From 4ed890c90eca9b1e3eb2395e21e0823e8333dd3f Mon Sep 17 00:00:00 2001 From: Curt Edmund Date: Sat, 15 Feb 2025 16:28:14 +0000 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..17dee5e --- /dev/null +++ b/DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart.-.md @@ -0,0 +1,93 @@ +
Today, we are [thrilled](http://www.vmeste-so-vsemi.ru) to announce 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 [AI](http://121.37.208.192:3000)'s first-generation frontier design, DeepSeek-R1, in addition to the distilled variations ranging from 1.5 to 70 billion specifications to construct, experiment, and responsibly scale your generative [AI](https://git.hmcl.net) ideas on AWS.
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In this post, [forum.pinoo.com.tr](http://forum.pinoo.com.tr/profile.php?id=1321148) we demonstrate how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable actions to release the distilled variations of the models as well.
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[Overview](https://www.social.united-tuesday.org) of DeepSeek-R1
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DeepSeek-R1 is a large language design (LLM) established by DeepSeek [AI](http://158.160.20.3:3000) that utilizes support discovering to boost thinking capabilities through a multi-stage training process from a DeepSeek-V3-Base structure. A key distinguishing function is its [reinforcement knowing](http://hellowordxf.cn) (RL) action, which was utilized to refine the model's reactions beyond the basic pre-training and fine-tuning procedure. By [integrating](https://git.kimcblog.com) RL, DeepSeek-R1 can adapt better to user feedback and objectives, eventually boosting both significance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) approach, indicating it's geared up to break down [intricate queries](https://gogs.zhongzhongtech.com) and factor through them in a detailed way. This assisted thinking procedure permits the design to produce more precise, transparent, and detailed answers. This model combines RL-based fine-tuning with CoT abilities, aiming to produce structured reactions while focusing on interpretability and user [interaction](https://smartcampus-seskoal.id). With its extensive abilities DeepSeek-R1 has actually recorded the market's attention as a versatile text-generation design that can be integrated into different workflows such as agents, sensible reasoning and information analysis tasks.
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DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture permits activation of 37 billion parameters, making it possible for efficient inference by [routing inquiries](https://bakery.muf-fin.tech) to the most relevant professional "clusters." This method enables the model to concentrate on various issue domains while maintaining total performance. DeepSeek-R1 requires 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 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 thinking abilities of the main R1 design to more efficient architectures based upon 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 efficient models to mimic the behavior and reasoning patterns of the bigger DeepSeek-R1 design, [utilizing](https://southwales.com) 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 model with guardrails in location. In this blog site, we will use Amazon Bedrock Guardrails to present safeguards, avoid hazardous content, and evaluate designs against essential safety 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 create multiple guardrails tailored to different usage cases and apply them to the DeepSeek-R1 design, improving user experiences and standardizing security controls throughout your generative [AI](https://karis.id) applications.
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Prerequisites
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To deploy the DeepSeek-R1 model, you require access to an ml.p5e circumstances. To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick 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 circumstances in the AWS Region you are deploying. To request a limit increase, develop a limit increase request and reach out to your account group.
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Because you will be releasing this design with [Amazon Bedrock](https://git.xantxo-coquillard.fr) Guardrails, make certain you have the proper AWS Identity and Gain Access To Management (IAM) authorizations to utilize Amazon Bedrock Guardrails. For directions, see Establish consents to use guardrails for content filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails enables you to introduce safeguards, prevent hazardous material, and examine designs against essential security requirements. You can carry out precaution for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This permits you to use guardrails to assess user inputs and design reactions deployed on Amazon Bedrock Marketplace and [systemcheck-wiki.de](https://systemcheck-wiki.de/index.php?title=Benutzer:FBYNoble8794) SageMaker JumpStart. You can develop a guardrail using the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo.
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The basic flow includes the following actions: First, the system receives an input for the design. This input is then processed through the ApplyGuardrail API. If the input passes the [guardrail](http://124.221.255.92) check, it's sent to the model for reasoning. After receiving 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 [occurred](https://moojijobs.com) at the input or output phase. The examples showcased in the following sections show inference utilizing this API.
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Deploy DeepSeek-R1 in [Amazon Bedrock](http://gitea.zyimm.com) Marketplace
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Amazon Bedrock Marketplace gives 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 actions:
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1. On the Amazon Bedrock console, pick Model brochure under Foundation models in the navigation pane. +At the time of composing this post, you can use the InvokeModel API to conjure up the design. It doesn't support Converse APIs and other Amazon Bedrock [tooling](https://ssconsultancy.in). +2. Filter for DeepSeek as a service provider and pick the DeepSeek-R1 design.
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The model detail page supplies necessary details about the design's abilities, rates structure, and implementation guidelines. You can find detailed usage guidelines, including sample API calls and code snippets for integration. The design supports different text generation tasks, consisting of material development, code generation, and question answering, utilizing its support learning optimization and CoT thinking abilities. +The page likewise includes deployment alternatives and [licensing](http://ggzypz.org.cn8664) details to help you get going with DeepSeek-R1 in your applications. +3. To begin utilizing DeepSeek-R1, pick Deploy.
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You will be prompted to set up the deployment details for DeepSeek-R1. The design ID will be pre-populated. +4. For Endpoint name, go into an endpoint name (between 1-50 alphanumeric characters). +5. For Variety of instances, enter 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 circumstances](https://git.fpghoti.com) type like ml.p5e.48 xlarge is recommended. +Optionally, you can [configure advanced](https://shinjintech.co.kr) security and facilities settings, consisting of virtual private cloud (VPC) networking, service role consents, and file encryption settings. For many utilize cases, the default settings will work well. However, for production deployments, you might desire to evaluate these settings to line up with your organization's security and compliance requirements. +7. Choose Deploy to start using the design.
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When the release is total, you can test DeepSeek-R1's abilities straight in the Amazon Bedrock play area. +8. Choose Open in play area to access an interactive interface where you can explore different triggers and adjust design criteria like temperature and maximum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for optimum results. For instance, material for inference.
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This is an outstanding way to check out the design's thinking and text generation abilities before incorporating it into your applications. The play area provides instant feedback, helping you understand how the design reacts to different inputs and letting you fine-tune your [triggers](https://eukariyer.net) for ideal results.
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You can quickly test the model in the play area through the UI. However, to invoke the released model programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
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Run reasoning using guardrails with the [deployed](https://whotube.great-site.net) DeepSeek-R1 endpoint
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The following code example demonstrates how to carry out inference utilizing a [released](https://www.istorya.net) DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can develop a guardrail using the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo. After you have actually produced the guardrail, use the following code to execute guardrails. The script initializes the bedrock_runtime client, sets up inference parameters, and sends a demand to produce text based on 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, built-in algorithms, and prebuilt ML services that you can deploy with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your use case, with your data, and release them into [production utilizing](http://git.meloinfo.com) either the UI or SDK.
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Deploying DeepSeek-R1 design through SageMaker JumpStart offers 2 convenient approaches: utilizing the user-friendly SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both methods to assist you choose the method that finest matches your needs.
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Deploy DeepSeek-R1 through [SageMaker JumpStart](https://gitea.viamage.com) UI
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Complete the following steps to release DeepSeek-R1 using SageMaker JumpStart:
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1. On the SageMaker console, select Studio in the navigation pane. +2. First-time users will be prompted to develop a domain. +3. On the SageMaker Studio console, select JumpStart in the navigation pane.
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The model internet browser shows available models, with details like the company name and model capabilities.
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4. Look for DeepSeek-R1 to see the DeepSeek-R1 design card. +Each model card shows key details, consisting of:
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- Model name +- Provider name +- Task category (for instance, Text Generation). +Bedrock Ready badge (if suitable), suggesting that this model can be signed up with Amazon Bedrock, enabling you to use Amazon Bedrock APIs to invoke the model
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5. Choose the design card to see the model details page.
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The model details page includes the following details:
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- The model name and supplier details. +Deploy button to release the model. +About and Notebooks tabs with detailed details
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The About tab consists of crucial details, such as:
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- Model description. +- License details. +- Technical specs. +- Usage standards
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Before you deploy the model, it's recommended to review the model details and license terms to validate compatibility with your use case.
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6. Choose Deploy to proceed with release.
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7. For Endpoint name, use the instantly generated name or develop a custom one. +8. For example type ΒΈ choose an instance type (default: ml.p5e.48 xlarge). +9. For Initial instance count, enter the number of instances (default: [wiki.vst.hs-furtwangen.de](https://wiki.vst.hs-furtwangen.de/wiki/User:LiliaBoston3284) 1). +Selecting appropriate instance types and counts is important for expense and performance optimization. Monitor your implementation to adjust these settings as needed.Under Inference type, Real-time inference is picked by default. This is optimized for sustained traffic and low latency. +10. Review all setups for precision. For this design, we highly suggest adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in location. +11. [Choose Deploy](http://www.hyakuyichi.com3000) to release the model.
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The release process can take several minutes to complete.
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When [deployment](https://jp.harmonymart.in) is total, your endpoint status will change to InService. At this point, the model is ready to accept reasoning requests through the [endpoint](http://114.111.0.1043000). You can keep track of the deployment development on the SageMaker console Endpoints page, which will display relevant metrics and status details. When the implementation is complete, [garagesale.es](https://www.garagesale.es/author/shennaburch/) 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 get started with DeepSeek-R1 utilizing the SageMaker Python SDK, you will need to set up the SageMaker Python SDK and make certain you have the needed AWS permissions and environment setup. The following is a [detailed](http://oj.algorithmnote.cn3000) code example that shows how to release and use DeepSeek-R1 for inference programmatically. The code for releasing 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 extra 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 use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can create a guardrail using the Amazon Bedrock console or the API, and execute it as revealed in the following code:
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Tidy up
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To prevent unwanted charges, finish the actions in this section to tidy 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 designs in the navigation pane, pick Marketplace implementations. +2. In the Managed implementations section, find the endpoint you desire to erase. +3. Select the endpoint, and on the Actions menu, select Delete. +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 costs if you leave it [running](https://wkla.no-ip.biz). Use the following code to delete 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 [checked](https://209rocks.com) out how you can access and deploy the DeepSeek-R1 design using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to begin. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker [JumpStart](https://event.genie-go.com) models, SageMaker JumpStart pretrained models, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, [trademarketclassifieds.com](https://trademarketclassifieds.com/user/profile/2672496) 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](https://eliteyachtsclub.com) [AI](https://git.chir.rs) companies develop innovative options using AWS services and sped up calculate. Currently, he is concentrated on establishing techniques for fine-tuning and enhancing the inference efficiency of big language designs. In his downtime, Vivek enjoys hiking, enjoying motion pictures, and attempting various cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](http://124.222.7.180:3000) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His location of focus is AWS [AI](https://cvmobil.com) (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.
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Jonathan Evans is a Specialist Solutions Architect dealing with generative [AI](https://actu-info.fr) with the Third-Party Model Science team at AWS.
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Banu Nagasundaram leads item, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://gitea.ruwii.com) center. She is enthusiastic about developing solutions that help customers accelerate their [AI](https://liveyard.tech:4443) journey and unlock company value.
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