commit 56cf4e86dc7af0ee4e2eb2b21df28f7fc024c9ad Author: ceceliaschiffe Date: Fri Feb 7 14:26:20 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..a1633ca --- /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 designs are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](https://134.209.236.143)'s first-generation frontier model, DeepSeek-R1, along with the [distilled versions](http://120.77.205.309998) ranging from 1.5 to 70 billion criteria to construct, experiment, and responsibly scale your generative [AI](http://121.5.25.246:3000) 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 deploy the distilled variations of the models also.
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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://git.youxiner.com) that utilizes reinforcement discovering to [improve thinking](https://maibuzz.com) abilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. An essential identifying feature is its reinforcement learning (RL) action, which was utilized to refine the model's responses beyond the basic pre-training and fine-tuning process. By incorporating RL, DeepSeek-R1 can adjust more effectively to user feedback and objectives, ultimately enhancing both importance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) method, meaning it's geared up to break down intricate questions and reason through them in a detailed manner. This assisted thinking procedure allows the design to [produce](https://git.creeperrush.fun) more accurate, transparent, and detailed answers. This model combines RL-based [fine-tuning](http://zeus.thrace-lan.info3000) with CoT abilities, aiming to produce structured actions while focusing on interpretability and [garagesale.es](https://www.garagesale.es/author/kierakeys13/) user interaction. With its extensive abilities DeepSeek-R1 has actually recorded the market's attention as a versatile text-generation design that can be integrated into different workflows such as representatives, rational reasoning and information analysis 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 criteria, [archmageriseswiki.com](http://archmageriseswiki.com/index.php/User:KarissaGleason) making it possible for effective inference by routing questions to the most pertinent expert "clusters." This approach allows the design to concentrate on different issue domains while maintaining overall performance. DeepSeek-R1 requires a minimum of 800 GB of [HBM memory](https://www.flirtywoo.com) in FP8 format for [reasoning](https://parentingliteracy.com). In this post, we will use an ml.p5e.48 xlarge circumstances to deploy the design. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.
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DeepSeek-R1 distilled models bring the thinking capabilities of the main R1 design to more effective architectures based on popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a process of training smaller sized, more effective models to imitate the behavior and reasoning patterns of the larger DeepSeek-R1 design, utilizing it as a teacher design.
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You can release 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 place. In this blog site, we will use Amazon Bedrock Guardrails to present safeguards, prevent damaging material, and evaluate models against crucial safety criteria. At the time of writing this blog site, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails [supports](https://dandaelitetransportllc.com) only the ApplyGuardrail API. You can produce numerous guardrails tailored to different use cases and use them to the DeepSeek-R1 model, improving user experiences and standardizing safety controls across your generative [AI](http://121.4.154.189:3000) 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 inspect 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](http://209.87.229.347080) use. Make certain that you have at least one ml.P5e.48 xlarge instance in the AWS Region you are deploying. To ask for a limit increase, develop a limitation increase demand and connect to your account team.
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Because you will be releasing this model with Amazon Bedrock Guardrails, make certain you have the correct AWS Identity and Gain Access To Management (IAM) authorizations to use Amazon Bedrock Guardrails. For directions, see Set up authorizations to use guardrails 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 key security requirements. You can execute precaution for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to examine user inputs and model responses deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a guardrail using the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo.
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The basic circulation includes the following steps: First, the system gets an input for the design. This input is then processed through the [ApplyGuardrail API](https://git.novisync.com). If the input passes the guardrail check, it's sent to the design 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 outcome. However, if either the input or output is intervened by the guardrail, a message is returned showing the nature of the intervention and whether it took place at the input or [output phase](https://www.cbl.health). The examples showcased in the following sections show 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 structure designs (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 catalog under Foundation designs in the navigation pane. +At the time of composing this post, you can use the InvokeModel API to conjure up the model. It doesn't support Converse APIs and other [Amazon Bedrock](http://git.datanest.gluc.ch) tooling. +2. Filter for DeepSeek as a provider and select the DeepSeek-R1 design.
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The model detail page provides vital details about the design's abilities, pricing structure, and execution standards. You can find detailed use guidelines, consisting of sample API calls and code snippets for combination. The design supports various text generation jobs, including [material](http://gitlab.ileadgame.net) creation, [larsaluarna.se](http://www.larsaluarna.se/index.php/User:RosauraYcm) code generation, and concern answering, utilizing its support learning optimization and CoT reasoning capabilities. +The page likewise consists of release choices and licensing details to assist you start with DeepSeek-R1 in your applications. +3. To begin utilizing DeepSeek-R1, pick Deploy.
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You will be triggered to configure 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 characters). +5. For Variety of circumstances, go into a number of circumstances (between 1-100). +6. For Instance type, choose your instance type. For optimum performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is advised. +Optionally, you can set up advanced security and facilities settings, including virtual personal cloud (VPC) networking, service function permissions, and encryption settings. For most utilize cases, the default settings will work well. However, for production implementations, you may want to review these settings to align with your company's security and compliance requirements. +7. Choose Deploy to begin using the model.
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When the implementation is complete, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock play area. +8. Choose Open in playground to access an interactive interface where you can experiment with different triggers and adjust design criteria like temperature level and optimum length. +When using 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 excellent method to explore the design's thinking and text generation abilities before incorporating it into your applications. The play area supplies immediate feedback, assisting you understand how the design reacts to different inputs and letting you tweak your triggers for optimal results.
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You can rapidly test the design in the play area through the UI. However, to conjure up the released model programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
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Run reasoning utilizing guardrails with the released DeepSeek-R1 endpoint
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The following code example demonstrates how to perform reasoning using a released DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can develop a [guardrail utilizing](http://101.34.228.453000) the Amazon [Bedrock console](http://idesys.co.kr) or the API. For the example code to create 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, configures inference specifications, and sends a demand to create text based upon 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 services that you can deploy with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage case, with your data, and [release](http://58.34.54.469092) them into production utilizing either the UI or SDK.
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Deploying DeepSeek-R1 design through SageMaker JumpStart uses 2 practical methods: using the user-friendly SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's explore both techniques to assist you choose the technique that finest fits 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, select Studio in the navigation pane. +2. First-time users will be triggered to develop a domain. +3. On the SageMaker Studio console, choose JumpStart in the navigation pane.
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The model web browser displays available designs, with details like the supplier name and design abilities.
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4. Look for DeepSeek-R1 to see the DeepSeek-R1 design card. +Each model card shows key details, including:
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- Model name +- Provider name +- Task category (for instance, Text Generation). +Bedrock Ready badge (if applicable), indicating that this model can be registered with Amazon Bedrock, permitting you to use Amazon Bedrock APIs to invoke the design
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5. Choose the design card to see the model details page.
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The [model details](https://woodsrunners.com) page includes the following details:
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- The design 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 details, such as:
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- Model description. +- License [details](https://social.acadri.org). +- Technical specifications. +- Usage guidelines
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Before you release the model, it's recommended to review the design details and license terms to confirm compatibility with your use case.
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6. Choose Deploy to proceed with deployment.
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7. For Endpoint name, utilize the instantly produced name or produce a custom-made one. +8. For Instance type ΒΈ choose a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, enter the number of circumstances (default: 1). +Selecting suitable circumstances types and counts is essential for cost and efficiency optimization. Monitor your release to adjust these settings as needed.Under Inference type, Real-time reasoning is selected by default. This is enhanced for [sustained traffic](http://35.207.205.183000) and low [latency](https://gitea.johannes-hegele.de). +10. Review all setups for precision. For this model, we highly advise sticking to SageMaker JumpStart [default](http://47.110.248.4313000) settings and making certain that network isolation remains in location. +11. Choose Deploy to release the model.
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The release process can take numerous 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 requests through the endpoint. You can keep track of the deployment development on the SageMaker console Endpoints page, which will display pertinent metrics and status details. When the deployment is complete, you can invoke the model 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 start with DeepSeek-R1 utilizing the SageMaker Python SDK, you will need to set up the SageMaker Python SDK and make certain you have the necessary AWS consents and [environment setup](http://101.33.225.953000). The following is a detailed code example that demonstrates how to release and use DeepSeek-R1 for reasoning programmatically. The code for deploying the model is offered in the Github here. You can clone the note pad and range from SageMaker Studio.
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You can run additional 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 utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can create a guardrail utilizing the Amazon Bedrock console or the API, and implement it as shown 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 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, total the following actions:
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1. On the Amazon Bedrock console, under [Foundation models](https://source.futriix.ru) in the navigation pane, pick Marketplace implementations. +2. In the Managed releases area, locate the endpoint you wish to erase. +3. Select the endpoint, and on the Actions menu, pick Delete. +4. Verify the endpoint details to make certain you're erasing the correct release: 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 expenses if you leave it running. Use the following code to erase the endpoint if you desire 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 deploy 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, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Starting 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 generative [AI](https://www.lotusprotechnologies.com) business develop innovative solutions using AWS services and accelerated calculate. Currently, he is focused on developing strategies for [fine-tuning](http://git.setech.ltd8300) and enhancing the reasoning performance of big language models. In his spare time, Vivek takes pleasure in treking, enjoying films, and trying different cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://wiki.team-glisto.com) Specialist Solutions Architect with the Third-Party Model [Science](https://maibuzz.com) team at AWS. His area of focus is AWS [AI](https://ready4hr.com) [accelerators](https://cosplaybook.de) (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.
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Jonathan Evans is an Expert Solutions Architect dealing with generative [AI](https://git.noisolation.com) with the Third-Party Model Science group at AWS.
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[Banu Nagasundaram](https://test.gamesfree.ca) leads item, engineering, and strategic collaborations for Amazon SageMaker JumpStart, [SageMaker's artificial](https://gitlab.syncad.com) intelligence and generative [AI](https://gofleeks.com) center. She is passionate about constructing services that help customers accelerate their [AI](https://code.estradiol.cloud) journey and unlock business value.
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