From 22710f2d5ecaf072cd01bdc823831f63f61cb988 Mon Sep 17 00:00:00 2001 From: redacato672264 Date: Sat, 31 May 2025 03:23:50 +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..3b6a628 --- /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 announce that DeepSeek R1 distilled Llama and Qwen designs are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](https://git.liubin.name)'s first-generation frontier design, DeepSeek-R1, along with the distilled versions [varying](http://www.zjzhcn.com) from 1.5 to 70 billion criteria to build, experiment, and properly scale your [generative](https://www.trueposter.com) [AI](https://jobs.salaseloffshore.com) ideas on AWS.
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In this post, we demonstrate how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and [SageMaker JumpStart](http://119.45.49.2123000). You can follow comparable steps to deploy the distilled versions of the models as well.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a large language design (LLM) developed by DeepSeek [AI](http://117.50.220.191:8418) that uses support discovering to enhance reasoning abilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. A key distinguishing feature is its support learning (RL) step, which was utilized to refine the design's responses beyond the basic pre-training and fine-tuning procedure. By incorporating RL, DeepSeek-R1 can adjust better to user feedback and goals, ultimately improving both relevance and [clarity](http://101.52.220.1708081). In addition, DeepSeek-R1 uses a chain-of-thought (CoT) technique, suggesting it's equipped to break down intricate inquiries and reason through them in a detailed way. This guided thinking process enables the design to produce more accurate, transparent, and detailed answers. This model combines RL-based fine-tuning with CoT abilities, aiming to create [structured actions](https://www.personal-social.com) while [concentrating](http://37.187.2.253000) on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has actually caught the industry's attention as a flexible text-generation model that can be incorporated into numerous workflows such as representatives, sensible [thinking](https://geniusactionblueprint.com) and data interpretation 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, allowing effective inference by routing inquiries to the most relevant professional "clusters." This approach allows the model to specialize in different problem domains while maintaining total effectiveness. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will use an ml.p5e.48 xlarge circumstances to release the design. 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 capabilities of the main R1 design to more efficient architectures based on [popular](https://git.privateger.me) open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a process of training smaller, more efficient models to [simulate](https://ozoms.com) the behavior and thinking patterns of the larger DeepSeek-R1 model, using it as a teacher model.
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You can release DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an [emerging](https://source.futriix.ru) design, we suggest [deploying](https://videopromotor.com) this model with guardrails in place. In this blog site, we will utilize Amazon Bedrock Guardrails to introduce safeguards, avoid damaging content, and examine models against essential safety requirements. At the time of writing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create several guardrails tailored to various use cases and use them to the DeepSeek-R1 model, improving user experiences and standardizing safety controls throughout your generative [AI](http://git.picaiba.com) 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, choose Amazon SageMaker, and confirm you're using ml.p5e.48 xlarge for endpoint usage. Make certain that you have at least one ml.P5e.48 xlarge circumstances in the AWS Region you are releasing. To request a limitation increase, [develop](https://forum.webmark.com.tr) a limitation increase request and reach out to your account group.
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Because you will be [deploying](http://hrplus.com.vn) this design with Amazon Bedrock Guardrails, make certain you have the right [AWS Identity](https://music.lcn.asia) and Gain Access To Management (IAM) permissions to utilize Amazon Bedrock Guardrails. For instructions, see Set up authorizations to use guardrails for content filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails permits you to present safeguards, avoid hazardous content, and examine models against key security criteria. You can implement precaution for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This permits you to [apply guardrails](https://sahabatcasn.com) to assess 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 basic circulation includes the following steps: First, the system gets an input for the design. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it's sent out to the model 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 outcome. However, if either the input or output is intervened by the guardrail, [bio.rogstecnologia.com.br](https://bio.rogstecnologia.com.br/chantedarbon) a message is returned suggesting the nature of the intervention and whether it occurred at the input or output phase. The examples showcased in the following sections 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 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, pick Model brochure under Foundation models in the navigation pane. +At the time of writing 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 company and pick the DeepSeek-R1 model.
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The model detail page supplies necessary details about the design's capabilities, rates structure, and [systemcheck-wiki.de](https://systemcheck-wiki.de/index.php?title=Benutzer:CarriSides75326) implementation guidelines. You can find detailed usage guidelines, consisting of [sample API](https://whotube.great-site.net) calls and code snippets for [hb9lc.org](https://www.hb9lc.org/wiki/index.php/User:KeithSpina077) combination. The design supports numerous text generation jobs, consisting of material creation, code generation, and [concern](https://rassi.tv) answering, utilizing its [support discovering](http://78.108.145.233000) optimization and CoT thinking abilities. +The page likewise consists of deployment choices and licensing details to help you begin with DeepSeek-R1 in your applications. +3. To start utilizing DeepSeek-R1, pick Deploy.
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You will be triggered to set up the implementation details for DeepSeek-R1. The model ID will be pre-populated. +4. For Endpoint name, get in an endpoint name (between 1-50 alphanumeric characters). +5. For Variety of circumstances, go into a number of [instances](http://git.picaiba.com) (in between 1-100). +6. For example type, pick your circumstances type. For ideal efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is suggested. +Optionally, you can set up advanced security and infrastructure settings, including virtual private cloud (VPC) networking, service role approvals, and file encryption settings. For a lot of utilize cases, the default settings will work well. However, for production implementations, you might desire to evaluate 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 capabilities straight in the Amazon Bedrock play area. +8. Choose Open in play ground to access an interactive user interface where you can try out various triggers and adjust design specifications like temperature level and maximum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for optimum outcomes. For example, material for reasoning.
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This is an [exceptional method](http://120.46.139.31) to explore the design's thinking and text generation abilities before integrating it into your [applications](http://24.233.1.3110880). The [play ground](http://175.27.189.803000) provides instant feedback, assisting you comprehend how the design reacts to numerous inputs and letting you tweak your prompts for optimum results.
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You can [rapidly evaluate](https://git.todayisyou.co.kr) the design in the play area through the UI. However, to conjure up the released design [programmatically](https://ttaf.kr) with any Amazon Bedrock APIs, you need to get the endpoint ARN.
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Run reasoning utilizing guardrails with the deployed DeepSeek-R1 endpoint
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The following code example shows how to carry out reasoning utilizing a [released](https://www.2dudesandalaptop.com) 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, utilize the following code to carry out guardrails. The script initializes the bedrock_runtime customer, sets up reasoning parameters, and sends out a demand to generate text based upon a user prompt.
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Deploy DeepSeek-R1 with [SageMaker](https://git.thomasballantine.com) JumpStart
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SageMaker JumpStart is an artificial intelligence (ML) hub with FMs, integrated algorithms, and prebuilt ML options that you can release with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your information, and release them into production using either the UI or SDK.
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Deploying DeepSeek-R1 design through SageMaker JumpStart offers 2 hassle-free techniques: utilizing the instinctive SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's explore both techniques to assist you select the approach that best matches your needs.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following steps to release DeepSeek-R1 utilizing 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, choose JumpStart in the [navigation pane](http://www.grandbridgenet.com82).
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The model internet browser shows available models, with details like the service provider name and model abilities.
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4. Search for DeepSeek-R1 to see the DeepSeek-R1 design card. +Each design card shows key details, consisting of:
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- Model name +- Provider name +- Task category (for instance, Text Generation). +Bedrock Ready badge (if applicable), showing that this model can be registered with Amazon Bedrock, permitting you to use Amazon Bedrock APIs to invoke the design
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5. Choose the model card to see the design 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 essential details, such as:
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[- Model](https://rightlane.beparian.com) description. +- License details. +- Technical specifications. +- Usage guidelines
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Before you deploy the model, it's suggested to examine the design details and license terms to validate compatibility with your use case.
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6. Choose Deploy to continue with implementation.
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7. For Endpoint name, utilize the immediately generated name or produce a customized one. +8. For Instance type ΒΈ select an instance type (default: ml.p5e.48 xlarge). +9. For Initial instance count, get in the number of circumstances (default: 1). +Selecting appropriate circumstances types and counts is crucial for expense and performance optimization. Monitor your implementation to adjust these settings as needed.Under Inference type, Real-time inference is by default. This is enhanced for sustained traffic and low latency. +10. Review all setups for precision. For this model, we highly recommend adhering to SageMaker JumpStart default settings and making certain that network isolation remains in place. +11. Choose Deploy to deploy the model.
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The implementation procedure can take a number of minutes to complete.
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When release is complete, your endpoint status will alter to InService. At this moment, the model is ready to accept inference demands through the endpoint. You can keep an eye on the release progress on the SageMaker console Endpoints page, which will display pertinent metrics and status details. When the release is total, you can invoke the design using a SageMaker runtime client and incorporate it with your applications.
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Deploy DeepSeek-R1 using the SageMaker Python SDK
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To start with DeepSeek-R1 utilizing the SageMaker Python SDK, you will need to install the SageMaker Python SDK and make certain you have the required AWS [permissions](http://president-park.co.kr) and environment setup. The following is a detailed code example that demonstrates how to deploy and utilize DeepSeek-R1 for [inference programmatically](https://spaceballs-nrw.de). The code for [wiki.myamens.com](http://wiki.myamens.com/index.php/User:CarinStrachan9) releasing the design is supplied in the Github here. You can clone the notebook and range from SageMaker Studio.
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You can run extra requests 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 also utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a guardrail utilizing 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 undesirable charges, finish the actions in this section to tidy 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](http://peterlevi.com) console, under Foundation designs in the navigation pane, choose Marketplace deployments. +2. In the Managed releases area, [wiki.myamens.com](http://wiki.myamens.com/index.php/User:VirgieBoldt4) find the endpoint you wish to erase. +3. Select the endpoint, and on the Actions menu, pick Delete. +4. Verify the endpoint details to make certain you're erasing the correct 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 design you deployed will sustain expenses 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 design utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to start. For more details, refer to Use [Amazon Bedrock](http://upleta.rackons.com) tooling with Amazon SageMaker [JumpStart](https://82.65.204.63) designs, 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 helps emerging generative [AI](https://ari-sound.aurumai.io) business construct innovative options utilizing AWS services and accelerated compute. Currently, he is concentrated on establishing methods for fine-tuning and enhancing the inference performance of large language designs. In his spare time, Vivek takes pleasure in treking, watching movies, and attempting various foods.
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Niithiyn Vijeaswaran is a Generative [AI](https://www.uaehire.com) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His location of focus is AWS [AI](http://62.234.223.238:3000) [accelerators](https://git.fanwikis.org) (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.
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Jonathan Evans is a Professional Solutions Architect dealing with generative [AI](http://122.112.209.52) with the Third-Party Model Science group at AWS.
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Banu Nagasundaram leads product, engineering, and [strategic collaborations](http://219.150.88.23433000) for Amazon SageMaker JumpStart, SageMaker's [artificial intelligence](http://124.222.48.2033000) and generative [AI](https://carrieresecurite.fr) center. She is enthusiastic about developing services that assist customers accelerate their [AI](https://jobidream.com) journey and unlock business value.
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