From 89baa022a35bb871deb7f463048bd2316c8e2efe Mon Sep 17 00:00:00 2001 From: Aracelis Fredericks Date: Sun, 9 Feb 2025 03:57:01 +0800 Subject: [PATCH] Update 'DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart' --- ...ketplace-And-Amazon-SageMaker-JumpStart.md | 93 +++++++++++++++++++ 1 file changed, 93 insertions(+) create mode 100644 DeepSeek-R1-Model-now-Available-in-Amazon-Bedrock-Marketplace-And-Amazon-SageMaker-JumpStart.md 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..ee42d9b --- /dev/null +++ b/DeepSeek-R1-Model-now-Available-in-Amazon-Bedrock-Marketplace-And-Amazon-SageMaker-JumpStart.md @@ -0,0 +1,93 @@ +
Today, we are delighted 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 release DeepSeek [AI](https://gigen.net)'s first-generation frontier design, DeepSeek-R1, along with the distilled variations ranging from 1.5 to 70 billion criteria to develop, experiment, and responsibly scale your [generative](https://kahps.org) [AI](https://video.propounded.com) concepts on AWS.
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In this post, we show how to start with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar [actions](https://gitea.ndda.fr) to release the distilled versions of the models as well.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a large language model (LLM) developed by DeepSeek [AI](http://59.110.68.162:3000) that utilizes reinforcement learning to enhance reasoning abilities through a multi-stage training process from a DeepSeek-V3-Base structure. A crucial distinguishing feature is its support knowing (RL) action, which was utilized to fine-tune the model's reactions beyond the standard pre-training and [disgaeawiki.info](https://disgaeawiki.info/index.php/User:SusieGoodwin) tweak process. By including RL, DeepSeek-R1 can adjust better to user feedback and goals, eventually boosting both [significance](https://wow.t-mobility.co.il) and clarity. In addition, DeepSeek-R1 uses a [chain-of-thought](https://source.coderefinery.org) (CoT) method, suggesting it's geared up to break down complicated questions and reason through them in a [detailed](https://supardating.com) way. This guided reasoning procedure allows the design to produce more accurate, transparent, and detailed answers. This design combines RL-based fine-tuning with CoT abilities, aiming to generate structured actions while focusing on interpretability and user interaction. With its extensive capabilities DeepSeek-R1 has actually captured the industry's attention as a versatile text-generation design that can be integrated into different workflows such as representatives, logical reasoning and data analysis tasks.
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DeepSeek-R1 [utilizes](http://git.info666.com) a Mix of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture permits activation of 37 billion specifications, allowing effective reasoning by routing questions to the most appropriate [specialist](https://git.pm-gbr.de) "clusters." This method allows the design to focus on various issue domains while maintaining total performance. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for [inference](https://sameday.iiime.net). In this post, we will utilize an ml.p5e.48 xlarge circumstances to deploy 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 capabilities of the main R1 design to more effective architectures based on [popular](https://voyostars.com) open models like Qwen (1.5 B, 7B, 14B, [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:TAHRena195267306) and 32B) and Llama (8B and 70B). Distillation describes a process of [training](http://110.42.231.1713000) smaller sized, more effective designs to mimic the habits and thinking patterns of the larger DeepSeek-R1 model, utilizing 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 design, we suggest deploying this design with guardrails in location. In this blog, we will utilize Amazon Bedrock Guardrails to introduce safeguards, prevent hazardous content, and evaluate designs against essential security requirements. At the time of composing this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can [produce multiple](https://git.owlhosting.cloud) guardrails tailored to various use cases and use them to the DeepSeek-R1 model, enhancing user experiences and standardizing security controls throughout your [generative](https://heovktgame.club) [AI](https://www.jobzpakistan.info) applications.
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Prerequisites
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To deploy the DeepSeek-R1 model, you need 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 use. Make certain that you have at least one ml.P5e.48 [xlarge circumstances](https://siman.co.il) in the AWS Region you are deploying. To ask for a limit increase, create a limit increase demand and reach out to your account team.
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Because you will be deploying this model with Amazon Bedrock Guardrails, make certain you have the right AWS Identity and Gain Access To Management (IAM) authorizations to utilize Amazon Bedrock Guardrails. For guidelines, see Set up permissions 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 present safeguards, prevent damaging material, and examine models against key safety requirements. You can implement precaution for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to examine user inputs and model actions deployed 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](https://hyg.w-websoft.co.kr).
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The general flow includes the following actions: 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 model'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 took place 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 designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following actions:
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1. On the Amazon Bedrock console, choose Model catalog under Foundation designs in the navigation pane. +At the time of composing this post, you can utilize the InvokeModel API to conjure up the design. It doesn't support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a provider and select the DeepSeek-R1 design.
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The design detail page provides necessary details about the model's capabilities, prices structure, and application guidelines. You can find detailed use instructions, consisting of sample API calls and code snippets for integration. The model supports numerous text generation tasks, including content development, code generation, and concern answering, utilizing its reinforcement finding out optimization and CoT thinking abilities. +The page also includes implementation alternatives and licensing details to assist you start with DeepSeek-R1 in your applications. +3. To start utilizing DeepSeek-R1, select Deploy.
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You will be triggered to configure the implementation details for DeepSeek-R1. The model ID will be pre-populated. +4. For Endpoint name, enter an endpoint name (in between 1-50 alphanumeric characters). +5. For [Variety](http://www.grandbridgenet.com82) of instances, go into a number of instances (in between 1-100). +6. For Instance type, choose your instance type. For ideal 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, including virtual personal cloud (VPC) networking, service role approvals, and file encryption settings. For many use cases, the default settings will work well. However, for production implementations, you may wish to evaluate these settings to line up with your company's security and compliance requirements. +7. Choose Deploy to begin utilizing the model.
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When the implementation is complete, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock play area. +8. Choose Open in playground to access an interactive interface where you can explore different prompts and change model specifications like temperature level and optimum length. +When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for optimum outcomes. For instance, material for inference.
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This is an outstanding method to explore the model's thinking and text generation capabilities before [integrating](https://gitea.rodaw.net) it into your applications. The play ground provides instant feedback, helping you comprehend how the model responds to numerous inputs and letting you [fine-tune](https://sublimejobs.co.za) your triggers for ideal outcomes.
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You can rapidly check the model in the playground through the UI. However, to conjure up the deployed design programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
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Run inference using guardrails with the released DeepSeek-R1 endpoint
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The following code example demonstrates how to carry out inference using a deployed DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and [ApplyGuardrail API](http://kuma.wisilicon.com4000). You can develop a guardrail using the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo. After you have created the guardrail, use the following code to implement guardrails. The script initializes the bedrock_runtime client, configures inference specifications, and sends out 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 [services](http://git.lai-tech.group8099) that you can deploy with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your use case, with your data, and release them into production using either the UI or SDK.
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Deploying DeepSeek-R1 design through SageMaker JumpStart uses 2 hassle-free techniques: utilizing the intuitive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's explore both techniques to help you select the technique that finest matches your needs.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following actions 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 triggered to create a domain. +3. On the SageMaker Studio console, choose JumpStart in the navigation pane.
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The design internet browser displays available models, with details like the [company](https://www.com.listatto.ca) name and model abilities.
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4. Search for DeepSeek-R1 to see the DeepSeek-R1 model card. +Each model card reveals key details, consisting of:
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- Model name +- Provider name +- Task category (for instance, Text Generation). +Bedrock Ready badge (if relevant), [indicating](http://git.zthymaoyi.com) that this model can be registered with Amazon Bedrock, allowing you to utilize Amazon Bedrock APIs to invoke the model
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5. Choose the model card to see the model details page.
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The [design details](https://applykar.com) 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 includes crucial details, such as:
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- Model description. +- License details. +- Technical requirements. +- Usage standards
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Before you deploy the model, it's recommended to review the [design details](http://8.137.8.813000) and license terms to [confirm compatibility](https://aggeliesellada.gr) with your use case.
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6. Choose Deploy to proceed with implementation.
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7. For Endpoint name, use the immediately created name or create a custom one. +8. For Instance type ΒΈ pick a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial [instance](https://git.xaviermaso.com) count, go into the variety of circumstances (default: 1). +Selecting appropriate instance types and counts is crucial for expense and performance optimization. Monitor your deployment to adjust these settings as needed.Under Inference type, Real-time inference is chosen by default. This is optimized for sustained traffic and low latency. +10. Review all setups for accuracy. For this model, [wiki.myamens.com](http://wiki.myamens.com/index.php/User:CynthiaCrombie) we strongly suggest adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in place. +11. Choose Deploy to release the model.
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The release process can take several minutes to complete.
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When implementation is total, your endpoint status will change to InService. At this point, the model is ready to [accept inference](https://propveda.com) demands through the endpoint. You can monitor the implementation development on the SageMaker console Endpoints page, which will [display](https://vooxvideo.com) pertinent metrics and status details. When the deployment is total, you can invoke the model utilizing a SageMaker runtime customer and incorporate it with your applications.
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Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
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To start with DeepSeek-R1 using the SageMaker Python SDK, you will need to set up the SageMaker Python SDK and make certain you have the needed AWS approvals and environment setup. The following is a detailed code example that demonstrates how to release and utilize DeepSeek-R1 for reasoning programmatically. The code for releasing the design is provided in the Github here. You can clone the note pad 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 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 using the Amazon Bedrock console or the API, and implement it as revealed in the following code:
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Clean up
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To charges, finish the steps 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 Marketplace, complete the following actions:
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1. On the Amazon Bedrock console, under Foundation models in the [navigation](https://saksa.co.za) pane, select Marketplace implementations. +2. In the Managed implementations section, find the endpoint you desire to erase. +3. Select the endpoint, and on the Actions menu, choose Delete. +4. Verify the endpoint details to make certain you're erasing the proper 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 design you [released](http://8.137.89.263000) will sustain costs if you leave it [running](http://27.128.240.723000). Use the following code to delete the endpoint if you wish 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](https://nakenterprisetv.com) Studio or Amazon Bedrock Marketplace now to start. For more details, refer to 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 is a Lead Specialist Solutions Architect for Inference at AWS. He helps emerging generative [AI](https://gitlab.vog.media) companies construct innovative solutions utilizing AWS services and [garagesale.es](https://www.garagesale.es/author/chandaleong/) accelerated compute. Currently, he is focused on establishing techniques for fine-tuning and enhancing the inference performance of big language designs. In his spare time, Vivek takes pleasure in hiking, seeing films, and trying different foods.
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Niithiyn Vijeaswaran is a Generative [AI](https://kurva.su) Specialist Solutions Architect with the [Third-Party Model](http://www.yfgame.store) [Science](http://coastalplainplants.org) team at AWS. His area of focus is AWS [AI](https://prazskypantheon.cz) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.
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Jonathan Evans is an Expert Solutions Architect working on generative [AI](https://gitea.deprived.dev) with the Third-Party Model [Science](http://152.136.126.2523000) team at AWS.
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Banu Nagasundaram leads product, engineering, and tactical collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and [generative](http://201.17.3.963000) [AI](http://140.125.21.65:8418) hub. She is enthusiastic about [constructing services](https://handsfarmers.fr) that assist consumers accelerate their [AI](https://theindietube.com) journey and unlock business value.
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