commit 62ea96390dfbb2bfef97409fdcbb965598661d20 Author: Ada Fortner Date: Fri Apr 4 12:18:36 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..1baf59f --- /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 designs](https://gitlab.digineers.nl) are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](https://candays.com)'s first-generation frontier design, DeepSeek-R1, together with the distilled versions ranging from 1.5 to 70 billion specifications to develop, experiment, and [responsibly scale](http://xrkorea.kr) your [generative](https://git.cbcl7.com) [AI](https://gitlab.rlp.net) ideas 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 comparable actions to deploy 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 design (LLM) developed by DeepSeek [AI](http://git.keliuyun.com:55676) that utilizes reinforcement finding out to boost reasoning [abilities](https://www.jobmarket.ae) through a multi-stage training procedure from a DeepSeek-V3-Base structure. A crucial identifying function is its [reinforcement learning](http://82.156.184.993000) (RL) action, which was used to improve the design's responses beyond the basic pre-training and tweak procedure. By [incorporating](https://healthcarejob.cz) RL, DeepSeek-R1 can adjust better to user feedback and objectives, eventually boosting both significance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) method, suggesting it's geared up to break down intricate questions and reason through them in a detailed way. This guided reasoning procedure enables the design to produce more precise, [wiki.dulovic.tech](https://wiki.dulovic.tech/index.php/User:KatrinaPolding1) transparent, and detailed responses. This model combines RL-based fine-tuning with CoT abilities, aiming to produce structured reactions while concentrating on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has actually captured the market's attention as a versatile text-generation design that can be incorporated into different workflows such as representatives, sensible reasoning and data analysis jobs.
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DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion parameters in size. The [MoE architecture](http://47.108.92.883000) allows activation of 37 billion specifications, enabling efficient inference by routing queries to the most appropriate professional "clusters." This method allows the design to specialize in various problem domains while maintaining total [efficiency](https://gitlab.lizhiyuedong.com). DeepSeek-R1 needs a minimum of 800 GB of [HBM memory](http://113.177.27.2002033) in FP8 format for inference. In this post, we will utilize an ml.p5e.48 xlarge circumstances to deploy the design. ml.p5e.48 xlarge comes with 8 Nvidia H200 [GPUs supplying](http://www.vmeste-so-vsemi.ru) 1128 GB of GPU memory.
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DeepSeek-R1 distilled designs bring the [thinking capabilities](https://git.hxps.ru) of the main R1 design to more efficient architectures based on [popular](https://git.easytelecoms.fr) open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a procedure of training smaller, more efficient designs to mimic the behavior and reasoning patterns of the larger DeepSeek-R1 design, [utilizing](https://phdjobday.eu) it as an instructor design.
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You can release DeepSeek-R1 model either through SageMaker JumpStart or [Bedrock](https://chemitube.com) Marketplace. Because DeepSeek-R1 is an emerging model, [it-viking.ch](http://it-viking.ch/index.php/User:ToryVkp588337606) we advise deploying this model with guardrails in location. In this blog, we will use Amazon Bedrock Guardrails to present safeguards, avoid harmful material, and assess designs against essential safety criteria. 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 create multiple guardrails tailored to different use cases and apply them to the DeepSeek-R1 model, enhancing user experiences and standardizing safety controls throughout your generative [AI](https://videopromotor.com) applications.
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Prerequisites
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To deploy the DeepSeek-R1 design, 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, pick Amazon SageMaker, and [confirm](http://1.94.30.13000) 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](https://nakshetra.com.np) a limit increase, develop a limitation boost demand and connect to your account group.
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Because you will be deploying this design with Amazon Bedrock Guardrails, make certain you have the proper AWS Identity and Gain Access To Management (IAM) consents to utilize Amazon Bedrock Guardrails. For directions, see Establish consents 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 material, and assess models against crucial security criteria. You can carry out [security measures](https://service.aicloud.fit50443) for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to evaluate user inputs and design reactions released on Amazon Bedrock Marketplace and SageMaker JumpStart. 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.
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The general flow includes the following actions: 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 reasoning. After getting the design's output, another guardrail check is used. If the output passes this last check, it's returned as the final 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 reasoning using 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, total the following steps:
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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 utilize 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 provider and pick the DeepSeek-R1 design.
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The model detail page supplies important details about the model's capabilities, rates structure, and implementation guidelines. You can find detailed usage instructions, including sample API calls and code bits for integration. The model supports various text generation tasks, consisting of material production, code generation, and question answering, utilizing its reinforcement finding out optimization and CoT reasoning capabilities. +The page likewise consists of [deployment options](https://git.fpghoti.com) and licensing details to assist you begin with DeepSeek-R1 in your applications. +3. To start using DeepSeek-R1, pick Deploy.
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You will be triggered to [configure](http://git.moneo.lv) 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](http://8.217.113.413000) of instances, enter a variety of instances (between 1-100). +6. For example type, choose your instance type. For optimal efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is advised. +Optionally, you can set up sophisticated security and infrastructure settings, including virtual personal cloud (VPC) networking, [archmageriseswiki.com](http://archmageriseswiki.com/index.php/User:JorgSelleck17) service function consents, and encryption settings. For a lot of use cases, the default settings will work well. However, for production deployments, you may want to review these settings to align with your company's security and compliance requirements. +7. Choose Deploy to begin utilizing the design.
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When the implementation is complete, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock playground. +8. Choose Open in playground to access an interactive user interface where you can try out different triggers and change 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, content for inference.
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This is an exceptional method to explore the model's thinking and text generation abilities before integrating it into your applications. The playground provides immediate feedback, assisting you comprehend how the design reacts to different inputs and letting you tweak your prompts for optimal outcomes.
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You can rapidly evaluate the design in the play ground through the UI. However, to conjure up the deployed model programmatically with any Amazon Bedrock APIs, [archmageriseswiki.com](http://archmageriseswiki.com/index.php/User:Janessa98P) you require to get the endpoint ARN.
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Run [reasoning utilizing](https://jobsscape.com) guardrails with the released DeepSeek-R1 endpoint
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The following code example shows how to carry out reasoning using a deployed DeepSeek-R1 design through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can create a guardrail utilizing the Amazon Bedrock [console](https://tayseerconsultants.com) or the API. For the example code to produce the guardrail, see the GitHub repo. After you have developed the guardrail, use the following code to execute guardrails. The script initializes the bedrock_runtime customer, [configures reasoning](http://106.15.41.156) parameters, and sends out a request to create text based upon a user timely.
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Deploy DeepSeek-R1 with SageMaker JumpStart
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SageMaker JumpStart is an [artificial intelligence](https://www.lotusprotechnologies.com) (ML) hub with FMs, built-in algorithms, and prebuilt ML options that you can release with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your use case, with your data, and release them into production utilizing either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart provides 2 convenient approaches: [utilizing](https://git.gz.internal.jumaiyx.cn) the instinctive SageMaker [JumpStart](http://39.99.134.1658123) UI or executing programmatically through the SageMaker Python SDK. Let's explore both methods to help 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 actions to release DeepSeek-R1 using SageMaker JumpStart:
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1. On the SageMaker console, pick Studio in the navigation pane. +2. First-time users will be prompted to create a domain. +3. On the SageMaker Studio console, select JumpStart in the navigation pane.
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The model internet browser displays available models, with details like the provider name and design capabilities.
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4. Search for DeepSeek-R1 to see the DeepSeek-R1 design card. +Each model card shows key details, including:
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[- Model](https://findschools.worldofdentistry.org) name +- Provider name +- Task category (for instance, Text Generation). +Bedrock Ready badge (if relevant), indicating that this design can be signed up with Amazon Bedrock, enabling you to [utilize Amazon](https://www.hireprow.com) Bedrock APIs to invoke the model
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5. Choose the design card to see the design details page.
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The model details page consists of the following details:
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- The design name and service provider details. +Deploy button to deploy the design. +About and Notebooks tabs with detailed details
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The About tab includes crucial details, such as:
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- Model description. +- License details. +- Technical specifications. +- Usage standards
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Before you deploy the design, it's suggested to review the model details and license terms to confirm compatibility 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 created name or create a customized one. +8. For example type ΒΈ select an instance type (default: ml.p5e.48 xlarge). +9. For Initial [circumstances](http://soho.ooi.kr) count, get in the variety of circumstances (default: 1). +Selecting suitable instance types and counts is essential for expense and efficiency optimization. Monitor your implementation to adjust these settings as needed.Under Inference type, Real-time reasoning is selected by default. This is enhanced for sustained traffic and low latency. +10. Review all [configurations](https://papersoc.com) 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 release the model.
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The deployment procedure can take several 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 reasoning requests 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 deployment is total, you can conjure up the model utilizing a SageMaker runtime client and integrate it with your applications.
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Deploy DeepSeek-R1 using the SageMaker Python SDK
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To get begun with DeepSeek-R1 utilizing the SageMaker Python SDK, you will need to set up the SageMaker Python SDK and make certain you have the needed AWS permissions and environment setup. The following is a detailed code example that shows how to deploy and utilize DeepSeek-R1 for inference programmatically. The code for releasing the design is supplied in the Github here. You can clone the notebook and run from SageMaker Studio.
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You can run [additional](https://brightworks.com.sg) demands against the predictor:
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[Implement guardrails](http://tfjiang.cn32773) and run inference with your SageMaker JumpStart predictor
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Similar to Amazon Bedrock, you can also utilize the [ApplyGuardrail API](https://www.hireprow.com) with your SageMaker JumpStart predictor. 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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Clean up
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To prevent undesirable charges, finish the actions in this area to clean up your resources.
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Delete the Amazon Bedrock Marketplace release
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If you deployed the model using Amazon Bedrock Marketplace, total the following actions:
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1. On the Amazon Bedrock console, under Foundation models in the navigation pane, choose Marketplace releases. +2. In the Managed deployments section, find the endpoint you desire to delete. +3. Select the endpoint, and on the Actions menu, select 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 erase the endpoint if you desire 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 explored 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 get going. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained designs, 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 for Inference at AWS. He assists emerging generative [AI](https://octomo.co.uk) companies construct ingenious options utilizing AWS services and sped up compute. Currently, he is concentrated on establishing strategies for fine-tuning and enhancing the inference performance of big language designs. In his downtime, Vivek takes pleasure in treking, enjoying motion pictures, and attempting different cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](http://mpowerstaffing.com) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His location of focus is AWS [AI](http://47.92.218.215:3000) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.
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Jonathan Evans is an Expert working on generative [AI](http://git.gupaoedu.cn) with the Third-Party Model Science group at AWS.
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Banu Nagasundaram leads product, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://ivebo.co.uk) hub. She is [passionate](https://hireblitz.com) about building services that help clients accelerate their [AI](http://git.dgtis.com) journey and unlock service worth.
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