commit 2ac32af23168b2e8a6228a064866481ffa7932d4 Author: selenayan76835 Date: Sun Apr 13 21:42:57 2025 +0800 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..30afd75 --- /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 reveal 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](http://yun.pashanhoo.com:9090)'s first-generation frontier design, DeepSeek-R1, in addition to the distilled variations varying from 1.5 to 70 billion specifications to construct, experiment, and properly scale your generative [AI](https://paxlook.com) concepts on AWS.
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In this post, 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 variations of the designs too.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a big language model (LLM) developed by DeepSeek [AI](https://servergit.itb.edu.ec) that utilizes support discovering to improve reasoning abilities through a [multi-stage training](https://studiostilesandtotalfitness.com) [procedure](https://git.pleasantprogrammer.com) from a DeepSeek-V3-Base structure. An essential differentiating function is its reinforcement learning (RL) action, which was utilized to improve the model's responses beyond the basic pre-training and tweak procedure. By including RL, DeepSeek-R1 can adapt more successfully to user feedback and goals, eventually enhancing both importance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) technique, [meaning](http://kiwoori.com) it's geared up to break down complex questions and reason through them in a detailed way. This assisted reasoning process permits the model to produce more precise, transparent, and detailed answers. This design integrates RL-based fine-tuning with CoT capabilities, aiming to generate structured responses while concentrating on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has actually recorded the industry's attention as a versatile text-generation design that can be integrated into different workflows such as representatives, rational thinking and data interpretation jobs.
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DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture [permits activation](http://appleacademy.kr) of 37 billion specifications, enabling efficient inference by routing questions to the most appropriate expert "clusters." This method permits the model to specialize in different problem domains while maintaining total performance. 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 to release the model. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.
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DeepSeek-R1 distilled designs bring the reasoning abilities of the main R1 design to more effective architectures based upon [popular](https://www.jobsires.com) open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a procedure of training smaller sized, more effective designs to mimic the habits and thinking patterns of the larger DeepSeek-R1 design, [utilizing](http://barungogi.com) it as an instructor design.
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You can release DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we recommend deploying this model with guardrails in place. In this blog, we will use Amazon Bedrock Guardrails to present safeguards, avoid hazardous material, and evaluate models against crucial security requirements. At the time of [composing](http://www.getfundis.com) this blog site, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create several [guardrails](https://gitlab.isc.org) tailored to various usage cases and apply them to the DeepSeek-R1 design, improving user experiences and standardizing safety controls across your generative [AI](http://66.85.76.122:3000) applications.
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Prerequisites
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To release the DeepSeek-R1 design, you require access to an ml.p5e [instance](https://git.cbcl7.com). To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick Amazon SageMaker, and verify you're utilizing ml.p5e.48 xlarge for endpoint usage. Make certain that you have at least one ml.P5e.48 xlarge circumstances in the AWS Region you are deploying. To request a limit boost, develop a limit increase request and [raovatonline.org](https://raovatonline.org/author/gailziegler/) 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 proper AWS Identity and Gain Access To Management (IAM) authorizations to use Amazon Bedrock Guardrails. For guidelines, see Establish authorizations to [utilize](http://hmkjgit.huamar.com) [guardrails](https://121gamers.com) for material 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 material, and examine designs against [essential safety](https://www.joboptimizers.com) criteria. You can [execute security](https://sportsprojobs.net) steps for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to examine user inputs and design responses released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce 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 [basic flow](https://crownmatch.com) includes the following actions: First, the system gets an input for the model. This input is then processed through the [ApplyGuardrail API](https://www.pkgovtjobz.site). If the input passes the guardrail check, it's sent to the design for inference. After receiving the design's output, another guardrail check is used. If the output passes this last check, it's returned as the result. However, if either the input or output is intervened by the guardrail, a message is returned indicating the nature of the intervention and whether it happened at the input or output phase. 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 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, complete the following actions:
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1. On the Amazon Bedrock console, pick Model brochure under Foundation designs in the navigation pane. +At the time of writing this post, you can use the InvokeModel API to conjure up the model. It doesn't support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a supplier and pick the DeepSeek-R1 model.
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The design detail page offers important [details](http://47.100.72.853000) about the [design's](http://xingyunyi.cn3000) abilities, rates structure, and execution guidelines. You can discover detailed usage guidelines, consisting of sample API calls and code snippets for combination. The model supports different text generation tasks, [including material](http://120.36.2.2179095) production, code generation, and question answering, using its reinforcement discovering optimization and CoT reasoning abilities. +The page likewise consists of deployment options and licensing 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 [prompted](https://jobsubscribe.com) to configure the release details for DeepSeek-R1. The model ID will be pre-populated. +4. For Endpoint name, enter an endpoint name (between 1-50 alphanumeric characters). +5. For Number of circumstances, go into a number of circumstances (between 1-100). +6. For Instance type, choose your circumstances type. For optimal performance 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 personal cloud (VPC) networking, service function consents, and file encryption settings. For most use cases, the default settings will work well. However, for production implementations, you may desire to examine these settings to line up with your organization's security and compliance requirements. +7. Choose Deploy to start using the model.
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When the implementation is complete, you can test DeepSeek-R1's abilities straight in the Amazon Bedrock playground. +8. Choose Open in playground to access an interactive user interface where you can experiment with different triggers and adjust design criteria like temperature level and optimum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for optimum outcomes. For example, content for reasoning.
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This is an excellent method to check out the model's thinking and text generation abilities before integrating it into your applications. The playground supplies immediate feedback, helping you comprehend how the model reacts to numerous inputs and letting you tweak 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 released model programmatically with any Amazon Bedrock APIs, you require to get the [endpoint ARN](http://tesma.co.kr).
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Run inference using guardrails with the deployed 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 create a guardrail using the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo. After you have actually developed the guardrail, use the following code to execute guardrails. The script initializes the bedrock_runtime customer, configures inference criteria, and sends out a request to create 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, integrated algorithms, and prebuilt ML options that you can release with just a few clicks. With [SageMaker](https://social.instinxtreme.com) JumpStart, you can tailor [gratisafhalen.be](https://gratisafhalen.be/author/lucindagipp/) pre-trained models 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 design through SageMaker JumpStart uses 2 practical approaches: using the user-friendly SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's explore both [techniques](http://gitea.infomagus.hu) to assist you select the method that finest suits 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 console, pick Studio in the navigation pane. +2. First-time users will be prompted to produce a domain. +3. On the SageMaker Studio console, pick JumpStart in the navigation pane.
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The model web browser shows available models, with details like the company name and design abilities.
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4. Search for DeepSeek-R1 to view the DeepSeek-R1 design card. +Each model card reveals key details, consisting of:
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- Model name +- Provider name +- Task classification (for example, Text Generation). +Bedrock Ready badge (if appropriate), indicating that this model can be signed up with Amazon Bedrock, enabling you to utilize Amazon Bedrock APIs to conjure up the design
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5. Choose the model card to view the design details page.
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The design details page includes the following details:
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- The model name and supplier details. +Deploy button to release the model. +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 requirements. +- Usage guidelines
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Before you release the design, it's recommended to the design details and license terms to confirm compatibility with your usage case.
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6. Choose Deploy to continue with deployment.
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7. For Endpoint name, utilize the immediately generated name or produce a customized one. +8. For example type ΒΈ select an instance type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, go into the variety of instances (default: 1). +Selecting suitable [instance](http://218.17.2.1033000) types and counts is essential for cost and performance optimization. Monitor your deployment to change these settings as needed.Under Inference type, Real-time inference is selected by default. This is optimized for sustained traffic and low latency. +10. Review all setups for accuracy. For [wiki.dulovic.tech](https://wiki.dulovic.tech/index.php/User:BernieTovar105) this model, we highly advise adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in place. +11. Choose Deploy to release the design.
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The deployment process can take several minutes to finish.
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When release is complete, your endpoint status will change to InService. At this moment, the model is ready to accept inference requests through the endpoint. You can keep an eye on the implementation development on the SageMaker console Endpoints page, [oeclub.org](https://oeclub.org/index.php/User:RebekahOSullivan) which will display relevant metrics and status details. When the deployment is total, you can [conjure](https://git.esc-plus.com) up the design using a SageMaker runtime customer and [incorporate](https://www.execafrica.com) it with your applications.
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Deploy DeepSeek-R1 using the [SageMaker Python](https://i10audio.com) SDK
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To get begun with DeepSeek-R1 utilizing the SageMaker Python SDK, you will need to install the SageMaker Python SDK and make certain you have the essential AWS approvals and environment setup. The following is a [detailed code](http://qiriwe.com) example that demonstrates how to release and utilize DeepSeek-R1 for reasoning programmatically. The code for deploying the model is [supplied](https://git.andrewnw.xyz) in the Github here. You can clone the note pad 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 inference with your SageMaker JumpStart predictor
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Similar to Amazon Bedrock, you can also utilize the [ApplyGuardrail API](https://repo.myapps.id) with your SageMaker JumpStart predictor. You can develop a guardrail using the Amazon Bedrock console or the API, and [gratisafhalen.be](https://gratisafhalen.be/author/danarawson/) implement it as revealed in the following code:
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Clean up
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To prevent unwanted charges, finish the steps in this section to tidy up your [resources](http://xn--ok0bw7u60ff7e69dmyw.com).
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Delete the Amazon Bedrock [Marketplace](https://git.thetoc.net) implementation
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If you released the model utilizing Amazon Bedrock Marketplace, complete the following actions:
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1. On the Amazon Bedrock console, under Foundation models in the navigation pane, choose Marketplace [implementations](https://degroeneuitzender.nl). +2. In the Managed implementations section, locate the endpoint you want to erase. +3. Select the endpoint, and on the Actions menu, [choose Delete](https://git.partners.run). +4. Verify the endpoint details to make certain you're erasing the appropriate release: 1. Endpoint name. +2. Model name. +3. [Endpoint](https://77.248.49.223000) status
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Delete the SageMaker JumpStart predictor
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The SageMaker JumpStart design you released will sustain expenses if you leave it [running](http://39.99.158.11410080). 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 explored how you can access and release the DeepSeek-R1 model using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker [JumpStart](https://ruofei.vip) in SageMaker Studio or Amazon Bedrock Marketplace now to begin. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, 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](https://jobs.assist-staffing.com) Architect for Inference at AWS. He assists emerging generative [AI](https://geniusactionblueprint.com) companies develop innovative solutions utilizing AWS services and sped up compute. Currently, he is focused on developing strategies for fine-tuning and enhancing the reasoning performance of large language designs. In his totally free time, Vivek delights in hiking, viewing motion pictures, and attempting different foods.
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Niithiyn Vijeaswaran is a Generative [AI](https://xn--939a42kg7dvqi7uo.com) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His area of focus is AWS [AI](https://pattonlabs.com) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer [technology](https://git.hxps.ru) and Bioinformatics.
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Jonathan Evans is a Professional Solutions Architect working on generative [AI](https://superblock.kr) with the Third-Party Model [Science](https://right-fit.co.uk) team at AWS.
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Banu Nagasundaram leads item, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://learninghub.fulljam.com) hub. She is enthusiastic about developing options that assist consumers accelerate their [AI](https://fcschalke04fansclub.com) journey and unlock company worth.
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