commit ab457a6baa13d9c0022897d90856c015cc576ded Author: desmondschroed Date: Fri May 30 18:49:15 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..3041f60 --- /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 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 release DeepSeek [AI](https://villahandle.com)'s first-generation frontier design, DeepSeek-R1, together with the distilled variations varying from 1.5 to 70 billion criteria to build, experiment, and responsibly scale your generative [AI](http://rm.runfox.com) concepts on AWS.
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In this post, we show how to get going with DeepSeek-R1 on [Amazon Bedrock](https://gitea.malloc.hackerbots.net) Marketplace and SageMaker JumpStart. You can follow comparable actions to release the distilled variations of the [designs](https://mixup.wiki) too.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a large language model (LLM) established by DeepSeek [AI](http://jobs.freightbrokerbootcamp.com) that utilizes support finding out to enhance thinking capabilities through a multi-stage training process from a DeepSeek-V3-Base foundation. An essential identifying feature is its reinforcement knowing (RL) action, which was used to fine-tune the design's reactions beyond the basic pre-training and fine-tuning process. By incorporating RL, DeepSeek-R1 can adjust better to user feedback and goals, ultimately boosting both significance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) approach, implying it's [equipped](http://120.77.209.1763000) to break down complex questions and factor through them in a detailed way. This assisted thinking process enables the model to produce more accurate, transparent, and detailed responses. This design combines RL-based fine-tuning with CoT abilities, aiming to produce structured reactions while focusing on interpretability and user interaction. With its wide-ranging capabilities DeepSeek-R1 has actually caught the industry's attention as a flexible [text-generation design](https://git.noisolation.com) that can be incorporated into various workflows such as representatives, rational thinking and data interpretation tasks.
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DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture allows activation of 37 billion criteria, making it possible for efficient inference by routing questions to the most appropriate professional "clusters." This approach permits the model to specialize in different problem domains while maintaining overall efficiency. 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 features 8 Nvidia H200 [GPUs supplying](http://www.hxgc-tech.com3000) 1128 GB of [GPU memory](https://impactosocial.unicef.es).
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DeepSeek-R1 distilled models bring the reasoning capabilities of the main R1 design to more [effective architectures](http://git.pushecommerce.com) based on popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a process of training smaller, more effective models to simulate the behavior and thinking patterns of the larger DeepSeek-R1 design, using it as a teacher model.
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You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we suggest releasing this model with guardrails in place. In this blog site, we will use Amazon Bedrock Guardrails to introduce safeguards, prevent damaging material, and examine designs against essential safety requirements. At the time of composing this blog site, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can develop several guardrails tailored to various usage cases and use them to the DeepSeek-R1 model, improving user experiences and standardizing safety controls across your generative [AI](https://gitea.ndda.fr) applications.
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Prerequisites
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To deploy the DeepSeek-R1 design, you need access to an ml.p5e circumstances. To examine if you have quotas for P5e, open the Service Quotas [console](http://1.12.255.88) and under AWS Services, pick Amazon SageMaker, and confirm you're utilizing ml.p5e.48 xlarge for endpoint use. Make certain that you have at least one ml.P5e.48 xlarge circumstances in the AWS Region you are releasing. To request a limit increase, produce a limit boost request and connect to your account team.
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Because you will be [deploying](https://video.lamsonsaovang.com) this design with Amazon Bedrock Guardrails, make certain you have the proper AWS Identity and Gain Access To Management (IAM) permissions to utilize Amazon Bedrock Guardrails. For directions, see [Establish consents](https://ourehelp.com) to use guardrails for content filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails enables you to present safeguards, avoid harmful content, and evaluate designs against essential safety requirements. You can carry out precaution for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This allows you to use [guardrails](https://chatgay.webcria.com.br) to [evaluate](http://git.gupaoedu.cn) user inputs and model reactions released on Amazon Bedrock Marketplace and [SageMaker JumpStart](https://tweecampus.com). You can develop a guardrail using 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 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](http://git.chuangxin1.com) the guardrail check, it's sent out to the model for inference. After getting the model's output, another guardrail check is applied. If the output passes this final check, it's returned as the final 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 took place at the input or output phase. The examples showcased in the following [sections](http://park8.wakwak.com) show inference 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 structure models (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, choose Model catalog under Foundation designs 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 supplier and select the DeepSeek-R1 model.
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The design detail page provides essential details about the model's abilities, rates structure, and [execution standards](http://git.cattech.org). You can find detailed usage directions, consisting of sample API calls and code bits for combination. The model supports different text generation tasks, including material production, code generation, and question answering, utilizing its support discovering optimization and CoT thinking capabilities. +The page likewise consists of implementation options and licensing details to assist you get going with DeepSeek-R1 in your applications. +3. To start using DeepSeek-R1, pick Deploy.
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You will be [prompted](https://git.aiadmin.cc) to configure the release details for DeepSeek-R1. The model ID will be pre-populated. +4. For [Endpoint](https://git.bwt.com.de) name, get in an endpoint name (in between 1-50 alphanumeric characters). +5. For Variety of instances, go into a variety of instances (in 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 advised. +Optionally, you can set up innovative security and infrastructure settings, consisting of virtual private cloud (VPC) networking, service role approvals, and encryption settings. For many use cases, the default settings will work well. However, for production deployments, you may want to evaluate these settings to align with your company's security and compliance requirements. +7. Choose Deploy to begin [utilizing](http://gitlab.abovestratus.com) the design.
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When the deployment is total, you can test DeepSeek-R1's abilities straight in the Amazon Bedrock playground. +8. Choose Open in play ground to access an interactive interface where you can explore various triggers and change model criteria like temperature and optimum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for ideal results. For instance, material for reasoning.
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This is an exceptional method to [explore](https://git.logicp.ca) the model's reasoning and text generation capabilities before integrating it into your applications. The play area supplies instant feedback, helping you comprehend how the design responds to various inputs and letting you tweak your triggers for optimal results.
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You can rapidly check the model in the [play ground](https://chhng.com) through the UI. However, to invoke the released design programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
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Run reasoning utilizing guardrails with the deployed DeepSeek-R1 endpoint
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The following code example demonstrates how to perform inference utilizing a deployed DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can develop a guardrail utilizing the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo. After you have actually created the guardrail, use the following code to implement guardrails. The script initializes the bedrock_runtime customer, configures inference criteria, and sends out a demand to generate text based on a user timely.
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Deploy DeepSeek-R1 with [SageMaker](https://sistemagent.com8081) JumpStart
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SageMaker JumpStart is an artificial intelligence (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 [yewiki.org](https://www.yewiki.org/User:DeniseTomholt25) 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 uses two hassle-free methods: utilizing the instinctive SageMaker [JumpStart](https://service.aicloud.fit50443) UI or executing programmatically through the SageMaker Python SDK. Let's check out both techniques to help you choose the approach that finest matches your needs.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following steps 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 [develop](https://www.punajuaj.com) a domain. +3. On the SageMaker Studio console, pick JumpStart in the navigation pane.
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The design web browser shows available designs, with details like the company name and design abilities.
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4. Look for DeepSeek-R1 to view the DeepSeek-R1 design card. +Each design card reveals essential details, including:
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- Model name +- Provider name +- Task category (for example, Text Generation). +Bedrock Ready badge (if appropriate), showing that this design can be signed up with Amazon Bedrock, permitting you to use Amazon Bedrock APIs to conjure up the model
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5. Choose the design card to view 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 company details. +Deploy button to deploy the design. +About and Notebooks tabs with detailed details
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The About tab includes [crucial](http://zerovalueentertainment.com3000) details, such as:
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- Model description. +- License details. +- Technical specifications. +- Usage standards
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Before you deploy the model, it's suggested to examine the design details and license terms to [verify compatibility](https://www.freetenders.co.za) with your use case.
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6. Choose Deploy to proceed with [release](https://losangelesgalaxyfansclub.com).
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7. For Endpoint name, use the immediately generated name or develop a customized one. +8. For example type ΒΈ select an instance type (default: ml.p5e.48 xlarge). +9. For Initial [circumstances](http://163.228.224.1053000) count, go into the number of circumstances (default: 1). +Selecting suitable instance types and counts is vital for expense and efficiency optimization. Monitor your implementation to change these settings as needed.Under Inference type, Real-time inference is picked by default. This is enhanced for sustained traffic and low latency. +10. Review all setups for precision. For this model, we highly suggest sticking to SageMaker JumpStart default settings and making certain that [network isolation](https://corerecruitingroup.com) remains in place. +11. Choose Deploy to release the model.
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The deployment process can take numerous minutes to complete.
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When implementation is complete, your endpoint status will alter to InService. At this point, the design is prepared to accept inference [requests](https://social.mirrororg.com) through the endpoint. You can keep track of the deployment development on the SageMaker console Endpoints page, which will [display relevant](http://111.8.36.1803000) metrics and status details. 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 using the SageMaker Python SDK, you will need to set up the SageMaker and make certain you have the necessary AWS consents and environment setup. The following is a detailed code example that demonstrates how to deploy and use DeepSeek-R1 for reasoning programmatically. The code for [releasing](https://git.mhurliman.net) the model is provided in the Github here. You can clone the note pad and run from SageMaker Studio.
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You can run extra demands against the predictor:
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Implement guardrails and run inference 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 implement it as displayed in the following code:
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Tidy up
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To avoid unwanted charges, finish the actions in this area to tidy up your resources.
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Delete the Amazon Bedrock Marketplace deployment
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If you deployed 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, choose Marketplace releases. +2. In the Managed deployments area, locate the [endpoint](https://www.medexmd.com) you want to erase. +3. Select the endpoint, and on the Actions menu, choose Delete. +4. Verify the endpoint details to make certain you're erasing the proper implementation: 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 model you deployed will sustain costs if you leave it running. 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](https://yourfoodcareer.com) the DeepSeek-R1 design using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get started. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting going with Amazon SageMaker JumpStart.
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About the Authors
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Vivek Gangasani is a Lead Specialist Solutions [Architect](https://pakkjob.com) for Inference at AWS. He assists emerging generative [AI](https://telecomgurus.in) companies develop innovative services utilizing AWS services and accelerated calculate. Currently, he is concentrated on establishing methods for fine-tuning and optimizing the inference efficiency of big language [designs](https://www.speedrunwiki.com). In his leisure time, Vivek enjoys treking, seeing movies, and trying various cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://repos.ubtob.net) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His area of focus is AWS [AI](http://ipc.gdguanhui.com:3001) [accelerators](https://www.blatech.co.uk) (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
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Jonathan Evans is an Expert Solutions Architect working on generative [AI](http://www.thehispanicamerican.com) with the Third-Party Model Science team 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://gitea.deprived.dev) hub. She is enthusiastic about constructing services that assist customers accelerate their [AI](https://www.dataalafrica.com) journey and [unlock organization](http://cwscience.co.kr) worth.
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