From 5ecbbc2f98620b8a4e8cd61874fb42477657312e Mon Sep 17 00:00:00 2001 From: maxierutherfor Date: Thu, 29 May 2025 08:44:32 +0700 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..8fb4b15 --- /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](http://connect.lankung.com) to announce that DeepSeek R1 distilled Llama and Qwen models are available through [Amazon Bedrock](https://circassianweb.com) Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](https://git.christophhagen.de)'s first-generation frontier design, DeepSeek-R1, in addition to the distilled variations ranging from 1.5 to 70 billion criteria to build, experiment, and properly scale your generative [AI](https://livesports808.biz) concepts on AWS.
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In this post, we show how to begin with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable actions to deploy the distilled variations 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) established by DeepSeek [AI](https://accountshunt.com) that uses reinforcement discovering to enhance reasoning capabilities through a multi-stage training process from a DeepSeek-V3-Base structure. An essential differentiating function is its reinforcement learning (RL) step, which was utilized to fine-tune the design's reactions beyond the [standard pre-training](https://galsenhiphop.com) and tweak procedure. By incorporating RL, DeepSeek-R1 can adjust more efficiently to user feedback and goals, eventually improving both relevance and clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) method, meaning it's [equipped](https://git.arachno.de) to break down [complicated questions](https://git.nothamor.com3000) and reason through them in a detailed way. This assisted thinking procedure enables the model to [produce](https://gitlab.syncad.com) more precise, transparent, and detailed answers. This model integrates RL-based fine-tuning with CoT abilities, aiming to generate structured reactions while concentrating on interpretability and user interaction. With its extensive capabilities DeepSeek-R1 has caught the market's attention as a flexible text-generation model that can be incorporated into various workflows such as representatives, rational reasoning and information interpretation jobs.
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DeepSeek-R1 utilizes a Mixture of [Experts](http://www.mitt-slide.com) (MoE) architecture and is 671 billion [parameters](http://39.100.93.1872585) in size. The MoE architecture permits activation of 37 billion parameters, allowing effective reasoning by routing queries to the most pertinent specialist "clusters." This technique allows the design to specialize in various issue domains while maintaining total efficiency. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for inference. In this post, we will utilize an ml.p5e.48 xlarge instance to [release](https://myjobapply.com) the model. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.
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DeepSeek-R1 distilled designs bring the thinking abilities of the main R1 design to more efficient architectures based on popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a process of training smaller, more effective models to imitate the behavior and reasoning patterns of the larger DeepSeek-R1 design, utilizing it as a teacher model.
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You can release DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we suggest releasing this model with guardrails in place. In this blog site, we will utilize Amazon Bedrock Guardrails to present safeguards, avoid harmful content, and examine models against crucial safety criteria. At the time of writing this blog site, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can create numerous guardrails tailored to various use cases and use them to the DeepSeek-R1 design, improving user experiences and standardizing safety controls across your generative [AI](https://infinirealm.com) applications.
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Prerequisites
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To deploy the DeepSeek-R1 design, 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](https://gitea.nasilot.me) SageMaker, and verify you're using ml.p5e.48 xlarge for endpoint use. Make certain that you have at least one ml.P5e.48 [xlarge circumstances](https://www.ourstube.tv) in the AWS Region you are deploying. To request a limit increase, develop a limit increase demand and reach out to your account group.
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Because you will be releasing this design with Amazon Bedrock Guardrails, make certain you have the appropriate AWS Identity and Gain Access To Management (IAM) consents to utilize Amazon Bedrock Guardrails. For directions, see Establish 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 introduce safeguards, avoid damaging material, and examine models against key safety criteria. You can carry out precaution for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This enables you to use guardrails to assess user inputs and model actions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can create a guardrail using the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo.
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The general circulation includes the following actions: First, the system receives an input for the model. This input is then processed through the [ApplyGuardrail API](https://git.daviddgtnt.xyz). If the input passes the guardrail check, it's sent to the design for [inference](https://actv.1tv.hk). After getting the design's output, another guardrail check is used. If the output passes this final check, it's returned as the last result. However, if either the input or output is stepped in by the guardrail, a message is returned suggesting the nature of the intervention and whether it happened at the input or output stage. The examples showcased in the following areas demonstrate reasoning using this API.
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Deploy DeepSeek-R1 in [Amazon Bedrock](http://git.hiweixiu.com3000) Marketplace
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Amazon Bedrock Marketplace offers you access to over 100 popular, emerging, and specialized foundation models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following steps:
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1. On the Amazon Bedrock console, choose Model [brochure](https://probando.tutvfree.com) under Foundation models in the [navigation](https://syndromez.ai) pane. +At the time of writing this post, you can utilize the InvokeModel API to conjure up the model. It does not support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a service provider and choose the DeepSeek-R1 model.
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The model detail page provides necessary details about the design's capabilities, pricing structure, and [execution guidelines](https://cambohub.com3000). You can discover detailed use directions, of sample API calls and code snippets for integration. The design supports various text generation jobs, including material creation, code generation, and concern answering, utilizing its support finding out optimization and CoT reasoning abilities. +The page likewise consists of deployment alternatives and licensing details to assist you get begun with DeepSeek-R1 in your applications. +3. To start using DeepSeek-R1, choose Deploy.
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You will be prompted to configure the deployment details for DeepSeek-R1. The design ID will be pre-populated. +4. For Endpoint name, go into an endpoint name (between 1-50 alphanumeric characters). +5. For Variety of circumstances, enter a variety of instances (between 1-100). +6. For Instance type, choose your circumstances type. For optimum performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is advised. +Optionally, you can configure sophisticated security and infrastructure settings, consisting of virtual private cloud (VPC) networking, service function approvals, and encryption settings. For most use cases, the default settings will work well. However, for production implementations, you might wish to review these settings to align with your [organization's security](https://playtube.ann.az) and compliance requirements. +7. Choose Deploy to start utilizing the model.
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When the implementation is complete, you can evaluate DeepSeek-R1's capabilities straight in the Amazon Bedrock playground. +8. Choose Open in play ground to access an interactive interface where you can explore various prompts and adjust design parameters like temperature level and maximum length. +When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for optimum outcomes. For instance, content for inference.
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This is an exceptional method to check out the design's thinking and text generation capabilities before incorporating it into your applications. The play ground supplies immediate feedback, helping you understand how the design responds to [numerous](https://safeway.com.bd) inputs and letting you fine-tune your triggers for optimum outcomes.
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You can rapidly test the design in the play area through the UI. However, to invoke the deployed design programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
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Run reasoning utilizing guardrails with the deployed DeepSeek-R1 endpoint
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The following code example demonstrates how to perform reasoning using a deployed DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can develop a guardrail using the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo. After you have produced the guardrail, use the following code to implement guardrails. The script initializes the bedrock_runtime customer, sets up reasoning criteria, and sends out a request to create [text based](http://www.hyingmes.com3000) 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 options that you can release with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage case, with your information, and deploy them into production utilizing either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart uses two hassle-free methods: using the instinctive SageMaker [JumpStart](https://app.joy-match.com) UI or implementing programmatically through the SageMaker Python SDK. Let's explore both methods to assist you choose the [approach](https://followmypic.com) that finest matches your needs.
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Deploy DeepSeek-R1 through [SageMaker JumpStart](https://git.christophhagen.de) UI
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Complete the following steps to release DeepSeek-R1 using 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, select JumpStart in the navigation pane.
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The design browser displays available models, with details like the company name and model abilities.
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4. Look for DeepSeek-R1 to view the DeepSeek-R1 design card. +Each design card shows essential details, consisting of:
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- Model name +- Provider name +- Task classification (for example, Text Generation). +Bedrock Ready badge (if suitable), suggesting that this model can be registered with Amazon Bedrock, permitting you to use Amazon Bedrock APIs to conjure up the design
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5. Choose the model card to see the [design details](https://www.longisland.com) page.
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The [model details](http://202.164.44.2463000) page includes the following details:
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- The design name and provider 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. +- Technical specifications. +- Usage standards
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Before you release the design, it's [advised](https://aji.ghar.ku.jaldi.nai.aana.ba.tume.dont.tach.me) to examine the model details and license terms to confirm compatibility with your use case.
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6. Choose Deploy to continue with implementation.
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7. For Endpoint name, utilize the immediately generated name or create a customized one. +8. For Instance type ΒΈ select a circumstances type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, enter the number of circumstances (default: 1). +Selecting proper instance types and [wiki.asexuality.org](https://wiki.asexuality.org/w/index.php?title=User_talk:RIFFlorencia) counts is essential for cost and performance optimization. [Monitor](https://www.homebasework.net) your [release](http://zerovalueentertainment.com3000) to adjust these [settings](http://47.110.248.4313000) as needed.Under Inference type, Real-time reasoning is chosen by default. This is optimized for sustained traffic and low latency. +10. Review all setups for precision. 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 release the model.
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The release procedure can take a number of minutes to complete.
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When implementation is complete, your endpoint status will change to InService. At this moment, the model is all set to accept reasoning demands through the endpoint. You can monitor the deployment development on the SageMaker console Endpoints page, which will display appropriate metrics and status details. When the implementation is complete, you can conjure up the model utilizing a SageMaker runtime client and incorporate it with your [applications](https://gitlab.bzzndata.cn).
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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 set up the SageMaker Python SDK and make certain you have the required AWS permissions and environment setup. The following is a detailed code example that demonstrates how to deploy and use DeepSeek-R1 for inference programmatically. The code for deploying the design is provided in the Github here. You can clone the notebook 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 reasoning with your SageMaker JumpStart predictor
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Similar to Amazon Bedrock, you can likewise use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can develop a guardrail using the Amazon Bedrock console or the API, and execute it as displayed in the following code:
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Clean up
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To prevent unwanted charges, complete 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 designs in the navigation pane, choose Marketplace implementations. +2. In the Managed releases section, find the endpoint you wish to delete. +3. Select the endpoint, and on the Actions menu, [select Delete](https://suomalaistajalkapalloa.com). +4. Verify the endpoint details to make certain you're erasing the right implementation: 1. [Endpoint](http://git.hiweixiu.com3000) 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 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 deploy the DeepSeek-R1 design utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in [SageMaker Studio](http://jobee.cubixdesigns.com) or Amazon [Bedrock Marketplace](https://www.securityprofinder.com) now to start. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained models, 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://dongawith.com) companies develop ingenious services [utilizing AWS](https://pedulidigital.com) [services](https://speeddating.co.il) and accelerated compute. Currently, he is concentrated on developing techniques for fine-tuning and enhancing the reasoning performance of large language models. In his leisure time, Vivek delights in treking, watching films, and attempting different cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://chancefinders.com) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His location of focus is AWS [AI](http://dev.zenith.sh.cn) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
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Jonathan Evans is a Professional Solutions Architect working on generative [AI](https://surgiteams.com) with the Third-Party Model Science group 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](http://201.17.3.96:3000) hub. She is passionate about developing options that assist clients accelerate their [AI](http://82.146.58.193) journey and unlock company worth.
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