From 360ebd37c03ec0918855d26a700b0244b6555aec Mon Sep 17 00:00:00 2001 From: eptkoby1845571 Date: Tue, 8 Apr 2025 00:27:54 +0300 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..e76c618 --- /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](http://106.52.121.976088) 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](http://163.228.224.105:3000)'s first-generation frontier design, DeepSeek-R1, in addition to the distilled variations varying from 1.5 to 70 billion [parameters](https://aggm.bz) to develop, experiment, and responsibly scale your generative [AI](http://116.62.118.242) ideas on AWS.
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In this post, we show how to get begun with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar steps to release 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 model (LLM) developed by DeepSeek [AI](https://www.almanacar.com) that utilizes support discovering to enhance reasoning abilities through a multi-stage training process from a DeepSeek-V3-Base structure. A crucial differentiating feature is its reinforcement knowing (RL) step, which was used to improve the model's responses beyond the basic pre-training and tweak procedure. By integrating RL, DeepSeek-R1 can adjust more efficiently to user feedback and objectives, eventually enhancing both importance and clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) technique, suggesting it's geared up to break down complex queries and reason through them in a detailed way. This [directed thinking](https://15.164.25.185) procedure enables the model to produce more accurate, transparent, and detailed responses. This design combines RL-based fine-tuning with CoT abilities, aiming to create [structured reactions](https://www.jobzpakistan.info) while concentrating on interpretability and user interaction. With its comprehensive abilities DeepSeek-R1 has caught the market's attention as a flexible text-generation model that can be integrated into different workflows such as agents, logical reasoning and information analysis tasks.
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DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture enables activation of 37 billion specifications, making it possible for effective inference by routing questions to the most relevant expert "clusters." This approach enables the design to concentrate on various issue domains while maintaining general effectiveness. DeepSeek-R1 needs at least 800 GB of HBM memory in FP8 format for inference. In this post, we will utilize an ml.p5e.48 xlarge circumstances to deploy the design. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.
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DeepSeek-R1 distilled designs bring the reasoning abilities of the main R1 design 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 process of training smaller, more effective designs to simulate the [behavior](http://git.nikmaos.ru) and thinking patterns of the larger DeepSeek-R1 model, using it as a teacher model.
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You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we suggest deploying this design with guardrails in place. In this blog, we will use Amazon Bedrock Guardrails to present safeguards, prevent harmful content, and [evaluate models](http://115.159.107.1173000) against crucial security criteria. At the time of [composing](http://turtle.tube) this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can develop numerous guardrails tailored to different usage cases and use them to the DeepSeek-R1 model, enhancing user experiences and standardizing security controls throughout your [generative](https://dztrader.com) [AI](https://dev.worldluxuryhousesitting.com) [applications](https://git.torrents-csv.com).
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Prerequisites
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To release the DeepSeek-R1 design, you require access to an ml.p5e circumstances. To examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick Amazon SageMaker, and validate you're utilizing 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 deploying. To ask for a limitation boost, create a limit increase 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 correct AWS Identity and Gain Access To Management (IAM) permissions to use [Amazon Bedrock](https://supremecarelink.com) Guardrails. For instructions, see Set up approvals to use guardrails for content filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails permits you to introduce safeguards, avoid damaging material, and examine designs against crucial safety criteria. You can execute precaution for the DeepSeek-R1 design using the Amazon Bedrock [ApplyGuardrail](https://89.22.113.100) API. This enables you to use guardrails to assess user inputs and model responses released 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 general circulation involves the following steps: 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 model for reasoning. After getting the design's output, another [guardrail check](https://premiergitea.online3000) is applied. 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 suggesting](http://krasnoselka.od.ua) the nature of the intervention and whether it took place at the input or output stage. 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 provides 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 steps:
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1. On the Amazon Bedrock console, choose Model brochure under Foundation models in the navigation pane. +At the time of writing this post, you can utilize the InvokeModel API to conjure up the design. It does not support Converse APIs and other Amazon Bedrock tooling. +2. Filter for DeepSeek as a service provider and pick the DeepSeek-R1 model.
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The model detail page supplies important details about the model's capabilities, prices structure, and application standards. You can discover detailed usage directions, consisting of sample API calls and code snippets for integration. The design supports numerous text generation tasks, including material creation, code generation, and concern answering, utilizing its reinforcement discovering optimization and CoT reasoning abilities. +The page likewise includes deployment alternatives and licensing details to assist you get going with DeepSeek-R1 in your applications. +3. To begin using DeepSeek-R1, pick Deploy.
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You will be prompted to configure the deployment details for DeepSeek-R1. The design ID will be pre-populated. +4. For Endpoint name, get in an endpoint name (between 1-50 alphanumeric characters). +5. For Variety of instances, get in a variety of circumstances (between 1-100). +6. For example type, pick your circumstances type. For [optimal performance](https://laborando.com.mx) with DeepSeek-R1, a type like ml.p5e.48 xlarge is recommended. +Optionally, you can set up [innovative security](http://1024kt.com3000) and facilities settings, including virtual private cloud (VPC) networking, service function approvals, and file encryption settings. For a lot of use cases, the default settings will work well. However, for production implementations, you might want to evaluate these settings to line up with your organization's security and compliance requirements. +7. Choose Deploy to begin utilizing the design.
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When the deployment is complete, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock play ground. +8. Choose Open in playground to access an interactive user interface where you can explore different prompts and change design criteria like temperature and optimum length. +When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for ideal results. For example, material for inference.
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This is an exceptional way to check out the design's reasoning and text generation capabilities before integrating it into your applications. The [playground supplies](https://gogs.eldarsoft.com) immediate feedback, assisting you comprehend how the model reacts to numerous inputs and letting you fine-tune your prompts for ideal results.
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You can rapidly check the design in the play ground through the UI. However, to invoke the deployed model programmatically with any Amazon Bedrock APIs, you require 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 demonstrates how to perform reasoning utilizing a deployed DeepSeek-R1 design through Amazon Bedrock using the invoke_model and ApplyGuardrail API. 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. After you have created the guardrail, use the following code to execute guardrails. The script initializes the bedrock_runtime client, sets up reasoning criteria, and sends a request to create 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 release with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained [designs](https://kibistudio.com57183) to your usage case, with your data, and deploy them into production using either the UI or SDK.
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Deploying DeepSeek-R1 design through SageMaker JumpStart uses 2 hassle-free methods: utilizing the intuitive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's explore both [methods](https://git.gra.phite.ro) to assist you pick the method that best suits your needs.
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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 prompted to create a domain. +3. On the SageMaker Studio console, choose JumpStart in the navigation pane.
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The model internet browser displays available models, with details like the company name and model abilities.
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4. Search for DeepSeek-R1 to view the DeepSeek-R1 design card. +Each model card shows crucial details, consisting of:
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- Model name +- Provider name +- Task classification (for instance, Text Generation). +Bedrock Ready badge (if relevant), suggesting 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 to view the design details page.
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The design details page includes the following details:
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- The model name and supplier details. +Deploy button to release the design. +About and Notebooks tabs with detailed details
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The About tab includes essential details, such as:
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- Model description. +- License [details](http://git.armrus.org). +- Technical specifications. +- Usage standards
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Before you release the model, it's suggested to review the [model details](https://teachersconsultancy.com) and license terms to [validate compatibility](https://afrocinema.org) with your usage case.
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6. Choose Deploy to proceed with implementation.
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7. For Endpoint name, utilize the instantly generated 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://kibistudio.com57183) count, go into the variety of circumstances (default: [pediascape.science](https://pediascape.science/wiki/User:JohnLongstreet) 1). +Selecting suitable instance types and counts is important for cost and performance optimization. Monitor your release to change these settings as needed.Under Inference type, Real-time reasoning is selected by default. This is optimized for sustained traffic and low latency. +10. Review all setups for precision. For this model, we strongly suggest sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in location. +11. Choose Deploy to deploy the design.
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The implementation procedure can take several minutes to finish.
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When deployment is total, your endpoint status will change to InService. At this point, the design is ready to accept inference demands through the endpoint. You can keep track of the deployment progress on the SageMaker console Endpoints page, which will display relevant metrics and status details. When the implementation is complete, you can invoke the design utilizing 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 begin with DeepSeek-R1 using the [SageMaker](https://git.rongxin.tech) Python SDK, you will need to install the SageMaker Python SDK and make certain you have the required AWS consents and environment setup. The following is a detailed code example that demonstrates how to release and utilize DeepSeek-R1 for inference programmatically. The code for deploying the design is supplied in the Github here. You can clone the note pad 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 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 using the Amazon Bedrock [console](https://dainiknews.com) or the API, and execute it as displayed in the following code:
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Tidy up
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To avoid unwanted charges, finish the actions in this area to tidy up your resources.
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Delete the Amazon Bedrock Marketplace release
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If you released the model 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, 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, choose Delete. +4. Verify the endpoint details to make certain you're erasing the correct 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 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 explored how you can access and release the DeepSeek-R1 model using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get begun. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker [JumpStart](http://wiki.pokemonspeedruns.com) designs, SageMaker JumpStart pretrained models, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting started with Amazon SageMaker JumpStart.
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About the Authors
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Vivek Gangasani is a [Lead Specialist](https://truthbook.social) Solutions Architect for Inference at AWS. He assists emerging generative [AI](http://60.209.125.238:20010) business build innovative solutions using AWS services and accelerated calculate. Currently, he is focused on developing strategies for fine-tuning and enhancing the reasoning efficiency of big language models. In his free time, Vivek takes pleasure in treking, enjoying movies, and attempting various cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://www.allclanbattles.com) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His area of focus is AWS [AI](https://code.paperxp.com) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.
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Jonathan Evans is an Expert Solutions Architect dealing with generative [AI](http://dev.catedra.edu.co:8084) with the [Third-Party Model](https://sebagai.com) Science group at AWS.
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Banu Nagasundaram leads product, engineering, and [tactical partnerships](http://gitea.zyimm.com) for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](http://140.82.32.174) center. She is passionate about building solutions that help customers accelerate their [AI](https://talentmatch.somatik.io) [journey](https://www.infinistation.com) and unlock company value.
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