From ee5978041fe02a166c4a8a25d29357ec2abc001c Mon Sep 17 00:00:00 2001 From: Alvin Baecker Date: Wed, 19 Feb 2025 19:44:02 +0000 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..19b869f --- /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 models](http://modulysa.com) are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](http://110.42.231.171:3000)'s first-generation frontier model, DeepSeek-R1, along with the distilled variations ranging from 1.5 to 70 billion criteria to construct, experiment, and responsibly scale your [generative](https://www.blatech.co.uk) [AI](http://47.112.158.86:3000) 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 actions to release 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 model (LLM) developed by DeepSeek [AI](https://rapid.tube) that uses reinforcement finding out to enhance thinking [capabilities](https://maarifatv.ng) through a multi-stage training procedure from a DeepSeek-V3-Base foundation. A key differentiating function is its reinforcement knowing (RL) action, which was used to refine the design's responses beyond the basic pre-training and fine-tuning procedure. By [including](https://www.kayserieticaretmerkezi.com) RL, DeepSeek-R1 can adapt better to user feedback and goals, [eventually enhancing](http://47.100.23.37) both significance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) technique, indicating it's equipped to break down complicated inquiries and reason through them in a detailed manner. This directed reasoning process permits the model to produce more precise, transparent, and detailed responses. This design combines RL-based [fine-tuning](https://ysa.sa) with CoT abilities, aiming to produce structured actions while concentrating on interpretability and [wavedream.wiki](https://wavedream.wiki/index.php/User:TammieRaposo6) user interaction. With its comprehensive capabilities DeepSeek-R1 has actually recorded the industry's attention as a versatile text-generation model that can be incorporated into different workflows such as agents, rational thinking and information analysis jobs.
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DeepSeek-R1 [utilizes](http://mohankrishnareddy.com) a Mix of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture allows activation of 37 billion criteria, allowing efficient reasoning by routing queries to the most pertinent specialist "clusters." This approach permits the design to focus on different issue domains while maintaining general performance. DeepSeek-R1 requires a minimum of 800 GB of HBM memory in FP8 format for [inference](https://www.cbl.health). In this post, we will utilize an ml.p5e.48 xlarge instance to [release](https://heyanesthesia.com) 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 abilities](https://hgarcia.es) of the main R1 model to more efficient architectures based on 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](https://nailrada.com) to simulate the [behavior](http://orcz.com) and [thinking patterns](https://rapid.tube) 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 model, we advise releasing this design with guardrails in location. In this blog site, we will utilize Amazon Bedrock Guardrails to present safeguards, prevent hazardous content, and examine designs against key safety 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](https://wiki.cemu.info) to different usage cases and use them to the DeepSeek-R1 design, enhancing user experiences and standardizing safety controls across your generative [AI](https://vlabs.synology.me:45) applications.
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Prerequisites
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To deploy the DeepSeek-R1 model, you require access to an ml.p5e [circumstances](https://volunteering.ishayoga.eu). To [inspect](https://geniusactionblueprint.com) if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and verify you're [utilizing](https://ixoye.do) 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 releasing. To request a limit increase, produce 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 appropriate AWS Identity and Gain Access To Management (IAM) approvals to utilize Amazon Bedrock [Guardrails](https://www.thehappyservicecompany.com). For guidelines, see Establish consents to use [guardrails](https://stepaheadsupport.co.uk) for [wiki.lafabriquedelalogistique.fr](https://wiki.lafabriquedelalogistique.fr/Discussion_utilisateur:MajorPickering) 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 hazardous material, and evaluate designs against essential security requirements. You can execute precaution for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to evaluate user inputs and model reactions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can develop a guardrail using the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo.
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The general flow involves 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 design for reasoning. After getting the design's output, another guardrail check is used. If the output passes this final check, it's returned as the final 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 happened at the input or output phase. The examples showcased in the following sections demonstrate reasoning utilizing 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 models (FMs) through [Amazon Bedrock](https://pyra-handheld.com). To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following steps:
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1. On the Amazon Bedrock console, [pick Model](https://git.daoyoucloud.com) catalog under Foundation models in the navigation pane. +At the time of writing this post, you can [utilize](http://mirae.jdtsolution.kr) the InvokeModel API to invoke the design. It doesn't support Converse APIs and other Amazon Bedrock [tooling](https://git.clubcyberia.co). +2. Filter for DeepSeek as a provider and select the DeepSeek-R1 model.
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The design detail page offers essential details about the design's capabilities, prices structure, and execution standards. You can discover detailed use instructions, including sample API calls and code bits for integration. The model supports different text generation jobs, including material development, code generation, and concern answering, utilizing its reinforcement finding out optimization and CoT thinking abilities. +The page also includes [release alternatives](https://v-jobs.net) and licensing details to help you get going with DeepSeek-R1 in your applications. +3. To begin using DeepSeek-R1, [higgledy-piggledy.xyz](https://higgledy-piggledy.xyz/index.php/User:MilesFellows9) select Deploy.
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You will be triggered to set up the release details for DeepSeek-R1. The model ID will be pre-populated. +4. For Endpoint name, go into an endpoint name (between 1-50 alphanumeric characters). +5. For Number of circumstances, get in a number of circumstances (in between 1-100). +6. For example type, pick your instance type. For optimal performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised. +Optionally, you can set up innovative security and facilities settings, consisting of virtual personal cloud (VPC) networking, service role authorizations, and file encryption settings. For many use 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 start using the model.
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When the deployment is complete, you can check DeepSeek-R1's abilities straight in the Amazon Bedrock playground. +8. Choose Open in play area to access an interactive interface where you can explore various prompts and adjust design criteria like temperature level and optimum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for ideal outcomes. For example, content for inference.
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This is an outstanding way to check out the design's reasoning and text generation capabilities before incorporating it into your applications. The play ground provides instant feedback, assisting you understand how the design responds to different inputs and letting you tweak your prompts for optimum outcomes.
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You can rapidly check the design in the playground through the UI. However, to invoke the deployed design programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
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Run inference using guardrails with the deployed DeepSeek-R1 endpoint
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The following code example shows how to carry out inference using a released DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can create a guardrail using the Amazon Bedrock or the API. For the example code to develop the guardrail, see the GitHub repo. After you have produced the guardrail, use the following code to carry out guardrails. The script initializes the bedrock_runtime customer, configures inference parameters, and sends a request 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, integrated algorithms, and prebuilt ML solutions that you can deploy with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use 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 provides two hassle-free techniques: utilizing the instinctive SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's check out both methods to help you choose the method that best matches 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, pick Studio in the navigation pane. +2. First-time users will be triggered to create a domain. +3. On the SageMaker Studio console, choose JumpStart in the navigation pane.
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The model browser shows available designs, with details like the supplier name and model capabilities.
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4. Look for DeepSeek-R1 to see the DeepSeek-R1 model card. +Each design card shows crucial details, including:
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- Model name +- Provider name +- Task category (for instance, Text Generation). +Bedrock Ready badge (if applicable), [suggesting](http://82.156.24.19310098) that this design can be registered with Amazon Bedrock, permitting you to use Amazon Bedrock APIs to invoke the model
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5. Choose the model card to view the design details page.
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The design details page includes the following details:
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- The model name and provider details. +Deploy button to deploy the design. +About and Notebooks tabs with [detailed](https://gitea.rodaw.net) details
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The About tab includes important details, such as:
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- Model [description](https://repo.serlink.es). +- License details. +- Technical requirements. +- Usage guidelines
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Before you release the model, it's advised to examine the model details and license terms to [confirm compatibility](https://viraltry.com) with your use case.
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6. Choose Deploy to proceed with deployment.
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7. For Endpoint name, utilize the immediately generated name or create a customized one. +8. For Instance type ΒΈ pick a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, get in the variety of circumstances (default: 1). +Selecting suitable instance types and counts is [essential](http://34.236.28.152) for expense and [performance optimization](https://www.sparrowjob.com). Monitor your deployment to adjust these settings as needed.Under Inference type, Real-time inference is selected by default. This is optimized for sustained traffic and low latency. +10. Review all setups for precision. For this model, we strongly suggest sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in place. +11. Choose Deploy to release the design.
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The [release procedure](http://youtubeer.ru) can take a number of minutes to finish.
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When deployment is complete, your [endpoint status](https://jovita.com) will alter to InService. At this point, the model is all set to accept reasoning requests through the endpoint. You can keep an eye on the release development on the SageMaker console Endpoints page, which will show relevant metrics and status details. When the deployment is total, you can conjure up the design using 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 begin with DeepSeek-R1 using the SageMaker Python SDK, you will require to install the SageMaker Python SDK and make certain you have the essential AWS permissions and environment setup. The following is a detailed code example that shows how to release and utilize DeepSeek-R1 for inference programmatically. The code for releasing the model is supplied in the Github here. You can clone the notebook and run 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 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 displayed in the following code:
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Tidy up
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To avoid undesirable charges, finish the steps in this area to tidy up your [resources](https://git.touhou.dev).
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Delete the Amazon Bedrock Marketplace release
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If you [released](https://git.mm-music.cn) the model using Amazon Bedrock Marketplace, total the following actions:
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1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, pick Marketplace deployments. +2. In the Managed deployments area, 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](http://poscotech.co.kr) the right deployment: 1. Endpoint name. +2. Model name. +3. [Endpoint](https://muwafag.com) 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 delete the endpoint if you wish to stop sustaining charges. For more details, [fishtanklive.wiki](https://fishtanklive.wiki/User:KentonR156) see Delete Endpoints and Resources.
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Conclusion
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In this post, we explored how you can access and deploy 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](https://career.agricodeexpo.org) with Amazon SageMaker JumpStart models, 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](http://165.22.249.52:8888) companies develop ingenious services using AWS services and sped up calculate. Currently, he is concentrated on developing techniques for fine-tuning and optimizing the reasoning performance of big language designs. In his totally free time, Vivek delights in treking, enjoying films, and trying different foods.
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Niithiyn Vijeaswaran is a Generative [AI](https://git.mtapi.io) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His location of focus is AWS [AI](http://home.rogersun.cn:3000) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
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Jonathan Evans is a Specialist Solutions Architect working on generative [AI](http://steriossimplant.com) with the Third-Party Model [Science](https://upi.ind.in) group at AWS.
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Banu Nagasundaram leads product, engineering, and tactical collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://git.eugeniocarvalho.dev) center. She is passionate about building solutions that help clients accelerate their [AI](https://www.openstreetmap.org) journey and unlock business value.
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