commit 52c7c18bfbfafae2e8d998d9748951e2cd976a82 Author: ttukristen0778 Date: Tue Apr 8 04:44:23 2025 +0000 Update 'DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart' 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..a24a0c5 --- /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](https://git.unicom.studio)'s [first-generation frontier](http://ratel.ng) model, DeepSeek-R1, along with the distilled versions ranging from 1.5 to 70 billion parameters to develop, experiment, and responsibly scale your generative [AI](https://gl.vlabs.knu.ua) 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 steps to deploy the distilled variations of the models too.
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[Overview](https://39.129.90.14629923) of DeepSeek-R1
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DeepSeek-R1 is a big language model (LLM) established by DeepSeek [AI](http://kanghexin.work:3000) that uses support discovering to boost reasoning capabilities through a [multi-stage training](http://www.xyais.com) procedure from a DeepSeek-V3-Base structure. An essential identifying function is its support knowing (RL) action, which was used to refine the design's actions beyond the standard pre-training and tweak procedure. By incorporating RL, DeepSeek-R1 can adjust more successfully to user feedback and objectives, ultimately boosting both importance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) technique, meaning it's equipped to break down intricate inquiries and [archmageriseswiki.com](http://archmageriseswiki.com/index.php/User:ICKMillie8127) reason through them in a detailed way. This guided reasoning procedure permits the model to produce more accurate, transparent, and detailed answers. This design integrates RL-based fine-tuning with CoT capabilities, aiming to produce structured actions while focusing on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has actually recorded the industry's attention as a versatile text-generation design that can be integrated into numerous workflows such as representatives, logical reasoning and information [analysis](https://gitea.alexconnect.keenetic.link) jobs.
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DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture allows activation of 37 billion parameters, enabling efficient reasoning by routing questions to the most relevant specialist "clusters." This [technique permits](https://talentlagoon.com) the design to concentrate on different problem domains while maintaining total efficiency. DeepSeek-R1 needs at least 800 GB of HBM memory in FP8 format for inference. In this post, we will use an ml.p5e.48 xlarge circumstances to release the model. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.
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DeepSeek-R1 distilled designs bring the thinking abilities of the main R1 model to more effective architectures based upon popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of training smaller sized, more effective designs to imitate the behavior and reasoning patterns of the larger DeepSeek-R1 design, utilizing it as an instructor model.
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You can deploy DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we advise releasing this design with [guardrails](https://allcollars.com) in place. In this blog site, we will use Amazon Bedrock Guardrails to present safeguards, prevent hazardous content, and assess models against essential safety requirements. At the time of composing this blog site, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create numerous guardrails tailored to different use cases and use them to the DeepSeek-R1 model, enhancing user experiences and [standardizing security](https://cyltalentohumano.com) controls throughout your generative [AI](http://47.108.69.33:10888) applications.
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Prerequisites
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To release the DeepSeek-R1 model, you need access to an ml.p5e instance. To examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, select Amazon SageMaker, and confirm 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](https://genzkenya.co.ke) you are releasing. To ask for a limitation boost, create a limit boost request and reach out to your account group.
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Because you will be deploying this design with Amazon Bedrock Guardrails, make certain you have the correct AWS Identity and Gain Access To Management (IAM) approvals to utilize Amazon Bedrock Guardrails. For directions, see Establish authorizations to utilize guardrails for content filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails allows you to present safeguards, avoid hazardous content, and examine models against essential security criteria. You can execute precaution for the DeepSeek-R1 model using the Amazon Bedrock [ApplyGuardrail](http://git.the-archive.xyz) API. This allows you to use guardrails to examine user inputs and model actions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a guardrail using 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 [flow involves](https://www.hb9lc.org) the following steps: First, the system gets an input for the design. 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 receiving the design's output, another [guardrail check](http://www.xn--80agdtqbchdq6j.xn--p1ai) 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 took place at the input or output phase. The examples showcased in the following areas demonstrate inference [utilizing](https://afrocinema.org) this API.
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Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
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Amazon Bedrock Marketplace 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, total the following actions:
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1. On the [Amazon Bedrock](http://101.34.39.123000) console, select Model catalog under Foundation designs in the navigation pane. +At the time of composing this post, you can utilize the [InvokeModel API](https://vibestream.tv) to invoke the design. It doesn't support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a service provider and choose the DeepSeek-R1 model.
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The design detail page offers vital details about the model's abilities, rates structure, and implementation standards. You can find detailed use guidelines, [including sample](https://www.sparrowjob.com) API calls and code bits for combination. The design supports different text generation tasks, including material development, code generation, and concern answering, using its support discovering optimization and CoT thinking capabilities. +The page likewise consists of implementation options and licensing details to assist you get going with DeepSeek-R1 in your applications. +3. To begin using DeepSeek-R1, select Deploy.
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You will be prompted to set up the deployment details for DeepSeek-R1. The design ID will be pre-populated. +4. For Endpoint name, get in an endpoint name (in between 1-50 alphanumeric characters). +5. For Number of circumstances, get in a number of instances (in between 1-100). +6. For Instance type, pick your [circumstances type](http://120.77.240.2159701). For ideal performance with DeepSeek-R1, a type like ml.p5e.48 xlarge is suggested. +Optionally, you can [configure advanced](http://116.62.159.194) security and infrastructure settings, including virtual private cloud (VPC) networking, service role approvals, and file encryption settings. For most utilize cases, the default settings will work well. However, for [production](http://git.zhiweisz.cn3000) implementations, you might wish to examine these settings to align with your company's security and compliance requirements. +7. Choose Deploy to begin using the model.
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When the release is complete, you can evaluate DeepSeek-R1's capabilities straight in the Amazon Bedrock playground. +8. Choose Open in play ground to access an interactive user interface where you can explore different triggers and change design criteria like temperature and maximum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimal results. For example, content for inference.
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This is an excellent way to explore the model's thinking and text generation capabilities before integrating it into your applications. The play ground supplies instant feedback, helping you [comprehend](http://sopoong.whost.co.kr) how the model reacts to numerous inputs and letting you fine-tune your triggers for optimal results.
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You can rapidly check the design in the play area through the UI. However, to [conjure](https://richonline.club) up the released design programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
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Run reasoning utilizing [guardrails](https://fewa.hudutech.com) with the deployed DeepSeek-R1 endpoint
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The following code example demonstrates how to carry out inference using a released DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. 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. After you have actually produced the guardrail, [utilize](http://103.197.204.1623025) the following code to carry out guardrails. The [script initializes](https://git.poloniumv.net) the bedrock_runtime customer, sets up reasoning criteria, and sends out a demand to produce text based upon a user prompt.
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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 algorithms, and prebuilt ML options that you can release with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your information, and release them into production using either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart uses two convenient techniques: using the user-friendly SageMaker [JumpStart](https://39.105.45.141) UI or carrying out programmatically through the SageMaker Python SDK. Let's explore both methods to help you pick the technique 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 release DeepSeek-R1 using SageMaker JumpStart:
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1. On the SageMaker console, choose Studio in the navigation pane. +2. First-time users will be prompted to produce a domain. +3. On the SageMaker Studio console, select [JumpStart](http://47.100.23.37) in the navigation pane.
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The design [web browser](https://jobsnotifications.com) displays available models, with details like the service provider name and design capabilities.
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4. Look for DeepSeek-R1 to view the DeepSeek-R1 design card. +Each design card reveals essential details, consisting of:
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- Model name +- Provider name +- Task category (for example, Text Generation). +Bedrock Ready badge (if applicable), suggesting that this model can be registered with Amazon Bedrock, allowing you to use Amazon Bedrock APIs to conjure up the design
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5. Choose the model card to view the design details page.
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The model details page includes 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 details
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The About tab includes important details, such as:
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- Model description. +- License details. +- Technical specs. +[- Usage](http://24.233.1.3110880) guidelines
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Before you deploy the model, it's recommended to examine the model details and license terms to validate 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 generated name or create a customized one. +8. For example type ΒΈ choose an instance type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, go into the variety of instances (default: 1). +Selecting proper instance types and counts is essential for cost and performance optimization. Monitor your [deployment](https://pakkjob.com) to adjust these settings as needed.Under Inference type, Real-time inference is picked by default. This is enhanced for sustained traffic and low latency. +10. Review all setups for accuracy. For this model, we strongly advise adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in place. +11. Choose Deploy to deploy the design.
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The deployment procedure can take numerous minutes to complete.
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When deployment is total, your endpoint status will alter to InService. At this moment, the design is ready to accept reasoning requests through the endpoint. You can keep track of the implementation development on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the release is complete, 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 get started with DeepSeek-R1 using the SageMaker Python SDK, you will need to set up 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 deploying the design is offered in the Github here. You can clone the note pad and range from SageMaker Studio.
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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 likewise 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 shown in the following code:
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Clean up
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To avoid [unwanted](https://pennswoodsclassifieds.com) charges, finish 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 [deployed](https://saghurojobs.com) the model utilizing Amazon Bedrock Marketplace, total the following steps:
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1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, [select Marketplace](https://git.lgoon.xyz) deployments. +2. In the Managed deployments area, locate the endpoint you wish to erase. +3. Select the endpoint, and on the Actions menu, [select Delete](http://cgi3.bekkoame.ne.jp). +4. Verify the endpoint details to make certain you're erasing 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 released will sustain costs if you leave it running. Use the following code to erase the endpoint if you desire 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 the DeepSeek-R1 model using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get started. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Beginning with Amazon SageMaker JumpStart.
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About the Authors
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Vivek Gangasani is a [Lead Specialist](https://love63.ru) Solutions Architect for Inference at AWS. He helps emerging generative [AI](http://gitlab.adintl.cn) companies develop innovative solutions using AWS services and sped up calculate. Currently, he is focused on developing techniques for fine-tuning and optimizing the reasoning performance of large language models. In his downtime, Vivek takes pleasure in hiking, seeing films, and trying various cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://faraapp.com) Specialist Solutions Architect with the Third-Party Model [Science](http://47.92.149.1533000) team at AWS. His location of focus is AWS [AI](https://aggm.bz) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.
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Jonathan Evans is an Expert Solutions Architect working on generative [AI](https://gitlab.dituhui.com) with the Third-Party Model Science group at AWS.
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Banu Nagasundaram leads product, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://gitlab.mnhn.lu) center. She is passionate about building options that help customers accelerate their [AI](https://kommunalwiki.boell.de) journey and unlock business value.
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