commit b14e6a007b56389c19ce5aa0fffadd9fbc32870d Author: kaitlynbendrod Date: Fri Apr 11 16:45:43 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..78bd8d6 --- /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 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://yooobu.com)'s first-generation frontier design, [genbecle.com](https://www.genbecle.com/index.php?title=Utilisateur:LouanneMeece331) DeepSeek-R1, together with the distilled versions varying from 1.5 to 70 billion specifications to develop, experiment, and properly scale your generative [AI](http://tv.houseslands.com) concepts on AWS.
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In this post, we demonstrate how to begin with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar actions to release the distilled versions of the models too.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a big language model (LLM) established by DeepSeek [AI](https://freedomlovers.date) that utilizes reinforcement learning to boost thinking abilities through a multi-stage training process from a DeepSeek-V3-Base structure. A key distinguishing feature is its support knowing (RL) action, which was [utilized](https://ransomware.design) to fine-tune the design's responses beyond the standard pre-training and tweak procedure. By including RL, DeepSeek-R1 can adjust more effectively to user feedback and goals, eventually improving both significance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) method, indicating it's equipped to break down complex questions and reason through them in a detailed manner. This assisted thinking process allows the design to produce more precise, transparent, and detailed answers. This design integrates RL-based fine-tuning with CoT abilities, aiming to produce structured responses while focusing on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has captured the market's attention as a versatile text-generation model that can be integrated into numerous workflows such as representatives, rational reasoning and information interpretation jobs.
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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 reasoning by routing queries to the most appropriate expert "clusters." This method enables the model to specialize in various issue domains while maintaining general effectiveness. DeepSeek-R1 needs at least 800 GB of HBM memory in FP8 format for inference. In this post, we will utilize an ml.p5e.48 xlarge circumstances to deploy the model. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs offering 1128 GB of [GPU memory](http://bingbinghome.top3001).
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DeepSeek-R1 distilled designs bring the reasoning capabilities of the main R1 model to more effective architectures based upon [popular](https://employme.app) open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and [hb9lc.org](https://www.hb9lc.org/wiki/index.php/User:MarkusNeustadt1) 70B). [Distillation refers](http://www.pelletkorea.net) to a process of training smaller sized, more effective models to imitate the behavior and thinking patterns of the larger DeepSeek-R1 model, utilizing it as an instructor model.
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You can deploy DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we suggest releasing this model with guardrails in location. In this blog site, we will utilize Amazon Bedrock Guardrails to present safeguards, avoid hazardous material, and evaluate designs against crucial security criteria. At the time of writing this blog, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can [produce](https://followmylive.com) [multiple guardrails](http://linyijiu.cn3000) tailored to various use cases and use them to the DeepSeek-R1 design, enhancing user experiences and standardizing security controls throughout your [generative](https://gitea.mierzala.com) [AI](https://git.hackercan.dev) applications.
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Prerequisites
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To [release](https://git.vhdltool.com) the DeepSeek-R1 design, you need access to an ml.p5e instance. To [examine](https://quierochance.com) if you have quotas for P5e, open the Service Quotas console and under AWS Services, [pick Amazon](https://code.nwcomputermuseum.org.uk) 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 releasing. To ask for a limitation increase, produce a limitation boost demand and reach out to your account team.
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Because you will be deploying this model with Amazon Bedrock Guardrails, make certain you have the correct AWS Identity and Gain Access To Management (IAM) authorizations to use Amazon Bedrock Guardrails. For directions, see Set up permissions to utilize guardrails for content filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails allows you to present safeguards, prevent damaging material, and evaluate models against crucial safety criteria. You can carry out precaution for the DeepSeek-R1 model utilizing the Amazon Bedrock [ApplyGuardrail](http://118.195.204.2528080) API. This permits you to apply guardrails to evaluate user inputs and model actions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can develop a [guardrail utilizing](https://gitea.alaindee.net) 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 circulation involves 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 check, it's sent to the design for reasoning. After getting the model's output, another guardrail check is applied. If the output passes this last check, it's returned as the result. However, if either the input or output is intervened by the guardrail, a message is returned suggesting 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 using 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 structure 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, 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 doesn't support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a provider and select the DeepSeek-R1 model.
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The design detail page provides important details about the design's abilities, [wiki.snooze-hotelsoftware.de](https://wiki.snooze-hotelsoftware.de/index.php?title=Benutzer:MattSkene928) pricing structure, and implementation standards. You can find detailed usage instructions, including sample API calls and code snippets for combination. The design supports various text generation tasks, including material creation, code generation, and concern answering, using its support learning optimization and CoT reasoning abilities. +The page also includes release alternatives and licensing details to help you begin with DeepSeek-R1 in your applications. +3. To begin using DeepSeek-R1, select Deploy.
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You will be prompted to configure the [deployment details](https://forum.batman.gainedge.org) for DeepSeek-R1. The design ID will be pre-populated. +4. For Endpoint name, enter an endpoint name (between 1-50 alphanumeric characters). +5. For Number of circumstances, enter a variety of circumstances (between 1-100). +6. For example type, choose your instance type. For ideal efficiency with DeepSeek-R1, a [GPU-based instance](http://120.77.221.1993000) type like ml.p5e.48 xlarge is suggested. +Optionally, you can configure advanced security and infrastructure settings, consisting of virtual private cloud (VPC) networking, [service role](https://gitlab.lizhiyuedong.com) approvals, and file encryption settings. For the majority of 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 begin using the model.
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When the deployment is total, you can check DeepSeek-R1's abilities straight in the Amazon Bedrock play area. +8. Choose Open in play area to access an interactive user interface where you can explore various prompts and adjust design specifications like temperature level and maximum length. +When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for ideal outcomes. For instance, content for reasoning.
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This is an excellent method to explore the design's reasoning and text generation capabilities before integrating it into your applications. The play area supplies immediate feedback, assisting you understand how the model reacts to various inputs and letting you tweak your prompts for ideal results.
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You can rapidly evaluate the design in the playground through the UI. However, to invoke the released design programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
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Run inference utilizing guardrails with the [released](https://www.medicalvideos.com) DeepSeek-R1 endpoint
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The following code example shows how to carry out reasoning utilizing a deployed DeepSeek-R1 model through Amazon Bedrock utilizing 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 developed the guardrail, use the following code to implement guardrails. The script initializes the bedrock_runtime customer, configures inference parameters, and sends a request to create text 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) center with FMs, built-in algorithms, and prebuilt ML services that you can deploy with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your information, and deploy them into production using either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart offers 2 convenient methods: using the [user-friendly SageMaker](https://followmylive.com) JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's check out both [techniques](https://gitea.tgnotify.top) to help you select the approach that best fits 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 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, pick JumpStart in the navigation pane.
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The design internet browser shows available models, with details like the company name and design capabilities.
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4. Search for DeepSeek-R1 to view the DeepSeek-R1 model card. +Each model card reveals essential details, including:
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- Model name +- Provider name +- Task classification (for instance, Text Generation). +Bedrock Ready badge (if applicable), [indicating](https://social.engagepure.com) that this model can be registered with Amazon Bedrock, permitting you to use Amazon Bedrock APIs to conjure up the design
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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 design name and company details. +Deploy button to release the design. +About and Notebooks tabs with detailed details
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The About tab consists of important details, such as:
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- Model description. +- License details. +- Technical requirements. +- Usage guidelines
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Before you release the design, 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 continue with release.
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7. For Endpoint name, the instantly produced name or produce a custom one. +8. For Instance type ΒΈ pick a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, enter the variety of circumstances (default: 1). +Selecting suitable [instance types](https://squishmallowswiki.com) and counts is important for expense and performance optimization. Monitor your release to adjust these settings as needed.Under Inference type, Real-time inference is chosen by default. This is enhanced for sustained traffic and low latency. +10. Review all configurations for [accuracy](https://gitea.v-box.cn). For this design, we highly suggest sticking to SageMaker JumpStart default settings and making certain that network isolation remains in location. +11. Choose Deploy to deploy the design.
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The release procedure can take a number of minutes to finish.
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When implementation is total, [classificados.diariodovale.com.br](https://classificados.diariodovale.com.br/author/mavisroger/) your endpoint status will change to InService. At this moment, the design is ready to accept inference requests through the endpoint. You can monitor the deployment progress on the SageMaker console [Endpoints](https://git.flandre.net) page, which will show relevant metrics and status details. When the deployment is total, you can invoke the design utilizing a SageMaker runtime client and incorporate it with your [applications](https://wiki.atlantia.sca.org).
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Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
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To start with DeepSeek-R1 using the SageMaker Python SDK, you will require to set up the SageMaker Python SDK and [forum.pinoo.com.tr](http://forum.pinoo.com.tr/profile.php?id=1322189) make certain you have the necessary AWS approvals and [environment](http://120.78.74.943000) setup. The following is a detailed code example that demonstrates how to release and use DeepSeek-R1 for [inference programmatically](http://139.199.191.273000). The code for deploying the model is offered 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](https://www.luckysalesinc.com) 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 shown in the following code:
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Clean up
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To prevent unwanted charges, finish the actions in this area to clean up your resources.
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Delete the Amazon Bedrock Marketplace release
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If you released the design utilizing Amazon Bedrock Marketplace, complete 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 area, locate the [endpoint](http://8.137.89.263000) you want to erase. +3. Select the endpoint, and on the Actions menu, choose Delete. +4. Verify the endpoint details to make certain you're deleting the right release: 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 expenses if you leave it running. Use the following code to delete the [endpoint](https://www.smfsimple.com) if you desire to stop [sustaining charges](http://221.182.8.1412300). 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 deploy the DeepSeek-R1 model 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 models, SageMaker JumpStart pretrained models, 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 assists emerging generative [AI](http://gitlab.andorsoft.ad) business build [innovative solutions](https://www.smfsimple.com) utilizing AWS services and sped up calculate. Currently, he is focused on developing strategies for fine-tuning and enhancing the inference efficiency of large [language designs](https://git.intelgice.com). In his downtime, Vivek enjoys treking, enjoying movies, and attempting different cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](http://106.55.3.105:20080) Specialist Solutions Architect with the [Third-Party Model](http://www.iway.lk) Science group at AWS. His location of focus is AWS [AI](http://ev-gateway.com) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.
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Jonathan Evans is a [Professional](https://southwales.com) Solutions Architect working on generative [AI](https://codeincostarica.com) with the Third-Party Model Science team 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://gitea.sync-web.jp) center. She is passionate about developing services that help customers accelerate their [AI](https://www.florevit.com) journey and unlock organization worth.
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