From 5c4fd72a6ab355e8dc102014210d070f679bc332 Mon Sep 17 00:00:00 2001 From: robyngoode254 Date: Fri, 21 Feb 2025 06:46:05 +0800 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..f9de1f7 --- /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://www.blatech.co.uk)'s first-generation frontier model, DeepSeek-R1, together with the distilled variations varying from 1.5 to 70 billion parameters to construct, experiment, and properly scale your [AI](https://soucial.net) ideas on AWS.
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In this post, we show how to start with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar steps to release 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) established by DeepSeek [AI](https://tube.zonaindonesia.com) that uses reinforcement discovering to improve thinking capabilities through a multi-stage training [process](http://122.51.51.353000) from a DeepSeek-V3-Base foundation. A [key distinguishing](https://proputube.com) function is its reinforcement learning (RL) step, which was used to refine the [design's reactions](https://www.fionapremium.com) beyond the standard pre-training and [wiki.asexuality.org](https://wiki.asexuality.org/w/index.php?title=User_talk:MyraCollocott58) tweak process. By integrating RL, DeepSeek-R1 can adapt better to user feedback and objectives, ultimately improving both relevance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) technique, indicating it's equipped to break down complex questions and factor through them in a detailed manner. This assisted reasoning process allows the design to produce more precise, transparent, and detailed responses. This model combines RL-based fine-tuning with CoT abilities, aiming to create structured reactions while focusing on interpretability and user interaction. With its wide-ranging capabilities DeepSeek-R1 has actually captured the market's attention as a versatile [text-generation model](http://metis.lti.cs.cmu.edu8023) that can be integrated into numerous workflows such as agents, logical reasoning and information [analysis](https://co2budget.nl) jobs.
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DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture enables activation of 37 billion criteria, enabling effective reasoning by [routing](http://hoenking.cn3000) inquiries to the most appropriate specialist "clusters." This method allows the model to specialize in different [issue domains](https://dubai.risqueteam.com) while maintaining total [effectiveness](https://www.eadvisor.it). DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for reasoning. In this post, we will use an ml.p5e.48 [xlarge instance](http://wecomy.co.kr) to deploy 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 [reasoning abilities](http://178.44.118.232) of the main R1 design to more efficient architectures based upon popular open [designs](https://trademarketclassifieds.com) like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of training smaller sized, more efficient models to mimic the habits and reasoning patterns of the larger DeepSeek-R1 design, 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 recommend releasing this design with guardrails in location. In this blog site, we will use Amazon Bedrock Guardrails to present safeguards, avoid harmful material, and evaluate models against key safety criteria. At the time of composing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create several guardrails tailored to different use cases and apply them to the DeepSeek-R1 model, improving user experiences and standardizing security controls across your [generative](http://gitea.anomalistdesign.com) [AI](https://git.viorsan.com) applications.
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Prerequisites
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To deploy the DeepSeek-R1 design, you require access to an ml.p5e circumstances. 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 instance in the AWS Region you are deploying. To ask for a limit increase, create a limit increase demand 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 correct AWS Identity and Gain Access To Management (IAM) permissions to utilize Amazon Bedrock Guardrails. For guidelines, see Set up authorizations to utilize guardrails for content 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 evaluate designs against essential security criteria. You can carry out security procedures for the DeepSeek-R1 design utilizing the Amazon Bedrock [ApplyGuardrail](https://inicknet.com) API. This allows you to use guardrails to evaluate user inputs and model reactions deployed 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 [basic flow](http://git.ai-robotics.cn) includes the following actions: 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 design for inference. After getting the model's output, another [guardrail check](https://vcanhire.com) is used. If the output passes this last check, it's returned as the result. However, if either the input or output is stepped in by the guardrail, a message is returned suggesting the nature of the intervention and whether it occurred 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 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, complete the following actions:
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1. On the Amazon Bedrock console, [pick Model](http://49.234.213.44) 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 service provider and pick the DeepSeek-R1 design.
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The model detail page supplies vital details about the model's capabilities, rates structure, and implementation guidelines. You can discover detailed use instructions, including sample API calls and code snippets for combination. The model supports numerous text generation tasks, including content production, code generation, and question answering, using its support learning optimization and CoT reasoning capabilities. +The page also consists of deployment alternatives and licensing details to assist you get begun with DeepSeek-R1 in your applications. +3. To begin using DeepSeek-R1, select Deploy.
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You will be triggered to set up the release details for DeepSeek-R1. The design ID will be pre-populated. +4. For Endpoint name, go into an endpoint name (in between 1-50 alphanumeric characters). +5. For Variety of circumstances, enter a number of [circumstances](http://152.136.126.2523000) (between 1-100). +6. For Instance type, choose your circumstances type. For optimum performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is suggested. +Optionally, you can configure sophisticated security and infrastructure settings, consisting of virtual personal cloud (VPC) networking, service role permissions, and encryption settings. For a lot of utilize cases, the default settings will work well. However, for production implementations, you might wish to examine these settings to line up with your organization's security and compliance requirements. +7. Choose Deploy to begin utilizing the model.
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When the implementation is total, you can check 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 triggers and change model specifications like temperature level and optimum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for optimal results. For example, content for reasoning.
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This is an outstanding method to check out the model's reasoning and [bio.rogstecnologia.com.br](https://bio.rogstecnologia.com.br/chantedarbon) text generation abilities before incorporating it into your applications. The play ground offers immediate feedback, assisting you comprehend how the design reacts to numerous inputs and letting you fine-tune your prompts for [optimum](http://101.43.112.1073000) results.
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You can quickly evaluate the model in the playground through the UI. However, to invoke the released model programmatically with any Amazon Bedrock APIs, you require 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 perform reasoning using a released DeepSeek-R1 design 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 created the guardrail, use the following code to execute guardrails. The [script initializes](https://gogs.fytlun.com) the bedrock_runtime client, sets up inference criteria, and [wavedream.wiki](https://wavedream.wiki/index.php/User:Kristie6813) sends a request to [generate text](https://git.wyling.cn) 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, 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 use case, with your information, and deploy them into production utilizing either the UI or SDK.
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Deploying DeepSeek-R1 design through SageMaker JumpStart offers two convenient techniques: using the user-friendly SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's check out both methods to help you choose the technique 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 deploy DeepSeek-R1 utilizing SageMaker JumpStart:
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1. On the SageMaker console, choose Studio in the [navigation](http://47.96.15.2433000) pane. +2. First-time users will be prompted to produce a domain. +3. On the [SageMaker Studio](http://ep210.co.kr) console, select JumpStart in the navigation pane.
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The design browser displays available designs, with details like the company name and model abilities.
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4. Look for DeepSeek-R1 to view the DeepSeek-R1 model card. +Each model card shows crucial details, consisting 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 registered with Amazon Bedrock, enabling you to use Amazon Bedrock APIs to invoke 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 consists of the following details:
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- The model name and service provider details. +Deploy button to [release](https://jobskhata.com) the design. +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](http://120.237.152.2188888). +- Technical specifications. +- Usage guidelines
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Before you release the design, it's recommended to examine the design details and license terms to verify compatibility with your use case.
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6. Choose Deploy to continue with deployment.
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7. For Endpoint name, utilize the automatically generated name or develop a custom-made one. +8. For example type ΒΈ pick an instance type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, get in the variety of circumstances (default: 1). +Selecting proper circumstances types and counts is essential for cost and [performance optimization](https://servergit.itb.edu.ec). Monitor your implementation to change these settings as needed.Under Inference type, [raovatonline.org](https://raovatonline.org/author/antoniocope/) Real-time reasoning is picked by default. This is enhanced for sustained traffic and low latency. +10. Review all setups for accuracy. For this design, we strongly advise sticking to SageMaker JumpStart default settings and making certain that network isolation remains in [location](https://healthcarestaff.org). +11. Choose Deploy to release the design.
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The implementation procedure can take a number of minutes to finish.
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When implementation is total, your endpoint status will change to InService. At this point, the design is all set to accept reasoning demands through the endpoint. You can keep an eye on the deployment development on the SageMaker console Endpoints page, which will display pertinent metrics and status details. When the deployment is total, you can invoke the design utilizing a SageMaker runtime client and integrate it with your applications.
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Deploy DeepSeek-R1 using the SageMaker Python SDK
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To start with DeepSeek-R1 utilizing the SageMaker Python SDK, you will require to set up the [SageMaker Python](https://git.lmh5.com) SDK and make certain you have the necessary AWS permissions and environment setup. The following is a detailed code example that demonstrates how to release and utilize DeepSeek-R1 for inference programmatically. The code for releasing the design is provided in the Github here. You can clone the notebook and range from SageMaker Studio.
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You can run extra demands against the predictor:
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Implement guardrails and run [reasoning](https://p1partners.co.kr) 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 create a guardrail utilizing the Amazon Bedrock console or the API, and implement it as revealed in the following code:
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Tidy up
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To avoid unwanted charges, finish the actions in this area to tidy up your resources.
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Delete the Amazon Bedrock [Marketplace](https://truejob.co) deployment
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If you deployed the design using Amazon Bedrock Marketplace, complete the following steps:
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1. On the Amazon Bedrock console, under Foundation models in the navigation pane, select Marketplace deployments. +2. In the Managed deployments section, locate the endpoint you want to erase. +3. Select the endpoint, [wavedream.wiki](https://wavedream.wiki/index.php/User:JoseLabarre6648) and on the Actions menu, [select Delete](https://gt.clarifylife.net). +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 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 going. For more details, refer to Use Amazon Bedrock [tooling](https://www.complete-jobs.com) with Amazon SageMaker JumpStart designs, SageMaker JumpStart pretrained models, [Amazon SageMaker](http://118.190.145.2173000) 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](http://investicos.com) business construct innovative services using AWS services and sped up compute. Currently, he is focused on establishing techniques for fine-tuning and optimizing the reasoning performance of large language designs. In his spare time, Vivek enjoys hiking, enjoying films, and attempting different foods.
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Niithiyn Vijeaswaran is a Generative [AI](https://git.lmh5.com) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His area of focus is AWS [AI](https://repos.ubtob.net) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and [archmageriseswiki.com](http://archmageriseswiki.com/index.php/User:IsidroPerrone) Bioinformatics.
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Jonathan Evans is an Expert Solutions Architect working on generative [AI](https://git.antonshubin.com) with the Third-Party Model Science group at AWS.
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Banu Nagasundaram leads product, engineering, and strategic collaborations for [archmageriseswiki.com](http://archmageriseswiki.com/index.php/User:LienGoshorn40) Amazon SageMaker JumpStart, SageMaker's artificial intelligence and [generative](https://sundaycareers.com) [AI](https://realmadridperipheral.com) center. She is passionate about developing solutions that help clients accelerate their [AI](https://repo.amhost.net) journey and unlock business worth.
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