commit 040d89af17e84052ca7135d605c9a929e7dacd30 Author: lontowle615544 Date: Fri Apr 4 17:55:15 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..6e57710 --- /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 deploy DeepSeek [AI](https://weeddirectory.com)'s first-generation frontier model, DeepSeek-R1, along with the distilled versions ranging from 1.5 to 70 billion parameters to develop, experiment, and responsibly scale your [generative](http://artsm.net) [AI](http://ggzypz.org.cn:8664) 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 release the distilled variations of the models also.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a large language model (LLM) established by DeepSeek [AI](http://forum.moto-fan.pl) that utilizes support finding out to improve thinking capabilities through a multi-stage training process from a DeepSeek-V3-Base foundation. An essential differentiating feature is its reinforcement learning (RL) action, which was used to improve the model's reactions beyond the standard pre-training and fine-tuning procedure. By including RL, DeepSeek-R1 can adjust better to user feedback and objectives, eventually boosting both significance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) technique, indicating it's geared up to break down complex inquiries and factor through them in a detailed way. This guided reasoning process permits the design to produce more accurate, transparent, and detailed responses. This design combines RL-based fine-tuning with CoT capabilities, aiming to create structured responses while focusing on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has caught the industry's attention as a versatile text-generation model that can be integrated into various workflows such as representatives, sensible reasoning and information analysis jobs.
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DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture permits activation of 37 billion specifications, allowing efficient inference by routing queries to the most appropriate specialist "clusters." This approach permits the model to specialize in different problem domains while maintaining general effectiveness. DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for inference. In this post, we will utilize an ml.p5e.48 xlarge instance to release the design. ml.p5e.48 xlarge comes with 8 Nvidia H200 [GPUs providing](https://unitenplay.ca) 1128 GB of GPU memory.
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DeepSeek-R1 distilled models bring the reasoning abilities of the main R1 model to more efficient architectures based upon popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). [Distillation refers](https://gigsonline.co.za) to a procedure of training smaller sized, more efficient designs to simulate the habits and reasoning patterns of the bigger DeepSeek-R1 model, using 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 design with guardrails in location. In this blog, we will utilize Amazon Bedrock Guardrails to introduce safeguards, avoid hazardous material, and assess models against essential safety criteria. At the time of writing this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the [ApplyGuardrail API](https://git.kicker.dev). 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](http://148.66.10.103000) across your generative [AI](http://101.200.127.15:3000) applications.
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Prerequisites
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To deploy the DeepSeek-R1 design, you require access to an ml.p5e [circumstances](http://8.138.140.943000). To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick Amazon SageMaker, and confirm you're utilizing 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 ask for a limit boost, develop a limitation boost demand and connect to your account group.
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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 [utilize Amazon](http://47.119.160.1813000) Bedrock Guardrails. For directions, 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 allows you to introduce safeguards, prevent hazardous content, and assess models against crucial safety criteria. You can [execute precaution](https://prosafely.com) for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This permits you to use guardrails to evaluate user inputs and model reactions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can create a guardrail utilizing the Amazon Bedrock [console](https://gitea.ravianand.me) or the API. For the example code to [produce](https://wiki.rolandradio.net) the guardrail, see the GitHub repo.
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The general circulation involves the following actions: First, the system receives an input for the model. This input is then processed through the ApplyGuardrail API. If the input passes the [guardrail](https://ezworkers.com) check, it's sent out to the design for reasoning. After receiving the design's output, another guardrail check is applied. If the output passes this last check, it's [returned](https://git.dadunode.com) as the last 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 happened at the input or output phase. The examples showcased in the following areas demonstrate inference using 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 foundation designs (FMs) through [Amazon Bedrock](http://47.97.161.14010080). To gain access to DeepSeek-R1 in Amazon Bedrock, total the following steps:
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1. On the Amazon Bedrock console, choose Model catalog under Foundation models in the navigation pane. +At the time of composing 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 service provider and select the DeepSeek-R1 design.
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The design detail page offers vital details about the model's capabilities, pricing structure, and application standards. You can find detailed usage directions, including sample API calls and code snippets for combination. The design supports various text generation tasks, including material development, code generation, and question answering, utilizing its reinforcement finding out optimization and CoT thinking capabilities. +The page likewise consists of release choices and licensing details to assist you begin with DeepSeek-R1 in your [applications](https://rassi.tv). +3. To start using DeepSeek-R1, choose Deploy.
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You will be triggered to configure the release details for DeepSeek-R1. The design ID will be pre-populated. +4. For Endpoint name, enter an endpoint name (between 1-50 alphanumeric characters). +5. For Variety of circumstances, get in a variety of circumstances (in between 1-100). +6. For example type, select your circumstances type. For optimum efficiency with DeepSeek-R1, a [GPU-based circumstances](https://granthers.com) type like ml.p5e.48 xlarge is recommended. +Optionally, you can configure sophisticated security and facilities settings, [including virtual](http://motojic.com) personal cloud (VPC) networking, service role consents, and file encryption [settings](https://job4thai.com). For a lot of utilize cases, the default settings will work well. However, for [production](https://plamosoku.com) deployments, you may want to review these settings to line up with your company's security and compliance requirements. +7. Choose Deploy to start using the design.
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When the release is complete, you can evaluate DeepSeek-R1's abilities straight in the Amazon Bedrock play area. +8. Choose Open in playground to access an interactive user interface where you can explore different prompts and change model parameters like temperature level and maximum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for optimal outcomes. For example, material for inference.
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This is an [exceptional method](https://gitea.neoaria.io) to explore the design's thinking and text generation capabilities before integrating it into your [applications](http://47.108.182.667777). The play area supplies immediate feedback, assisting you understand how the design responds to numerous inputs and letting you tweak your prompts for ideal results.
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You can quickly test the model in the playground through the UI. However, to invoke the released model programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
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Run inference utilizing guardrails with the [released](https://social.nextismyapp.com) DeepSeek-R1 endpoint
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The following code example demonstrates how to perform inference utilizing a deployed DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can develop a guardrail utilizing the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo. After you have actually produced the guardrail, utilize the following code to carry out guardrails. The script initializes the bedrock_runtime customer, configures inference criteria, and sends a demand to create text based upon a user timely.
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Deploy DeepSeek-R1 with SageMaker JumpStart
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[SageMaker JumpStart](https://www.blatech.co.uk) is an artificial intelligence (ML) center with FMs, built-in algorithms, and prebuilt ML options that you can release with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your use case, with your data, and release them into production using either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart offers 2 practical techniques: using the user-friendly SageMaker JumpStart UI or executing programmatically through the [SageMaker Python](http://tian-you.top7020) SDK. Let's explore both techniques to assist you pick the approach that best suits your requirements.
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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 develop a domain. +3. On the SageMaker Studio console, select JumpStart in the navigation pane.
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The model web browser shows available designs, with details like the supplier name and [model abilities](http://git.motr-online.com).
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4. Search for DeepSeek-R1 to view the DeepSeek-R1 model card. +Each design card reveals key details, [consisting](http://39.108.86.523000) of:
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- Model name +- Provider name +- Task classification (for example, Text Generation). +Bedrock Ready badge (if appropriate), indicating that this model can be signed up with Amazon Bedrock, permitting 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 design name and supplier details. +Deploy button to [release](http://git.airtlab.com3000) the model. +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 specifications. +[- Usage](https://git.bwt.com.de) standards
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Before you deploy the model, it's advised to review the design details and license terms to verify compatibility with your use case.
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6. Choose Deploy to proceed with release.
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7. For Endpoint name, utilize the automatically produced name or create a custom-made one. +8. For Instance type ΒΈ choose a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial instance count, get in the number of circumstances (default: 1). +Selecting appropriate instance types and counts is crucial for expense and performance optimization. Monitor your implementation to adjust these settings as needed.Under Inference type, Real-time reasoning is selected by default. This is optimized for sustained traffic and low [latency](http://116.198.225.843000). +10. Review all configurations for precision. For this model, we strongly recommend sticking to SageMaker JumpStart default settings and making certain that network isolation remains in place. +11. Choose Deploy to release the model.
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The implementation procedure can take a number of minutes to complete.
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When deployment is total, your [endpoint status](https://deadreckoninggame.com) will change to InService. At this moment, the design is prepared to accept inference demands through the endpoint. You can monitor the implementation progress on the SageMaker console Endpoints page, which will show [relevant metrics](http://13.228.87.95) and status details. When the deployment is complete, you can conjure up the design 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 essential AWS authorizations and environment setup. The following is a [detailed](http://git.estoneinfo.com) code example that demonstrates how to deploy and use DeepSeek-R1 for [inference programmatically](http://gsend.kr). The code for releasing the model is offered in the Github here. You can clone the notebook and run from SageMaker Studio.
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You can run extra requests 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 likewise use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can develop a guardrail utilizing the Amazon Bedrock console or the API, and implement it as shown in the following code:
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Clean up
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To avoid unwanted charges, complete the steps in this area to clean up your resources.
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Delete the Amazon Bedrock Marketplace deployment
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If you deployed 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 implementations. +2. In the Managed releases section, locate the endpoint you wish to erase. +3. Select the endpoint, and on the Actions menu, pick Delete. +4. Verify the endpoint details to make certain you're deleting the appropriate 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 deployed will sustain expenses if you leave it running. Use the following code to delete the [endpoint](http://123.111.146.2359070) 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 deploy the DeepSeek-R1 model utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get started. For more details, describe 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 Solutions Architect for Inference at AWS. He helps emerging generative [AI](https://wkla.no-ip.biz) companies construct ingenious services utilizing AWS services and accelerated calculate. Currently, he is concentrated on establishing strategies for fine-tuning and the inference efficiency of big language models. In his leisure time, Vivek delights in treking, enjoying motion pictures, and trying various cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://lubuzz.com) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His area of focus is AWS [AI](http://111.53.130.194:3000) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and [Bioinformatics](http://www.pygrower.cn58081).
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Jonathan Evans is a Professional Solutions Architect working on generative [AI](https://www.remotejobz.de) with the Third-Party Model Science team at AWS.
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Banu Nagasundaram leads item, [raovatonline.org](https://raovatonline.org/author/ajadst45283/) engineering, and strategic collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://git.l1.media) hub. She is enthusiastic about building solutions that help consumers accelerate their [AI](http://168.100.224.79:3000) journey and unlock company value.
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