.kr) to release the distilled variations of the designs as well.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a large language model (LLM) developed by DeepSeek [AI](https://git.goatwu.com) that utilizes support finding out to enhance thinking capabilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. An essential distinguishing feature is its reinforcement knowing (RL) step, which was used to refine the design's actions beyond the standard pre-training and tweak process. By incorporating RL, DeepSeek-R1 can adapt more successfully to user feedback and goals, ultimately boosting both relevance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) method, [implying](https://3rrend.com) it's geared up to break down intricate queries and reason through them in a detailed manner. This assisted thinking process allows the design to produce more precise, transparent, and detailed answers. This model integrates RL-based fine-tuning with CoT capabilities, aiming to generate structured responses while focusing on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has actually caught the market's attention as a flexible [text-generation model](https://git.gday.express) that can be integrated into various workflows such as agents, logical thinking and information jobs.
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DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture allows activation of 37 billion criteria, enabling effective inference by routing queries to the most appropriate expert "clusters." This method allows the model to specialize in different issue domains while maintaining general performance. DeepSeek-R1 needs at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will use an ml.p5e.48 xlarge instance to release the design. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.
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DeepSeek-R1 distilled designs bring the reasoning capabilities 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 describes a procedure of training smaller, more effective models to mimic the behavior and thinking patterns of the larger DeepSeek-R1 design, utilizing 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 advise releasing this design with guardrails in location. In this blog, we will utilize Amazon Bedrock Guardrails to present safeguards, prevent damaging material, and examine designs against [essential safety](https://git.mm-music.cn) requirements. At the time of writing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can create multiple guardrails tailored to various usage cases and use them to the DeepSeek-R1 model, improving user experiences and standardizing safety controls throughout your generative [AI](https://corvestcorp.com) applications.
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Prerequisites
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To release 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 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](http://bc.zycoo.com3000) you are releasing. To ask for a limit boost, develop a limit increase demand and connect to your account team.
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Because you will be deploying this design with Amazon Bedrock Guardrails, make certain you have the right [AWS Identity](https://git.kimcblog.com) and Gain Access To Management (IAM) approvals to utilize Amazon Bedrock Guardrails. For guidelines, see Establish authorizations to utilize guardrails for material filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails enables you to present safeguards, prevent damaging content, and assess designs against essential safety criteria. You can implement safety steps for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to evaluate user inputs and model responses 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 develop the guardrail, see the GitHub repo.
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The general circulation includes the following actions: First, the system receives an input for the design. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it's sent out to the design 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 outcome. However, if either the input or output is stepped in 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 areas show 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 foundation designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following actions:
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1. On the Amazon Bedrock console, choose Model catalog under Foundation designs in the navigation pane. +At the time of writing 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 provider and choose the DeepSeek-R1 model.
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The model detail page supplies important details about the design's capabilities, pricing structure, and implementation standards. You can find detailed use instructions, including sample API calls and code snippets for integration. The [model supports](https://familyworld.io) various text generation tasks, including content production, [pediascape.science](https://pediascape.science/wiki/User:HymanRangel8) code generation, and question answering, utilizing its reinforcement finding out optimization and CoT reasoning capabilities. +The page also consists of release choices and licensing details to help you start with DeepSeek-R1 in your applications. +3. To begin utilizing DeepSeek-R1, pick Deploy.
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You will be triggered to configure the deployment details for DeepSeek-R1. The design ID will be pre-populated. +4. For Endpoint name, get in an endpoint name (between 1-50 alphanumeric characters). +5. For Variety of circumstances, enter a number of instances (between 1-100). +6. For [archmageriseswiki.com](http://archmageriseswiki.com/index.php/User:JorgSelleck17) example type, choose your instance type. For optimum performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is recommended. +Optionally, you can configure sophisticated security and facilities settings, including virtual private cloud (VPC) networking, service function consents, and [encryption settings](https://club.at.world). For many use cases, the default settings will work well. However, for production releases, you may want to evaluate 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 release is complete, you can check DeepSeek-R1's abilities straight in the Amazon Bedrock play ground. +8. Choose Open in playground to access an interactive interface where you can try out various prompts and change model specifications like temperature level and optimum length. +When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for ideal results. For example, content for reasoning.
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This is an [exceptional](https://gitstud.cunbm.utcluj.ro) way to explore the model's reasoning and text generation capabilities before integrating it into your [applications](https://superblock.kr). The play area supplies immediate feedback, assisting you comprehend how the [model reacts](http://139.199.191.19715000) to different inputs and letting you fine-tune your triggers for ideal outcomes.
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You can quickly test the design in the playground 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 reasoning utilizing guardrails with the deployed DeepSeek-R1 endpoint
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The following code example demonstrates how to perform reasoning using a deployed DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can [develop](https://ttemployment.com) 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 created the guardrail, utilize the following code to execute guardrails. The script initializes the bedrock_runtime customer, sets up reasoning parameters, and sends out a demand to produce 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](https://gogs.jublot.com) (ML) center with FMs, built-in algorithms, and prebuilt ML services that you can release with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage case, with your information, and deploy them into production using either the UI or SDK.
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Deploying DeepSeek-R1 design through SageMaker JumpStart provides two hassle-free techniques: using the instinctive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both techniques to help you choose the method that best suits 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 utilizing SageMaker JumpStart:
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1. On the SageMaker console, select Studio in the navigation pane. +2. First-time users will be triggered to produce a domain. +3. On the SageMaker Studio console, choose JumpStart in the navigation pane.
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The model web browser shows available designs, with details like the provider name and design capabilities.
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4. Search for DeepSeek-R1 to view the DeepSeek-R1 [model card](https://zenithgrs.com). +Each design card reveals essential details, including:
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- Model name +- Provider name +- Task category (for instance, Text Generation). +Bedrock Ready badge (if relevant), showing that this design can be signed up with Amazon Bedrock, enabling you to use Amazon Bedrock APIs to conjure up the model
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5. Choose the design card to view the model details page.
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The design details page includes the following details:
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- The design name and [supplier details](https://www.celest-interim.fr). +Deploy button to deploy the design. +About and Notebooks tabs with [detailed](http://jobsgo.co.za) details
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The About tab includes essential details, such as:
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- Model description. +- License details. +- Technical specifications. +[- Usage](https://www.wakewiki.de) guidelines
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Before you deploy the design, it's advised to examine the design details and license terms to verify compatibility with your usage case.
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6. Choose Deploy to continue with release.
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7. For Endpoint name, use the instantly produced name or produce a custom one. +8. For example type ΒΈ select a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial instance count, go into the number of circumstances (default: 1). +Selecting suitable instance types and counts is important for cost and performance optimization. Monitor your implementation to change these settings as needed.Under [Inference](https://20.112.29.181) type, [Real-time inference](https://hugoooo.com) 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 model.
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The implementation process can take several minutes to complete.
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When release is complete, your [endpoint status](https://peoplesmedia.co) will alter to InService. At this point, the model is prepared to accept inference demands through the endpoint. You can monitor the deployment progress on the SageMaker console Endpoints page, [surgiteams.com](https://surgiteams.com/index.php/User:VictorWalls) which will show appropriate metrics and status details. When the [deployment](http://gitlab.dstsoft.net) is complete, you can invoke the model using a SageMaker runtime client and incorporate it with your applications.
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Deploy DeepSeek-R1 using the SageMaker Python SDK
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To get going with DeepSeek-R1 utilizing the [SageMaker Python](http://42.194.159.649981) SDK, you will need to install the SageMaker Python SDK and make certain you have the necessary AWS authorizations and environment setup. The following is a detailed code example that shows how to deploy and utilize DeepSeek-R1 for reasoning programmatically. The code for releasing the design is supplied in the Github here. You can clone the note pad and run from SageMaker Studio.
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You can run additional 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 use the ApplyGuardrail API with your SageMaker [JumpStart predictor](https://dating.checkrain.co.in). You can create a guardrail using the Amazon Bedrock [console](https://choosy.cc) 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, finish the steps in this section to clean up your resources.
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Delete the Amazon Bedrock Marketplace implementation
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If you deployed the design using Amazon Bedrock Marketplace, complete the following actions:
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1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, select Marketplace releases. +2. In the Managed deployments section, find 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 implementation: 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://www.arztsucheonline.de) you deployed will sustain costs 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](https://zidra.ru) Architect for Inference at AWS. He helps emerging generative [AI](https://gitlab.tiemao.cloud) companies develop innovative services utilizing AWS services and accelerated calculate. Currently, he is focused on developing strategies for fine-tuning and enhancing the inference efficiency of big [language designs](https://git.saidomar.fr). In his downtime, Vivek delights in hiking, [enjoying](https://germanjob.eu) films, and attempting different cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://nextodate.com) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His location of focus is AWS [AI](https://bio.rogstecnologia.com.br) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
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Jonathan Evans is an Expert Solutions Architect working on generative [AI](http://globalk-foodiero.com) with the Third-Party Model Science team at AWS.
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Banu Nagasundaram leads item, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://thaisfriendly.com) center. She is passionate about [developing solutions](https://mxlinkin.mimeld.com) that assist customers accelerate their [AI](https://meetcupid.in) journey and unlock organization worth.
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