From 32a870ca1827bf67553107421a5529de1e6af8fc Mon Sep 17 00:00:00 2001 From: jxsagnes364446 Date: Tue, 8 Apr 2025 14:00:16 +0900 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..35e3a4f --- /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 announce 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://www.tinguj.com)'s first-generation frontier design, DeepSeek-R1, in addition to the distilled variations ranging from 1.5 to 70 billion parameters to construct, experiment, and responsibly scale your generative [AI](http://rm.runfox.com) concepts 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 steps to deploy the distilled variations of the designs as well.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a large language design (LLM) established by [DeepSeek](https://virtualoffice.com.ng) [AI](http://43.143.46.76:3000) that utilizes support finding out to enhance thinking capabilities through a multi-stage training process from a DeepSeek-V3-Base foundation. An [essential identifying](http://fridayad.in) feature is its reinforcement knowing (RL) action, which was utilized to fine-tune the model's reactions beyond the basic pre-training and fine-tuning process. By incorporating RL, DeepSeek-R1 can adjust better to user feedback and goals, eventually enhancing both importance and clarity. In addition, DeepSeek-R1 [employs](https://talentrendezvous.com) a chain-of-thought (CoT) approach, meaning it's geared up to break down complicated questions and reason through them in a detailed manner. This guided reasoning procedure enables the model to produce more accurate, transparent, and detailed responses. This model combines RL-based [fine-tuning](http://git.sanshuiqing.cn) with CoT abilities, aiming to create structured actions while focusing on interpretability and user interaction. With its comprehensive abilities DeepSeek-R1 has actually captured the industry's attention as a versatile text-generation model that can be incorporated into numerous workflows such as representatives, sensible reasoning and information analysis jobs.
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DeepSeek-R1 [utilizes](https://muwafag.com) a Mix of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture [permits activation](https://redebuck.com.br) of 37 billion parameters, making it possible for effective reasoning by routing questions to the most pertinent professional "clusters." This technique enables the design to concentrate on various problem domains while maintaining general efficiency. 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 instance](http://www.litehome.top) to release the design. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.
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DeepSeek-R1 distilled designs bring the reasoning capabilities of the main R1 model to more efficient architectures based on popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of training smaller sized, more effective designs to imitate the habits and [reasoning patterns](https://nakenterprisetv.com) of the larger DeepSeek-R1 model, utilizing it as an instructor 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 [recommend releasing](https://kaykarbar.com) this design with guardrails in place. In this blog site, we will use Amazon Bedrock Guardrails to present safeguards, prevent damaging content, and assess designs against key security requirements. At the time of writing this blog, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can create multiple guardrails tailored to different usage cases and apply them to the DeepSeek-R1 design, improving user experiences and standardizing security controls across your generative [AI](http://47.97.161.140:10080) applications.
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Prerequisites
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To release the DeepSeek-R1 design, you need access to an ml.p5e instance. To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and confirm you're [utilizing](https://m1bar.com) ml.p5e.48 xlarge for [endpoint usage](http://git.vimer.top3000). Make certain that you have at least one ml.P5e.48 xlarge instance in the AWS Region you are deploying. To ask for a limitation increase, develop a limit increase request and reach out to your account team.
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Because you will be deploying this design with Amazon Bedrock Guardrails, make certain you have the right AWS Identity and Gain Access To Management (IAM) permissions to use 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 hazardous material, and evaluate designs against essential security criteria. You can implement security measures for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This permits you to use guardrails to assess user inputs and design reactions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a guardrail utilizing the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo.
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The basic circulation involves the following actions: First, the system gets an input for the design. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it's sent out to the model for inference. After getting the model's output, another guardrail check is used. If the output passes this last check, it's returned as the final outcome. However, if either the input or output is intervened by the guardrail, a message is returned indicating the nature of the intervention and whether it occurred at the input or output phase. 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 gives you access to over 100 popular, emerging, and specialized structure 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, select Model catalog under Foundation models in the navigation pane. +At the time of writing this post, you can utilize the InvokeModel API to invoke the model. It doesn't support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a [supplier](http://chkkv.cn3000) and choose the DeepSeek-R1 design.
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The design detail page supplies vital details about the design's abilities, pricing structure, and implementation standards. You can discover detailed use directions, consisting of sample API calls and code snippets for combination. The design supports different text generation jobs, including material development, code generation, and concern answering, utilizing its support discovering optimization and CoT thinking abilities. +The page likewise includes deployment alternatives and licensing details to help you get going with DeepSeek-R1 in your applications. +3. To start using DeepSeek-R1, select Deploy.
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You will be triggered to configure the deployment details for DeepSeek-R1. The model ID will be pre-populated. +4. For Endpoint name, enter an endpoint name (in between 1-50 alphanumeric characters). +5. For Variety of circumstances, go into a variety of instances (between 1-100). +6. For Instance type, pick your circumstances type. For optimal efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is suggested. +Optionally, you can configure sophisticated security and infrastructure settings, consisting of virtual private cloud (VPC) networking, service function authorizations, and file encryption settings. For many utilize cases, the will work well. However, for production implementations, you might want to review these settings to line up with your organization's security and compliance requirements. +7. Choose Deploy to begin using the model.
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When the release is complete, you can check 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 different prompts and adjust design specifications like temperature and maximum length. +When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for ideal outcomes. For instance, material for reasoning.
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This is an outstanding method to explore the model's thinking and text generation abilities before integrating it into your [applications](https://circassianweb.com). The play ground provides instant feedback, [assisting](https://autogenie.co.uk) you understand how the model reacts to various inputs and letting you tweak your triggers for ideal results.
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You can rapidly check the model in the play area 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](https://www.kenpoguy.com) DeepSeek-R1 endpoint
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The following code example shows how to carry out reasoning utilizing a released DeepSeek-R1 design through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can produce 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 actually developed the guardrail, utilize the following code to execute guardrails. The script initializes the bedrock_runtime customer, sets up reasoning parameters, and sends 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, integrated algorithms, and prebuilt ML solutions that you can release with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your data, and release them into production utilizing either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart uses two practical techniques: utilizing the instinctive SageMaker JumpStart UI or executing programmatically through the SageMaker [Python SDK](https://jobsscape.com). Let's explore both techniques to assist you choose the method that best fits your needs.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following steps to deploy 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 prompted to create a domain. +3. On the SageMaker Studio console, choose JumpStart in the navigation pane.
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The design browser displays available designs, with details like the service provider name and design capabilities.
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4. Search for DeepSeek-R1 to view the DeepSeek-R1 design card. +Each model card shows crucial details, including:
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- Model name +- Provider name +- Task [category](https://tobesmart.co.kr) (for instance, Text Generation). +Bedrock Ready badge (if relevant), showing that this design can be registered with Amazon Bedrock, allowing you to use Amazon Bedrock APIs to invoke the design
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5. Choose the design card to view the model details page.
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The design details page consists of the following details:
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- The design name and company details. +Deploy button to deploy 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 specifications. +- Usage guidelines
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Before you release the design, it's recommended to review the model details and license terms to verify compatibility with your usage case.
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6. Choose Deploy to continue with implementation.
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7. For Endpoint name, use the instantly generated name or develop a [customized](http://154.209.4.103001) one. +8. For example type ΒΈ choose an instance type (default: ml.p5e.48 xlarge). +9. For Initial instance count, go into the variety of circumstances (default: 1). +Selecting suitable circumstances types and counts is essential for cost and efficiency optimization. Monitor your implementation 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 accuracy. For this design, we strongly recommend sticking to [SageMaker JumpStart](http://suvenir51.ru) default [settings](https://scienetic.de) and making certain that network seclusion remains in location. +11. Choose Deploy to release the design.
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The release procedure can take several minutes to complete.
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When release is total, your endpoint status will change to InService. At this moment, the model is prepared to accept inference demands through the endpoint. You can keep an eye on the deployment development on the SageMaker [console Endpoints](http://ecoreal.kr) page, which will show relevant metrics and status details. When the implementation is total, you can conjure up the design utilizing a SageMaker runtime client and integrate it with your applications.
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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 necessary AWS approvals and environment setup. The following is a detailed code example that shows how to deploy and use DeepSeek-R1 for reasoning programmatically. The code for releasing the design is provided 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 inference with your [SageMaker JumpStart](https://copyright-demand-letter.com) predictor
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Similar to Amazon Bedrock, you can also utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can create a guardrail utilizing 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 charges, complete the actions in this section to clean up your resources.
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Delete the Amazon Bedrock Marketplace release
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If you released the model using Amazon Bedrock Marketplace, complete the following actions:
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1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, select Marketplace implementations. +2. In the Managed releases section, locate the endpoint you desire to erase. +3. Select the endpoint, and on the Actions menu, pick Delete. +4. Verify the [endpoint details](http://demo.qkseo.in) to make certain you're deleting the correct release: 1. [Endpoint](https://git.touhou.dev) 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 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 explored how you can access and deploy the DeepSeek-R1 design using Bedrock Marketplace and SageMaker [JumpStart](https://asromafansclub.com). 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](https://kaykarbar.com) 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://39.98.253.192:3000) companies build ingenious options using AWS services and accelerated calculate. Currently, he is concentrated on developing techniques for fine-tuning and optimizing the reasoning efficiency of big language models. In his leisure time, Vivek delights in treking, seeing motion pictures, and [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11860868) attempting different cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](http://120.79.7.122:3000) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His location of focus is AWS [AI](https://bnsgh.com) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.
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Jonathan Evans is a Professional Solutions Architect dealing with generative [AI](http://ecoreal.kr) with the Third-Party Model Science team at AWS.
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Banu Nagasundaram leads product, engineering, and strategic collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://git.brodin.rocks) hub. She is enthusiastic about building services that help clients accelerate their [AI](https://grailinsurance.co.ke) journey and unlock organization worth.
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