From 77974c4d67405a75228a5d387ae907a0cc7508a0 Mon Sep 17 00:00:00 2001 From: freyagillingha Date: Wed, 5 Mar 2025 03:25:43 +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..451039c --- /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 models are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](https://redmonde.es)'s [first-generation frontier](https://jobs.colwagen.co) model, DeepSeek-R1, along with the distilled variations varying from 1.5 to 70 billion [criteria](https://nmpeoplesrepublick.com) to construct, [wiki.snooze-hotelsoftware.de](https://wiki.snooze-hotelsoftware.de/index.php?title=Benutzer:EricGooding) experiment, and properly scale your [generative](http://turtle.tube) [AI](https://videoflixr.com) concepts on AWS.
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In this post, [wiki.myamens.com](http://wiki.myamens.com/index.php/User:ChanaWroe521668) we show how to get started with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar steps to release the distilled versions of the designs too.
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[Overview](https://nse.ai) of DeepSeek-R1
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DeepSeek-R1 is a large language design (LLM) developed by DeepSeek [AI](http://119.45.49.212:3000) that uses support discovering to improve reasoning capabilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. A key distinguishing feature is its reinforcement knowing (RL) action, which was used to refine the design's responses beyond the standard pre-training and tweak process. By integrating RL, DeepSeek-R1 can adapt better to user feedback and objectives, ultimately boosting both relevance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) approach, [implying](https://git.gumoio.com) it's geared up to break down complex inquiries and reason through them in a detailed manner. This directed thinking process enables the design to produce more precise, transparent, and detailed responses. This model combines RL-based fine-tuning with CoT abilities, aiming to generate structured responses while concentrating on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has captured the market's attention as a versatile text-generation design that can be incorporated into numerous workflows such as agents, rational reasoning and data interpretation tasks.
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DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion [criteria](http://182.92.202.1133000) in size. The MoE architecture permits activation of 37 billion parameters, allowing efficient inference by routing questions to the most appropriate specialist "clusters." This technique allows the design to focus on different issue domains while maintaining general effectiveness. DeepSeek-R1 needs at least 800 GB of HBM memory 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 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.
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DeepSeek-R1 distilled models bring the thinking capabilities of the main R1 design to more effective architectures based upon popular open [designs](https://octomo.co.uk) like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a process of training smaller sized, more efficient models to mimic the habits and thinking patterns of the bigger DeepSeek-R1 model, utilizing it as an instructor model.
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You can release DeepSeek-R1 model either through SageMaker JumpStart or Bedrock [Marketplace](https://www.jangsuori.com). Because DeepSeek-R1 is an emerging design, we advise releasing this model with guardrails in place. In this blog, we will use Amazon Bedrock Guardrails to introduce safeguards, avoid hazardous material, and examine designs against key security criteria. At the time of composing this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can produce several guardrails tailored to various usage cases and [surgiteams.com](https://surgiteams.com/index.php/User:LatanyaZiegler) apply them to the DeepSeek-R1 model, improving user experiences and standardizing safety controls across your generative [AI](http://git.fmode.cn:3000) 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](https://astonvillafansclub.com) 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 a limit increase, create a limit boost request and reach out to your account group.
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Because you will be deploying this model with Amazon Bedrock Guardrails, make certain you have the correct AWS Identity and Gain Access To Management (IAM) approvals to use Amazon Bedrock Guardrails. For guidelines, see Establish approvals 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, prevent hazardous content, and evaluate models against key safety [requirements](http://nysca.net). You can carry out precaution for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This [enables](https://hafrikplay.com) you to apply guardrails to assess user inputs and model reactions released on [Amazon Bedrock](https://oyotunji.site) Marketplace and SageMaker JumpStart. You can create a guardrail [utilizing](https://abalone-emploi.ch) 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 [circulation](https://sudanre.com) involves the following steps: First, the system [receives](https://www.ntcinfo.org) an input for the model. This input is then processed through the [ApplyGuardrail API](https://git.fhlz.top). 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](http://keenhome.synology.me) as the result. However, if either the input or output is intervened by the guardrail, a message is returned showing the nature of the [intervention](https://coverzen.co.zw) and whether it happened at the input or output stage. The examples showcased in the following sections show 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](https://complexityzoo.net). 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 writing this post, you can utilize the InvokeModel API to invoke the design. It does not support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a company and [it-viking.ch](http://it-viking.ch/index.php/User:GennieRedman012) select the DeepSeek-R1 design.
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The design detail page provides essential details about the model's capabilities, pricing structure, and implementation guidelines. You can find detailed usage guidelines, consisting of [sample API](https://gitlab.donnees.incubateur.anct.gouv.fr) calls and code bits for integration. The model supports various text generation tasks, including content development, code generation, and question answering, utilizing its reinforcement finding out optimization and CoT reasoning abilities. +The page likewise consists of release options and licensing details to assist you begin with DeepSeek-R1 in your applications. +3. To begin utilizing DeepSeek-R1, choose Deploy.
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You will be [triggered](https://77.248.49.223000) 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 Number of circumstances, get in a number of circumstances (between 1-100). +6. For example type, select your circumstances type. For ideal efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised. +Optionally, you can set up sophisticated security and infrastructure settings, consisting of virtual private cloud (VPC) networking, service function consents, and encryption settings. For many utilize cases, the default settings will work well. However, for production releases, you may wish to review these settings to line up with your company's security and [compliance requirements](http://fuxiaoshun.cn3000). +7. Choose Deploy to start using the design.
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When the deployment is complete, you can test DeepSeek-R1's abilities straight in the Amazon Bedrock playground. +8. Choose Open in play area to access an interactive interface where you can try out various triggers and adjust model parameters like temperature and maximum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for optimal results. For instance, material for reasoning.
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This is an exceptional way to check out the model's thinking and text generation abilities before integrating it into your applications. The play area [supplies instant](https://nationalcarerecruitment.com.au) feedback, assisting you understand how the [design responds](https://swahilihome.tv) to numerous inputs and letting you fine-tune your triggers for optimum results.
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You can rapidly test the design in the play area through the UI. However, to invoke the deployed model programmatically with any Amazon Bedrock APIs, you [require](https://www.infiniteebusiness.com) 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 demonstrates how to perform inference utilizing a deployed DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. 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. After you have actually produced the guardrail, [wiki.dulovic.tech](https://wiki.dulovic.tech/index.php/User:Freeman0349) utilize the following code to carry out guardrails. The script initializes the bedrock_runtime client, configures inference criteria, and sends out a demand 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 (ML) hub with FMs, built-in algorithms, and prebuilt ML services that you can release with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your use case, with your information, and deploy them into production utilizing either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart offers 2 convenient approaches: utilizing the intuitive SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's explore both techniques to assist you select the method that finest suits your requirements.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following actions to deploy DeepSeek-R1 using SageMaker JumpStart:
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1. On the SageMaker console, pick Studio in the [navigation](https://gitlab.dev.cpscz.site) pane. +2. First-time users will be prompted to create a domain. +3. On the SageMaker Studio console, choose JumpStart in the navigation pane.
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The model internet browser displays available designs, with details like the provider name and model capabilities.
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4. Look for DeepSeek-R1 to see the DeepSeek-R1 design card. +Each model card [reveals](https://centraldasbiblias.com.br) key details, including:
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- Model name +- Provider name +- [Task classification](http://47.116.115.15610081) (for instance, Text Generation). +Bedrock Ready badge (if appropriate), [suggesting](https://phones2gadgets.co.uk) that this model can be signed up with Amazon Bedrock, enabling you to use Amazon Bedrock APIs to [conjure](https://git.declic3000.com) up the model
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5. Choose the design card to see the page.
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The model details page consists of the following details:
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- The design name and provider details. +Deploy button to deploy the model. +About and Notebooks tabs with [detailed](http://adbux.shop) details
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The About tab includes crucial details, such as:
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- Model description. +- License details. +- Technical specs. +- Usage standards
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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 use case.
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6. Choose Deploy to continue with release.
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7. For Endpoint name, utilize the automatically generated name or produce a custom-made one. +8. For Instance type [ΒΈ select](https://mp3talpykla.com) an instance type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, get in the number of circumstances (default: 1). +Selecting suitable circumstances types and counts is vital for expense and performance optimization. Monitor your implementation to adjust these settings as needed.Under Inference type, Real-time inference is selected by default. This is enhanced for sustained traffic and low latency. +10. Review all setups for accuracy. For this design, we highly advise adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in location. +11. Choose Deploy to deploy the model.
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The deployment procedure can take a number of minutes to finish.
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When deployment is total, your endpoint status will change to InService. At this moment, the model is all set to accept inference requests through the endpoint. You can keep an eye on the deployment development on the SageMaker console Endpoints page, which will display pertinent metrics and status details. When the deployment is complete, you can conjure up the design utilizing a SageMaker runtime customer 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 need to install the SageMaker Python SDK and make certain you have the needed AWS consents and environment setup. The following is a [detailed code](https://elitevacancies.co.za) example that demonstrates how to release and [engel-und-waisen.de](http://www.engel-und-waisen.de/index.php/Benutzer:JWOPearline) use DeepSeek-R1 for inference programmatically. The code for deploying the model is offered in the Github here. You can clone the notebook and range from SageMaker Studio.
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You can run additional [demands](https://vibestream.tv) against the predictor:
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Implement guardrails and [surgiteams.com](https://surgiteams.com/index.php/User:Eddy957157) 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 using the Amazon Bedrock console or the API, and implement it as shown in the following code:
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Tidy up
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To prevent unwanted charges, finish the actions in this area to tidy up your resources.
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Delete the Amazon Bedrock Marketplace implementation
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If you released the model 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, choose Marketplace deployments. +2. In the [Managed deployments](https://git.luoui.com2443) section, locate the endpoint you want 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 proper 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 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 utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to start. 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 begun 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://workforceselection.eu) companies build innovative solutions using AWS services and accelerated compute. Currently, he is [focused](http://www.forwardmotiontx.com) on establishing techniques for fine-tuning and optimizing the reasoning efficiency of big language models. In his spare time, Vivek delights in treking, seeing movies, and trying various cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](http://128.199.125.93:3000) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His location of focus is AWS [AI](https://abalone-emploi.ch) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.
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Jonathan Evans is a Specialist Solutions Architect dealing with generative [AI](https://157.56.180.169) with the Third-Party Model Science group at AWS.
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Banu Nagasundaram leads item, engineering, and strategic collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](http://epsontario.com) center. She is passionate about constructing options that help customers accelerate their [AI](http://24insite.com) journey and [unlock business](https://git.daoyoucloud.com) worth.
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