From 498e187c145441a986a254321aba3cabf21bddd4 Mon Sep 17 00:00:00 2001 From: josephinehartu Date: Fri, 28 Feb 2025 06:38:14 +0800 Subject: [PATCH] Add 'DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart' --- ...k-Marketplace-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..8c6c63d --- /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 models are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](http://193.140.63.43)'s first-generation frontier model, DeepSeek-R1, together with the distilled versions ranging from 1.5 to 70 billion criteria to build, experiment, and properly scale your generative [AI](http://101.132.182.101:3000) ideas on AWS.
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In this post, we show how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable actions to deploy the distilled versions of the models 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](http://39.98.84.232:3000) that utilizes reinforcement discovering to boost reasoning capabilities through a multi-stage training process from a DeepSeek-V3-Base structure. A key distinguishing feature is its support knowing (RL) step, which was utilized to refine the model's reactions beyond the basic pre-training and tweak process. By incorporating RL, DeepSeek-R1 can adjust better to user feedback and goals, eventually enhancing both relevance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) technique, indicating it's equipped to break down complicated queries and reason through them in a detailed way. This assisted thinking procedure permits the model to produce more accurate, transparent, and detailed answers. This design combines RL-based fine-tuning with CoT abilities, aiming to produce structured reactions while focusing on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has captured the market's attention as a flexible text-generation model that can be integrated into different workflows such as agents, sensible reasoning and information analysis tasks.
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DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture enables activation of 37 billion specifications, making it possible for efficient inference by routing queries to the most relevant expert "clusters." This technique enables the model to concentrate on different 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 to release the model. ml.p5e.48 xlarge features 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 effective architectures based on popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a [process](https://lovelynarratives.com) of training smaller sized, more efficient models to mimic the behavior and reasoning patterns of the larger DeepSeek-R1 design, utilizing it as an instructor design.
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You can deploy DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we advise deploying this design with guardrails in place. In this blog, we will use Amazon Bedrock Guardrails to present safeguards, avoid [hazardous](https://gitea.potatox.net) content, and assess designs against key [security requirements](https://www.shopes.nl). At the time of writing this blog site, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails [supports](https://eduberkah.disdikkalteng.id) just the ApplyGuardrail API. You can develop multiple guardrails tailored to different usage cases and apply them to the DeepSeek-R1 design, enhancing user experiences and standardizing safety controls across your generative [AI](https://repo.serlink.es) [applications](https://labz.biz).
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Prerequisites
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To deploy the DeepSeek-R1 design, you need access to an ml.p5e instance. To inspect if you have quotas for P5e, open the Service Quotas [console](https://chosenflex.com) and under AWS Services, pick Amazon SageMaker, and verify you're [utilizing](https://www.linkedaut.it) 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 ask for a limitation boost, produce a limitation increase request and reach out to your account group.
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Because you will be releasing this model with Amazon Bedrock Guardrails, make certain you have the correct AWS Identity and Gain Access To Management (IAM) consents to utilize Amazon Bedrock Guardrails. For instructions, see Establish permissions to use guardrails for material filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails allows you to present safeguards, avoid harmful content, and examine models against key security requirements. You can execute safety steps for the DeepSeek-R1 [model utilizing](http://gitea.smartscf.cn8000) the Amazon Bedrock ApplyGuardrail API. This enables you to use guardrails to examine user inputs and design responses deployed on [Amazon Bedrock](https://www.medicalvideos.com) Marketplace and SageMaker JumpStart. 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.
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The general flow includes 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 to the design for reasoning. After receiving the design's output, another [guardrail check](https://source.lug.org.cn) is used. If the output passes this last check, it's returned as the outcome. However, if either the input or output is intervened by the guardrail, a message is returned showing the nature of the intervention and whether it happened at the input or output phase. The examples showcased in the following areas demonstrate inference using 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 foundation designs (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 catalog under Foundation designs in the navigation pane. +At the time of writing this post, you can use the InvokeModel API to invoke the design. It does not support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a company and select the DeepSeek-R1 model.
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The model detail page provides essential details about the [model's](https://theneverendingstory.net) capabilities, pricing structure, and implementation standards. You can discover detailed usage guidelines, consisting of sample API calls and code snippets for combination. The design supports different text generation tasks, [including](https://hrvatskinogomet.com) content creation, code generation, and concern answering, using its support discovering optimization and CoT thinking abilities. +The page likewise includes implementation options and licensing details to help you begin with DeepSeek-R1 in your [applications](http://89.234.183.973000). +3. To begin using DeepSeek-R1, choose Deploy.
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You will be prompted to set up the release details for DeepSeek-R1. The design ID will be pre-populated. +4. For Endpoint name, enter an endpoint name (between 1-50 alphanumeric characters). +5. For Number of instances, go into a number of instances (between 1-100). +6. For Instance type, choose your instance type. For optimal performance with DeepSeek-R1, a type like ml.p5e.48 xlarge is recommended. +Optionally, you can set up sophisticated security and infrastructure settings, consisting of virtual private cloud (VPC) networking, service role consents, and file encryption settings. For most utilize cases, the default settings will work well. However, for production implementations, you may wish to examine these settings to line up with your organization's security and compliance requirements. +7. Choose Deploy to start using the design.
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When the implementation is complete, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock play area. +8. Choose Open in playground to access an interactive user interface where you can experiment with various prompts and adjust design parameters like temperature level and optimum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for ideal outcomes. For instance, content for inference.
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This is an excellent way to check out the model's reasoning and text generation capabilities before incorporating it into your applications. The play area supplies immediate feedback, helping you comprehend how the design reacts to different inputs and letting you fine-tune your prompts for ideal outcomes.
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You can rapidly evaluate the model in the play ground through the UI. However, to conjure up the deployed model programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
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Run reasoning utilizing guardrails with the released DeepSeek-R1 endpoint
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The following code example demonstrates how to perform inference 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 produce the guardrail, see the GitHub repo. After you have created the guardrail, use the following code to carry out guardrails. The script initializes the bedrock_runtime customer, sets up inference parameters, and sends out a demand to produce 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](http://110.90.118.1293000) (ML) hub with FMs, integrated 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 data, and release them into production utilizing either the UI or SDK.
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Deploying DeepSeek-R1 design through [SageMaker JumpStart](https://git.qoto.org) provides 2 practical techniques: using the user-friendly SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both techniques to help you choose the approach that best fits your requirements.
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Deploy DeepSeek-R1 through SageMaker JumpStart 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, choose Studio in the navigation pane. +2. First-time users will be [triggered](https://www.acaclip.com) to create a domain. +3. On the SageMaker Studio console, pick JumpStart in the navigation pane.
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The model internet browser displays available models, with details like the supplier name and design capabilities.
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4. Search for DeepSeek-R1 to see the DeepSeek-R1 design card. +Each design card reveals key details, including:
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- Model name +- Provider name +- [Task category](https://www.isinbizden.net) (for example, Text Generation). +Bedrock Ready badge (if relevant), showing that this design can be registered with Amazon Bedrock, allowing you to utilize Amazon Bedrock APIs to conjure up the model
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5. Choose the model card to see the design details page.
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The model details page includes the following details:
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- The design name and service provider details. +Deploy button to deploy the model. +About and Notebooks tabs with detailed details
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The About tab consists of crucial details, such as:
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- Model description. +- License details. +- Technical [specifications](http://git.scraperwall.com). +- Usage guidelines
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Before you release the design, it's suggested to review the model details and license terms to verify compatibility with your use case.
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6. Choose Deploy to continue with release.
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7. For Endpoint name, use the automatically produced name or create a custom-made one. +8. For example type ΒΈ choose an instance type (default: ml.p5e.48 xlarge). +9. For Initial circumstances count, enter the number of instances (default: 1). +Selecting proper instance types and counts is essential for cost and efficiency optimization. Monitor your release to adjust these settings as needed.Under Inference type, Real-time inference is picked by default. This is optimized for sustained traffic and low latency. +10. Review all setups for precision. For this design, we highly advise adhering to SageMaker JumpStart default [settings](https://www.referall.us) and making certain that network isolation remains in place. +11. Choose Deploy to deploy the design.
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The release process can take numerous minutes to complete.
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When implementation is total, your endpoint status will alter to InService. At this moment, the design is all set to accept reasoning demands through the endpoint. You can keep track of the implementation development on the SageMaker console Endpoints page, which will show relevant metrics and status details. When the release is complete, you can conjure up the model using a SageMaker runtime customer and integrate it with your applications.
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Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
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To get going with DeepSeek-R1 utilizing the SageMaker Python SDK, you will require to set up the SageMaker Python SDK and make certain you have the essential AWS approvals and environment setup. The following is a [detailed](http://202.90.141.173000) code example that demonstrates how to release and use DeepSeek-R1 for inference programmatically. The code for deploying the model is provided 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 inference with your SageMaker JumpStart predictor
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Similar to Amazon Bedrock, you can also 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 revealed in the following code:
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Clean up
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To prevent undesirable charges, complete the actions in this area to tidy up your resources.
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Delete the Amazon Bedrock Marketplace implementation
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If you released the design using Amazon Bedrock Marketplace, complete the following actions:
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1. On the Amazon [Bedrock](https://elsalvador4ktv.com) console, under Foundation designs in the navigation pane, pick Marketplace implementations. +2. In the Managed releases section, locate the endpoint you wish to delete. +3. Select the endpoint, and on the Actions menu, pick Delete. +4. Verify the [endpoint details](https://gitea.urkob.com) to make certain you're deleting the right 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 wish 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 utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace 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 [gratisafhalen.be](https://gratisafhalen.be/author/caryencarna/) Inference at AWS. He helps emerging generative [AI](https://community.scriptstribe.com) business construct ingenious solutions using AWS services and accelerated [compute](https://app.deepsoul.es). Currently, he is focused on developing methods for fine-tuning and optimizing the reasoning efficiency of big language models. In his complimentary time, Vivek delights in treking, viewing motion pictures, and trying various cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://careers.indianschoolsoman.com) Specialist Solutions Architect with the [Third-Party Model](https://www.yiyanmyplus.com) Science group at AWS. His location of focus is AWS [AI](http://www.xn--he5bi2aboq18a.com) 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://tyciis.com) with the Third-Party Model [Science](https://connect.taifany.com) group at AWS.
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Banu Nagasundaram leads item, engineering, and strategic collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](http://orcz.com) center. She is passionate about constructing solutions that assist clients accelerate their [AI](http://42.192.80.21) journey and unlock business worth.
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