commit ca8c02a89984081ec0865ee47efe2beeccdcc8a2 Author: majororourke03 Date: Sat Mar 1 04:17:52 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..018bcf8 --- /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://applykar.com)'s first-generation frontier design, DeepSeek-R1, in addition to the distilled versions ranging from 1.5 to 70 billion criteria to build, experiment, and properly scale your generative [AI](http://api.cenhuy.com:3000) concepts on AWS.
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In this post, we demonstrate how to get begun with DeepSeek-R1 on Amazon Bedrock [Marketplace](https://gurjar.app) and SageMaker JumpStart. You can follow comparable actions to deploy 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 design (LLM) developed by DeepSeek [AI](https://raisacanada.com) that uses support discovering to improve reasoning capabilities through a multi-stage training process from a DeepSeek-V3-Base structure. A crucial distinguishing feature is its support learning (RL) step, which was utilized to improve the model's responses beyond the standard pre-training and tweak procedure. By integrating RL, DeepSeek-R1 can adjust better to user feedback and objectives, ultimately enhancing both importance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) technique, suggesting it's equipped to break down complicated queries and reason through them in a detailed manner. This directed thinking procedure allows the model to produce more precise, transparent, and detailed responses. This model integrates RL-based fine-tuning with CoT capabilities, aiming to generate structured reactions while focusing on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has actually captured the market's attention as a flexible text-generation model that can be integrated into various workflows such as agents, rational thinking and information analysis tasks.
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DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture allows activation of 37 billion parameters, making it possible for efficient reasoning by routing questions to the most appropriate professional "clusters." This method enables the design to concentrate on different issue domains while maintaining general [effectiveness](http://artsm.net). DeepSeek-R1 needs a minimum of 800 GB of [HBM memory](http://git.storkhealthcare.cn) in FP8 format for reasoning. In this post, we will use an ml.p5e.48 xlarge circumstances to release the design. 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 abilities of the main R1 model to more efficient architectures based upon popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a process of training smaller sized, more efficient models to mimic the and reasoning patterns of the bigger DeepSeek-R1 model, utilizing it as a teacher model.
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You can deploy DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we suggest deploying this design with guardrails in place. In this blog, we will [utilize Amazon](https://www.meetgr.com) Bedrock Guardrails to present safeguards, prevent damaging content, and examine models against crucial safety [requirements](https://job.bzconsultant.in). At the time of composing this blog site, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can produce multiple guardrails tailored to different usage cases and use them to the DeepSeek-R1 model, enhancing user experiences and standardizing safety controls throughout your generative [AI](http://api.cenhuy.com:3000) applications.
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Prerequisites
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To release 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, pick Amazon SageMaker, and validate 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 releasing. To request a limit boost, create a limit boost demand and reach out to your account team.
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Because you will be [deploying](http://gitlab.ideabeans.myds.me30000) this design 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 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, prevent hazardous content, and evaluate models against crucial security requirements. You can carry out security measures for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This permits you to use guardrails to assess user inputs and model actions deployed 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 develop 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 [surgiteams.com](https://surgiteams.com/index.php/User:CliftonZ60) reasoning. After getting the model's output, another guardrail check is applied. If the output passes this last check, it's returned as the [final outcome](http://63.32.145.226). However, if either the input or output is intervened by the guardrail, a [message](http://gitlab.nsenz.com) is returned indicating the nature of the intervention and whether it happened at the input or output stage. The examples showcased in the following areas show reasoning utilizing this API.
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Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
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Amazon Bedrock Marketplace gives you access to over 100 popular, emerging, and specialized foundation 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, choose Model catalog under Foundation designs in the navigation pane. +At the time of [writing](http://39.106.8.2463003) this post, you can utilize the InvokeModel API to invoke the design. It does not support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a company and choose the DeepSeek-R1 model.
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The model detail page offers [essential details](https://careers.webdschool.com) about the model's abilities, pricing structure, and execution standards. You can discover detailed usage guidelines, including sample API calls and code bits for [gratisafhalen.be](https://gratisafhalen.be/author/graigchirns/) combination. The model supports different text generation jobs, including content production, code generation, and question answering, using its reinforcement learning optimization and CoT reasoning abilities. +The page also includes release choices and licensing details to help you start with DeepSeek-R1 in your applications. +3. To begin using DeepSeek-R1, pick Deploy.
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You will be triggered to set up the implementation details for DeepSeek-R1. The design ID will be pre-populated. +4. For Endpoint name, enter an endpoint name (in between 1-50 alphanumeric characters). +5. For [Variety](https://www.soundofrecovery.org) of circumstances, go into a variety of instances (in between 1-100). +6. For Instance type, pick your instance type. For ideal performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised. +Optionally, you can configure advanced security and infrastructure settings, consisting of virtual private cloud (VPC) networking, service function permissions, and encryption settings. For many use cases, the default settings will work well. However, for production deployments, you may want to examine these settings to line up with your organization's security and compliance requirements. +7. Choose Deploy to begin utilizing the design.
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When the release is total, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock playground. +8. Choose Open in playground to access an interactive interface where you can explore different prompts and change design criteria like temperature level and optimum length. +When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for ideal results. For instance, material for reasoning.
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This is an exceptional way to explore the model's thinking and text generation capabilities before integrating it into your applications. The play area provides immediate feedback, helping you understand how the model reacts to different inputs and letting you tweak your prompts for ideal results.
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You can quickly check the design in the playground through the UI. However, to invoke the [released design](https://spudz.org) programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
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Run [reasoning](http://47.122.66.12910300) utilizing guardrails with the released DeepSeek-R1 endpoint
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The following code example [demonstrates](https://aladin.tube) how to perform reasoning using a released DeepSeek-R1 model through Amazon Bedrock [utilizing](http://120.55.164.2343000) the invoke_model and ApplyGuardrail API. You can create a guardrail utilizing the Amazon Bedrock [console](https://okk-shop.com) or the API. For the example code to develop 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 customer, configures inference parameters, and sends a demand to [produce text](https://git.qiucl.cn) based on a user timely.
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Deploy DeepSeek-R1 with SageMaker JumpStart
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SageMaker JumpStart is an [artificial intelligence](https://kurva.su) (ML) hub with FMs, built-in algorithms, and prebuilt ML solutions that you can deploy with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use case, with your information, and release them into production utilizing either the UI or SDK.
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[Deploying](https://git.cbcl7.com) DeepSeek-R1 model through SageMaker JumpStart uses 2 hassle-free methods: using the [user-friendly SageMaker](http://greenmk.co.kr) JumpStart UI or carrying out [programmatically](http://git.andyshi.cloud) through the SageMaker Python SDK. Let's check out both methods to help you pick the technique that best matches your requirements.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following actions to release DeepSeek-R1 utilizing SageMaker JumpStart:
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1. On the [SageMaker](http://223.68.171.1508004) console, choose Studio in the navigation pane. +2. First-time users will be triggered to develop a domain. +3. On the SageMaker Studio console, pick JumpStart in the navigation pane.
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The model internet browser shows available models, with details like the service provider name and model abilities.
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4. Search for DeepSeek-R1 to view the DeepSeek-R1 design card. +Each model card shows crucial details, consisting of:
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- Model name +- Provider name +- Task classification (for instance, Text Generation). +Bedrock Ready badge (if relevant), [suggesting](http://gs1media.oliot.org) 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 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 supplier details. +Deploy button to deploy the design. +About and Notebooks tabs with detailed details
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The About tab includes important details, such as:
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- Model description. +- License details. +- Technical specs. +- Usage guidelines
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Before you deploy the model, it's recommended to examine the design details and [systemcheck-wiki.de](https://systemcheck-wiki.de/index.php?title=Benutzer:EloyCallahan) license terms to verify compatibility with your usage case.
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6. Choose Deploy to [proceed](https://dongawith.com) with deployment.
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7. For Endpoint name, use the instantly produced name or [develop](http://chillibell.com) a customized one. +8. For example type ΒΈ pick an instance type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, enter the variety of instances (default: 1). +Selecting appropriate [circumstances types](https://video-sharing.senhosts.com) and counts is crucial for expense and efficiency optimization. [Monitor](https://vidhiveapp.com) your release to change these settings as needed.Under Inference type, Real-time inference is chosen by default. This is enhanced for sustained traffic and low latency. +10. Review all configurations for precision. For this design, we highly suggest sticking 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 procedure can take several minutes to complete.
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When implementation is total, your endpoint status will alter to InService. At this moment, the design is ready to accept reasoning requests through the endpoint. You can keep an eye on the implementation progress on the SageMaker console Endpoints page, which will show appropriate 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](https://gitter.top).
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Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
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To begin with DeepSeek-R1 using the SageMaker Python SDK, you will need to install the SageMaker Python SDK and make certain you have the required AWS permissions and environment setup. The following is a detailed code example that shows how to deploy and utilize DeepSeek-R1 for inference programmatically. The code for releasing the model is supplied in the Github here. You can clone the note pad and range 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 also use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a guardrail utilizing the Amazon Bedrock console or the API, and execute it as shown in the following code:
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Tidy up
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To avoid unwanted charges, complete the actions 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 steps:
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1. On the Amazon Bedrock console, under [Foundation models](https://miggoo.com.br) in the navigation pane, select Marketplace deployments. +2. In the Managed deployments section, find the [endpoint](https://social-lancer.com) you want 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 right deployment: 1. [Endpoint](http://123.249.20.259080) 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 will [sustain expenses](http://93.104.210.1003000) if you leave it [running](https://animployment.com). Use the following code to delete the [endpoint](https://flowndeveloper.site) 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 deploy the DeepSeek-R1 model using Bedrock Marketplace and SageMaker JumpStart. [Visit SageMaker](https://baripedia.org) JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get going. 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 Beginning 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](http://121.37.166.0:3000) companies build [innovative](http://git.andyshi.cloud) options utilizing AWS services and accelerated calculate. Currently, he is concentrated on developing strategies for fine-tuning and optimizing the inference performance of large language designs. In his downtime, Vivek enjoys treking, viewing movies, and attempting different cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://gitlab.iue.fh-kiel.de) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His area of focus is AWS [AI](https://www.mediarebell.com) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
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Jonathan Evans is a Specialist Solutions Architect dealing with generative [AI](https://dispatchexpertscudo.org.uk) 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.tx.pl) center. She is passionate about developing options that help clients accelerate their [AI](http://globalk-foodiero.com) journey and unlock business worth.
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