commit 0786ea1d392e6e29beab705ea1cdb1c5531b4a1a Author: pqzosvaldo309 Date: Sat Apr 5 18:07:49 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..4aab405 --- /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 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 release DeepSeek [AI](http://124.71.134.146:3000)'s first-generation frontier design, DeepSeek-R1, in addition to the distilled versions ranging from 1.5 to 70 billion criteria to develop, experiment, and responsibly scale your generative [AI](https://empregos.acheigrandevix.com.br) ideas on AWS.
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In this post, we demonstrate how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable steps to deploy the distilled versions of the designs also.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a big language model (LLM) [established](https://gogolive.biz) by DeepSeek [AI](https://www.belizetalent.com) that uses reinforcement finding out to [improve](https://git.ombreport.info) reasoning capabilities through a multi-stage training process from a DeepSeek-V3-Base structure. An essential differentiating feature is its reinforcement knowing (RL) action, which was utilized to improve the model's actions beyond the [standard](http://forum.pinoo.com.tr) pre-training and fine-tuning process. By incorporating RL, DeepSeek-R1 can adjust more efficiently to user feedback and objectives, ultimately boosting both significance and clarity. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) technique, meaning it's geared up to break down intricate questions and factor through them in a detailed way. This directed reasoning procedure permits the model to produce more accurate, transparent, and detailed answers. This design integrates RL-based [fine-tuning](http://ptube.site) with CoT abilities, aiming to generate structured reactions while concentrating on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has captured the market's attention as a flexible text-generation design that can be incorporated into numerous workflows such as representatives, rational reasoning and information interpretation jobs.
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DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture enables activation of 37 billion parameters, enabling efficient inference by routing questions to the most relevant professional "clusters." This technique allows the model to concentrate on different issue domains while [maintaining](https://mmsmaza.in) total effectiveness. DeepSeek-R1 needs at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will utilize an ml.p5e.48 [xlarge instance](https://www.virsocial.com) to deploy the model. ml.p5e.48 xlarge features 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 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 sized, more effective designs to mimic the habits and thinking patterns of the larger DeepSeek-R1 design, using it as a teacher design.
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You can deploy DeepSeek-R1 design either through SageMaker JumpStart or [Bedrock Marketplace](http://moyora.today). Because DeepSeek-R1 is an emerging design, we [recommend deploying](http://www.asiapp.co.kr) this design with guardrails in place. In this blog site, we will use Amazon Bedrock Guardrails to present safeguards, [prevent damaging](http://119.45.49.2123000) content, and [assess designs](http://194.87.97.823000) against key security criteria. At the time of composing this blog, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can create multiple guardrails tailored to various use cases and apply them to the DeepSeek-R1 model, enhancing user experiences and standardizing safety controls across your generative [AI](http://47.120.70.16:8000) applications.
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Prerequisites
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To release the DeepSeek-R1 design, you require access to an ml.p5e circumstances. To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, select Amazon SageMaker, and verify you're using 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 ask for a limit boost, produce a [limitation increase](http://modulysa.com) demand and reach out to your account group.
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Because you will be releasing this model with Amazon Bedrock Guardrails, make certain you have the [proper AWS](https://git.bugwc.com) Identity and Gain Access To Management (IAM) permissions to utilize Amazon Bedrock Guardrails. For instructions, see Establish consents to utilize guardrails for material filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails enables you to introduce safeguards, avoid damaging content, and evaluate designs against key safety requirements. You can carry out security procedures for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to evaluate user inputs and model reactions released on Amazon Bedrock Marketplace and SageMaker JumpStart. 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.
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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 reasoning. After getting the design's output, another guardrail check is used. If the output passes this last check, it's returned as the final outcome. 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 areas demonstrate reasoning 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 models (FMs) through [Amazon Bedrock](https://www.muslimtube.com). 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 models in the navigation pane. +At the time of composing this post, you can utilize the InvokeModel API to conjure up the model. It does not support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a [company](http://82.223.37.137) and choose the DeepSeek-R1 model.
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The design detail page supplies necessary details about the design's abilities, rates structure, and application standards. You can find detailed usage guidelines, consisting of sample API calls and code bits for combination. The design supports numerous text generation tasks, including content creation, code generation, and question answering, utilizing its support discovering optimization and CoT thinking capabilities. +The page also consists of implementation choices and licensing details to help you get going with DeepSeek-R1 in your applications. +3. To begin utilizing DeepSeek-R1, choose Deploy.
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You will be triggered to configure the [release details](http://gitlab.andorsoft.ad) for DeepSeek-R1. The model ID will be pre-populated. +4. For Endpoint name, go into an endpoint name (in between 1-50 alphanumeric characters). +5. For Number of instances, go into a number of instances (in between 1-100). +6. For Instance type, pick your instance type. For efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is recommended. +Optionally, you can set up sophisticated security and infrastructure settings, consisting of virtual private cloud (VPC) networking, service role consents, and encryption settings. For many use cases, the default settings will work well. However, for production releases, you might desire to evaluate these settings to line up with your company's security and compliance requirements. +7. Choose Deploy to start using the model.
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When the implementation is complete, you can evaluate DeepSeek-R1's capabilities straight in the Amazon Bedrock play area. +8. Choose Open in play area to access an interactive user interface where you can try out different triggers and change model criteria like temperature level and maximum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for optimal results. For example, content for reasoning.
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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 ground supplies immediate feedback, assisting you understand how the design reacts to numerous inputs and letting you [fine-tune](https://www.racingfans.com.au) your triggers for optimum results.
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You can quickly check the model in the playground through the UI. However, to conjure up the deployed model programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
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Run inference utilizing guardrails with the [deployed](https://playtube.app) DeepSeek-R1 endpoint
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The following code example demonstrates how to perform inference using 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](https://gruppl.com) or the API. For the example code to create the guardrail, see the GitHub repo. After you have developed the guardrail, utilize the following code to implement guardrails. The script initializes the bedrock_runtime customer, sets up inference parameters, [trademarketclassifieds.com](https://trademarketclassifieds.com/user/profile/2672496) and sends a demand 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) center with FMs, built-in algorithms, and prebuilt ML solutions that you can deploy with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage 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 uses two practical techniques: utilizing the user-friendly SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both methods to assist you pick the method that finest suits your requirements.
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Deploy DeepSeek-R1 through [SageMaker JumpStart](https://gitea.dokm.xyz) UI
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Complete the following [actions](http://www.thynkjobs.com) to release DeepSeek-R1 utilizing SageMaker JumpStart:
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1. On the SageMaker console, pick Studio in the navigation pane. +2. First-time users will be triggered to produce a domain. +3. On the SageMaker Studio console, select JumpStart in the navigation pane.
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The design internet browser shows available models, with details like the provider name and model abilities.
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4. Search for DeepSeek-R1 to see the DeepSeek-R1 design card. +Each design card reveals key details, consisting of:
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- Model name +- Provider name +- Task classification (for example, Text Generation). +Bedrock Ready badge (if relevant), showing that this design can be signed up with Amazon Bedrock, allowing you to utilize Amazon Bedrock APIs to invoke the design
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5. Choose the design card to view the design details 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 release the design. +About and Notebooks tabs with detailed details
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The About tab includes essential details, such as:
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- Model description. +- License details. +- Technical specifications. +- Usage guidelines
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Before you deploy the design, it's recommended to evaluate the design details and license terms to verify compatibility with your usage case.
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6. Choose Deploy to proceed with deployment.
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7. For Endpoint name, use the automatically generated name or create a custom one. +8. For example [type ΒΈ](https://zeroth.one) pick an instance type (default: ml.p5e.48 xlarge). +9. For Initial instance count, get in the variety of instances (default: 1). +Selecting proper instance types and counts is crucial for cost and efficiency optimization. Monitor your implementation to adjust these settings as needed.Under Inference type, Real-time reasoning is chosen by default. This is enhanced for sustained traffic and low latency. +10. Review all configurations for accuracy. 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 design.
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The deployment procedure can take several minutes to complete.
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When release is total, your endpoint status will change to InService. At this point, the design is prepared to accept inference [demands](https://hip-hop.id) through the endpoint. You can keep an eye on the implementation progress on the SageMaker console Endpoints page, which will display relevant metrics and status details. When the implementation 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 using the SageMaker Python SDK
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To get going with DeepSeek-R1 utilizing the SageMaker Python SDK, you will require to set up the SageMaker Python SDK and make certain you have the essential AWS approvals and environment setup. The following is a detailed code example that demonstrates how to release and utilize DeepSeek-R1 for inference programmatically. The code for deploying the design is supplied in the Github here. You can clone the notebook and run from SageMaker Studio.
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You can run additional demands 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 create a guardrail using the Amazon Bedrock console or the API, and execute it as revealed in the following code:
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Tidy up
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To prevent undesirable charges, complete the steps in this area to clean up your resources.
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Delete the Amazon Bedrock Marketplace release
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If you released the model utilizing 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, find the endpoint you wish to erase. +3. Select the endpoint, and on the Actions menu, pick Delete. +4. Verify the [endpoint details](http://47.107.29.613000) 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](https://tube.leadstrium.com) out how you can access and release the DeepSeek-R1 design using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock [Marketplace](https://apyarx.com) 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 started 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://www.top5stockbroker.com) companies build [innovative services](http://doosung1.co.kr) using AWS services and sped up calculate. Currently, he is concentrated on developing methods for fine-tuning and optimizing the reasoning efficiency of large [language](https://yourgreendaily.com) models. In his free time, Vivek delights in hiking, enjoying films, and attempting various cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://sabiile.com) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His area of focus is AWS [AI](https://vsbg.info) 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.obo.cash) 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](http://1.15.187.67) [AI](https://service.lanzainc.xyz:10281) hub. She is passionate about developing services that help clients accelerate their [AI](https://www.iratechsolutions.com) journey and unlock company worth.
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