From a344872b71ac2a6dec01380d1f60797d506ab38b Mon Sep 17 00:00:00 2001 From: Amber Pinnock Date: Fri, 14 Mar 2025 22:01:53 +0100 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..27914a7 --- /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://wolvesbaneuo.com)'s first-generation frontier design, DeepSeek-R1, along with the distilled variations ranging from 1.5 to 70 billion criteria to build, experiment, and responsibly scale your generative [AI](https://www.joboptimizers.com) ideas on AWS.
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In this post, we show how to start with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable actions to release the distilled versions of the designs also.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a large language design (LLM) established by DeepSeek [AI](http://otyjob.com) that utilizes reinforcement [finding](https://laborando.com.mx) out to enhance thinking abilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. A crucial differentiating function is its support knowing (RL) step, which was utilized to fine-tune the model's responses beyond the basic pre-training and tweak process. By integrating RL, DeepSeek-R1 can adapt more effectively to user feedback and objectives, ultimately boosting both relevance and [raovatonline.org](https://raovatonline.org/author/roxanalechu/) clearness. In addition, [pediascape.science](https://pediascape.science/wiki/User:LuisPrentice) DeepSeek-R1 utilizes a chain-of-thought (CoT) approach, suggesting it's geared up to break down complicated queries and factor through them in a detailed manner. This assisted reasoning process enables the design to [produce](https://dongochan.id.vn) more accurate, transparent, and detailed responses. This design combines RL-based fine-tuning with CoT capabilities, aiming to generate structured responses while focusing on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has caught the industry's attention as a versatile text-generation design that can be incorporated into various workflows such as agents, rational thinking and data interpretation jobs.
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DeepSeek-R1 uses a Mixture of [Experts](https://git.vhdltool.com) (MoE) [architecture](https://git.tool.dwoodauto.com) and is 671 billion specifications in size. The MoE architecture permits activation of 37 billion specifications, enabling effective reasoning by routing inquiries to the most relevant professional "clusters." This technique allows the model to concentrate on various problem domains while maintaining overall effectiveness. DeepSeek-R1 requires a minimum of 800 GB of HBM memory in FP8 format for reasoning. In this post, we will use an ml.p5e.48 [xlarge instance](https://audioedu.kyaikkhami.com) to deploy the design. ml.p5e.48 xlarge comes with 8 Nvidia H200 [GPUs offering](https://kahps.org) 1128 GB of GPU memory.
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DeepSeek-R1 distilled designs bring the thinking capabilities of the main R1 model to more efficient architectures based upon popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of training smaller, more efficient designs to imitate the habits and thinking patterns of the bigger DeepSeek-R1 model, [utilizing](http://1.14.125.63000) 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 model, we suggest deploying this design with guardrails in place. In this blog site, we will use Amazon Bedrock Guardrails to present safeguards, avoid damaging content, and assess designs against key security criteria. 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 multiple guardrails tailored to various use cases and apply them to the DeepSeek-R1 model, enhancing user experiences and standardizing safety controls throughout your generative [AI](http://211.159.154.98:3000) applications.
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Prerequisites
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To release the DeepSeek-R1 design, you need 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 validate you're utilizing 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 limitation boost, develop a limitation increase request and connect to your account group.
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Because you will be releasing this model with Amazon Bedrock Guardrails, make certain you have the right AWS Identity and Gain Access To Management (IAM) authorizations to utilize Amazon Bedrock Guardrails. For directions, see Set up 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 introduce safeguards, prevent damaging content, and assess designs against crucial safety criteria. You can execute safety measures for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This permits you to use guardrails to examine user inputs and design reactions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a guardrail using the Amazon Bedrock [console](http://git.guandanmaster.com) or the API. For the example code to produce the guardrail, see the GitHub repo.
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The general circulation includes the following steps: First, the system receives 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 getting the design's output, another guardrail check is used. If the output passes this last check, it's returned as the last result. However, if either the input or output is stepped in by the guardrail, a message is returned indicating the nature of the intervention and whether it occurred at the input or output stage. The examples showcased in the following sections show reasoning using this API.
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Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
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Amazon Bedrock Marketplace provides you access to over 100 popular, emerging, and specialized foundation models (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, choose Model brochure under Foundation models in the navigation pane. +At the time of writing this post, you can use the InvokeModel API to [conjure](http://8.137.54.2139000) up the model. It does not support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a service provider and select the DeepSeek-R1 model.
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The model detail page offers vital details about the design's abilities, pricing structure, and execution guidelines. You can discover detailed use directions, including sample [API calls](http://apps.iwmbd.com) and code bits for combination. The [model supports](https://infinirealm.com) different text generation tasks, including material creation, code generation, and question answering, using its [support learning](https://pioneercampus.ac.in) optimization and CoT thinking capabilities. +The page also consists of release alternatives and licensing details to help you get going with DeepSeek-R1 in your applications. +3. To start using DeepSeek-R1, select Deploy.
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You will be triggered to configure the [deployment details](https://sangha.live) for DeepSeek-R1. The design ID will be pre-populated. +4. For Endpoint name, go into an endpoint name (in between 1-50 alphanumeric characters). +5. For Variety of circumstances, get in a number of instances (between 1-100). +6. For example type, pick your circumstances type. For optimum efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is suggested. +Optionally, you can configure innovative security and facilities settings, including virtual private cloud (VPC) networking, service role authorizations, and encryption settings. For most use cases, the default settings will work well. However, for production deployments, you may wish to examine these settings to align with your organization's security and [compliance requirements](http://39.98.84.2323000). +7. Choose Deploy to begin utilizing the design.
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When the release is total, you can evaluate DeepSeek-R1's capabilities straight in the Amazon Bedrock play area. +8. Choose Open in playground to access an interactive interface where you can explore different prompts and change model criteria like temperature level and maximum length. +When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for ideal outcomes. For example, material for reasoning.
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This is an excellent method to explore the [design's thinking](http://110.42.178.1133000) and text generation abilities before integrating it into your applications. The playground provides instant feedback, helping you comprehend how the design responds to different inputs and letting you fine-tune your prompts for [optimum](https://git.youxiner.com) results.
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You can quickly test the design in the playground through the UI. However, to conjure up the deployed model [programmatically](https://media.motorsync.co.uk) 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](https://git.yqfqzmy.monster) utilizing a [deployed](http://8.222.247.203000) DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can create a guardrail using the Amazon Bedrock [console](https://sosmed.almarifah.id) or the API. For [pipewiki.org](https://pipewiki.org/wiki/index.php/User:TonyaBorowski04) the example code to create the guardrail, see the GitHub repo. After you have produced the guardrail, utilize the following code to execute guardrails. The script initializes the bedrock_runtime client, sets up inference parameters, and sends a request 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 algorithms, and [wiki.lafabriquedelalogistique.fr](https://wiki.lafabriquedelalogistique.fr/Discussion_utilisateur:MajorPickering) prebuilt ML solutions that you can deploy with just a couple of clicks. With [SageMaker](https://gitea.linuxcode.net) JumpStart, you can tailor pre-trained models to your use 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 provides two practical techniques: utilizing the user-friendly SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's explore both approaches to help you choose the approach that best fits your requirements.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following [actions](http://47.108.239.2023001) to deploy 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 triggered to create a domain. +3. On the SageMaker Studio console, pick JumpStart in the navigation pane.
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The design browser shows available designs, with details like the service provider name and model capabilities.
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4. Search for DeepSeek-R1 to see the DeepSeek-R1 design card. +Each design card shows essential details, including:
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- Model name +- Provider name +- Task classification (for example, Text Generation). +Bedrock Ready badge (if appropriate), suggesting that this model can be signed up with Amazon Bedrock, permitting you to use Amazon Bedrock APIs to invoke the model
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5. Choose the model card to see the model details page.
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The design details page consists of 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 specifications. +- Usage standards
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Before you deploy the design, it's suggested to review the [model details](http://git.andyshi.cloud) and license terms to validate compatibility with your usage case.
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6. Choose Deploy to proceed with release.
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7. For Endpoint name, utilize the automatically created name or create a custom-made one. +8. For Instance type ΒΈ pick an instance type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, enter the variety of circumstances (default: 1). +Selecting appropriate circumstances types and counts is vital for cost and performance optimization. Monitor your deployment to adjust 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 accuracy. For this model, we strongly recommend adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in location. +11. Choose Deploy to release the design.
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The [implementation process](https://gitea.gm56.ru) can take numerous minutes to complete.
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When [implementation](http://git.papagostore.com) is complete, your endpoint status will alter to InService. At this moment, the design is ready to accept inference requests through the endpoint. You can monitor the deployment progress on the SageMaker console Endpoints page, which will display relevant metrics and [status details](http://101.200.220.498001). When the implementation is complete, you can invoke the design 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 to set up the SageMaker Python SDK and make certain you have the necessary AWS approvals and environment setup. The following is a detailed code example that shows how to deploy and use DeepSeek-R1 for inference programmatically. The code for deploying the design is provided in the Github here. You can clone the notebook and run from SageMaker Studio.
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You can run extra 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 likewise use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can create a guardrail using the Amazon Bedrock console or the API, and implement it as displayed in the following code:
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Tidy up
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To avoid unwanted charges, complete the actions in this area to clean up your resources.
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Delete the Amazon Bedrock Marketplace deployment
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If you released the model using Amazon Bedrock Marketplace, complete the following steps:
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1. On the Amazon Bedrock console, under Foundation models in the navigation pane, select Marketplace implementations. +2. In the Managed deployments area, locate the endpoint you wish to delete. +3. Select the endpoint, and on the Actions menu, pick Delete. +4. Verify the endpoint details to make certain you're deleting the appropriate deployment: 1. Endpoint name. +2. Model name. +3. [Endpoint](https://login.discomfort.kz) status
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Delete the SageMaker JumpStart predictor
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The SageMaker JumpStart model you deployed will sustain expenses if you leave it [running](http://forum.ffmc59.fr). Use the following code to delete the endpoint if you want 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 begin. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, [gratisafhalen.be](https://gratisafhalen.be/author/starlao635/) 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](https://121gamers.com) for Inference at AWS. He assists emerging generative [AI](https://genzkenya.co.ke) companies construct innovative solutions utilizing AWS services and [accelerated calculate](https://www.celest-interim.fr). Currently, he is concentrated on [establishing techniques](https://code.flyingtop.cn) for fine-tuning and optimizing the inference efficiency of large [language](https://www.hb9lc.org) models. In his leisure time, [surgiteams.com](https://surgiteams.com/index.php/User:ArcherOchoa9) Vivek enjoys treking, enjoying motion pictures, and [forum.pinoo.com.tr](http://forum.pinoo.com.tr/profile.php?id=1335129) trying various foods.
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Niithiyn Vijeaswaran is a Generative [AI](https://git.mm-music.cn) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His location of focus is AWS [AI](https://techtalent-source.com) accelerators (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://modulysa.com) with the Third-Party Model Science group at AWS.
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Banu Nagasundaram leads product, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://www.jobsires.com) hub. She is enthusiastic about building solutions that help consumers accelerate their [AI](http://www.asystechnik.com) journey and unlock business value.
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