From 3834ce747bcbdbb0bb5b90de9f6a67f5189fd6ed Mon Sep 17 00:00:00 2001 From: Adalberto Saucedo Date: Mon, 7 Apr 2025 14:12:16 +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..1c5d355 --- /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 designs are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](http://47.97.161.140:10080)'s first-generation frontier model, DeepSeek-R1, in addition to the distilled variations varying from 1.5 to 70 billion specifications to construct, experiment, and responsibly scale your generative [AI](https://matchpet.es) ideas on AWS.
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In this post, we demonstrate how to start with DeepSeek-R1 on Amazon Bedrock Marketplace 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 model (LLM) developed by DeepSeek [AI](https://slovenskymedved.sk) that uses support learning to improve thinking abilities through a multi-stage training process from a DeepSeek-V3-Base structure. A key identifying function is its [reinforcement knowing](https://huconnect.org) (RL) action, which was used to fine-tune the model's actions beyond the basic pre-training and fine-tuning process. By incorporating RL, DeepSeek-R1 can adjust more effectively to user feedback and objectives, eventually boosting both significance and clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) method, meaning it's equipped to break down intricate questions and factor through them in a detailed manner. This guided reasoning procedure allows the model to produce more precise, transparent, and detailed responses. This model integrates RL-based [fine-tuning](https://git.thomasballantine.com) with CoT capabilities, aiming to generate structured reactions while concentrating on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has recorded the market's attention as a versatile text-generation design that can be integrated into different workflows such as representatives, sensible thinking and information interpretation tasks.
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DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture permits activation of 37 billion criteria, enabling effective inference by [routing queries](http://www.becausetravis.com) to the most pertinent professional "clusters." This approach enables the design to focus on various problem [domains](http://175.178.199.623000) while maintaining overall performance. DeepSeek-R1 needs a minimum of 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 designs bring the thinking abilities of the main R1 model to more [efficient architectures](https://smaphofilm.com) 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 imitate the habits and thinking patterns of the bigger DeepSeek-R1 model, using it as an instructor design.
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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 recommend deploying this model with guardrails in place. In this blog, we will use Amazon Bedrock Guardrails to introduce safeguards, prevent harmful material, and assess designs against essential security criteria. At the time of writing this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create several guardrails tailored to various usage cases and use them to the DeepSeek-R1 design, enhancing user experiences and standardizing safety controls throughout your generative [AI](https://sangha.live) applications.
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Prerequisites
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To release the DeepSeek-R1 design, you need access to an ml.p5e [instance](https://git.apps.calegix.net). To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, select 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 instance in the AWS Region you are releasing. To [request](https://kronfeldgit.org) a limit boost, produce a limit boost request and connect to your account team.
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Because you will be deploying this design with Amazon Bedrock Guardrails, make certain you have the correct AWS Identity and Gain Access To Management (IAM) authorizations to use Amazon Bedrock . For instructions, 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](https://atomouniversal.com.br) you to present safeguards, prevent damaging content, and evaluate designs against [essential safety](http://www.hanmacsamsung.com) criteria. You can implement safety measures for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This enables you to apply guardrails to assess user inputs and model reactions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can develop 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 basic flow involves the following steps: First, the system receives an input for the model. This input is then processed through the ApplyGuardrail API. If the input passes the [guardrail](https://oninabresources.com) check, it's sent to the design for inference. After getting the model's output, another guardrail check is applied. If the [output passes](http://git.twopiz.com8888) this final check, it's returned as the [outcome](http://ja7ic.dxguy.net). However, if either the input or output is stepped in by the guardrail, a [message](http://ccrr.ru) is returned showing 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 offers you access to over 100 popular, emerging, and specialized structure designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following steps:
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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 design. It does not support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a [supplier](https://yourgreendaily.com) and pick the DeepSeek-R1 design.
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The design detail page supplies important details about the design's abilities, prices structure, and implementation standards. You can discover detailed use instructions, consisting of [sample API](https://upi.ind.in) calls and code snippets for integration. The design supports various text generation tasks, consisting of [material](https://freeworld.global) development, code generation, and question answering, using its reinforcement learning optimization and CoT reasoning abilities. +The page also consists of release choices and licensing details to assist you start with DeepSeek-R1 in your applications. +3. To begin utilizing DeepSeek-R1, pick Deploy.
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You will be prompted to set up the release details for DeepSeek-R1. The model ID will be pre-populated. +4. For Endpoint name, get in an endpoint name (in between 1-50 alphanumeric characters). +5. For Number of instances, go into a variety of circumstances (in between 1-100). +6. For example type, select your circumstances type. For ideal performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is advised. +Optionally, you can set up innovative security and facilities settings, including virtual personal cloud (VPC) networking, service function approvals, and encryption [settings](https://gitlab.anc.space). For a lot of utilize cases, the default settings will work well. However, for production implementations, you may desire to review these settings to align with your organization's security and compliance requirements. +7. Choose Deploy to start using the design.
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When the deployment is total, you can check DeepSeek-R1's abilities straight in the Amazon Bedrock play area. +8. Choose Open in play ground to access an interactive interface where you can explore different triggers and adjust design specifications like temperature and maximum length. +When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for ideal outcomes. For instance, content for reasoning.
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This is an outstanding method to check out the design's thinking and text generation capabilities before incorporating it into your applications. The playground provides immediate feedback, helping you understand how the model reacts to various inputs and letting you tweak your prompts for optimal outcomes.
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You can [rapidly test](http://59.110.125.1643062) the model in the playground through the UI. However, to conjure up the deployed model programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
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Run inference utilizing guardrails with the deployed DeepSeek-R1 endpoint
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The following code example demonstrates how to carry out inference using a [released](https://videobox.rpz24.ir) DeepSeek-R1 design through Amazon Bedrock using the invoke_model and [ApplyGuardrail API](https://git.thomasballantine.com). You can [develop](https://www.cbtfmytube.com) a [guardrail utilizing](http://plus-tube.ru) the Amazon [Bedrock console](https://geetgram.com) or the API. For the example code to develop the guardrail, see the GitHub repo. After you have actually produced the guardrail, [utilize](http://wiki.lexserve.co.ke) the following code to carry out guardrails. The script initializes the bedrock_runtime client, sets up reasoning criteria, and sends out a demand to generate text 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 (ML) center with FMs, built-in algorithms, and prebuilt ML options that you can deploy with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your use case, with your information, and release them into production utilizing either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart uses 2 practical methods: utilizing the user-friendly SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both approaches to assist you choose the [technique](http://git.emagenic.cl) 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 release DeepSeek-R1 using SageMaker JumpStart:
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1. On the SageMaker console, select Studio in the navigation pane. +2. First-time users will be triggered to create a domain. +3. On the SageMaker Studio console, pick JumpStart in the navigation pane.
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The design [internet browser](https://aceme.ink) displays available models, with details like the supplier name and model abilities.
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4. Search for DeepSeek-R1 to view the DeepSeek-R1 design card. +Each design card reveals crucial details, consisting of:
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- Model name +- Provider name +- Task category (for example, Text Generation). +Bedrock Ready badge (if relevant), [indicating](http://106.52.242.1773000) that this design can be signed up with Amazon Bedrock, allowing you to utilize Amazon Bedrock APIs to conjure up the design
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5. Choose the design card 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 [provider details](http://dcmt.co.kr). +Deploy button to deploy the design. +About and Notebooks tabs with detailed details
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The About tab consists of essential details, such as:
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- Model description. +- License details. +- Technical requirements. +- Usage guidelines
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Before you deploy the model, it's advised to examine the model details and license terms to validate compatibility with your usage case.
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6. Choose Deploy to continue with release.
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7. For Endpoint name, use the immediately produced name or create a custom-made one. +8. For Instance type ΒΈ choose an instance type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, enter the number of circumstances (default: 1). +Selecting appropriate circumstances types and counts is vital for cost and [pipewiki.org](https://pipewiki.org/wiki/index.php/User:ArlenKershaw) performance optimization. Monitor your implementation to change these settings as needed.Under Inference type, Real-time inference is selected by default. This is optimized for sustained traffic and low latency. +10. Review all configurations for accuracy. For this design, we strongly advise sticking to SageMaker JumpStart default settings and making certain that network isolation remains in place. +11. Choose Deploy to deploy the model.
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The [release procedure](http://jenkins.stormindgames.com) can take numerous minutes to complete.
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When implementation 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 keep an eye on the deployment progress on the [SageMaker](https://applykar.com) console Endpoints page, which will [display](https://dirkohlmeier.de) relevant metrics and status details. When the release is complete, you can conjure up the model 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 utilizing the SageMaker Python SDK, you will require to install the SageMaker Python SDK and make certain you have the essential AWS approvals and environment setup. The following is a detailed code example that demonstrates how to release and utilize DeepSeek-R1 for [reasoning programmatically](http://whai.space3000). The code for deploying the model is supplied in the Github here. You can clone the notebook and run 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 execute it as shown in the following code:
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Clean up
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To avoid unwanted charges, finish the actions in this section to tidy up your resources.
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Delete the [Amazon Bedrock](http://www.topverse.world3000) Marketplace implementation
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If you deployed the design utilizing Amazon Bedrock Marketplace, total the following steps:
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1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, pick Marketplace deployments. +2. In the Managed releases section, find the endpoint you want to erase. +3. Select the endpoint, and on the [Actions](http://111.61.77.359999) menu, choose Delete. +4. Verify the endpoint details to make certain you're deleting the proper deployment: 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 costs if you leave it running. Use the following code to delete the endpoint if you want 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 Studio or Amazon Bedrock Marketplace now to begin. 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 helps emerging generative [AI](https://www.cupidhive.com) business build ingenious options using AWS services and accelerated calculate. Currently, he is focused on establishing methods for fine-tuning and optimizing the reasoning performance of big language designs. In his leisure time, Vivek enjoys hiking, watching motion pictures, and [attempting](http://112.74.102.696688) different foods.
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Niithiyn Vijeaswaran is a Generative [AI](http://111.9.47.105:10244) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His location of focus is AWS [AI](https://fotobinge.pincandies.com) 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](https://careerworksource.org) 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](https://wavedream.wiki) hub. She is enthusiastic about developing options that assist consumers accelerate their [AI](http://124.220.187.142:3000) [journey](https://demo.titikkata.id) and unlock business value.
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