From 34d8857b918dde13341107b44f233fb982fd917d Mon Sep 17 00:00:00 2001 From: fionanemeth660 Date: Fri, 7 Feb 2025 17:24:13 +0800 Subject: [PATCH] Add DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart --- ...tplace 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..9f5e4cd --- /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 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](https://starfc.co.kr)'s first-generation frontier model, DeepSeek-R1, along with the distilled variations ranging from 1.5 to 70 billion parameters to construct, experiment, [it-viking.ch](http://it-viking.ch/index.php/User:AngelicaSnowball) and properly scale your generative [AI](https://www.careermakingjobs.com) ideas on AWS.
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In this post, we demonstrate how to get started with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable actions to release the distilled variations of the models too.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a large language design (LLM) established by [DeepSeek](https://www.arztsucheonline.de) [AI](https://www.imf1fan.com) that utilizes reinforcement learning to enhance reasoning capabilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A key identifying function is its reinforcement learning (RL) step, which was utilized to improve the design's responses beyond the basic pre-training and fine-tuning procedure. By incorporating RL, DeepSeek-R1 can adjust better to user feedback and objectives, ultimately boosting both importance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) method, suggesting it's [equipped](https://teachinthailand.org) to break down complicated questions and reason through them in a detailed manner. This directed thinking procedure enables the design to produce more precise, transparent, and detailed responses. This design integrates RL-based [fine-tuning](https://git.peaksscrm.com) with CoT capabilities, aiming to produce structured reactions while concentrating on interpretability and user interaction. With its wide-ranging capabilities DeepSeek-R1 has actually recorded the market's attention as a flexible text-generation design that can be integrated into various workflows such as representatives, rational reasoning and data interpretation tasks.
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DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture permits activation of 37 billion criteria, allowing efficient [reasoning](http://revoltsoft.ru3000) by routing inquiries to the most pertinent professional "clusters." This technique permits the model to focus on different issue domains while maintaining overall performance. DeepSeek-R1 needs a minimum of 800 GB of HBM memory 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](https://newtheories.info).
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DeepSeek-R1 distilled designs bring the thinking abilities of the main R1 model to more [efficient architectures](https://derivsocial.org) based on [popular](https://satyoptimum.com) open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a [process](https://git.ffho.net) of training smaller, more effective designs to simulate the habits and reasoning patterns of the bigger DeepSeek-R1 design, utilizing it as a teacher 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 releasing this design with guardrails in place. In this blog site, we will use Amazon Bedrock Guardrails to introduce safeguards, avoid harmful material, and assess designs against [essential](https://coolroomchannel.com) safety criteria. At the time of composing this blog site, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create multiple guardrails tailored to various usage cases and use them to the DeepSeek-R1 design, enhancing user experiences and standardizing safety controls across your generative [AI](https://eleeo-europe.com) 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, select Amazon SageMaker, and verify 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 request a limitation boost, create a limitation increase demand [wiki.dulovic.tech](https://wiki.dulovic.tech/index.php/User:DewittMosely09) and reach out to your [account team](http://gagetaylor.com).
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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 Establish authorizations to use guardrails for material filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails allows you to introduce safeguards, prevent damaging material, and evaluate designs against essential safety requirements. You can implement safety measures for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This enables you to apply guardrails to assess user inputs and model responses deployed on Amazon Bedrock Marketplace and [SageMaker JumpStart](https://gitea.qi0527.com). You can produce a guardrail utilizing the Amazon [Bedrock console](https://git.noisolation.com) or the API. For the example code to create the guardrail, see the GitHub repo.
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The general flow includes the following actions: 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 is applied. If the output passes this final check, it's returned as the outcome. However, if either the input or output is stepped in by the guardrail, a message is returned showing the nature of the [intervention](https://sosyalanne.com) and whether it happened at the input or output stage. The examples showcased in the following sections demonstrate 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 foundation 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, choose Model catalog under Foundation models in the navigation pane. +At the time of composing this post, you can utilize the InvokeModel API to invoke the design. It does not support Converse APIs and other Amazon Bedrock [tooling](https://git.nothamor.com3000). +2. Filter for DeepSeek as a supplier and pick the DeepSeek-R1 model.
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The model detail page offers vital details about the model's abilities, pricing structure, and execution standards. You can discover detailed usage instructions, consisting of sample API calls and code bits for combination. The model supports different text generation tasks, including material production, code generation, and concern answering, using its support discovering optimization and CoT thinking abilities. +The page also includes release options and licensing details to help you begin with DeepSeek-R1 in your applications. +3. To start using DeepSeek-R1, select Deploy.
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You will be prompted to set up the [deployment details](https://git.lmh5.com) for DeepSeek-R1. The model ID will be pre-populated. +4. For Endpoint name, go into an endpoint name (in between 1-50 alphanumeric characters). +5. For Number of circumstances, enter a variety of circumstances (between 1-100). +6. For Instance type, pick your circumstances type. For optimal performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is suggested. +Optionally, you can configure sophisticated [security](https://www.webthemes.ca) and infrastructure settings, including virtual private cloud (VPC) networking, service function consents, and file encryption settings. For the majority of use cases, the default settings will work well. However, for production implementations, you may desire to examine 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 deployment is complete, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock play area. +8. Choose Open in play area to access an interactive user interface where you can experiment with various prompts and adjust design parameters like temperature and maximum length. +When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for optimal outcomes. For example, content for reasoning.
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This is an outstanding method to check out the model's thinking and text generation abilities before incorporating it into your applications. The playground supplies immediate feedback, assisting you comprehend how the design responds to various inputs and letting you tweak your prompts for optimum results.
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You can quickly check the design in the play ground 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 [reasoning utilizing](https://dakresources.com) guardrails with the deployed DeepSeek-R1 endpoint
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The following code example demonstrates how to carry out reasoning using a deployed DeepSeek-R1 design through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can create a guardrail utilizing the [Amazon Bedrock](http://193.30.123.1883500) console or the API. For the example code to [develop](https://lovetechconsulting.net) the guardrail, see the GitHub repo. After you have actually created the guardrail, use the following code to execute guardrails. The script initializes the bedrock_runtime customer, sets up [reasoning](http://121.37.138.2) parameters, and sends 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) center with FMs, built-in algorithms, and prebuilt ML solutions that you can deploy with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage case, with your data, [systemcheck-wiki.de](https://systemcheck-wiki.de/index.php?title=Benutzer:SusieChipman) and deploy them into production utilizing either the UI or SDK.
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Deploying DeepSeek-R1 design through SageMaker JumpStart provides 2 hassle-free techniques: utilizing the instinctive SageMaker JumpStart UI or carrying out programmatically through the [SageMaker Python](https://asromafansclub.com) SDK. Let's check out both [methods](https://mixup.wiki) to assist you pick the method that best suits 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, choose Studio in the navigation pane. +2. First-time users will be triggered to produce a domain. +3. On the SageMaker Studio console, choose JumpStart in the navigation pane.
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The design browser shows available models, with details like the service provider name and model capabilities.
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4. Look for DeepSeek-R1 to see the DeepSeek-R1 design card. +Each design card reveals crucial details, including:
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- Model name +- Provider name +- Task category (for instance, Text Generation). +Bedrock Ready badge (if applicable), indicating that this design can be signed up with Amazon Bedrock, permitting you to use Amazon Bedrock APIs to conjure up the design
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5. Choose the design card to see the [design details](https://git.kansk-tc.ru) page.
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The design details page consists of the following details:
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- The design name and service provider details. +Deploy button to release the design. +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 specs. +- Usage guidelines
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Before you deploy the design, it's recommended to review the design details and license terms to validate compatibility with your usage case.
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6. Choose Deploy to proceed with [release](https://git.hichinatravel.com).
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7. For Endpoint name, utilize the instantly created name or develop a custom one. +8. For example type ΒΈ select an instance type (default: ml.p5e.48 xlarge). +9. For [bio.rogstecnologia.com.br](https://bio.rogstecnologia.com.br/chantedarbon) Initial circumstances count, enter the number of instances (default: 1). +Selecting appropriate instance types and counts is [essential](https://codecraftdb.eu) for cost and performance optimization. Monitor your implementation to adjust these settings as needed.Under Inference type, Real-time reasoning is selected by default. This is optimized for sustained traffic and low latency. +10. Review all configurations for accuracy. For this design, we strongly recommend adhering to SageMaker JumpStart default settings and making certain that network [isolation](http://81.68.246.1736680) remains in location. +11. Choose Deploy to release the design.
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The [deployment process](http://www.lucaiori.it) can take numerous minutes to finish.
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When release is complete, your endpoint status will change to InService. At this point, the model is all set to accept inference demands through the endpoint. You can keep an eye on the release progress on the SageMaker console Endpoints page, which will display relevant metrics and [status details](http://www.radioavang.org). When the deployment is total, you can conjure up the model utilizing a SageMaker runtime customer and integrate it with your applications.
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Deploy DeepSeek-R1 using the SageMaker Python SDK
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To begin with DeepSeek-R1 utilizing the SageMaker Python SDK, you will need to set up the [SageMaker Python](https://git.wo.ai) SDK and make certain you have the essential AWS approvals and environment setup. The following is a detailed code example that shows how to deploy and use DeepSeek-R1 for inference programmatically. The code for [releasing](https://gitea.ashcloud.com) 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 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 likewise utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can develop a guardrail using 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 [prevent undesirable](http://git.huxiukeji.com) 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 design 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 deployments. +2. In the Managed implementations area, find the endpoint you wish to erase. +3. Select the endpoint, and on the Actions menu, select Delete. +4. Verify the endpoint details to make certain you're deleting the proper deployment: 1. [Endpoint](https://21fun.app) 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 costs if you leave it running. Use the following code to erase the endpoint if you want to stop sustaining charges. For more details, see Delete [Endpoints](http://git.huxiukeji.com) 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 begin. For more details, describe Use [Amazon Bedrock](https://miggoo.com.br) tooling with Amazon SageMaker JumpStart designs, SageMaker JumpStart designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting begun 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://47.119.160.181:3000) companies build innovative options using AWS services and accelerated compute. Currently, he is concentrated on developing methods for fine-tuning and enhancing the reasoning performance of large language designs. In his free time, Vivek enjoys treking, enjoying movies, and attempting different cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://animeportal.cl) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His location of focus is AWS [AI](http://git.vimer.top:3000) [accelerators](http://git.superiot.net) (AWS Neuron). He holds a [Bachelor's degree](https://jobstaffs.com) in Computer technology and Bioinformatics.
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Jonathan Evans is a Specialist Solutions Architect dealing with generative [AI](http://jobteck.com) with the Third-Party Model Science 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://youarealways.online) center. She is enthusiastic about constructing options that help customers accelerate their [AI](https://clousound.com) journey and unlock company value.
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