commit 89544a55040171ed5733b5ab791256d82687db02 Author: leonardmagnuso Date: Thu Apr 3 02:17:36 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..6eebef5 --- /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 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://git.cacpaper.com)'s first-generation frontier design, DeepSeek-R1, in addition to the distilled variations varying from 1.5 to 70 billion specifications to develop, experiment, and properly scale your generative [AI](https://activitypub.software) concepts on AWS.
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In this post, we demonstrate how to start with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable actions to release the distilled variations of the designs as well.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a large language model (LLM) established by DeepSeek [AI](https://gitlab-dev.yzone01.com) that utilizes support learning to improve reasoning capabilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. A key distinguishing feature is its support learning (RL) action, which was utilized to refine the [model's reactions](https://dongawith.com) beyond the basic pre-training and fine-tuning process. By incorporating RL, DeepSeek-R1 can adapt more effectively to user [feedback](https://repo.komhumana.org) and objectives, eventually enhancing both importance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) approach, meaning it's equipped to break down intricate inquiries and reason through them in a detailed manner. This assisted thinking procedure allows the design to produce more accurate, transparent, and detailed responses. This model integrates RL-based fine-tuning with CoT capabilities, aiming to create structured actions while concentrating on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has actually recorded the industry's attention as a versatile text-generation design that can be [incorporated](https://yezidicommunity.com) into various workflows such as agents, [wiki.lafabriquedelalogistique.fr](https://wiki.lafabriquedelalogistique.fr/Discussion_utilisateur:Kraig8717051) sensible thinking and information analysis tasks.
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DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture enables activation of 37 billion criteria, making it possible for effective inference by routing queries to the most relevant specialist "clusters." This technique allows the design to concentrate on different problem domains while [maintaining](https://www.lakarjobbisverige.se) general efficiency. DeepSeek-R1 requires a minimum of 800 GB of [HBM memory](https://farmjobsuk.co.uk) in FP8 format for inference. In this post, we will utilize an ml.p5e.48 xlarge instance to release the design. ml.p5e.48 [xlarge features](https://goalsshow.com) 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.
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DeepSeek-R1 distilled designs bring the reasoning capabilities of the main R1 design to more effective architectures based upon popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a process of training smaller sized, more efficient models to imitate the habits and thinking patterns of the larger DeepSeek-R1 model, using 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 deploying this design with guardrails in place. In this blog, we will use Amazon Bedrock Guardrails to [introduce](http://www.brightching.cn) safeguards, prevent harmful content, and examine models against crucial security requirements. At the time of writing this blog site, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can produce multiple guardrails tailored to different use cases and use them to the DeepSeek-R1 design, [surgiteams.com](https://surgiteams.com/index.php/User:NXOFrancisco) improving user experiences and standardizing safety controls throughout your generative [AI](https://prsrecruit.com) applications.
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Prerequisites
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To release the DeepSeek-R1 design, you require access to an ml.p5e circumstances. To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, select Amazon SageMaker, and validate 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 releasing. To ask for [wiki.snooze-hotelsoftware.de](https://wiki.snooze-hotelsoftware.de/index.php?title=Benutzer:BrandyVail1) a limitation boost, create a limit boost demand and reach out to your account group.
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Because you will be deploying this design with Amazon Bedrock Guardrails, make certain you have the correct [AWS Identity](https://granthers.com) and Gain Access To Management (IAM) approvals to use Amazon Bedrock Guardrails. For guidelines, see Establish consents to utilize 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 harmful material, and examine designs against key security criteria. You can carry out security steps for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to examine user inputs and design reactions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can develop 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 flow involves the following actions: First, the system gets 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 reasoning. After receiving the model'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 intervened by the guardrail, a message is returned indicating the nature of the intervention and whether it took place at the input or output stage. The examples showcased in the following sections show 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 designs (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, select Model brochure under Foundation models in the navigation pane. +At the time of composing this post, you can use the InvokeModel API to invoke the model. It does not 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 model detail page supplies vital details about the design's capabilities, rates structure, and implementation guidelines. You can find detailed use instructions, consisting of sample API calls and code snippets for integration. The design supports different text generation tasks, consisting of content production, code generation, and question answering, utilizing its support finding out optimization and CoT reasoning capabilities. +The page likewise consists of release options and licensing details to help you begin with DeepSeek-R1 in your applications. +3. To begin using DeepSeek-R1, pick 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 name, get in an endpoint name (between 1-50 alphanumeric characters). +5. For Number of instances, enter a variety of circumstances (between 1-100). +6. For example type, pick your circumstances type. For optimal performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised. +Optionally, you can set up innovative security and facilities settings, including virtual personal cloud (VPC) networking, service role authorizations, and [encryption](https://gryzor.info) settings. For the majority of use cases, the default settings will work well. However, [forum.pinoo.com.tr](http://forum.pinoo.com.tr/profile.php?id=1324171) for production implementations, you may wish to examine these settings to line up with your organization's security and compliance [requirements](https://gitlab.interjinn.com). +7. Choose Deploy to begin utilizing the model.
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When the deployment is complete, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock play area. +8. Choose Open in playground to access an interactive interface where you can experiment with different triggers and change model criteria like temperature and maximum length. +When using 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 outstanding way to check out the design's reasoning and text generation abilities before incorporating it into your applications. The play area offers immediate feedback, helping you [understand](https://tmsafri.com) how the design responds to various inputs and [wiki.rolandradio.net](https://wiki.rolandradio.net/index.php?title=User:TimAmbrose899) letting you tweak your triggers for optimum outcomes.
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You can rapidly check the design in the play area through the UI. However, to conjure up the released model programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
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Run inference using guardrails with the [deployed](https://nujob.ch) DeepSeek-R1 endpoint
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The following code example demonstrates how to perform reasoning utilizing a released DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can produce a guardrail utilizing the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo. After you have created the guardrail, use the following code to carry out guardrails. The script initializes the bedrock_runtime customer, sets up inference criteria, and sends out a demand to produce text based on a user timely.
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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](https://amigomanpower.com) algorithms, and prebuilt ML options that you can release with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained models 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 uses two hassle-free techniques: using the instinctive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's explore both techniques to help you choose the technique that finest matches your requirements.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following steps 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 develop a domain. +3. On the SageMaker Studio console, [wiki.dulovic.tech](https://wiki.dulovic.tech/index.php/User:RaulHuot3542) choose JumpStart in the navigation pane.
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The design web browser [displays](https://classtube.ru) available designs, with details like the company name and model capabilities.
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4. Look for DeepSeek-R1 to see the DeepSeek-R1 model card. +Each design card shows key details, including:
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- Model name +- Provider name +- Task classification (for example, Text Generation). +Bedrock Ready badge (if appropriate), suggesting that this design can be signed up with Amazon Bedrock, allowing you to use Amazon Bedrock APIs to invoke the design
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5. Choose the design card to see the model details page.
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The design details page includes the following details:
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- The model name and supplier details. +[Deploy button](https://www.kenpoguy.com) to deploy the model. +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 specifications. +- Usage guidelines
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Before you release the design, it's advised to review the model details and license terms to validate compatibility with your usage case.
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6. Choose Deploy to continue with deployment.
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7. For Endpoint name, use the immediately generated name or develop a custom one. +8. For Instance type ΒΈ select an instance type (default: [wavedream.wiki](https://wavedream.wiki/index.php/User:TarenRuby13528) ml.p5e.48 xlarge). +9. For Initial instance count, go into the variety of instances (default: 1). +Selecting proper circumstances types and counts is important for cost and efficiency optimization. [Monitor](https://jmusic.me) your [deployment](http://testyourcharger.com) to adjust these [settings](https://nujob.ch) as needed.Under Inference type, Real-time inference is picked by default. This is optimized for sustained traffic and low latency. +10. Review all setups for accuracy. For this model, we highly advise adhering to SageMaker JumpStart default and making certain that network seclusion remains in location. +11. Choose Deploy to deploy the model.
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The deployment process can take numerous minutes to finish.
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When implementation is complete, your [endpoint status](http://140.143.208.1273000) will alter to InService. At this point, the model is prepared to accept reasoning demands through the endpoint. You can monitor the release development on the SageMaker console Endpoints page, which will display pertinent metrics and status details. When the release is complete, you can invoke the model using a SageMaker runtime client and incorporate 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 using the SageMaker Python SDK, you will require to set up the SageMaker Python SDK and make certain you have the required AWS permissions and environment setup. The following is a detailed code example that shows how to release and use DeepSeek-R1 for inference programmatically. The code for deploying the model is supplied in the Github here. You can clone the [notebook](http://git.cyjyyjy.com) and range from SageMaker Studio.
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You can run additional [requests](https://18plus.fun) 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 also utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a guardrail using 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 prevent undesirable charges, complete the steps in this section to tidy up your resources.
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Delete the [Amazon Bedrock](http://121.36.37.7015501) Marketplace release
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If you released the design using Amazon Bedrock Marketplace, total the following actions:
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1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, pick Marketplace deployments. +2. In the Managed deployments section, locate the endpoint you wish to delete. +3. Select the endpoint, and on the Actions menu, select Delete. +4. Verify the endpoint details to make certain you're deleting the correct 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 will sustain costs 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 using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get going. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker [JumpStart](http://shiningon.top) models, SageMaker JumpStart pretrained models, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Starting with Amazon SageMaker JumpStart.
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About the Authors
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Vivek Gangasani is a Lead Specialist Solutions [Architect](https://rrallytv.com) for Inference at AWS. He assists emerging generative [AI](https://projobs.dk) companies develop ingenious options [utilizing](http://27.128.240.723000) AWS services and accelerated calculate. Currently, he is concentrated on developing strategies for fine-tuning and optimizing the reasoning performance of large language models. In his downtime, Vivek delights in hiking, seeing films, and trying different foods.
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Niithiyn Vijeaswaran is a Generative [AI](https://openedu.com) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His location of focus is AWS [AI](https://uspublicsafetyjobs.com) accelerators (AWS Neuron). He holds a [Bachelor's degree](https://git.tea-assets.com) in Computer Science and Bioinformatics.
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Jonathan Evans is an Expert Solutions Architect working on [generative](http://119.3.9.593000) [AI](http://yijichain.com) with the Third-Party Model Science group 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://tweecampus.com) hub. She is passionate about building solutions that help clients accelerate their [AI](https://localglobal.in) journey and unlock organization value.
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