commit 0ed49d78f407a434859e988a5a7352c16fa7587c Author: grovercarroll Date: Sun Apr 6 20:16:42 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..9651988 --- /dev/null +++ b/DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart.-.md @@ -0,0 +1,93 @@ +
Today, we are excited to reveal that [DeepSeek](https://minka.gob.ec) 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://scholarpool.com)'s first-generation frontier model, DeepSeek-R1, together with the distilled versions varying from 1.5 to 70 billion specifications to construct, experiment, and properly scale your generative [AI](https://gogs.sxdirectpurchase.com) ideas on AWS.
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In this post, we demonstrate how to start with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar actions to release the distilled variations of the designs as well.
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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://133.242.131.226:3003) that uses support learning to boost thinking capabilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A crucial identifying feature is its [support learning](https://comunidadebrasilbr.com) (RL) action, which was used to improve the design's responses beyond the basic pre-training and tweak process. By integrating RL, DeepSeek-R1 can adjust better to user feedback and objectives, ultimately improving both relevance and clarity. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) technique, [implying](http://43.139.182.871111) it's geared up to break down complicated queries and factor through them in a detailed way. This guided reasoning process enables the model to produce more accurate, transparent, and detailed responses. This model combines RL-based [fine-tuning](http://210.236.40.2409080) with CoT abilities, aiming to create structured actions while concentrating on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has recorded the market's attention as a versatile text-generation model that can be integrated into numerous workflows such as agents, rational thinking and information interpretation jobs.
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DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture [enables activation](https://taelimfwell.com) of 37 billion parameters, making it possible for effective [inference](http://042.ne.jp) by routing queries to the most appropriate professional "clusters." This technique permits the model to specialize in various problem domains while maintaining general performance. DeepSeek-R1 requires a minimum of 800 GB of HBM memory in FP8 format for inference. In this post, we will utilize an ml.p5e.48 xlarge circumstances to release the design. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.
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DeepSeek-R1 distilled designs bring the reasoning abilities of the main R1 model to more efficient architectures based on popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of training smaller sized, more effective designs to [simulate](https://git.lain.church) the behavior and reasoning patterns of the bigger DeepSeek-R1 model, using it as a teacher design.
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You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we recommend releasing this model with guardrails in location. In this blog site, we will use Amazon Bedrock Guardrails to introduce safeguards, avoid damaging material, and examine models against key security requirements. At the time of writing this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can develop multiple guardrails tailored to different usage cases and use them to the DeepSeek-R1 model, enhancing user experiences and standardizing security controls across your generative [AI](http://60.205.104.179:3000) applications.
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Prerequisites
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To deploy the DeepSeek-R1 design, 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 [instance](https://gitea.adminakademia.pl) in the AWS Region you are deploying. To ask for a limit boost, produce a limit increase request and [connect](http://111.35.141.53000) to your account team.
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Because you will be deploying this design with Amazon Bedrock Guardrails, make certain you have the right AWS and Gain Access To Management (IAM) approvals to use Amazon Bedrock Guardrails. For directions, see Establish permissions to utilize guardrails for content filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails allows you to present safeguards, avoid hazardous material, and assess models against [crucial](https://safeway.com.bd) security requirements. You can [execute security](http://120.77.2.937000) steps for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This enables you to use guardrails to [evaluate](http://gitlab.ifsbank.com.cn) user inputs and design responses [deployed](https://gitlab.payamake-sefid.com) on Amazon Bedrock Marketplace and SageMaker JumpStart. You can develop a guardrail utilizing the [Amazon Bedrock](https://premiergitea.online3000) [console](https://menfucks.com) or the API. For the example code to create the guardrail, see the GitHub repo.
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The general flow involves the following steps: 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 inference. After receiving the design's output, another [guardrail check](https://git.andrewnw.xyz) is applied. If the output passes this last check, it's returned as the last outcome. However, if either the input or output is stepped in 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 areas demonstrate reasoning utilizing 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 designs in the navigation pane. +At the time of writing this post, you can utilize the InvokeModel API to [conjure](https://zurimeet.com) up the design. It does not [support Converse](https://getstartupjob.com) APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a company and select the DeepSeek-R1 design.
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The design detail page supplies vital details about the model's capabilities, prices structure, and implementation guidelines. You can find detailed use instructions, including sample API calls and code snippets for integration. The model supports various text generation tasks, consisting of material development, code generation, and concern answering, utilizing its support learning optimization and CoT reasoning abilities. +The page likewise consists of release options and [licensing](https://www.jobexpertsindia.com) 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 triggered to set up the release details for DeepSeek-R1. The design ID will be pre-populated. +4. For Endpoint name, go into an endpoint name (in between 1-50 [alphanumeric](https://ipmanage.sumedangkab.go.id) characters). +5. For Variety of instances, enter a variety of instances (between 1-100). +6. For Instance type, pick your circumstances type. For ideal performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised. +Optionally, you can configure sophisticated security and facilities settings, including virtual personal cloud (VPC) networking, service role authorizations, and file encryption settings. For most use cases, the default settings will work well. However, for production releases, you might desire to examine these settings to align with your company's security and compliance requirements. +7. Choose Deploy to start using the design.
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When the release is complete, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock play ground. +8. Choose Open in playground to access an interactive user interface where you can experiment with different triggers and adjust model specifications like temperature level and optimum length. +When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimal outcomes. For instance, content for inference.
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This is an excellent way to check out the design's reasoning and text generation abilities before integrating it into your applications. The playground offers immediate feedback, assisting you understand how the design reacts to numerous inputs and letting you tweak your prompts for optimum results.
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You can rapidly check the design in the playground through the UI. However, to invoke the released model programmatically with any Amazon Bedrock APIs, you need 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 shows how to carry out reasoning using a released DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can produce 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 produced the guardrail, utilize the following code to carry out guardrails. The script initializes the bedrock_runtime client, sets up inference criteria, and sends a request to produce text based on a user prompt.
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Deploy DeepSeek-R1 with SageMaker JumpStart
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SageMaker JumpStart is an artificial intelligence (ML) hub with FMs, integrated algorithms, and prebuilt ML options that you can release with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained [designs](https://xn--939a42kg7dvqi7uo.com) to your usage 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 provides 2 convenient techniques: using the intuitive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's check out both approaches to help you pick the method that best fits your needs.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following steps to deploy DeepSeek-R1 utilizing SageMaker JumpStart:
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1. On the [SageMaker](http://gitlab.pakgon.com) console, select Studio in the navigation pane. +2. First-time users will be prompted to develop a domain. +3. On the SageMaker Studio console, pick JumpStart in the navigation pane.
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The design web browser displays available designs, with details like the [company](https://git.mhurliman.net) name and [model abilities](https://wino.org.pl).
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4. Search for DeepSeek-R1 to view the DeepSeek-R1 model card. +Each design [card reveals](https://whoosgram.com) key details, including:
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- Model name +- Provider name +- Task category (for example, Text Generation). +Bedrock Ready badge (if appropriate), indicating that this design can be registered with Amazon Bedrock, allowing you to utilize 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 includes the following details:
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- The model name and service provider details. +Deploy button to deploy the model. +About and Notebooks tabs with detailed details
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The About [tab consists](https://git.owlhosting.cloud) of essential details, such as:
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- Model [description](http://8.140.205.1543000). +- License details. +- Technical requirements. +- Usage standards
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Before you release the model, it's recommended to examine the design details and license terms to confirm compatibility with your use case.
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6. Choose Deploy to continue with release.
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7. For Endpoint name, [it-viking.ch](http://it-viking.ch/index.php/User:ElmoVaude846) utilize the instantly produced name or produce a customized one. +8. For example type ΒΈ pick an instance type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, get in the number of circumstances (default: 1). +Selecting appropriate instance types and counts is vital for cost and efficiency optimization. Monitor your implementation to change these settings as needed.Under Inference type, Real-time reasoning is picked by default. This is enhanced for sustained traffic and low latency. +10. Review all configurations for accuracy. For this design, we strongly advise adhering to SageMaker JumpStart default settings and making certain that network isolation remains in location. +11. Choose Deploy to deploy the model.
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The deployment procedure can take several minutes to finish.
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When implementation is total, your endpoint status will alter to InService. At this moment, the design is ready to accept reasoning requests through the endpoint. You can keep an eye on the [release development](https://bld.lat) on the SageMaker console Endpoints page, which will show pertinent metrics and status details. When the release is total, you can conjure up the design utilizing a SageMaker runtime client and integrate 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 utilizing the SageMaker Python SDK, you will need to set up the SageMaker Python SDK and make certain you have the essential AWS consents and environment setup. The following is a detailed code example that [demonstrates](http://hrplus.com.vn) how to release and use DeepSeek-R1 for reasoning programmatically. The code for [deploying](https://www.genbecle.com) the model 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 inference 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 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 prevent unwanted charges, complete the actions in this section to clean up your resources.
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Delete the Amazon Bedrock Marketplace deployment
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If you released the design using [Amazon Bedrock](https://www.designxri.com) Marketplace, total the following actions:
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1. On the Amazon Bedrock console, under Foundation models in the [navigation](https://pioneercampus.ac.in) pane, choose Marketplace releases. +2. In the Managed implementations section, find the endpoint you desire to delete. +3. Select the endpoint, and on the Actions menu, choose Delete. +4. Verify the endpoint details to make certain you're erasing the right 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 released will [sustain expenses](https://www.lingualoc.com) if you leave it running. Use the following code to delete the endpoint if you wish to stop [sustaining charges](https://www.empireofember.com). 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 begin. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, 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](http://jobs.freightbrokerbootcamp.com) is a Lead Specialist Solutions Architect for Inference at AWS. He helps emerging generative [AI](https://realhindu.in) companies build innovative options utilizing [AWS services](http://gitlabhwy.kmlckj.com) and sped up calculate. Currently, he is concentrated on developing methods for fine-tuning and optimizing the inference performance of big [language](https://social.netverseventures.com) models. In his downtime, Vivek enjoys treking, enjoying films, and trying various foods.
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Niithiyn Vijeaswaran is a Generative [AI](http://gitea.shundaonetwork.com) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His area of focus is AWS [AI](http://39.106.177.160:8756) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
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Jonathan Evans is a Specialist Solutions Architect working on [generative](http://git.iloomo.com) [AI](https://git.cyu.fr) with the Third-Party Model [Science](https://hortpeople.com) 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://rapid.tube) center. She is passionate about constructing services that help consumers accelerate their [AI](http://52.23.128.62:3000) journey and unlock organization worth.
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