From f7b39112b9f90b9ae1b27ac77a8227519bdfb077 Mon Sep 17 00:00:00 2001 From: danewhitford0 Date: Fri, 7 Feb 2025 15:15:41 +0100 Subject: [PATCH] Add 'DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart' --- ...ketplace-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..19cd40d --- /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](http://103.205.66.473000) to announce that DeepSeek R1 distilled Llama and [Qwen models](http://www.mizmiz.de) are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](http://sehwaapparel.co.kr)'s first-generation frontier design, DeepSeek-R1, together with the distilled variations ranging from 1.5 to 70 billion parameters to construct, experiment, and responsibly scale your generative [AI](http://43.143.245.135:3000) ideas on AWS.
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In this post, we demonstrate how to begin with DeepSeek-R1 on Amazon Bedrock Marketplace and [SageMaker JumpStart](http://gitlab.suntrayoa.com). You can follow comparable actions to release the distilled versions of the designs 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](http://chotaikhoan.me) that utilizes reinforcement discovering to enhance reasoning capabilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A crucial distinguishing feature is its reinforcement knowing (RL) action, which was utilized to improve the model's actions beyond the standard pre-training and . By integrating RL, DeepSeek-R1 can adapt more efficiently to user feedback and objectives, ultimately enhancing both significance and clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) method, suggesting it's equipped to break down complicated queries and factor through them in a detailed way. This guided reasoning procedure enables the design to produce more accurate, transparent, and detailed responses. This model integrates RL-based fine-tuning with CoT capabilities, aiming to produce 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 versatile text-generation model that can be integrated into numerous workflows such as agents, logical reasoning and data analysis jobs.
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DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture permits activation of 37 billion parameters, enabling efficient inference by routing queries to the most appropriate expert "clusters." This method permits the design to specialize in different problem domains while maintaining general efficiency. DeepSeek-R1 needs at least 800 GB of [HBM memory](https://brightworks.com.sg) in FP8 format for reasoning. In this post, we will use an ml.p5e.48 xlarge circumstances to release the model. ml.p5e.48 xlarge includes 8 Nvidia H200 [GPUs offering](https://git.agent-based.cn) 1128 GB of GPU memory.
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DeepSeek-R1 distilled designs bring the reasoning capabilities of the main R1 design to more efficient architectures based on popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of training smaller sized, more efficient designs to imitate the behavior 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 deploying this design with guardrails in location. In this blog site, we will use Amazon Bedrock Guardrails to present safeguards, prevent hazardous material, and examine models against key safety requirements. At the time of composing this blog, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create numerous guardrails tailored to various usage cases and use them to the DeepSeek-R1 design, enhancing user experiences and standardizing security controls across your generative [AI](http://omkie.com:3000) 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 check if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick Amazon SageMaker, and validate 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 boost, develop a limitation boost 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 proper AWS Identity and [Gain Access](https://ysa.sa) To Management (IAM) approvals to utilize Amazon Bedrock Guardrails. For directions, see Establish approvals to utilize guardrails for material filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails enables you to introduce safeguards, prevent harmful content, and assess models against essential safety requirements. You can execute precaution for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This enables you to use [guardrails](https://lovelynarratives.com) to assess user inputs and design reactions [released](https://jobsleed.com) on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a guardrail utilizing the Amazon Bedrock [console](https://3flow.se) 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 steps: 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 inference. After receiving the model's output, another guardrail check is used. If the output passes this last check, it's returned as the last outcome. However, if either the input or output is intervened by the guardrail, a message is returned showing the nature of the intervention and whether it happened at the input or output phase. 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 designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following steps:
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1. On the [Amazon Bedrock](https://gitlab.dituhui.com) console, choose Model brochure under Foundation designs in the navigation pane. +At the time of composing this post, you can utilize the InvokeModel API to invoke the model. It doesn't support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a [provider](https://git.devinmajor.com) and pick the DeepSeek-R1 design.
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The model detail page supplies essential details about the model's abilities, rates structure, and execution guidelines. You can discover detailed usage guidelines, including sample API calls and code snippets for [combination](http://82.157.11.2243000). The design supports various text generation tasks, including content production, code generation, and question answering, utilizing its reinforcement learning optimization and CoT reasoning abilities. +The page also includes deployment choices and licensing details to help you begin with DeepSeek-R1 in your applications. +3. To begin using DeepSeek-R1, choose 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](https://flixtube.org) name, enter an endpoint name (in between 1-50 alphanumeric characters). +5. For Number of circumstances, go into a variety of circumstances (between 1-100). +6. For example type, select your instance type. For optimum performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is recommended. +Optionally, you can set up advanced security and facilities settings, including virtual personal cloud (VPC) networking, service role consents, and encryption settings. For most use cases, the default settings will work well. However, for production deployments, you may desire to review these settings to line up with your company's security and compliance requirements. +7. Choose Deploy to begin utilizing the design.
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When the deployment is total, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock play area. +8. Choose Open in playground to access an interactive user interface where you can try out different prompts and adjust design parameters like temperature and maximum length. +When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for ideal outcomes. For example, material for reasoning.
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This is an outstanding way to explore the design's reasoning and text generation abilities before integrating it into your applications. The play ground provides immediate feedback, [raovatonline.org](https://raovatonline.org/author/antoniocope/) assisting you comprehend how the model responds to various inputs and [letting](https://career.abuissa.com) you tweak your prompts for optimum outcomes.
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You can quickly test the design in the play area through the UI. However, to invoke the deployed model programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
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Run inference utilizing guardrails with the released DeepSeek-R1 endpoint
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The following code example shows how to perform reasoning utilizing a deployed DeepSeek-R1 model through Amazon Bedrock using 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 developed the guardrail, utilize the following code to execute guardrails. The script initializes the bedrock_runtime client, sets up inference parameters, and sends out 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) center with FMs, integrated algorithms, and prebuilt ML services that you can deploy with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use case, with your information, and release them into [production](https://git.muhammadfahri.com) using either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart uses two hassle-free methods: utilizing the intuitive SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's check out both techniques to assist you pick the [approach](https://bd.cane-recruitment.com) 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 using 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, choose JumpStart in the navigation pane.
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The model web browser displays 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 design card. +Each design card shows crucial details, including:
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[- Model](https://www.nenboy.com29283) name +- Provider name +- Task classification (for instance, Text Generation). +Bedrock Ready badge (if appropriate), indicating 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 see the model details page.
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The model details page includes the following details:
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- The design name and service provider [details](http://jobteck.com). +Deploy button to release the model. +About and Notebooks tabs with detailed details
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The About tab includes essential details, such as:
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- Model description. +- License details. +- Technical requirements. +- Usage standards
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Before you release the design, it's advised to evaluate the model details and license terms to validate compatibility with your usage case.
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6. Choose Deploy to continue with implementation.
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7. For Endpoint name, use the instantly produced name or develop a custom one. +8. For example type ΒΈ pick a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial instance count, enter the number of circumstances (default: 1). +Selecting proper circumstances types and counts is crucial 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 optimized for sustained traffic and low latency. +10. Review all setups for precision. For this design, we highly advise 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 deployment procedure can take a number of minutes to finish.
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When deployment is complete, your endpoint status will alter to InService. At this point, the model is ready to accept reasoning requests through the endpoint. You can keep track of the deployment development on the SageMaker console Endpoints page, which will display appropriate metrics and status details. When the deployment is complete, you can invoke 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 start with DeepSeek-R1 using the SageMaker Python SDK, you will need to set up the SageMaker Python SDK and make certain you have the necessary AWS consents and environment setup. The following is a detailed code example that shows how to deploy and utilize DeepSeek-R1 for [reasoning programmatically](https://cozwo.com). The code for deploying the design is provided in the Github here. You can clone the note pad 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](https://35.237.164.2). You can develop a guardrail using the Amazon Bedrock console or the API, and implement it as displayed in the following code:
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Tidy up
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To avoid undesirable charges, complete the actions in this area to tidy up your [resources](http://omkie.com3000).
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Delete the Amazon Bedrock Marketplace deployment
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If you deployed the model 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 releases. +2. In the Managed deployments section, locate the endpoint you want to delete. +3. Select the endpoint, and [raovatonline.org](https://raovatonline.org/author/dixietepper/) on the Actions menu, pick Delete. +4. Verify the endpoint details to make certain you're erasing the correct 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 design you deployed will sustain expenses if you leave it running. Use the following code to erase the endpoint if you wish to stop sustaining charges. For more details, see [Delete Endpoints](https://sagemedicalstaffing.com) 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 [higgledy-piggledy.xyz](https://higgledy-piggledy.xyz/index.php/User:WinstonNajera5) SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get started. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting started 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 [AI](http://66.112.209.2:3000) companies construct ingenious services using AWS services and accelerated calculate. Currently, he is concentrated on developing methods for fine-tuning and enhancing the reasoning performance of big language designs. In his complimentary time, Vivek takes [pleasure](https://2flab.com) in treking, viewing movies, and attempting different foods.
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Niithiyn Vijeaswaran is a Generative [AI](https://career.agricodeexpo.org) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His location of focus is AWS [AI](https://beta.talentfusion.vn) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer [Science](https://remote-life.de) and Bioinformatics.
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Jonathan Evans is a Professional Solutions Architect working on generative [AI](https://gitlab.amatasys.jp) with the Third-Party Model Science team at AWS.
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Banu Nagasundaram leads item, engineering, and tactical collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://bartists.info) center. She is passionate about [constructing options](http://blueroses.top8888) that assist clients accelerate their [AI](https://git.tbaer.de) journey and unlock company worth.
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