From 9d928be7f3119af804f1c13c616a8a34c1e6ad5b Mon Sep 17 00:00:00 2001 From: duaneearnhardt Date: Wed, 5 Mar 2025 09:03:00 +0000 Subject: [PATCH] Update '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..5b3535d --- /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](https://wakeuptaylor.boardhost.com) are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](http://222.121.60.40:3000)'s first-generation frontier design, DeepSeek-R1, together with the distilled versions varying from 1.5 to 70 billion specifications to develop, experiment, and properly scale your [generative](https://careerworksource.org) [AI](http://git.baige.me) ideas on AWS.
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In this post, we demonstrate how to get begun with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar steps to deploy the distilled variations of the designs 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://source.futriix.ru) that uses support finding out to boost reasoning capabilities through a multi-stage training process from a DeepSeek-V3-Base foundation. An essential identifying feature is its reinforcement knowing (RL) step, which was utilized to fine-tune the model's reactions beyond the standard pre-training and fine-tuning procedure. By incorporating RL, DeepSeek-R1 can adapt better to user feedback and objectives, eventually enhancing both significance and clarity. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) technique, implying it's equipped to break down complicated questions and reason through them in a detailed way. This assisted reasoning procedure allows the model to produce more precise, transparent, and detailed responses. This design combines RL-based fine-tuning with CoT abilities, [it-viking.ch](http://it-viking.ch/index.php/User:RaphaelLodewyckx) aiming to create structured reactions while concentrating on interpretability and user interaction. With its [extensive capabilities](https://textasian.com) DeepSeek-R1 has caught the industry's attention as a flexible text-generation design that can be integrated into various workflows such as representatives, sensible reasoning and data analysis jobs.
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DeepSeek-R1 [utilizes](https://www.cbmedics.com) a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture allows activation of 37 billion specifications, allowing effective inference by routing questions to the most relevant professional "clusters." This technique allows the design to concentrate on various problem domains while maintaining total effectiveness. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will utilize an ml.p5e.48 [xlarge instance](http://123.57.66.463000) to deploy the model. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.
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DeepSeek-R1 distilled designs bring the reasoning capabilities of the main R1 design to more effective architectures based on popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a procedure of training smaller sized, more efficient models to imitate the habits and thinking patterns of the larger DeepSeek-R1 model, using it as a teacher model.
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You can release DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we advise releasing this design with guardrails in place. In this blog, we will utilize Amazon Bedrock Guardrails to introduce safeguards, prevent damaging material, and assess models against essential safety criteria. At the time of composing this blog site, for [yewiki.org](https://www.yewiki.org/User:IMIKristin) DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can create several guardrails tailored to various use cases and apply them to the DeepSeek-R1 model, enhancing user experiences and standardizing safety controls throughout your generative [AI](https://autogenie.co.uk) applications.
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Prerequisites
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To deploy the DeepSeek-R1 design, you require access to an ml.p5e instance. To inspect 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 you are releasing. To ask for a limit boost, produce a limit increase demand and connect to your account group.
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Because you will be deploying this model with Amazon Bedrock Guardrails, make certain you have the right AWS Identity and Gain Access To Management (IAM) permissions to utilize Amazon Bedrock Guardrails. For guidelines, see Set up approvals to utilize 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, avoid hazardous content, [forum.altaycoins.com](http://forum.altaycoins.com/profile.php?id=1085681) and evaluate designs against crucial security criteria. You can carry out precaution for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to examine user inputs and [design reactions](http://178.44.118.232) released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can create a guardrail using the Amazon Bedrock [console](https://test1.tlogsir.com) or the API. For the example code to produce the guardrail, see the GitHub repo.
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The basic 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 inference. After getting the model's output, another guardrail check is applied. If the output passes this last check, it's returned as the outcome. However, if either the input or output is intervened by the guardrail, a message is returned indicating the nature of the intervention and whether it took place at the input or output stage. The examples showcased in the following sections show [inference utilizing](http://www.pelletkorea.net) this API.
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Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
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Amazon Bedrock Marketplace offers you access to over 100 popular, emerging, and specialized foundation designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following steps:
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1. On the Amazon Bedrock console, choose Model brochure 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 does not support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a company and select the DeepSeek-R1 model.
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The design detail page supplies necessary details about the model's capabilities, pricing structure, and execution standards. You can discover detailed usage directions, including sample API calls and code snippets for [wavedream.wiki](https://wavedream.wiki/index.php/User:AdeleTreloar) combination. The design supports various text generation jobs, including material creation, code generation, and question answering, [utilizing](https://weworkworldwide.com) its support discovering optimization and [CoT reasoning](http://122.51.46.213) capabilities. +The page likewise [consists](https://www.outletrelogios.com.br) of implementation choices and [engel-und-waisen.de](http://www.engel-und-waisen.de/index.php/Benutzer:LeilaniCable73) licensing details to help you begin with DeepSeek-R1 in your applications. +3. To start using DeepSeek-R1, 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, enter an endpoint name (in between 1-50 alphanumeric characters). +5. For Variety of circumstances, go into a variety of circumstances (between 1-100). +6. For example type, pick 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, including virtual personal cloud (VPC) networking, service function permissions, and file encryption settings. For most [utilize](http://120.77.213.1393389) cases, the default settings will work well. However, for production deployments, you might wish to evaluate these [settings](http://destruct82.direct.quickconnect.to3000) to align with your [organization's security](http://114.132.230.24180) and [compliance requirements](https://code.jigmedatse.com). +7. Choose Deploy to begin using the design.
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When the implementation is total, 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 different triggers and adjust design parameters 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 example, material for reasoning.
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This is an outstanding method to check out the design's reasoning and text generation capabilities before integrating it into your applications. The play ground provides immediate feedback, helping you comprehend how the design reacts to numerous inputs and letting you tweak your prompts for optimal outcomes.
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You can rapidly test the design in the play area through the UI. However, to conjure up the released design programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
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Run [inference](https://source.brutex.net) using guardrails with the released DeepSeek-R1 endpoint
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The following code example demonstrates how to perform reasoning using a [deployed](https://seedvertexnetwork.co.ke) DeepSeek-R1 design through Amazon Bedrock using the invoke_model and ApplyGuardrail API. 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. After you have created the guardrail, utilize the following code to carry out guardrails. The script initializes the bedrock_runtime client, sets up inference parameters, and sends a request to [generate text](https://supardating.com) 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 deploy with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your use case, with your data, and deploy them into [production](https://lifefriendsurance.com) using either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart provides two hassle-free approaches: using the intuitive SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's explore both methods to help you pick the [approach](http://128.199.125.933000) that best fits 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 utilizing SageMaker JumpStart:
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1. On the SageMaker console, choose Studio in the navigation pane. +2. First-time users will be triggered to create a domain. +3. On the SageMaker Studio console, select JumpStart in the navigation pane.
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The design internet browser [displays](http://120.201.125.1403000) available designs, with details like the supplier name and [design abilities](http://update.zgkw.cn8585).
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4. Search 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 suitable), indicating that this model can be signed up with Amazon Bedrock, [allowing](https://jobs.ahaconsultant.co.in) you to utilize Amazon Bedrock APIs to conjure up the model
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5. Choose the design card to see 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 release the design. +About and Notebooks tabs with detailed details
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The About tab includes crucial details, such as:
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[- Model](https://thunder-consulting.net) description. +- License details. +- Technical specifications. +- Usage standards
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Before you release the model, it's recommended to review the design details and license terms to validate compatibility 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 created name or create a custom one. +8. For example type ΒΈ choose an instance type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, enter the variety of circumstances (default: 1). +Selecting proper instance types and counts is essential for [expense](http://www.tuzh.top3000) and performance optimization. Monitor your implementation to adjust these settings as needed.Under Inference type, Real-time reasoning is [selected](https://forum.infinity-code.com) by default. This is optimized for sustained traffic and [larsaluarna.se](http://www.larsaluarna.se/index.php/User:SerenaM745) 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 seclusion](http://141.98.197.226000) remains in location. +11. Choose Deploy to deploy the design.
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The deployment process can take numerous minutes to complete.
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When deployment is total, your [endpoint status](https://git.synz.io) will alter to InService. At this point, the model is ready to accept reasoning demands through the endpoint. You can keep an eye on the deployment progress on the SageMaker console Endpoints page, which will display relevant metrics and status details. When the deployment is total, you can conjure up the design using a [SageMaker runtime](https://gitcode.cosmoplat.com) client and incorporate 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 utilizing the SageMaker Python SDK, you will require to install the SageMaker Python SDK and make certain you have the needed AWS approvals and environment setup. The following is a detailed code example that shows how to deploy and use DeepSeek-R1 for inference programmatically. The code for releasing the design is provided in the Github here. You can clone the [notebook](https://tmiglobal.co.uk) 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 reasoning with your SageMaker JumpStart predictor
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Similar to Amazon Bedrock, you can also utilize the [ApplyGuardrail API](https://hesdeadjim.org) with your SageMaker JumpStart [predictor](https://gitea.xiaolongkeji.net). You can create a guardrail utilizing the Amazon Bedrock console or the API, and execute it as shown in the following code:
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Tidy up
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To avoid undesirable charges, finish the steps in this section to tidy up your resources.
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Delete the Amazon Bedrock [Marketplace](https://git.blinkpay.vn) deployment
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If you the design using Amazon Bedrock Marketplace, total 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 wish to delete. +3. Select the endpoint, and on the [Actions](https://gmstaffingsolutions.com) menu, [pick Delete](https://esvoe.video). +4. Verify the endpoint details to make certain you're deleting the proper 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 design you deployed will sustain expenses if you leave it running. Use the following code to erase the endpoint 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](http://valueadd.kr) the DeepSeek-R1 design using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get going. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Starting 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://galsenhiphop.com) business develop innovative services utilizing AWS services and accelerated calculate. Currently, he is concentrated on establishing strategies for fine-tuning and optimizing the inference efficiency of big language models. In his complimentary time, [Vivek enjoys](http://123.60.103.973000) hiking, enjoying films, and trying different foods.
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Niithiyn Vijeaswaran is a Generative [AI](https://howtolo.com) Specialist Solutions Architect with the [Third-Party Model](https://almanyaisbulma.com.tr) Science group at AWS. His location of focus is AWS [AI](https://cvmira.com) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.
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Jonathan Evans is a Specialist Solutions Architect working on generative [AI](https://git.limework.net) with the Third-Party Model Science team at AWS.
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Banu Nagasundaram leads product, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and [trademarketclassifieds.com](https://trademarketclassifieds.com/user/profile/2931558) generative [AI](https://trustemployement.com) center. She is enthusiastic about constructing solutions that help consumers accelerate their [AI](http://doc.folib.com:3000) journey and unlock organization worth.
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