commit efbc2fa36c7f3c752e97325ab630daac332fc9cf Author: lorenespriggs Date: Sat Feb 22 02:59:18 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..e54a9ea --- /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 reveal that DeepSeek R1 distilled Llama and Qwen designs are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](https://service.aicloud.fit:50443)'s first-generation [frontier](http://motojic.com) design, DeepSeek-R1, together with the distilled variations ranging from 1.5 to 70 billion parameters to construct, [systemcheck-wiki.de](https://systemcheck-wiki.de/index.php?title=Benutzer:MonserrateHuntin) experiment, and responsibly scale your generative [AI](http://boiler.ttoslinux.org:8888) ideas on AWS.
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In this post, we demonstrate how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar actions to deploy the distilled versions of the models too.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a large language model (LLM) established by DeepSeek [AI](https://www.naukrinfo.pk) that uses reinforcement discovering to enhance reasoning abilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. An essential differentiating function is its reinforcement learning (RL) action, which was utilized to refine the [design's actions](https://trabaja.talendig.com) beyond the basic pre-training and fine-tuning procedure. By incorporating RL, DeepSeek-R1 can adjust better to user feedback and objectives, ultimately enhancing both importance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) technique, meaning it's equipped to break down complicated queries and reason through them in a detailed way. This directed thinking process permits the design to produce more accurate, transparent, and detailed answers. This design integrates RL-based fine-tuning with CoT capabilities, aiming to generate structured responses while concentrating on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has actually captured the market's attention as a flexible text-generation design that can be incorporated into various workflows such as representatives, rational reasoning and information [interpretation tasks](https://dimension-gaming.nl).
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DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion [criteria](https://bbs.yhmoli.com) in size. The MoE architecture permits activation of 37 billion parameters, enabling effective inference by routing queries to the most pertinent specialist "clusters." This method enables the model to specialize in various problem domains while maintaining general performance. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will use an ml.p5e.48 xlarge circumstances to release the design. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.
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DeepSeek-R1 distilled models bring the reasoning capabilities 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 describes a process of training smaller sized, more effective designs to mimic the habits and thinking patterns of the bigger DeepSeek-R1 model, utilizing it as a teacher 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 [recommend deploying](http://www.jedge.top3000) this model with guardrails in location. In this blog site, we will use Amazon Bedrock Guardrails to present safeguards, prevent damaging content, and assess designs against key security criteria. At the time of writing this blog, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, [Bedrock Guardrails](https://www.egomiliinteriors.com.ng) supports only the ApplyGuardrail API. You can develop multiple guardrails tailored to various usage cases and use them to the DeepSeek-R1 design, enhancing user experiences and standardizing safety controls throughout your generative [AI](https://trabajosmexico.online) 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 check if you have quotas for P5e, open the [Service Quotas](https://social.nextismyapp.com) console and under AWS Services, pick Amazon SageMaker, and validate you're using ml.p5e.48 xlarge for endpoint usage. Make certain that you have at least one ml.P5e.48 [xlarge instance](https://gitea.star-linear.com) in the AWS Region you are releasing. To ask for a limitation boost, create a limitation increase request and connect to your account team.
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Because you will be deploying this model with Amazon Bedrock Guardrails, make certain you have the proper AWS Identity and Gain Access To [Management](https://git.foxarmy.org) (IAM) approvals to utilize Amazon Bedrock Guardrails. For instructions, see Establish permissions to utilize guardrails for material filtering.
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Implementing guardrails with the [ApplyGuardrail](https://git.soy.dog) API
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Amazon Bedrock Guardrails allows you to introduce safeguards, avoid harmful material, and evaluate models against key security requirements. You can implement precaution for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to evaluate user inputs and design actions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can develop a guardrail utilizing 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 circulation includes the following steps: First, the system [receives](https://manpoweradvisors.com) an input for the model. This input is then processed through the ApplyGuardrail API. If the input passes the [guardrail](http://boiler.ttoslinux.org8888) check, it's sent to the model for reasoning. After getting the model's output, another guardrail check is used. If the output passes this final 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 took place at the input or output phase. The examples showcased in the following areas show inference 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 foundation models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following steps:
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1. On the Amazon Bedrock console, pick Model brochure under Foundation designs in the navigation pane. +At the time of composing this post, you can utilize the InvokeModel API to conjure up the model. It doesn't support Converse APIs and other Amazon Bedrock [tooling](http://anggrek.aplikasi.web.id3000). +2. Filter for DeepSeek as a supplier and choose the DeepSeek-R1 model.
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The design detail page supplies vital details about the model's abilities, prices structure, and application standards. You can discover detailed usage directions, including sample API calls and [code bits](http://39.106.43.96) for combination. The design supports numerous text generation tasks, including material development, code generation, and question answering, utilizing its reinforcement learning [optimization](https://www.miptrucking.net) and CoT thinking abilities. +The page likewise includes deployment 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 deployment details for DeepSeek-R1. The model ID will be [pre-populated](https://www.naukrinfo.pk). +4. For Endpoint name, enter an endpoint name (between 1-50 alphanumeric characters). +5. For Number of circumstances, enter a number of circumstances (in between 1-100). +6. For [engel-und-waisen.de](http://www.engel-und-waisen.de/index.php/Benutzer:Juliane3350) example type, select your circumstances type. For [optimal performance](https://stepaheadsupport.co.uk) with DeepSeek-R1, a [GPU-based instance](https://gitea.gm56.ru) type like ml.p5e.48 xlarge is suggested. +Optionally, you can configure innovative security and facilities settings, consisting of virtual personal cloud (VPC) networking, service role consents, and file encryption settings. For most utilize cases, the default settings will work well. However, for production implementations, you may want to review these settings to line up with your organization's security and compliance requirements. +7. Choose Deploy to begin utilizing the design.
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When the release is complete, you can check DeepSeek-R1's abilities straight in the Amazon Bedrock play ground. +8. Choose Open in playground to access an interactive user interface where you can experiment with different triggers and adjust design parameters like temperature and optimum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for optimum results. For instance, content for reasoning.
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This is an outstanding way to explore the design's thinking and text generation abilities before integrating it into your applications. The playground provides immediate feedback, helping you understand how the design responds to numerous inputs and letting you fine-tune your prompts for optimum outcomes.
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You can [rapidly](http://jsuntec.cn3000) test the model in the playground through the UI. However, to conjure up the deployed model programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
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Run inference utilizing [guardrails](https://www.h0sting.org) with the released DeepSeek-R1 endpoint
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The following code example shows how to carry out inference using a deployed DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can produce 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 created the guardrail, utilize the following code to implement guardrails. The script initializes the bedrock_runtime client, [configures inference](https://nycu.linebot.testing.jp.ngrok.io) specifications, and sends a demand to generate text based upon a user timely.
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Deploy DeepSeek-R1 with SageMaker JumpStart
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SageMaker JumpStart is an artificial intelligence (ML) center with FMs, integrated algorithms, and prebuilt ML [options](https://phpcode.ketofastlifestyle.com) that you can deploy with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your data, and release them into production using either the UI or SDK.
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Deploying DeepSeek-R1 design through SageMaker JumpStart provides two practical methods: utilizing the intuitive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's explore both techniques to help you choose the technique that finest fits your needs.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following [actions](https://www.sportfansunite.com) to deploy 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 prompted to develop a domain. +3. On the [SageMaker Studio](https://pl.velo.wiki) console, select JumpStart in the navigation pane.
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The design browser displays available designs, with details like the service provider name and model abilities.
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4. Search for DeepSeek-R1 to see the DeepSeek-R1 model card. +Each model card shows crucial details, including:
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- Model name +- Provider name +- Task classification (for example, Text Generation). +Bedrock Ready badge (if suitable), that this design can be registered with Amazon Bedrock, allowing you to [utilize Amazon](http://repo.z1.mastarjeta.net) [Bedrock](https://git.project.qingger.com) APIs to invoke the model
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5. Choose the design card to view the design details page.
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The model details page [consists](http://git.airtlab.com3000) of the following details:
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- The model name and company details. +Deploy button to deploy the design. +About and Notebooks tabs with detailed details
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The About tab includes important details, such as:
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- Model description. +- License details. +- Technical requirements. +- Usage guidelines
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Before you deploy the design, it's advised to examine the design details and license terms to confirm compatibility with your use case.
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6. Choose Deploy to [continue](https://jobs.cntertech.com) with implementation.
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7. For Endpoint name, use the automatically generated name or develop a custom one. +8. For example type ΒΈ select a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial instance count, enter the [variety](http://120.46.139.31) of circumstances (default: 1). +Selecting proper circumstances types and counts is vital for expense and performance optimization. Monitor your release to adjust these settings as needed.Under Inference type, Real-time reasoning is selected by default. This is enhanced for sustained traffic and [oeclub.org](https://oeclub.org/index.php/User:MilanNickson63) low latency. +10. Review all configurations for precision. For this model, we highly advise adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in location. +11. Choose Deploy to deploy the model.
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The deployment procedure can take numerous minutes to complete.
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When release is total, your endpoint status will alter to InService. At this moment, the model is ready to accept inference demands through the endpoint. You can monitor the deployment progress on the SageMaker console Endpoints page, which will display [relevant metrics](https://parentingliteracy.com) and status details. When the [release](https://v-jobs.net) is total, you can invoke the model utilizing a SageMaker runtime client and integrate it with your applications.
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Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
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To get going with DeepSeek-R1 utilizing the SageMaker Python SDK, you will require to set up the SageMaker Python SDK and make certain you have the essential AWS authorizations and environment setup. The following is a detailed code example that shows how to release and utilize DeepSeek-R1 for reasoning programmatically. The code for [yewiki.org](https://www.yewiki.org/User:MaisieRoldan5) deploying the design is supplied in the Github here. You can clone the note pad and range from SageMaker Studio.
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You can run additional [demands](http://secdc.org.cn) 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 produce 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, complete the actions in this area to clean up your resources.
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Delete the Amazon Bedrock Marketplace deployment
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If you released the design utilizing Amazon Bedrock Marketplace, total the following steps:
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1. On the Amazon Bedrock console, under Foundation models in the navigation pane, choose Marketplace releases. +2. In the Managed releases area, find the endpoint you desire 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 proper implementation: 1. Endpoint name. +2. Model name. +3. [Endpoint](https://radiothamkin.com) status
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Delete the SageMaker JumpStart predictor
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The SageMaker JumpStart design you released will sustain costs if you leave it running. Use the following code to delete 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 checked out 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](https://git.tedxiong.com) Marketplace 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 begun with Amazon SageMaker JumpStart.
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About the Authors
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Vivek Gangasani is a Lead [Specialist Solutions](https://ifairy.world) Architect for Inference at AWS. He helps emerging generative [AI](http://git.zhiweisz.cn:3000) companies develop ingenious solutions using AWS services and sped up compute. Currently, he is focused on establishing methods for fine-tuning and enhancing the reasoning performance of big language designs. In his [leisure](https://git.toolhub.cc) time, Vivek takes pleasure in hiking, seeing movies, and trying various cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](http://haiji.qnoddns.org.cn:3000) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His area of focus is AWS [AI](http://39.108.93.0) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
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[Jonathan Evans](https://job.firm.in) is an Expert Solutions Architect dealing with generative [AI](http://git.storkhealthcare.cn) with the Third-Party Model Science group at AWS.
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Banu Nagasundaram leads product, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://git.satori.love) center. She is passionate about constructing options that assist clients accelerate their [AI](https://www.highpriceddatinguk.com) journey and unlock business worth.
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