From f15d9ce92a2f1235fc3ec9a98687b9200adb30a9 Mon Sep 17 00:00:00 2001 From: bobbiereichert Date: Sat, 15 Feb 2025 15:21:34 +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..9cbfd39 --- /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 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 [AI](https://careerconnect.mmu.edu.my)'s first-generation frontier design, DeepSeek-R1, in addition to the distilled variations [varying](https://mulaybusiness.com) from 1.5 to 70 billion specifications to build, experiment, and responsibly scale your generative [AI](https://git.cooqie.ch) concepts on AWS.
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In this post, we show how to get started with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar actions to deploy the distilled versions of the models also.
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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://members.mcafeeinstitute.com) that uses reinforcement discovering to enhance reasoning capabilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. An essential distinguishing feature is its reinforcement learning (RL) action, which was used to improve the design's responses beyond the basic pre-training and tweak procedure. By incorporating RL, DeepSeek-R1 can adjust better to user feedback and goals, ultimately boosting both relevance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) technique, suggesting it's equipped to break down complex queries and reason through them in a detailed manner. This assisted thinking procedure permits the model to produce more precise, transparent, and detailed answers. This model integrates RL-based fine-tuning with CoT abilities, aiming to generate structured actions while focusing on interpretability and user interaction. With its comprehensive abilities DeepSeek-R1 has caught the market's attention as a flexible text-generation model that can be incorporated into different workflows such as agents, [engel-und-waisen.de](http://www.engel-und-waisen.de/index.php/Benutzer:LeilaniCable73) rational reasoning and information analysis tasks.
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DeepSeek-R1 utilizes a [Mixture](https://ssh.joshuakmckelvey.com) of Experts (MoE) [architecture](http://gitlab.hupp.co.kr) and is 671 billion criteria in size. The MoE architecture permits activation of 37 billion criteria, making it possible for efficient inference by [routing questions](http://barungogi.com) to the most pertinent specialist "clusters." This approach enables the design to specialize in different problem [domains](https://linkpiz.com) while [maintaining](https://executiverecruitmentltd.co.uk) general efficiency. 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 model. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.
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DeepSeek-R1 distilled designs bring the reasoning abilities of the main R1 model 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 process of training smaller, more efficient models to simulate the habits and reasoning patterns of the bigger DeepSeek-R1 design, utilizing it as a teacher design.
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You can deploy DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we suggest releasing this design with guardrails in place. In this blog site, we will utilize Amazon [Bedrock Guardrails](https://sudanre.com) to introduce safeguards, prevent harmful material, and [examine](http://101.33.255.603000) models against key safety requirements. At the time of writing this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can develop multiple guardrails tailored to various use cases and use them to the DeepSeek-R1 model, enhancing user experiences and standardizing security controls across your generative [AI](https://git.alternephos.org) applications.
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Prerequisites
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To deploy the DeepSeek-R1 model, [yewiki.org](https://www.yewiki.org/User:MayaGinn22) you need access to an ml.p5e instance. To examine 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 usage. Make certain that you have at least one ml.P5e.48 xlarge instance in the AWS Region you are deploying. To request a limit boost, develop a limit boost demand and reach out to your account team.
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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](https://forum.batman.gainedge.org) to utilize Amazon Bedrock Guardrails. For directions, see Set up authorizations to utilize guardrails for material filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails permits you to introduce safeguards, avoid damaging content, and assess designs against key security criteria. You can carry out precaution for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This enables you to [apply guardrails](http://168.100.224.793000) to examine user inputs and design 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 develop the guardrail, see the [GitHub repo](https://www.viewtubs.com).
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The general 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](https://www.tcrew.be) check, it's sent to the design for reasoning. After receiving the design's output, another guardrail check is applied. If the output passes this final 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](https://114jobs.com) at the input or output stage. The examples showcased in the following sections demonstrate 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 models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, complete 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 writing this post, you can use the InvokeModel API to conjure up the model. It doesn't support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a company and select the DeepSeek-R1 design.
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The design detail page supplies vital details about the model's capabilities, pricing structure, and execution standards. You can find detailed use directions, including sample API calls and code bits for integration. The model supports various text generation tasks, including material development, code generation, and question answering, utilizing its reinforcement finding out optimization and CoT reasoning capabilities. +The page also consists of deployment options and licensing details to help you begin with DeepSeek-R1 in your [applications](https://parentingliteracy.com). +3. To start using DeepSeek-R1, pick Deploy.
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You will be triggered to configure the release details for DeepSeek-R1. The design ID will be pre-populated. +4. For Endpoint name, enter an endpoint name (in between 1-50 alphanumeric characters). +5. For Variety of circumstances, enter a number of circumstances (in between 1-100). +6. For Instance type, select your circumstances type. For optimal efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is suggested. +Optionally, you can configure innovative security and infrastructure settings, consisting of virtual private cloud (VPC) networking, service function permissions, and file encryption settings. For the majority of utilize cases, the default settings will work well. However, for production deployments, you might wish to review these settings to align with your company's security and compliance requirements. +7. Choose Deploy to start using the model.
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When the implementation is total, you can test DeepSeek-R1's abilities straight in the Amazon Bedrock play ground. +8. Choose Open in play ground to access an interactive user interface where you can explore various prompts and change model criteria like temperature level and maximum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for optimum results. For example, content for reasoning.
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This is an excellent method to check out the model's thinking and text generation abilities before incorporating it into your applications. The playground supplies immediate feedback, helping you understand how the model reacts to numerous inputs and letting you tweak your prompts for ideal results.
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You can quickly test the design in the play ground 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 using guardrails with the released DeepSeek-R1 endpoint
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The following code example demonstrates 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](http://98.27.190.224) or the API. For the example code to create the guardrail, see the GitHub repo. After you have actually developed the guardrail, utilize the following code to implement guardrails. The script initializes the bedrock_runtime client, sets up [reasoning](https://executiverecruitmentltd.co.uk) specifications, and sends a demand to produce text based on 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 solutions that you can release with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage case, with your data, and [release](https://quickdatescript.com) them into production using either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart offers 2 hassle-free methods: using the [user-friendly SageMaker](https://www.dpfremovalnottingham.com) JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's check out both [techniques](https://18plus.fun) to assist you select 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 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 develop a domain. +3. On the SageMaker Studio console, pick JumpStart in the navigation pane.
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The design browser displays available models, with details like the supplier name and design capabilities.
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4. Search for DeepSeek-R1 to view the DeepSeek-R1 design card. +Each model card [reveals](http://linyijiu.cn3000) crucial details, including:
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- Model name +- Provider name +- Task category (for example, Text Generation). +Bedrock Ready badge (if appropriate), indicating that this model can be registered with Amazon Bedrock, allowing you to use Amazon Bedrock APIs to conjure up the design
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5. Choose the [design card](https://gitlab.appgdev.co.kr) 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 service provider details. +Deploy button to release the design. +About and [surgiteams.com](https://surgiteams.com/index.php/User:Darnell83J) Notebooks tabs with detailed details
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The About tab consists of crucial details, such as:
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- Model [description](https://members.advisorist.com). +- License details. +- Technical requirements. +- Usage standards
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Before you release the design, it's suggested to evaluate the design details and license terms to confirm compatibility with your use case.
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6. Choose Deploy to continue with implementation.
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7. For Endpoint name, use the automatically produced name or create a customized one. +8. For Instance type ΒΈ choose a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, enter the variety of instances (default: 1). +Selecting proper instance types and counts is important for cost and performance optimization. Monitor your release to change these settings as needed.Under [Inference](https://git.itk.academy) type, Real-time reasoning is chosen by default. This is optimized for sustained traffic and low latency. +10. Review all setups for precision. For this design, we strongly suggest sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in location. +11. Choose Deploy to release the design.
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The release procedure can take several minutes to complete.
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When implementation is complete, your endpoint status will alter to InService. At this moment, the model is ready to accept inference requests through the . You can keep track of the release progress on the SageMaker console Endpoints page, which will display relevant metrics and status details. When the implementation is complete, you can [conjure](https://tagreba.org) up the design 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 need to set up the SageMaker 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 deploy and use DeepSeek-R1 for inference programmatically. The code for releasing the design is provided in the Github here. You can clone the notebook and run from SageMaker Studio.
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You can run extra demands 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 utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can develop a guardrail utilizing the Amazon Bedrock console or the API, and execute it as displayed in the following code:
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Clean up
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To avoid unwanted charges, finish the steps in this section to clean up your [resources](https://mmatycoon.info).
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Delete the Amazon Bedrock Marketplace implementation
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If you deployed the model using Amazon Bedrock Marketplace, complete the following steps:
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1. On the Amazon [Bedrock](https://git.clubcyberia.co) console, under Foundation designs in the navigation pane, [choose Marketplace](http://gitlab.rainh.top) deployments. +2. In the Managed deployments area, find the endpoint you wish to delete. +3. Select the endpoint, and [surgiteams.com](https://surgiteams.com/index.php/User:AshliLent31607) on the Actions menu, [select Delete](https://www.viewtubs.com). +4. Verify the endpoint details to make certain you're erasing the right 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 design you [released](https://www.yewiki.org) will sustain costs if you leave it running. Use the following code to delete the endpoint if you want 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](http://111.61.77.359999) out how you can access and deploy the DeepSeek-R1 [model utilizing](https://redefineworksllc.com) Bedrock Marketplace and [SageMaker JumpStart](http://www.xn--80agdtqbchdq6j.xn--p1ai). Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to begin. For more details, describe Use [Amazon Bedrock](http://85.214.112.1167000) tooling with Amazon SageMaker JumpStart designs, SageMaker JumpStart pretrained models, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting going 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](https://meephoo.com) companies build ingenious options using AWS services and accelerated compute. Currently, he is concentrated on developing techniques for fine-tuning and enhancing the reasoning efficiency of big language models. In his spare time, Vivek delights in hiking, enjoying motion pictures, and attempting different cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](http://1.92.66.29:3000) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His location of focus is AWS [AI](http://git.zhongjie51.com) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.
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Jonathan Evans is an Expert Solutions Architect dealing with generative [AI](https://www.gabeandlisa.com) with the Third-Party Model Science team at AWS.
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Banu Nagasundaram leads item, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://www.jobmarket.ae) hub. She is passionate about developing solutions that assist consumers accelerate their [AI](https://pioneercampus.ac.in) journey and unlock company worth.
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