From a5539cf8f4cb0489f9cd18efe331d94ca2e0ba15 Mon Sep 17 00:00:00 2001 From: franciscolcn44 Date: Sat, 1 Mar 2025 03:50:28 +0800 Subject: [PATCH] Add '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..272a426 --- /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 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](https://intunz.com)'s [first-generation frontier](https://pioneercampus.ac.in) model, DeepSeek-R1, together with the distilled variations varying from 1.5 to 70 billion criteria to construct, experiment, and responsibly scale your generative [AI](https://itheadhunter.vn) ideas on AWS.
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In this post, we demonstrate how to begin with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar actions to deploy the distilled variations of the designs too.
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[Overview](https://arbeitswerk-premium.de) of DeepSeek-R1
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DeepSeek-R1 is a large language design (LLM) established by DeepSeek [AI](http://47.103.91.160:50903) that uses reinforcement finding out to improve thinking abilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A key differentiating feature is its support learning (RL) action, which was utilized to refine the design's reactions beyond the standard pre-training and fine-tuning process. By integrating RL, DeepSeek-R1 can adapt more efficiently to user feedback and goals, eventually improving both relevance and clarity. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) approach, implying it's geared up to break down complicated inquiries and factor through them in a [detailed manner](https://121.36.226.23). This assisted reasoning procedure permits the design to produce more precise, transparent, and detailed responses. This model integrates RL-based fine-tuning with CoT capabilities, aiming to generate structured actions while concentrating on interpretability and user interaction. With its comprehensive abilities DeepSeek-R1 has recorded the industry's attention as a versatile text-generation design that can be incorporated into different workflows such as representatives, rational reasoning and data interpretation tasks.
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DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture permits activation of 37 billion specifications, making it possible for efficient inference by routing queries to the most appropriate specialist "clusters." This [approach](https://git.danomer.com) allows the model to focus on different issue domains while maintaining general 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 deploy the design. ml.p5e.48 xlarge includes 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 designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a process of training smaller sized, more effective designs to imitate the behavior and reasoning patterns of the larger DeepSeek-R1 model, using it as a teacher 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 releasing this design with guardrails in location. In this blog site, we will utilize Amazon Bedrock Guardrails to introduce safeguards, prevent hazardous material, and assess models against key security criteria. At the time of writing this blog site, for DeepSeek-R1 implementations on [SageMaker JumpStart](https://xinh.pro.vn) and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can produce several guardrails tailored to different use cases and use them to the DeepSeek-R1 design, improving user experiences and standardizing security controls throughout your generative [AI](http://116.62.159.194) applications.
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Prerequisites
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To deploy the DeepSeek-R1 model, you require access to an ml.p5e instance. To check if you have quotas for P5e, open the [Service Quotas](https://taelimfwell.com) console and under AWS Services, select Amazon SageMaker, and validate you're using ml.p5e.48 xlarge for endpoint use. Make certain that you have at least one ml.P5e.48 xlarge circumstances in the AWS Region you are deploying. To ask for a limit increase, create a limit boost [request](http://mengqin.xyz3000) 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 appropriate AWS Identity and Gain Access To Management (IAM) authorizations to use Amazon Bedrock Guardrails. For guidelines, see Set up permissions 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 [introduce](https://strimsocial.net) safeguards, avoid damaging content, and examine models against essential security requirements. You can execute security procedures for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to use guardrails to examine user inputs and model reactions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can create a guardrail utilizing the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo.
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The basic flow involves 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](https://jobs.superfny.com) check, it's sent out to the model for reasoning. After receiving the model's output, another guardrail check is applied. If the output passes this final check, it's [returned](https://app.zamow-kontener.pl) as the last result. However, if either the input or output is stepped in by the guardrail, a message is returned showing the nature of the intervention and whether it [occurred](http://47.94.142.23510230) at the input or output phase. The [examples](https://solegeekz.com) showcased in the following sections demonstrate reasoning 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 steps:
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1. On the Amazon Bedrock console, select Model catalog under Foundation designs in the navigation pane. +At the time of composing this post, you can utilize the InvokeModel API to conjure up the design. 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 supplies essential details about the model's abilities, prices structure, and implementation standards. You can discover detailed use directions, consisting of sample API calls and code bits for combination. The design supports various text generation jobs, including content development, code generation, and question answering, using its reinforcement discovering optimization and CoT thinking abilities. +The page also consists of deployment options and licensing details to help you get begun 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 release details for DeepSeek-R1. The model ID will be pre-populated. +4. For Endpoint name, go into an endpoint name (between 1-50 alphanumeric characters). +5. For Variety of circumstances, get in a number of circumstances (between 1-100). +6. For example type, select your instance type. For optimal efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is recommended. +Optionally, you can configure sophisticated security and infrastructure settings, including virtual private cloud (VPC) networking, service function consents, and encryption settings. For most utilize cases, the default settings will work well. However, for [production](http://222.85.191.975000) deployments, you might wish to examine these settings to align with your organization's security and compliance requirements. +7. Choose Deploy to begin utilizing the design.
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When the implementation is complete, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock play area. +8. Choose Open in play ground to access an interactive user interface where you can explore various triggers and adjust model parameters like temperature and maximum length. +When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for ideal results. For example, material for inference.
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This is an excellent method to check out the model's reasoning and text generation abilities before incorporating it into your applications. The playground provides immediate feedback, helping you comprehend how the model responds to numerous inputs and letting you fine-tune your prompts for optimal results.
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You can rapidly test the design in the play area through the UI. However, to conjure up the deployed design programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.
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Run inference using guardrails with the released DeepSeek-R1 endpoint
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The following code example shows how to carry out reasoning using a deployed DeepSeek-R1 design through Amazon Bedrock using the invoke_model and [ApplyGuardrail API](http://git.baige.me). You can create a [guardrail](http://42.192.80.21) using the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo. After you have actually developed the guardrail, utilize the following code to implement guardrails. The script initializes the bedrock_runtime client, configures reasoning criteria, and sends a demand 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, built-in algorithms, and prebuilt ML services 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 data, and deploy them into [production](https://umindconsulting.com) using either the UI or SDK.
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Deploying DeepSeek-R1 design through SageMaker JumpStart uses two practical approaches: using the user-friendly SageMaker JumpStart UI or carrying out [programmatically](https://jobs.cntertech.com) through the SageMaker Python SDK. Let's check out both approaches to help you choose the approach that finest fits your requirements.
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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, select Studio in the [navigation](https://phdjobday.eu) pane. +2. First-time users will be prompted to produce a domain. +3. On the SageMaker Studio console, select JumpStart in the navigation pane.
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The design browser displays available models, [demo.qkseo.in](http://demo.qkseo.in/profile.php?id=998410) with details like the supplier name and [model abilities](https://dlya-nas.com).
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4. Search for DeepSeek-R1 to view the DeepSeek-R1 design card. +Each design card shows essential details, including:
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- Model name +- Provider name +- Task classification (for example, Text Generation). +Bedrock Ready badge (if appropriate), suggesting that this model can be signed up with Amazon Bedrock, allowing you to use Amazon Bedrock APIs to conjure up the design
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5. Choose the model card to see the model details page.
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The model details page includes the following details:
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- The model name and supplier details. +Deploy button to release the model. +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 release the model, it's advised to review the design details and license terms to validate compatibility with your usage case.
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6. Choose Deploy to proceed with implementation.
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7. For Endpoint name, use the automatically produced name or create a customized one. +8. For Instance type ΒΈ select a circumstances type (default: ml.p5e.48 xlarge). +9. For [Initial circumstances](https://git.molokoin.ru) count, get in the variety of instances (default: 1). +Selecting proper circumstances types and counts is essential for expense and efficiency optimization. [Monitor](https://jobs.ethio-academy.com) your implementation to change these settings as needed.Under Inference type, Real-time reasoning is selected by default. This is enhanced for sustained traffic and low latency. +10. Review all setups for accuracy. For this model, we highly advise sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in place. +11. Choose Deploy to release the design.
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The implementation process can take numerous minutes to finish.
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When deployment is total, your endpoint status will alter to InService. At this point, the design is prepared to accept inference requests through the endpoint. You can keep an eye on the release development on the SageMaker console Endpoints page, which will show relevant metrics and status details. When the deployment is complete, you can invoke the model utilizing a SageMaker runtime client and integrate 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 need to set up the SageMaker [Python SDK](http://sanaldunyam.awardspace.biz) and make certain you have the needed AWS approvals and environment setup. The following is a detailed code example that shows how to deploy and utilize 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 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 create 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 avoid undesirable charges, complete the actions in this section to clean up your resources.
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Delete the Amazon Bedrock Marketplace implementation
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If you deployed the model utilizing Amazon Bedrock Marketplace, complete the following actions:
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1. On the Amazon Bedrock console, under [Foundation designs](http://47.122.66.12910300) in the navigation pane, choose Marketplace deployments. +2. In the Managed implementations section, locate the endpoint you want to erase. +3. Select the endpoint, and on the Actions menu, [pick Delete](https://choosy.cc). +4. Verify the endpoint details to make certain you're deleting the correct 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](https://jobs.colwagen.co) will sustain costs if you leave it running. Use the following code to erase 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 design using Bedrock Marketplace and SageMaker 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 models, 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](https://git.tx.pl) at AWS. He assists emerging generative [AI](https://integramais.com.br) companies develop ingenious options using and sped up compute. Currently, he is focused on developing techniques for fine-tuning and optimizing the reasoning efficiency of large language models. In his leisure time, Vivek delights in hiking, watching movies, and trying different foods.
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Niithiyn Vijeaswaran is a Generative [AI](http://222.85.191.97:5000) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His area of focus is AWS [AI](https://open-gitlab.going-link.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 working on generative [AI](https://jobsubscribe.com) with the Third-Party Model Science group 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](http://www.fun-net.co.kr) center. She is enthusiastic about developing options that help customers [accelerate](https://comunidadebrasilbr.com) their [AI](https://wutdawut.com) journey and unlock service worth.
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