From 86b58a36dc488a2cec288af82682840b23b342d5 Mon Sep 17 00:00:00 2001 From: albert84688290 Date: Thu, 27 Feb 2025 12:58:53 +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..fd07abb --- /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 models are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now [deploy DeepSeek](https://music.worldcubers.com) [AI](http://47.101.187.29:8081)'s first-generation frontier design, DeepSeek-R1, along with the distilled variations ranging from 1.5 to 70 billion specifications to construct, experiment, and responsibly scale your generative [AI](https://wiki.snooze-hotelsoftware.de) ideas on AWS.
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In this post, we show how to begin with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar actions to release the distilled variations of the designs as well.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a big language design (LLM) established by DeepSeek [AI](http://httelecom.com.cn:3000) that uses reinforcement finding out to enhance thinking abilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. A key identifying feature is its [support](https://www.infinistation.com) learning (RL) action, which was utilized to improve the design's actions beyond the standard pre-training and tweak procedure. By including RL, DeepSeek-R1 can adapt more effectively to user feedback and goals, eventually enhancing both importance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) technique, indicating it's equipped to break down intricate inquiries and factor through them in a detailed manner. This guided reasoning process allows the model to produce more precise, transparent, and detailed responses. This model [combines](https://www.lingualoc.com) [RL-based fine-tuning](https://brotato.wiki.spellsandguns.com) with CoT capabilities, aiming to produce structured responses while concentrating on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has caught the industry's attention as a versatile text-generation model that can be integrated into different [workflows](https://actsfile.com) such as representatives, logical thinking 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 effective inference by [routing queries](http://www.xn--80agdtqbchdq6j.xn--p1ai) to the most appropriate professional "clusters." This approach permits the model to focus on different issue domains while maintaining total performance. DeepSeek-R1 requires a minimum of 800 GB of [HBM memory](https://git.boergmann.it) in FP8 format for inference. In this post, we will use an ml.p5e.48 xlarge instance to release 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 thinking abilities of the main R1 design to more effective architectures based upon popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a process of training smaller, more [efficient designs](http://47.108.140.33) to imitate the habits and [wiki.lafabriquedelalogistique.fr](https://wiki.lafabriquedelalogistique.fr/Utilisateur:CXOLazaro99) thinking patterns of the bigger DeepSeek-R1 model, utilizing it as an instructor design.
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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 suggest releasing this model with guardrails in place. In this blog, we will utilize Amazon Bedrock Guardrails to introduce safeguards, prevent damaging content, and evaluate designs against crucial security criteria. At the time of composing this blog site, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can produce several [guardrails tailored](https://www.mediarebell.com) to various usage cases and apply them to the DeepSeek-R1 model, user experiences and standardizing security controls throughout your generative [AI](https://www.mepcobill.site) 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 utilizing ml.p5e.48 xlarge for endpoint use. Make certain that you have at least one ml.P5e.48 xlarge instance in the AWS Region you are releasing. To request a limitation boost, create a limitation boost demand and reach out to your account group.
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Because you will be releasing this design with Amazon Bedrock Guardrails, make certain you have the proper AWS Identity and Gain Access To Management (IAM) approvals to use Amazon Bedrock Guardrails. For directions, see Establish consents 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 present safeguards, avoid hazardous material, and assess models against essential safety requirements. You can execute precaution for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This enables you to apply guardrails to assess user inputs and design reactions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can create a guardrail using the [Amazon Bedrock](https://volunteering.ishayoga.eu) [console](https://improovajobs.co.za) or the API. For the example code to produce the guardrail, see the GitHub repo.
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The basic circulation involves the following actions: [wakewiki.de](https://www.wakewiki.de/index.php?title=The_Verge_Stated_It_s_Technologically_Impressive) First, the system receives an input for the model. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it's sent out to the design for reasoning. After getting the design'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 stepped in by the guardrail, a message is returned suggesting the nature of the intervention and whether it occurred at the input or output stage. 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 offers you access to over 100 popular, emerging, and specialized foundation designs (FMs) through [Amazon Bedrock](http://110.41.143.1288081). To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following steps:
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1. On the Amazon Bedrock console, select Model brochure under Foundation designs in the navigation pane. +At the time of writing this post, you can use the InvokeModel API to conjure up the design. It does not support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a provider and choose the DeepSeek-R1 model.
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The model detail page supplies necessary details about the design's capabilities, rates structure, and [execution standards](https://jobs.ethio-academy.com). You can find detailed usage guidelines, including sample API calls and code snippets for integration. The design supports different text generation jobs, including content development, code generation, and concern answering, utilizing its support finding out optimization and CoT thinking abilities. +The page also includes deployment alternatives and licensing details to help you get going with DeepSeek-R1 in your applications. +3. To start utilizing DeepSeek-R1, select Deploy.
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You will be prompted to configure the implementation 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 Number of circumstances, go into a number of circumstances (between 1-100). +6. For Instance type, choose your [circumstances type](https://wiki.dulovic.tech). For optimal efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is suggested. +Optionally, you can configure advanced security and [forum.altaycoins.com](http://forum.altaycoins.com/profile.php?id=1077776) infrastructure settings, including 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 may wish to review these settings to line up with your organization's security and compliance requirements. +7. Choose Deploy to start utilizing the model.
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When the release is total, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock playground. +8. Choose Open in play ground to access an interactive interface where you can explore various prompts and change model specifications like temperature and optimum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for optimal outcomes. For example, material for inference.
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This is an exceptional method to check out the design's reasoning and text generation abilities before integrating it into your applications. The play ground offers immediate feedback, assisting you understand how the [model reacts](https://great-worker.com) to different inputs and letting you fine-tune your prompts for optimum results.
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You can [rapidly test](http://nysca.net) the model in the play ground 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 reasoning utilizing guardrails with the deployed DeepSeek-R1 endpoint
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The following code example shows how to carry out reasoning utilizing a released DeepSeek-R1 model through [Amazon Bedrock](http://120.46.139.31) using the invoke_model and [ApplyGuardrail API](https://shareru.jp). You can produce a guardrail using the Amazon Bedrock [console](https://git.hitchhiker-linux.org) or the API. For the example code to create the guardrail, see the GitHub repo. After you have developed the guardrail, utilize the following code to carry out guardrails. The script initializes the bedrock_runtime client, configures inference criteria, and sends out a request 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 solutions that you can release with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use case, with your information, and deploy them into [production](http://www.hcmis.cn) using either the UI or SDK.
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Deploying DeepSeek-R1 design through SageMaker JumpStart provides two hassle-free methods: using the intuitive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's check out both approaches to help you select 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 actions to release DeepSeek-R1 utilizing 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, pick JumpStart in the navigation pane.
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The model browser displays available models, with details like the supplier name and design capabilities.
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4. Look for DeepSeek-R1 to view the DeepSeek-R1 model card. +Each model card shows essential details, including:
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- Model name +- Provider name +- Task classification (for example, Text Generation). +Bedrock Ready badge (if appropriate), showing that this design can be signed up with Amazon Bedrock, allowing you to use Amazon Bedrock APIs to conjure up the design
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5. Choose the model card to see the model details page.
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The [design details](http://kuzeydogu.ogo.org.tr) page consists of the following details:
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- The model name and supplier details. +Deploy button to deploy the model. +About and Notebooks tabs with detailed details
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The About tab consists of crucial details, [genbecle.com](https://www.genbecle.com/index.php?title=Utilisateur:Randal50P9974608) such as:
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- Model description. +- License details. +- Technical specifications. +- Usage guidelines
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Before you release the design, it's advised 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, use the immediately created name or produce a customized one. +8. For Instance type ΒΈ pick a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial instance count, go into the variety of instances (default: 1). +Selecting proper instance types and counts is important for expense and performance optimization. Monitor your deployment to change these settings as needed.Under Inference type, Real-time reasoning is picked by default. This is enhanced for sustained traffic and low latency. +10. Review all setups for accuracy. For this model, we strongly recommend adhering to SageMaker JumpStart default settings and making certain that network isolation remains in location. +11. Choose Deploy to release the model.
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The implementation process can take a number of minutes to complete.
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When deployment is complete, your endpoint status will alter to InService. At this moment, the model is prepared to [accept reasoning](https://work-ofie.com) demands through the endpoint. You can keep track of the release progress on the SageMaker console Endpoints page, which will show relevant metrics and status details. When the implementation is complete, you can invoke the design using a SageMaker runtime client and incorporate it with your applications.
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Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
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To get begun with DeepSeek-R1 utilizing the [SageMaker Python](https://job.iwok.vn) SDK, you will require to install the SageMaker Python SDK and make certain you have the needed AWS consents and [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:CoreyZ5141346) environment setup. The following is a detailed code example that demonstrates how to release and use DeepSeek-R1 for inference programmatically. The code for [releasing](https://insta.tel) the design is supplied in the Github here. You can clone the note pad and run from SageMaker Studio.
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You can run additional demands 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 utilize the [ApplyGuardrail API](https://peekz.eu) with your SageMaker JumpStart predictor. You can produce a guardrail utilizing the Amazon Bedrock console or the API, and execute it as displayed in the following code:
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Tidy up
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To avoid unwanted charges, [wavedream.wiki](https://wavedream.wiki/index.php/User:PauletteMckinney) complete the actions in this section to tidy up your resources.
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Delete the Amazon Bedrock Marketplace release
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If you deployed the design utilizing Amazon Bedrock Marketplace, complete the following actions:
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1. On the Amazon Bedrock console, under Foundation models in the navigation pane, pick Marketplace implementations. +2. In the Managed implementations section, locate the endpoint you desire to delete. +3. Select the endpoint, and on the Actions menu, pick Delete. +4. Verify the endpoint details to make certain you're erasing the appropriate implementation: 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 costs 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 utilizing 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 with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained models, 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 assists emerging generative [AI](http://47.98.226.240:3000) companies develop innovative options utilizing AWS services and sped up compute. Currently, he is concentrated on establishing techniques for fine-tuning and enhancing the reasoning efficiency of big language designs. In his downtime, Vivek takes pleasure in treking, watching films, and attempting different cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://meephoo.com) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His area of focus is AWS [AI](https://www.footballclubfans.com) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer [Science](https://iklanbaris.id) and Bioinformatics.
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Jonathan Evans is a Professional Solutions Architect working on [generative](https://neejobs.com) [AI](https://www.refermee.com) with the Third-Party Model Science team at AWS.
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Banu Nagasundaram leads item, engineering, and strategic collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://music.worldcubers.com) center. She is enthusiastic about building solutions that assist customers accelerate their [AI](https://heyanesthesia.com) journey and unlock service worth.
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