commit d45baac044e2ae11830b6f1c9a4bd54bb27de299 Author: coletteingamel Date: Fri Apr 11 21:57:19 2025 +0900 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..8b165b0 --- /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](https://www.workinternational-df.com) to reveal 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://xn--289an1ad92ak6p.com)'s first-generation frontier design, DeepSeek-R1, along with the distilled versions ranging from 1.5 to 70 billion [criteria](https://jvptube.net) to construct, experiment, and properly scale your generative [AI](http://www.fasteap.cn:3000) concepts on AWS.
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In this post, we demonstrate how to begin with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar steps to deploy the distilled versions 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](http://121.36.27.6:3000) that utilizes reinforcement discovering to improve thinking capabilities through a multi-stage training process from a DeepSeek-V3-Base foundation. An essential identifying feature is its reinforcement knowing (RL) step, which was [utilized](http://dchain-d.com3000) to refine the model's actions beyond the standard pre-training and fine-tuning process. By including RL, DeepSeek-R1 can adapt more [efficiently](https://ubuntushows.com) to user feedback and goals, eventually enhancing both significance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) method, meaning it's geared up to break down complex inquiries and reason through them in a detailed way. This assisted thinking procedure allows the model to produce more precise, transparent, and detailed answers. This model integrates RL-based fine-tuning with CoT capabilities, aiming to create structured responses while focusing on interpretability and user [interaction](https://www.wtfbellingham.com). With its [extensive abilities](https://code.webpro.ltd) DeepSeek-R1 has caught the industry's attention as a flexible text-generation design that can be integrated into different workflows such as representatives, rational reasoning and data analysis tasks.
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DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture allows activation of 37 billion parameters, allowing efficient reasoning by routing questions to the most appropriate professional "clusters." This technique enables the model to specialize in different problem domains while maintaining total effectiveness. DeepSeek-R1 needs 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 release the model. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs offering 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 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 models to [simulate](https://git.goolink.org) the habits and thinking patterns of the bigger DeepSeek-R1 model, using it as an instructor model.
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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 advise deploying this design with guardrails in place. In this blog, we will use Amazon Bedrock Guardrails to present safeguards, prevent damaging content, and examine models against key [security criteria](https://kahps.org). At the time of writing this blog, [higgledy-piggledy.xyz](https://higgledy-piggledy.xyz/index.php/User:Lionel86Y523674) for DeepSeek-R1 deployments on SageMaker JumpStart and [Bedrock](https://gogs.dev.dazesoft.cn) Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can create several guardrails tailored to different use cases and use them to the DeepSeek-R1 model, enhancing user experiences and standardizing safety controls throughout your generative [AI](http://pinetree.sg) 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 examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, select Amazon SageMaker, and validate 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 deploying. To ask for a limitation increase, develop a limitation boost request and reach out to your account team.
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Because you will be releasing this design with Amazon Bedrock Guardrails, make certain you have the appropriate [AWS Identity](http://lespoetesbizarres.free.fr) and Gain Access To Management (IAM) consents to utilize Amazon Bedrock Guardrails. For directions, see [Establish](https://jobs.askpyramid.com) authorizations to utilize guardrails for material filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails permits you to introduce safeguards, avoid harmful content, and evaluate designs against crucial safety requirements. You can execute security procedures for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to evaluate user inputs and design actions 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 circulation includes the following steps: First, the system gets an input for the model. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it's sent to the model for inference. After receiving the model's output, another [guardrail check](https://gogs.rg.net) is applied. If the output passes this final 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 inference utilizing this API.
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Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
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[Amazon Bedrock](https://jobs.askpyramid.com) Marketplace gives you access to over 100 popular, emerging, and specialized foundation designs (FMs) through [Amazon Bedrock](http://slfood.co.kr). 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 models in the navigation pane. +At the time of writing this post, you can utilize the InvokeModel API to invoke the model. It does not [support Converse](http://luodev.cn) APIs and other Amazon Bedrock tooling. +2. Filter for [DeepSeek](https://git.devinmajor.com) as a service provider and choose the DeepSeek-R1 design.
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The design detail page offers necessary [details](http://www.hakyoun.co.kr) about the design's abilities, pricing structure, and execution standards. You can discover detailed use instructions, including sample API calls and code snippets for combination. The model supports numerous text generation jobs, consisting of material production, code generation, and question answering, utilizing its support finding out optimization and CoT thinking capabilities. +The page likewise includes deployment alternatives and licensing details to assist you start with DeepSeek-R1 in your [applications](https://tuxpa.in). +3. To start utilizing DeepSeek-R1, select Deploy.
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You will be triggered to configure the deployment details for DeepSeek-R1. The design ID will be pre-populated. +4. For Endpoint name, get in an endpoint name (in between 1-50 alphanumeric characters). +5. For Number of circumstances, go into a variety of instances (between 1-100). +6. For example type, pick your instance type. For ideal performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is suggested. +Optionally, you can set up innovative security and infrastructure settings, including virtual personal cloud (VPC) networking, service role permissions, and file encryption settings. For many use cases, the default settings will work well. However, [pipewiki.org](https://pipewiki.org/wiki/index.php/User:LynwoodBolling) for production releases, you might desire to examine 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 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 various triggers and change design parameters 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 instance, content for reasoning.
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This is an outstanding way to explore the model's thinking and [wavedream.wiki](https://wavedream.wiki/index.php/User:KristyMccartney) text generation capabilities before integrating it into your applications. The play area offers immediate feedback, helping you comprehend how the design reacts to various inputs and letting you tweak your triggers for optimal results.
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You can quickly check the design in the play ground through the UI. However, to invoke the [released design](https://www.menacopt.com) programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
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Run reasoning using guardrails with the released DeepSeek-R1 endpoint
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The following code example demonstrates how to perform reasoning utilizing a deployed DeepSeek-R1 design 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 create the guardrail, see the GitHub repo. After you have actually created the guardrail, utilize the following code to implement guardrails. The script initializes the bedrock_[runtime](https://bpx.world) customer, sets up inference parameters, and sends out a demand to generate 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 solutions that you can deploy with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your information, and [release](https://hellovivat.com) them into production utilizing either the UI or SDK.
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Deploying DeepSeek-R1 design through SageMaker JumpStart offers 2 hassle-free techniques: utilizing the user-friendly SageMaker JumpStart UI or carrying out programmatically through the [SageMaker Python](https://phones2gadgets.co.uk) SDK. Let's [explore](https://jobs.cntertech.com) both approaches to assist you pick the technique that finest suits your requirements.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following [actions](http://krzsyjtj.zlongame.co.kr9004) to deploy 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 triggered to produce a domain. +3. On the SageMaker Studio console, pick JumpStart in the navigation pane.
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The design internet browser shows available models, with details like the supplier name and model abilities.
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4. Search for DeepSeek-R1 to see the DeepSeek-R1 model card. +Each model card shows key details, consisting of:
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- Model name +- Provider name +- Task classification (for example, Text Generation). +Bedrock Ready badge (if applicable), indicating that this design can be registered with Amazon Bedrock, permitting you to utilize Amazon [Bedrock APIs](https://www.cbtfmytube.com) to invoke the model
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5. Choose the model card to see the model details page.
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The model details page includes 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 consists of essential 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 advised to evaluate 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, use the instantly generated name or develop a custom-made one. +8. For example type ΒΈ pick a circumstances type (default: ml.p5e.48 xlarge). +9. For [Initial circumstances](https://jandlfabricating.com) count, go into the variety of circumstances (default: 1). +Selecting appropriate circumstances types and counts is vital for cost and efficiency optimization. Monitor your implementation to adjust these settings as needed.Under Inference type, [genbecle.com](https://www.genbecle.com/index.php?title=How_Do_Chinese_AI_Bots_Stack_Up_Against_ChatGPT_) Real-time reasoning is chosen by default. This is optimized for [sustained traffic](https://investsolutions.org.uk) and low latency. +10. Review all configurations for accuracy. For this model, we highly recommend sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in place. +11. Choose Deploy to deploy the model.
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The deployment procedure can take a number of minutes to complete.
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When release is total, your endpoint status will alter to InService. At this point, the design is ready to accept reasoning requests through the endpoint. You can monitor the deployment progress on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the deployment is complete, 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 started with DeepSeek-R1 utilizing the SageMaker Python SDK, [archmageriseswiki.com](http://archmageriseswiki.com/index.php/User:GrazynaKoss711) you will need to set up the SageMaker Python SDK and make certain you have the required AWS approvals and [higgledy-piggledy.xyz](https://higgledy-piggledy.xyz/index.php/User:RevaBettis0) environment setup. The following is a detailed code example that demonstrates how to release and use DeepSeek-R1 for reasoning programmatically. The code for releasing the design is offered 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 reasoning with your SageMaker JumpStart predictor
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Similar to Amazon Bedrock, you can also utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a guardrail using the Amazon Bedrock console or the API, and implement it as shown in the following code:
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Clean up
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To avoid undesirable charges, complete the steps in this area to clean up your resources.
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Delete the Amazon Bedrock Marketplace implementation
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If you deployed the model utilizing Amazon Bedrock Marketplace, total the following actions:
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1. On the Amazon Bedrock console, under Foundation models in the navigation pane, pick Marketplace releases. +2. In the area, find the [endpoint](https://ibs3457.com) you wish 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 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](http://git.zthymaoyi.com) design you deployed will sustain costs if you leave it running. Use the following code to delete 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 checked out how you can access and release the DeepSeek-R1 model using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get begun. 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 going with Amazon SageMaker JumpStart.
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About the Authors
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[Vivek Gangasani](https://social.stssconstruction.com) is a Lead Specialist Solutions Architect for Inference at AWS. He helps emerging generative [AI](http://187.216.152.151:9999) companies develop innovative services utilizing AWS services and sped up compute. Currently, he is concentrated on establishing strategies for fine-tuning and optimizing the inference efficiency of large language models. In his leisure time, Vivek enjoys treking, enjoying movies, [systemcheck-wiki.de](https://systemcheck-wiki.de/index.php?title=Benutzer:JulianeStoker8) and trying different foods.
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Niithiyn Vijeaswaran is a Generative [AI](http://fatims.org) Specialist Solutions Architect with the [Third-Party Model](http://8.134.38.1063000) [Science](http://121.40.209.823000) group at AWS. His area of focus is AWS [AI](https://minka.gob.ec) accelerators (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](https://workonit.co) [AI](http://westec-immo.com) with the Third-Party Model Science team at AWS.
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Banu Nagasundaram leads item, engineering, and tactical collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://git.saphir.one) hub. She is passionate about building solutions that [assist consumers](https://usvs.ms) accelerate their [AI](https://git.easytelecoms.fr) journey and unlock business value.
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