From b07ab49dbd175418563c7d81c5abb8027934260c Mon Sep 17 00:00:00 2001 From: carleybligh712 Date: Sun, 6 Apr 2025 13:17:03 +0000 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..bca4f44 --- /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 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://ruofei.vip)'s first-generation frontier design, DeepSeek-R1, together with the distilled variations varying from 1.5 to 70 billion specifications to develop, experiment, and properly scale your generative [AI](http://gitlab.dstsoft.net) 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 release the distilled versions of the models too.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a big language design (LLM) developed by DeepSeek [AI](https://phdjobday.eu) that [utilizes reinforcement](http://test-www.writebug.com3000) finding out to improve thinking abilities through a multi-stage training process from a DeepSeek-V3-Base foundation. An essential distinguishing function is its reinforcement knowing (RL) action, [archmageriseswiki.com](http://archmageriseswiki.com/index.php/User:FKFSusie563778) which was utilized to refine the design's responses beyond the basic pre-training and fine-tuning procedure. By integrating RL, DeepSeek-R1 can adapt better to user feedback and goals, ultimately improving both significance and [clarity](https://jobs.but.co.id). In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) technique, meaning it's equipped to break down complex questions and reason through them in a detailed way. This assisted thinking procedure permits the design to produce more accurate, transparent, and detailed responses. This model combines RL-based fine-tuning with CoT capabilities, aiming to create structured reactions while concentrating on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has captured the market's attention as a versatile text-generation design that can be integrated into numerous workflows such as representatives, sensible thinking and data interpretation jobs.
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DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture enables activation of 37 billion specifications, making it possible for effective reasoning by routing inquiries to the most pertinent expert "clusters." This technique allows the design to focus on different problem [domains](http://121.4.154.1893000) while maintaining overall effectiveness. DeepSeek-R1 requires 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 to deploy 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 reasoning abilities of the main R1 design to more effective architectures based upon popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of training smaller, more efficient designs to simulate the behavior and reasoning patterns of the larger DeepSeek-R1 model, utilizing it as an instructor 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 recommend releasing this design with guardrails in location. In this blog, we will use Amazon Bedrock Guardrails to introduce safeguards, avoid harmful material, and assess designs against key security criteria. At the time of composing this blog, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can produce 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://suomalainennaikki.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, choose Amazon SageMaker, and confirm you're utilizing 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 deploying. To request a limitation increase, create a limitation boost demand and connect to your account group.
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Because you will be deploying this design with Amazon Bedrock Guardrails, make certain you have the appropriate AWS [Identity](https://placementug.com) and Gain Access To Management (IAM) consents to utilize Amazon Bedrock Guardrails. For guidelines, see Set up authorizations to use guardrails for content filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails allows you to present safeguards, avoid damaging content, and examine designs against essential security requirements. You can [execute](http://gogs.efunbox.cn) for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to evaluate user inputs and design responses deployed on Amazon Bedrock [Marketplace](https://hotjobsng.com) and SageMaker JumpStart. 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.
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The basic flow involves the following steps: First, the system receives an input for the design. 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 design's output, another guardrail check is used. If the output passes this final check, it's returned as the result. However, if either the input or output is stepped in by the guardrail, a message is returned indicating the nature of the [intervention](https://saghurojobs.com) and whether it occurred at the input or output phase. The examples showcased in the following areas show inference utilizing this API.
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Deploy DeepSeek-R1 in Amazon Bedrock Marketplace
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Amazon [Bedrock Marketplace](http://h.gemho.cn7099) 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, choose Model brochure under Foundation designs in the navigation pane. +At the time of writing this post, you can use the InvokeModel API to invoke the model. It doesn't 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 model detail page provides essential details about the model's capabilities, prices structure, and implementation guidelines. You can discover detailed usage instructions, consisting of sample API calls and code bits for integration. The design supports different text generation tasks, consisting of content production, code generation, and concern answering, [pediascape.science](https://pediascape.science/wiki/User:Jeremy54P355271) utilizing its reinforcement discovering optimization and CoT reasoning capabilities. +The page likewise consists of deployment options and licensing details to assist you start with DeepSeek-R1 in your applications. +3. To start using DeepSeek-R1, pick Deploy.
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You will be triggered to configure the implementation details 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 Variety of instances, get in a number of instances (in between 1-100). +6. For [Instance](https://charin-issuedb.elaad.io) type, pick your circumstances type. For ideal performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised. +Optionally, you can set up innovative security and infrastructure settings, including virtual personal cloud (VPC) networking, service role consents, and file encryption settings. For many utilize cases, the default settings will work well. However, for production deployments, you may desire to evaluate these settings to line up with your organization's security and compliance requirements. +7. Choose Deploy to begin using the design.
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When the release is total, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock play ground. +8. Choose Open in play ground to access an interactive user interface where you can try out various triggers and adjust design criteria like temperature level and maximum length. +When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for ideal results. For instance, content for reasoning.
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This is an outstanding way to explore the model's reasoning and text generation abilities before incorporating it into your applications. The play ground supplies immediate feedback, assisting you comprehend how the model reacts to different inputs and [letting](http://grainfather.asia) you tweak your [prompts](https://higgledy-piggledy.xyz) for [ideal outcomes](https://gogs.sxdirectpurchase.com).
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You can [rapidly](https://career.logictive.solutions) test the design in the play area through the UI. However, to invoke the released design programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.
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Run reasoning using guardrails with the deployed DeepSeek-R1 endpoint
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The following code example shows how to carry out reasoning utilizing a deployed DeepSeek-R1 model 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 actually developed the guardrail, use the following code to implement guardrails. The script initializes the bedrock_runtime customer, [configures inference](http://shenjj.xyz3000) parameters, and sends out a request to produce text based upon 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 services that you can deploy with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your information, and deploy them into [production utilizing](http://116.62.118.242) either the UI or SDK.
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Deploying DeepSeek-R1 design through SageMaker JumpStart provides 2 convenient methods: using the user-friendly SageMaker [JumpStart](http://zerovalueentertainment.com3000) UI or executing programmatically through the SageMaker Python SDK. Let's check out both methods to help you select the technique that best matches your requirements.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following actions 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 triggered to create a domain. +3. On the SageMaker Studio console, select JumpStart in the navigation pane.
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The model web browser shows available models, with details like the company name and design capabilities.
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4. Search for DeepSeek-R1 to see the DeepSeek-R1 [model card](http://122.51.46.213). +Each model card reveals key details, consisting of:
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- Model name +- Provider name +- Task classification (for instance, Text Generation). +Bedrock Ready badge (if appropriate), indicating that this design can be registered with Amazon Bedrock, enabling you to use Amazon Bedrock APIs to invoke the design
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5. Choose the [design card](http://www.sa1235.com) to see the model details page.
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The design details page includes the following details:
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- The design name and company details. +Deploy button to release the model. +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 model, it's recommended to review the design 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 automatically produced name or create a custom one. +8. For example type ΒΈ select an instance type (default: ml.p5e.48 xlarge). +9. For Initial instance count, enter the number of [instances](http://git.suxiniot.com) (default: 1). +Selecting proper circumstances types and counts is essential for expense and performance optimization. Monitor your deployment 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 setups for accuracy. For this design, we strongly recommend adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in location. +11. Choose Deploy to deploy the model.
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The release procedure can take a number of minutes to complete.
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When deployment is total, your endpoint status will change to InService. At this point, the design is ready to accept reasoning demands through the endpoint. You can monitor the release development on the SageMaker console Endpoints page, which will display pertinent metrics and status details. When the release is complete, you can conjure up the model using a SageMaker runtime client and integrate it with your [applications](https://gertsyhr.com).
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Deploy DeepSeek-R1 using the SageMaker Python SDK
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To get begun with DeepSeek-R1 using the SageMaker Python SDK, you will require to install the SageMaker Python SDK and make certain you have the necessary AWS consents and environment setup. The following is a detailed code example that shows how to release and use DeepSeek-R1 for reasoning programmatically. The code for deploying the model is provided in the Github here. You can clone the notebook and run from SageMaker Studio.
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You can run extra 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 also use 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 displayed in the following code:
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Clean up
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To avoid undesirable charges, complete the steps in this area to clean up your [resources](https://gitlab.xfce.org).
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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 releases. +2. In the Managed releases area, find the endpoint you desire to delete. +3. Select the endpoint, and on the Actions menu, select Delete. +4. Verify the endpoint details to make certain you're erasing the appropriate 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 released will sustain expenses if you leave it running. Use the following code to delete the [endpoint](https://career.finixia.in) 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](https://www.50seconds.com) how you can access and release the DeepSeek-R1 design using Bedrock Marketplace and [SageMaker](https://redefineworksllc.com) JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to begin. 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 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](https://aceme.ink) at AWS. He assists emerging generative [AI](https://gigen.net) companies develop ingenious services using AWS services and sped up calculate. Currently, he is concentrated on establishing techniques for fine-tuning and enhancing the reasoning performance of large language models. In his downtime, Vivek delights in hiking, watching motion pictures, and trying various foods.
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Niithiyn Vijeaswaran is a Generative [AI](https://www.globaltubedaddy.com) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His location of focus is AWS [AI](https://sss.ung.si) 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 dealing with generative [AI](https://git.gocasts.ir) with the Third-Party Model Science team at AWS.
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[Banu Nagasundaram](http://59.37.167.938091) leads product, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://e-sungwoo.co.kr) hub. She is enthusiastic about constructing services that help consumers accelerate their [AI](https://gogocambo.com) journey and unlock organization value.
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