commit 38dac5294e35e9117a249288a9a529e098894d72 Author: gwbetsuko01458 Date: Sun Apr 6 07:07:18 2025 +0200 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..e29919b --- /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](http://git.scdxtc.cn)'s first-generation frontier design, DeepSeek-R1, in addition to the distilled versions ranging from 1.5 to 70 billion parameters to construct, [pediascape.science](https://pediascape.science/wiki/User:JohnLongstreet) experiment, and properly scale your [generative](https://wikibase.imfd.cl) [AI](https://sowjobs.com) ideas on AWS.
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In this post, we show how to begin with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar steps to release the distilled variations of the models also.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a large language design (LLM) established by [DeepSeek](https://score808.us) [AI](https://git.mbyte.dev) that uses support discovering to improve thinking abilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. An essential differentiating function is its support knowing (RL) action, which was utilized to fine-tune the model's reactions beyond the basic pre-training and tweak process. By integrating RL, DeepSeek-R1 can adjust better to user feedback and goals, ultimately enhancing both importance and clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) technique, indicating it's equipped to break down complex queries and reason through them in a detailed manner. This guided reasoning process enables the model to produce more accurate, transparent, and [detailed responses](https://git.unicom.studio). This model combines [RL-based fine-tuning](https://gitea.umrbotech.com) with CoT abilities, aiming to create structured responses while concentrating on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has actually captured the industry's attention as a versatile text-generation design that can be incorporated into numerous workflows such as representatives, sensible reasoning and information interpretation jobs.
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DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture permits activation of 37 billion specifications, making it possible for efficient inference by routing inquiries to the most pertinent specialist "clusters." This method allows the design to focus on various issue domains while maintaining overall performance. DeepSeek-R1 requires a minimum of 800 GB of HBM memory in FP8 format for reasoning. In this post, we will utilize an ml.p5e.48 xlarge circumstances to deploy the model. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.
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DeepSeek-R1 distilled models bring the thinking capabilities of the main R1 model to more efficient 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 designs to simulate the behavior and reasoning patterns of the larger DeepSeek-R1 design, utilizing it as an instructor model.
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You can [release](https://suomalaistajalkapalloa.com) DeepSeek-R1 model either through SageMaker JumpStart or [Bedrock](https://careers.ecocashholdings.co.zw) Marketplace. Because DeepSeek-R1 is an emerging design, we advise releasing this model with guardrails in place. In this blog, we will utilize Amazon Bedrock Guardrails to present safeguards, avoid damaging content, and examine designs against crucial security requirements. At the time of composing this blog site, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can produce several guardrails tailored to different use cases and use them to the DeepSeek-R1 design, improving user experiences and standardizing security controls throughout your generative [AI](https://git.chocolatinie.fr) applications.
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Prerequisites
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To release 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 limit boost, develop a limitation boost request and reach out to your account team.
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Because you will be deploying this design with Amazon Bedrock Guardrails, make certain you have the correct [AWS Identity](https://mensaceuta.com) and Gain Access To Management (IAM) approvals to utilize Amazon Bedrock Guardrails. For guidelines, see Establish permissions 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 present safeguards, avoid hazardous content, and examine designs against key security criteria. You can execute precaution for the DeepSeek-R1 [design utilizing](http://124.71.40.413000) the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to examine user inputs and design actions released on Amazon Bedrock [Marketplace](https://almagigster.com) 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 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 to the design for reasoning. After receiving the model's output, another guardrail check is applied. If the output passes this last check, it's returned as the result. 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 at the input or output phase. The examples showcased in the following areas show inference 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 structure 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, pick Model brochure under Foundation designs in the [navigation pane](https://git.getmind.cn). +At the time of writing this post, you can utilize the InvokeModel API to invoke the model. It does not support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a and choose the DeepSeek-R1 design.
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The design detail page supplies important details about the model's abilities, pricing structure, and execution standards. You can discover detailed usage guidelines, including sample API calls and code bits for integration. The model supports different text generation jobs, including material production, code generation, and concern answering, utilizing its reinforcement learning optimization and CoT reasoning capabilities. +The page likewise consists of implementation options and licensing details to assist you start with DeepSeek-R1 in your applications. +3. To begin utilizing DeepSeek-R1, choose Deploy.
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You will be prompted to set up the [release details](https://castingnotices.com) for DeepSeek-R1. The design ID will be pre-populated. +4. For Endpoint name, get in an endpoint name (in between 1-50 alphanumeric characters). +5. For Variety of instances, go into a variety of circumstances (between 1-100). +6. For example type, pick your circumstances type. For optimum performance with DeepSeek-R1, a [GPU-based instance](http://logzhan.ticp.io30000) type like ml.p5e.48 xlarge is recommended. +Optionally, you can configure sophisticated security and infrastructure settings, including virtual personal cloud (VPC) networking, service function authorizations, and file encryption settings. For most utilize cases, the default settings will work well. However, for production releases, you may wish to review these [settings](https://gamberonmusic.com) to align with your company's security and compliance requirements. +7. Choose Deploy to begin using the design.
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When the release is complete, you can evaluate DeepSeek-R1's abilities straight in the Amazon Bedrock play area. +8. Choose Open in play ground to access an interactive interface where you can experiment with different prompts and change design parameters like temperature and maximum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for ideal results. For instance, content for inference.
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This is an outstanding way to check out the model's reasoning and text generation capabilities before incorporating it into your applications. The play area [supplies](http://47.100.3.2093000) immediate feedback, helping you comprehend how the design responds to various inputs and [letting](https://collegetalks.site) you tweak your triggers for [optimum outcomes](https://avajustinmedianetwork.com).
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You can rapidly test the design in the play area through the UI. However, to conjure up the deployed design programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
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Run reasoning using guardrails with the released DeepSeek-R1 endpoint
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The following code example demonstrates how to perform inference using a released DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can develop 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, utilize the following code to [implement guardrails](http://120.46.37.2433000). The script initializes the bedrock_runtime client, sets up reasoning specifications, and sends a demand to produce text based upon a user timely.
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Deploy DeepSeek-R1 with SageMaker JumpStart
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[SageMaker JumpStart](https://smaphofilm.com) is an artificial intelligence (ML) hub with FMs, built-in algorithms, and prebuilt ML options that you can deploy 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 utilizing either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart provides two convenient techniques: utilizing the intuitive SageMaker JumpStart UI or implementing programmatically through the SageMaker [Python SDK](http://123.206.9.273000). Let's check out both approaches to help you select the technique that best fits 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 utilizing SageMaker JumpStart:
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1. On the SageMaker console, choose Studio in the navigation pane. +2. First-time users will be prompted to produce a domain. +3. On the SageMaker Studio console, select JumpStart in the navigation pane.
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The design browser shows available models, with details like the service provider name and design capabilities.
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4. Search for DeepSeek-R1 to see the DeepSeek-R1 model card. +Each model card reveals crucial details, including:
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[- Model](https://git.mbyte.dev) name +- Provider name +- Task classification (for instance, Text Generation). +Bedrock Ready badge (if relevant), indicating that this design can be registered with Amazon Bedrock, permitting you to use [Amazon Bedrock](https://cello.cnu.ac.kr) APIs to conjure up the design
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5. Choose the design card to see the model details page.
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The model details page includes the following details:
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- The model name and provider details. +Deploy button to deploy the design. +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 specs. +- Usage guidelines
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Before you release the design, it's recommended to examine the design details and license terms to verify compatibility with your usage case.
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6. Choose Deploy to continue with release.
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7. For Endpoint name, use the automatically produced name or develop a custom-made one. +8. For Instance type ΒΈ select an instance type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, enter the number of instances (default: 1). +Selecting appropriate instance types and counts is [crucial](http://www.letts.org) for cost and performance optimization. Monitor your deployment to change these settings as needed.Under Inference type, Real-time reasoning is picked by default. This is optimized for sustained traffic and low latency. +10. Review all configurations for precision. For this model, we strongly suggest adhering to SageMaker JumpStart default settings and making certain that network isolation remains in place. +11. Choose Deploy to release the model.
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The implementation process can take numerous minutes to complete.
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When [implementation](https://spillbean.in.net) 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 keep an eye on the release development on the SageMaker console Endpoints page, which will show relevant metrics and status details. When the implementation is total, you can conjure up the model using a SageMaker runtime customer and incorporate it with your [applications](https://samisg.eu8443).
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Deploy DeepSeek-R1 using the SageMaker Python SDK
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To get started 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 consents and environment setup. The following is a detailed code example that demonstrates how to release and utilize DeepSeek-R1 for reasoning programmatically. The code for releasing the design is [offered](https://2flab.com) in the Github here. You can clone the note pad and range from SageMaker Studio.
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You can run additional requests 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 use 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 shown in the following code:
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Tidy up
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To prevent undesirable charges, complete the steps in this section to tidy up your [resources](https://wiki.rolandradio.net).
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Delete the [Amazon Bedrock](https://git.xantxo-coquillard.fr) Marketplace deployment
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If you deployed the [model utilizing](https://cristianoronaldoclub.com) Amazon Bedrock Marketplace, total the following actions:
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1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, choose Marketplace deployments. +2. In the Managed implementations area, find the endpoint you wish to delete. +3. Select the endpoint, and on the Actions menu, [pick Delete](https://mypetdoll.co.kr). +4. Verify the endpoint details to make certain you're deleting the right 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 released will sustain expenses if you leave it [running](https://ipmanage.sumedangkab.go.id). Use the following code to erase the endpoint if you wish to stop sustaining charges. For more details, see [Delete Endpoints](https://atfal.tv) 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 model utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in [SageMaker Studio](https://cello.cnu.ac.kr) or Amazon Bedrock [Marketplace](https://git.lewis.id) now to start. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained designs, 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](http://51.75.64.148) is a Lead Specialist Solutions Architect for Inference at AWS. He assists emerging generative [AI](https://fydate.com) [companies develop](http://www.topverse.world3000) ingenious options using AWS services and accelerated calculate. Currently, he is concentrated on establishing methods for fine-tuning and optimizing the inference efficiency of large language designs. In his spare time, Vivek enjoys treking, watching films, and attempting various foods.
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Niithiyn Vijeaswaran is a Generative [AI](https://git.serenetia.com) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His area of focus is AWS [AI](https://cyltalentohumano.com) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
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Jonathan Evans is a Professional Solutions Architect working on generative [AI](https://git.bubblesthebunny.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://www.teacircle.co.in) center. She is passionate about constructing services that assist clients accelerate their [AI](http://artsm.net) journey and unlock business worth.
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