commit 170fea65166b12b1d025062f2f24a6bb5c37577d Author: Angus McGruder Date: Fri Feb 7 03:00:18 2025 +0000 Add 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..03a6e2b --- /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 reveal that DeepSeek R1 distilled Llama and Qwen models are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](https://fmstaffingsource.com)'s first-generation frontier model, DeepSeek-R1, together with the distilled versions varying from 1.5 to 70 billion criteria to develop, experiment, and responsibly scale your generative [AI](https://mediawiki.hcah.in) concepts on AWS.
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In this post, we show how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar actions to deploy the distilled variations of the designs also.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a big [language design](https://www.bjs-personal.hu) (LLM) established by DeepSeek [AI](https://pakkjob.com) that uses reinforcement finding out to enhance thinking capabilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. A key identifying feature is its support learning (RL) action, which was used to refine the design's responses beyond the basic pre-training and fine-tuning process. By including RL, DeepSeek-R1 can adapt more successfully to user feedback and objectives, ultimately boosting both importance and clearness. In addition, DeepSeek-R1 [utilizes](https://rejobbing.com) a chain-of-thought (CoT) method, implying it's equipped to break down intricate questions and reason through them in a detailed way. This assisted thinking procedure permits the design to produce more precise, transparent, and detailed answers. This [design combines](https://jobedges.com) RL-based fine-tuning with CoT abilities, aiming to generate structured [responses](http://43.139.10.643000) while concentrating on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has recorded the [market's attention](http://slfood.co.kr) as a [versatile text-generation](https://dvine.tv) design that can be integrated into various workflows such as representatives, logical thinking and information analysis tasks.
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DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture allows [activation](https://xn--939a42kg7dvqi7uo.com) of 37 billion criteria, enabling efficient inference by routing queries to the most relevant expert "clusters." This technique enables the model to specialize in various problem domains while [maintaining](https://freelyhelp.com) 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 instance to release the model. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.
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DeepSeek-R1 distilled models bring the thinking capabilities of the main R1 design to more [efficient architectures](http://hoteltechnovalley.com) 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, more efficient models to simulate the behavior and thinking patterns of the bigger 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 design, we suggest releasing this model with guardrails in place. In this blog, we will utilize Amazon Bedrock Guardrails to present safeguards, avoid harmful material, and evaluate designs against crucial safety requirements. At the time of composing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can develop several guardrails tailored to different use cases and use them to the DeepSeek-R1 design, enhancing user experiences and standardizing security controls across your generative [AI](http://hmkjgit.huamar.com) 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 examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and [confirm](http://slfood.co.kr) you're using ml.p5e.48 xlarge for [endpoint usage](https://www.videochatforum.ro). Make certain that you have at least one ml.P5e.48 xlarge circumstances in the AWS Region you are releasing. To request a limit increase, develop a limitation increase request and reach out 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 and Gain Access To Management (IAM) permissions to use Amazon Bedrock Guardrails. For guidelines, see [Establish authorizations](http://120.79.218.1683000) to utilize [guardrails](https://disgaeawiki.info) 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 examine models against essential security [criteria](http://83.151.205.893000). You can implement precaution for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to examine user inputs and design reactions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can develop a guardrail using the [Amazon Bedrock](http://www.hnyqy.net3000) console or the API. For the example code to produce the guardrail, see the GitHub repo.
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The general circulation includes the following steps: 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 receiving the model's output, another guardrail check is used. If the output passes this last check, it's returned as the [outcome](http://git.wangtiansoft.com). 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 happened at the input or output phase. The examples showcased in the following areas [demonstrate reasoning](https://corevacancies.com) utilizing this API.
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Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
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Amazon Bedrock Marketplace gives you access to over 100 popular, emerging, and specialized foundation 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](https://mediawiki.hcah.in) console, select Model catalog under Foundation models in the navigation pane. +At the time of writing this post, you can utilize the InvokeModel API to invoke the design. It doesn't support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a [supplier](https://globalabout.com) and choose the DeepSeek-R1 design.
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The model detail page supplies important details about the [design's](http://47.105.104.2043000) capabilities, rates structure, and application guidelines. You can find detailed use directions, consisting of sample API calls and code snippets for integration. The [design supports](https://www.tqmusic.cn) different text generation jobs, including content development, code generation, and question answering, using its support discovering optimization and CoT thinking capabilities. +The page likewise includes release choices and licensing details to assist you get started with DeepSeek-R1 in your applications. +3. To start using DeepSeek-R1, pick 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](https://pioneercampus.ac.in) name, get in an endpoint name (between 1-50 alphanumeric characters). +5. For [Variety](http://27.154.233.18610080) of circumstances, enter a number of instances (between 1-100). +6. For Instance type, choose your instance type. For ideal efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is recommended. +Optionally, you can configure sophisticated security and facilities settings, consisting of virtual personal cloud (VPC) networking, service role approvals, and encryption settings. For a lot of utilize cases, the default settings will work well. However, for production deployments, you might wish to evaluate these settings to line up with your company's security and compliance requirements. +7. Choose Deploy to begin using the model.
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When the deployment is complete, you can evaluate DeepSeek-R1's capabilities straight in the Amazon Bedrock play area. +8. Choose Open in play area to access an interactive user interface where you can try out various triggers and change design specifications like temperature and maximum length. +When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for optimal results. For instance, material for reasoning.
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This is an excellent method to explore the design's reasoning and text generation abilities before incorporating it into your applications. The play ground provides immediate feedback, helping you understand how the to different inputs and letting you tweak your prompts for optimal outcomes.
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You can quickly test the design in the play ground through the UI. However, to invoke the released design programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
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Run reasoning utilizing guardrails with the released DeepSeek-R1 endpoint
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The following code example demonstrates how to carry out reasoning using a released DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can create a guardrail using the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo. After you have developed the guardrail, [utilize](http://43.139.182.871111) the following code to execute guardrails. The script initializes the bedrock_runtime customer, configures inference 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 (ML) hub with FMs, built-in algorithms, and prebuilt ML services that you can deploy with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage case, with your information, and [release](https://thewerffreport.com) them into production using either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart provides two hassle-free approaches: using the user-friendly SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's explore both methods to assist you choose the method that best fits 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, choose Studio in the navigation pane. +2. First-time users will be prompted to produce a domain. +3. On the SageMaker Studio console, pick JumpStart in the navigation pane.
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The design browser shows available models, with details like the provider name and model abilities.
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4. Search for DeepSeek-R1 to view the DeepSeek-R1 design card. +Each [model card](http://101.34.211.1723000) shows [essential](https://vooxvideo.com) details, consisting of:
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- Model name +- Provider name +- Task category (for instance, Text Generation). +Bedrock Ready badge (if suitable), indicating that this design can be registered 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 design details page.
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The design details page includes the following details:
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- The design name and provider details. +Deploy button to deploy the design. +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 [release](http://www.hcmis.cn) the model, it's recommended to examine the model details and license terms to confirm compatibility with your usage case.
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6. Choose Deploy to proceed with release.
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7. For Endpoint name, utilize the immediately created name or produce a customized one. +8. For Instance type ΒΈ choose a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial [circumstances](https://forum.freeadvice.com) count, enter the variety of circumstances (default: 1). +Selecting appropriate circumstances types and counts is crucial for expense and efficiency optimization. [Monitor](http://git.yang800.cn) your deployment to change these settings as needed.Under Inference type, Real-time inference is selected by [default](https://www.teamswedenclub.com). This is optimized for sustained traffic and low latency. +10. Review all configurations for precision. For this model, we highly suggest adhering to SageMaker JumpStart default settings and making certain that network isolation remains in place. +11. Choose Deploy to deploy the design.
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The deployment process can take several minutes to finish.
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When release is complete, your endpoint status will alter to InService. At this moment, the model is ready to accept inference requests through the endpoint. You can monitor the deployment progress on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the release 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 begun 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 demonstrates how to release and use DeepSeek-R1 for reasoning programmatically. The code for deploying the model is supplied 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 likewise use 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 displayed in the following code:
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Tidy up
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To avoid undesirable charges, complete the actions in this section to tidy up your resources.
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Delete the Amazon Bedrock Marketplace deployment
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If you deployed the [design utilizing](http://forum.altaycoins.com) Amazon Bedrock Marketplace, total 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 deployments section, find the endpoint you wish to erase. +3. Select the endpoint, and on the Actions menu, select Delete. +4. Verify the endpoint details to make certain you're deleting the appropriate 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 model you released will [sustain expenses](http://doosung1.co.kr) if you leave it running. Use the following code to delete the [endpoint](https://placementug.com) 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 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](http://117.50.100.23410080) now to get begun. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained models, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting started 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://git.aiadmin.cc) companies develop innovative services using AWS services and sped up calculate. Currently, he is focused on establishing strategies for fine-tuning and enhancing the inference performance of large [language models](http://gpra.jpn.org). In his [complimentary](https://mypocket.cloud) time, Vivek delights in treking, [35.237.164.2](https://35.237.164.2/wiki/User:BessieFitzRoy) viewing movies, and trying different foods.
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Niithiyn Vijeaswaran is a Generative [AI](http://code.snapstream.com) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His area of focus is AWS [AI](https://www.meetyobi.com) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and [Bioinformatics](http://gitlab.solyeah.com).
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Jonathan Evans is an Expert Solutions [Architect dealing](https://iamzoyah.com) with generative [AI](https://www.angevinepromotions.com) with the Third-Party Model Science team at AWS.
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Banu Nagasundaram leads item, engineering, and strategic collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](http://sbstaffing4all.com) center. She is passionate about developing services that assist clients accelerate their [AI](http://thinking.zicp.io:3000) journey and unlock business worth.
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