commit 9f86a42ee54374793eadd3afa118b7ab1c31a418 Author: Ara Donley Date: Tue Feb 18 14:17:54 2025 +0800 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..84b7edb --- /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](https://meebeek.com) [AI](https://cagit.cacode.net)'s first-generation frontier design, DeepSeek-R1, in addition to the distilled variations varying from 1.5 to 70 billion criteria to construct, experiment, and properly scale your generative [AI](http://www.grandbridgenet.com:82) concepts on AWS.
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In this post, we show how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar actions to release the distilled versions of the designs also.
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[Overview](https://guyanajob.com) of DeepSeek-R1
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DeepSeek-R1 is a large language model (LLM) developed by DeepSeek [AI](http://git.jaxc.cn) that utilizes reinforcement discovering to boost reasoning capabilities through a multi-stage training [process](https://cagit.cacode.net) from a DeepSeek-V3-Base foundation. A [key differentiating](https://doum.cn) function is its reinforcement knowing (RL) action, which was utilized to fine-tune the model's responses beyond the basic pre-training and fine-tuning process. By including RL, DeepSeek-R1 can adapt more effectively to user feedback and objectives, eventually enhancing both relevance and clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) approach, implying it's equipped to break down complex questions and reason through them in a detailed way. This directed reasoning procedure allows the model to produce more precise, [hb9lc.org](https://www.hb9lc.org/wiki/index.php/User:PaulineDowler) transparent, and detailed responses. This design combines RL-based fine-tuning with CoT abilities, aiming to create structured responses while concentrating on interpretability and user interaction. With its wide-ranging capabilities DeepSeek-R1 has actually recorded the industry's attention as a flexible text-generation design that can be integrated into different workflows such as agents, rational thinking and information interpretation jobs.
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DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture [enables](https://git.adminkin.pro) activation of 37 billion criteria, making it possible for efficient inference by routing questions to the most relevant specialist "clusters." This technique permits the design to concentrate on different problem domains while maintaining overall performance. 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 release the design. ml.p5e.48 xlarge comes with 8 Nvidia H200 [GPUs supplying](https://tuxpa.in) 1128 GB of GPU memory.
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DeepSeek-R1 distilled models bring the reasoning abilities of the main R1 design to more [efficient architectures](https://git.phyllo.me) based on popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a procedure of training smaller sized, more effective models to mimic the behavior and reasoning patterns of the larger DeepSeek-R1 design, utilizing 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 design, we recommend deploying this design with guardrails in place. In this blog, we will use Amazon Bedrock Guardrails to introduce safeguards, prevent hazardous content, and examine models against key safety criteria. At the time of writing this blog site, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create multiple guardrails tailored to various use cases and apply them to the DeepSeek-R1 model, improving user experiences and standardizing safety controls throughout your generative [AI](https://0miz2638.cdn.hp.avalon.pw:9443) applications.
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Prerequisites
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To deploy the DeepSeek-R1 model, you need access to an ml.p5e instance. To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and verify you're using 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, develop a limit increase request 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 correct AWS Identity and Gain Access To Management (IAM) consents to use [Amazon Bedrock](http://120.77.67.22383) Guardrails. For directions, see Set up 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 introduce safeguards, prevent hazardous material, and examine designs against crucial security requirements. You can execute precaution for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This enables you to apply guardrails to examine user inputs and model reactions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can create a guardrail utilizing the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo.
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The general [circulation](https://gitlab.t-salon.cc) includes the following actions: [wiki.snooze-hotelsoftware.de](https://wiki.snooze-hotelsoftware.de/index.php?title=Benutzer:ClaireNovak) First, the system gets an input for the model. This input is then processed through the [ApplyGuardrail API](http://152.136.232.1133000). If the input passes the guardrail check, it's sent to the model for inference. After receiving the design's output, another guardrail check is used. If the output passes this final check, it's returned as the last result. However, if either the input or output is stepped in by the guardrail, a message is returned indicating the nature of the intervention and whether it occurred at the input or output stage. The examples showcased in the following areas demonstrate reasoning utilizing this API.
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Deploy DeepSeek-R1 in Amazon Bedrock 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, choose Model catalog under Foundation designs in the navigation pane. +At the time of writing this post, you can utilize the InvokeModel API to conjure up the design. It does not [support Converse](https://gitea.alexandermohan.com) APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a service provider and choose the DeepSeek-R1 model.
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The model detail page supplies vital details about the design's abilities, rates structure, and implementation guidelines. You can find detailed usage directions, including sample API calls and code snippets for integration. The design supports numerous text generation tasks, including content development, code generation, and question answering, utilizing its reinforcement finding out optimization and CoT reasoning [abilities](https://cvmobil.com). +The page also includes deployment options and licensing details to help you begin 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 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](https://vydiio.com) characters). +5. For [yewiki.org](https://www.yewiki.org/User:EwanDyke4311656) Number of circumstances, enter a number of circumstances (between 1-100). +6. For [Instance](https://pedulidigital.com) type, choose your circumstances type. For ideal efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is suggested. +Optionally, you can set up sophisticated security and facilities settings, including virtual private cloud (VPC) networking, service role 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 evaluate these settings to align with your organization's security and compliance requirements. +7. to start utilizing the model.
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When the release is complete, you can test DeepSeek-R1's abilities straight in the Amazon Bedrock playground. +8. Choose Open in play area to access an interactive interface where you can explore various prompts and change model specifications like temperature level and optimum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for optimal results. For example, content for reasoning.
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This is an exceptional method to check out the design's reasoning and text generation abilities before incorporating it into your applications. The play area provides instant feedback, helping you understand how the model reacts to different inputs and letting you tweak your prompts for optimum results.
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You can rapidly evaluate the model in the playground through the UI. However, to invoke the deployed model programmatically with any Amazon Bedrock APIs, you [require](http://124.16.139.223000) to get the endpoint ARN.
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Run reasoning using guardrails with the deployed DeepSeek-R1 endpoint
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The following code example shows how to perform inference utilizing a released DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can produce a guardrail utilizing the Amazon [Bedrock console](http://www.evmarket.co.kr) or the API. For the example code to develop the guardrail, see the GitHub repo. After you have actually created the guardrail, utilize the following code to [execute guardrails](http://kanghexin.work3000). The script initializes the bedrock_runtime customer, sets up reasoning parameters, and sends a request to [produce text](https://probando.tutvfree.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](https://git.laser.di.unimi.it) designs to your use case, with your data, and release them into production using either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart provides two hassle-free methods: utilizing the user-friendly SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both approaches to help you pick the approach that finest suits your requirements.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following steps 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 create a domain. +3. On the SageMaker Studio console, [trademarketclassifieds.com](https://trademarketclassifieds.com/user/profile/2701513) select [JumpStart](http://59.57.4.663000) in the navigation pane.
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The design browser shows available models, with details like the company name and model abilities.
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4. Search for DeepSeek-R1 to view the DeepSeek-R1 model card. +Each design card shows key details, consisting of:
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- Model name +- Provider name +- Task classification (for example, Text Generation). +Bedrock Ready badge (if relevant), [systemcheck-wiki.de](https://systemcheck-wiki.de/index.php?title=Benutzer:RamonitaSikes00) suggesting that this design can be signed up with Amazon Bedrock, permitting you to use Amazon Bedrock APIs to invoke the model
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5. Choose the design card to view the model details page.
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The design details page includes the following details:
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- The design name and [service provider](http://git.fmode.cn3000) details. +Deploy button to deploy the model. +About and Notebooks tabs with detailed details
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The About tab consists of essential details, such as:
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- Model [description](https://www.airemploy.co.uk). +- License details. +- Technical requirements. +- Usage guidelines
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Before you deploy the design, it's suggested to review the design details and license terms to [confirm compatibility](https://dev.nebulun.com) 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 customized 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](https://git.lgoon.xyz) instance types and counts is crucial for cost and efficiency optimization. Monitor your implementation to adjust these settings as needed.Under Inference type, Real-time inference is selected by default. This is optimized for sustained traffic and low latency. +10. Review all setups for accuracy. For this model, we strongly recommend sticking to SageMaker JumpStart default settings and making certain that network isolation remains in place. +11. Choose Deploy to deploy the design.
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The deployment process can take several minutes to complete.
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When release is total, your endpoint status will change to InService. At this point, the model is ready to accept inference demands through the endpoint. You can monitor the release progress on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the [release](http://1.15.150.903000) is complete, you can invoke the model utilizing a SageMaker runtime customer and incorporate it with your applications.
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Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
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To begin with DeepSeek-R1 using the SageMaker Python SDK, you will need to install the [SageMaker](https://git.xinstitute.org.cn) Python SDK and make certain you have the necessary AWS approvals 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 [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11937574) releasing the design is offered in the Github here. You can clone the note pad and run from SageMaker Studio.
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You can run extra requests against the predictor:
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[Implement guardrails](https://138.197.71.160) 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](https://quickdatescript.com) predictor. You can create a guardrail utilizing the Amazon Bedrock console or the API, and implement it as revealed in the following code:
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Clean up
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To avoid undesirable charges, complete the steps 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, 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 wish 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 right 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](http://jejuanimalnow.org) if you leave it running. 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 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 Marketplace now to start. For more details, describe Use [Amazon Bedrock](http://www.isexsex.com) tooling with Amazon SageMaker JumpStart designs, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Starting with Amazon SageMaker [JumpStart](http://1.117.194.11510080).
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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://itconsulting.millims.com) companies develop innovative [options](https://www.diltexbrands.com) using AWS services and sped up compute. Currently, he is concentrated on developing techniques for fine-tuning and optimizing the inference efficiency of big language designs. In his downtime, Vivek enjoys treking, enjoying motion pictures, and [it-viking.ch](http://it-viking.ch/index.php/User:Carmel1395) attempting different foods.
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Niithiyn Vijeaswaran is a Generative [AI](https://applykar.com) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His area of focus is AWS [AI](http://jobshut.org) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer [technology](https://tokemonkey.com) and Bioinformatics.
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Jonathan Evans is an Expert Solutions Architect working on generative [AI](https://www.yewiki.org) with the Third-Party Model Science group 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://bibi-kai.com) hub. She is passionate about constructing services that help clients accelerate their [AI](https://vydiio.com) journey and unlock service worth.
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