From 9918d3f8e62ee64ab6e537ed3478ec369808caf6 Mon Sep 17 00:00:00 2001 From: geraldmckelvy Date: Sun, 1 Jun 2025 07:59:33 +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..3ce2f50 --- /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 [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=12029182) Qwen models are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](http://223.68.171.150:8004)'s first-generation frontier design, DeepSeek-R1, together with the distilled versions ranging from 1.5 to 70 billion parameters to develop, experiment, and properly scale your generative [AI](https://gitea.star-linear.com) ideas on AWS.
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In this post, we show how to get begun with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable steps to release the distilled variations of the [designs](https://git.xinstitute.org.cn) also.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a large language model (LLM) developed by DeepSeek [AI](https://jobs.sudburychamber.ca) that utilizes reinforcement learning to improve thinking capabilities through a multi-stage training process from a DeepSeek-V3-Base foundation. An essential [differentiating feature](http://111.230.115.1083000) is its support learning (RL) action, which was used to fine-tune the design's responses beyond the standard pre-training and fine-tuning process. By integrating RL, DeepSeek-R1 can adjust more effectively to user feedback and goals, [eventually improving](https://amore.is) both importance and clarity. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) technique, meaning it's equipped to break down intricate questions and reason through them in a detailed manner. This assisted reasoning procedure permits the model to produce more precise, transparent, and detailed responses. This model integrates RL-based fine-tuning with CoT abilities, aiming to produce structured responses while focusing on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has recorded the industry's attention as a flexible text-generation design that can be integrated into numerous workflows such as agents, sensible reasoning and information interpretation tasks.
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DeepSeek-R1 utilizes a Mixture of Experts (MoE) [architecture](http://zaxx.co.jp) and is 671 billion specifications in size. The MoE architecture permits activation of 37 billion criteria, [wiki.snooze-hotelsoftware.de](https://wiki.snooze-hotelsoftware.de/index.php?title=Benutzer:RigobertoGreene) enabling efficient inference by routing inquiries to the most pertinent professional "clusters." This approach allows the design to concentrate on different issue domains while [maintaining](https://git.aionnect.com) overall [effectiveness](http://101.132.73.143000). DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for inference. In this post, we will use an ml.p5e.48 xlarge circumstances to release the model. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.
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DeepSeek-R1 distilled models bring the reasoning capabilities of the main R1 design to more efficient architectures based on popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a procedure of training smaller, more effective designs to imitate the habits and thinking 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](http://shammahglobalplacements.com) design, we recommend releasing this design with guardrails in place. In this blog site, we will use Amazon Bedrock Guardrails to present safeguards, prevent harmful material, and evaluate designs against essential security criteria. At the time of writing this blog, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can create numerous guardrails tailored to various usage cases and use them to the DeepSeek-R1 design, enhancing user [experiences](https://www.kmginseng.com) and standardizing security controls across your generative [AI](https://hayhat.net) 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 check if you have quotas for P5e, open the Service Quotas console and under AWS Services, select Amazon SageMaker, and verify you're using ml.p5e.48 xlarge for endpoint usage. Make certain that you have at least one ml.P5e.48 xlarge instance in the AWS Region you are [releasing](https://candidates.giftabled.org). To request a limitation increase, develop a limitation increase request and reach out to your account group.
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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) [permissions](http://sp001g.dfix.co.kr) to use Amazon Bedrock Guardrails. For directions, see Set up consents to use guardrails for content filtering.
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Implementing guardrails with the [ApplyGuardrail](https://travel-friends.net) API
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Amazon Bedrock Guardrails enables you to present safeguards, prevent harmful content, and evaluate models against crucial security [criteria](https://git.xedus.ru). You can execute precaution for the DeepSeek-R1 [design utilizing](https://aji.ghar.ku.jaldi.nai.aana.ba.tume.dont.tach.me) the Amazon Bedrock ApplyGuardrail API. This permits you to use guardrails to evaluate user inputs and [wiki.asexuality.org](https://wiki.asexuality.org/w/index.php?title=User_talk:JanineKesler153) design reactions [released](https://remote-life.de) on Amazon Bedrock Marketplace and SageMaker JumpStart. You can develop a guardrail utilizing the Amazon [Bedrock](https://15.164.25.185) [console](http://www.stardustpray.top30009) or the API. For the example code to produce the guardrail, see the GitHub repo.
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The general flow 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 design for reasoning. After receiving 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 suggesting](http://190.117.85.588095) the nature of the intervention and whether it took place at the input or output stage. The examples showcased in the following areas show inference using 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, total the following actions:
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1. On the Amazon Bedrock console, pick Model catalog under Foundation designs in the navigation pane. +At the time of composing this post, you can utilize the InvokeModel API to invoke the model. It does not support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a provider and choose the DeepSeek-R1 design.
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The design detail page provides essential details about the model's abilities, rates structure, and implementation standards. You can find detailed usage instructions, consisting of sample API calls and code bits for integration. The model supports numerous text generation jobs, [including](http://117.50.100.23410080) content creation, code generation, and concern answering, utilizing its reinforcement learning optimization and CoT reasoning capabilities. +The page also includes deployment options and [licensing](http://git.nextopen.cn) details to assist you begin with DeepSeek-R1 in your applications. +3. To start utilizing DeepSeek-R1, select Deploy.
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You will be prompted to set up the implementation details for DeepSeek-R1. The model ID will be pre-populated. +4. For [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11857434) Endpoint name, get in an endpoint name (in between 1-50 alphanumeric characters). +5. For Variety of instances, enter a variety of circumstances (between 1-100). +6. For example type, pick your circumstances type. For ideal efficiency with DeepSeek-R1, a [GPU-based instance](https://savico.com.br) type like ml.p5e.48 xlarge is advised. +Optionally, you can configure innovative security and facilities settings, consisting of virtual private cloud (VPC) networking, service function authorizations, and file encryption settings. For most use cases, the default settings will work well. However, for production implementations, you might wish to evaluate these settings to align with your organization's security and compliance requirements. +7. Choose Deploy to begin using the design.
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When the implementation is total, you can evaluate DeepSeek-R1's abilities straight in the Amazon Bedrock play ground. +8. Choose Open in play area to access an interactive user interface where you can explore different prompts and adjust model criteria like temperature and optimum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for optimum outcomes. For example, material for reasoning.
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This is an outstanding method to explore the model's reasoning and text generation capabilities before integrating it into your applications. The play ground offers immediate feedback, helping you understand how the model reacts to various inputs and [letting](https://git.partners.run) you tweak your triggers for ideal outcomes.
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You can quickly [evaluate](http://34.236.28.152) the design in the play ground through the UI. However, to invoke the released model 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 utilizing a released 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 develop the guardrail, see the GitHub repo. After you have created the guardrail, use the following code to execute guardrails. The script initializes the bedrock_runtime client, configures reasoning parameters, and sends out a request to create 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, integrated algorithms, and prebuilt ML solutions that you can deploy with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage case, with your information, and release them into production using either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart offers 2 [hassle-free](https://www.ojohome.listatto.ca) approaches: using the instinctive SageMaker JumpStart UI or implementing programmatically through the SDK. Let's check out both techniques to assist you choose the approach that best suits 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 console, pick Studio in the navigation pane. +2. First-time users will be prompted to produce a domain. +3. On the SageMaker Studio console, choose JumpStart in the navigation pane.
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The design web browser displays available designs, with details like the company name and model capabilities.
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4. Search for DeepSeek-R1 to view the DeepSeek-R1 [design card](https://miderde.de). +Each model card shows essential details, including:
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- Model name +- Provider name +- Task classification (for example, Text Generation). +[Bedrock Ready](https://tradingram.in) badge (if applicable), showing that this model can be registered with Amazon Bedrock, [permitting](https://git.polycompsol.com3000) you to use Amazon Bedrock APIs to conjure up the model
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5. Choose the [model card](http://stackhub.co.kr) to view the design details page.
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The model details page consists of the following details:
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- The design name and [supplier details](https://code.nwcomputermuseum.org.uk). +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 requirements. +- Usage standards
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Before you deploy the design, it's advised to review the design details and license terms to verify 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 customized one. +8. For example type ΒΈ select a circumstances type (default: ml.p5e.48 xlarge). +9. For [wiki.rolandradio.net](https://wiki.rolandradio.net/index.php?title=User:ChaseVandermark) Initial instance count, go into the variety of circumstances (default: 1). +Selecting proper circumstances types and counts is important for cost and efficiency optimization. [Monitor](https://gitlab.profi.travel) your implementation to change these settings as needed.Under Inference type, Real-time reasoning is chosen by default. This is enhanced for [wavedream.wiki](https://wavedream.wiki/index.php/User:ShereeHein) sustained traffic and low latency. +10. Review all setups for precision. For this design, we strongly recommend sticking to SageMaker JumpStart default [settings](http://tigg.1212321.com) and making certain that network seclusion remains in place. +11. Choose Deploy to release the design.
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The implementation procedure can take a number of minutes to finish.
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When release is total, your endpoint status will change to InService. At this moment, the design is ready to accept inference requests through the endpoint. You can monitor the implementation progress on the SageMaker console Endpoints page, which will show pertinent metrics and status details. When the implementation is total, you can conjure up the design utilizing a SageMaker runtime customer and incorporate 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 using the SageMaker Python SDK, you will require to set up the SageMaker Python SDK and make certain you have the necessary [AWS authorizations](https://blogram.online) and [environment](https://pivotalta.com) setup. The following is a detailed code example that shows how to [release](http://43.138.236.39000) and use DeepSeek-R1 for inference programmatically. The code for [releasing](https://papersoc.com) the design is offered in the Github here. You can clone the note pad and range from SageMaker Studio.
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You can run additional 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 develop a guardrail utilizing 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 [prevent unwanted](https://git.chocolatinie.fr) 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 using [Amazon Bedrock](https://www.koumii.com) Marketplace, complete the following actions:
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1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, select Marketplace releases. +2. In the Managed implementations area, find the endpoint you want 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 right 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 model you deployed will sustain expenses if you leave it running. Use the following code to delete the [endpoint](https://gogs.koljastrohm-games.com) 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 going. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, SageMaker JumpStart pretrained models, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Starting 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](http://152.136.187.229) generative [AI](https://mssc.ltd) companies build innovative services using AWS services and accelerated calculate. Currently, he is focused on developing methods for fine-tuning and enhancing the reasoning performance of large language designs. In his leisure time, Vivek takes pleasure in hiking, enjoying films, and trying different cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](http://110.42.178.113:3000) [Specialist Solutions](https://warleaks.net) Architect with the Third-Party Model Science group at AWS. His location of focus is AWS [AI](http://175.178.113.220:3000) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.
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Jonathan Evans is a Specialist Solutions Architect dealing with generative [AI](http://41.111.206.175:3000) with the Third-Party Model Science team at AWS.
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Banu Nagasundaram leads product, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](http://gitlab.suntrayoa.com) center. She is enthusiastic about developing services that assist clients accelerate their [AI](https://gitea.rodaw.net) journey and unlock company value.
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