From c1044dd7cdbe865ddad43606f3cf2d3879ffef1c Mon Sep 17 00:00:00 2001 From: Charli Pilgrim Date: Mon, 17 Feb 2025 12:21:44 +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..5364b3d --- /dev/null +++ b/DeepSeek-R1-Model-now-Available-in-Amazon-Bedrock-Marketplace-And-Amazon-SageMaker-JumpStart.md @@ -0,0 +1,93 @@ +
Today, we are thrilled 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 release DeepSeek [AI](http://47.103.91.160:50903)'s first-generation frontier design, [engel-und-waisen.de](http://www.engel-und-waisen.de/index.php/Benutzer:JuniorBowser22) DeepSeek-R1, along with the distilled versions ranging from 1.5 to 70 billion specifications to construct, experiment, and properly scale your generative [AI](https://www.bridgewaystaffing.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 similar actions to deploy the distilled versions of the designs also.
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Overview of DeepSeek-R1
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DeepSeek-R1 is a big language model (LLM) established by DeepSeek [AI](https://dhivideo.com) that utilizes reinforcement discovering to improve thinking [abilities](https://volunteering.ishayoga.eu) through a multi-stage training procedure from a DeepSeek-V3-Base structure. An essential differentiating function is its reinforcement knowing (RL) action, which was used to improve the design's responses beyond the basic pre-training and tweak process. By integrating RL, DeepSeek-R1 can adjust more efficiently to user feedback and objectives, eventually enhancing both significance and clarity. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) method, suggesting it's [equipped](http://dev.catedra.edu.co8084) to break down complicated queries and reason through them in a detailed manner. This assisted thinking procedure permits the design to produce more accurate, transparent, and detailed responses. This model integrates RL-based fine-tuning with CoT abilities, aiming to create structured reactions while focusing on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has caught the market's attention as a flexible text-generation model that can be integrated into various workflows such as agents, sensible reasoning and data analysis jobs.
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DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture allows activation of 37 billion criteria, enabling efficient inference by routing questions to the most pertinent specialist "clusters." This approach allows the model to specialize in different problem domains while maintaining overall efficiency. DeepSeek-R1 requires at least 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 comes with 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.
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DeepSeek-R1 distilled designs bring the reasoning capabilities of the main R1 model 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 process of training smaller, more effective models to simulate the behavior and thinking patterns of the bigger DeepSeek-R1 design, utilizing it as an [instructor model](http://www.hanmacsamsung.com).
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You can release DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we advise deploying this model with guardrails in [location](http://103.242.56.3510080). In this blog site, we will utilize Amazon Bedrock Guardrails to present safeguards, prevent harmful content, and examine models against [crucial safety](http://193.140.63.43) requirements. 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 create multiple guardrails tailored to various use cases and use them to the DeepSeek-R1 model, improving user experiences and standardizing security controls across your generative [AI](http://nas.killf.info:9966) applications.
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Prerequisites
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To release the DeepSeek-R1 model, you need access to an ml.p5e instance. To inspect if you have quotas for P5e, open the Service Quotas [console](https://moojijobs.com) and under AWS Services, choose Amazon SageMaker, and verify 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 boost, produce a limit boost demand and connect to your account group.
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Because you will be deploying this model with Amazon Bedrock Guardrails, make certain you have the correct AWS Identity and Gain Access To Management (IAM) authorizations to use Amazon Bedrock Guardrails. For guidelines, see Set up approvals to utilize guardrails for content filtering.
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Implementing guardrails with the ApplyGuardrail API
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Amazon Bedrock Guardrails permits you to present safeguards, prevent harmful content, and evaluate models against crucial security requirements. You can carry out precaution for the DeepSeek-R1 design using the Amazon Bedrock [ApplyGuardrail](https://pedulidigital.com) API. This allows you to use guardrails to examine user inputs and design responses released on [Amazon Bedrock](https://wisewayrecruitment.com) [Marketplace](https://navar.live) and SageMaker JumpStart. You can create a guardrail using 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 flow includes the following steps: [it-viking.ch](http://it-viking.ch/index.php/User:Muhammad9849) 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 out to the design for inference. After receiving the model's output, another guardrail check is used. If the output passes this last check, it's returned as the result. However, if either the input or output is intervened by the guardrail, a message is returned suggesting the nature of the intervention and whether it happened 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](http://39.105.128.46) Marketplace
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Amazon [Bedrock Marketplace](https://git.l1.media) offers you access to over 100 popular, emerging, and specialized foundation designs (FMs) through [Amazon Bedrock](https://hireblitz.com). 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 models in the navigation pane. +At the time of writing 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 select the DeepSeek-R1 design.
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The design detail page offers important details about the model's abilities, prices structure, and implementation guidelines. You can find detailed usage guidelines, consisting of sample API calls and code bits for integration. The design supports numerous text generation tasks, including content development, code generation, and concern answering, utilizing its support learning optimization and CoT reasoning capabilities. +The page also includes implementation choices and licensing details to help you get going with DeepSeek-R1 in your applications. +3. To begin utilizing DeepSeek-R1, select Deploy.
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You will be prompted to set up the [implementation details](https://www.bridgewaystaffing.com) for DeepSeek-R1. The model ID will be pre-populated. +4. For Endpoint name, [ratemywifey.com](https://ratemywifey.com/author/christenaw4/) enter an endpoint name (in between 1-50 [alphanumeric](https://cn.wejob.info) characters). +5. For Number of circumstances, enter a number of instances (in between 1-100). +6. For Instance type, choose your circumstances type. For ideal efficiency with DeepSeek-R1, [garagesale.es](https://www.garagesale.es/author/roseannanas/) a GPU-based instance type like ml.p5e.48 xlarge is recommended. +Optionally, you can set up sophisticated security and facilities settings, [including virtual](https://git.kawen.site) personal cloud (VPC) networking, service function permissions, and file encryption settings. For many use cases, the default settings will work well. However, for production deployments, you may wish to review 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 total, you can test DeepSeek-R1's abilities straight in the Amazon Bedrock play ground. +8. Choose Open in play area to access an interactive interface where you can explore various prompts and adjust design parameters 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 method to check out the design's reasoning and [text generation](https://mypetdoll.co.kr) capabilities before incorporating it into your applications. The play ground offers immediate feedback, assisting you understand how the model responds to inputs and letting you fine-tune your prompts for optimal outcomes.
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You can rapidly evaluate the model in the play area through the UI. However, to invoke the deployed design programmatically with any Amazon Bedrock APIs, you need to get the [endpoint ARN](https://apkjobs.com).
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Run reasoning utilizing guardrails with the [deployed](https://deepsound.goodsoundstream.com) DeepSeek-R1 endpoint
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The following code example demonstrates how to carry out reasoning using 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 create the guardrail, see the GitHub repo. After you have actually created the guardrail, use the following code to carry out [guardrails](https://sebeke.website). The script initializes the bedrock_runtime client, configures reasoning criteria, and sends out 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 services that you can deploy with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage case, with your information, and release them into production utilizing either the UI or SDK.
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Deploying DeepSeek-R1 model through SageMaker JumpStart provides two convenient approaches: utilizing the intuitive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's check out both techniques to assist you select the method that finest suits your needs.
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Deploy DeepSeek-R1 through SageMaker JumpStart UI
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Complete the following actions to deploy DeepSeek-R1 utilizing SageMaker JumpStart:
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1. On the SageMaker console, choose Studio in the [navigation](http://jobjungle.co.za) pane. +2. First-time users will be prompted to develop a domain. +3. On the SageMaker Studio console, pick JumpStart in the navigation pane.
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The model internet browser [displays](https://familytrip.kr) available models, with details like the supplier name and [design abilities](http://42.192.130.833000).
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4. Look for DeepSeek-R1 to see the DeepSeek-R1 model card. +Each model card shows key details, consisting of:
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- Model name +- Provider name +- Task classification (for instance, Text Generation). +Bedrock Ready badge (if suitable), suggesting that this model can be signed up with Amazon Bedrock, [enabling](https://fogel-finance.org) you to utilize Amazon Bedrock APIs to invoke the model
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5. Choose the design card to see the design details page.
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The model details page consists of the following details:
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- The model name and provider details. +Deploy button to release the design. +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 [specifications](https://doum.cn). +- Usage guidelines
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Before you release the model, it's recommended to evaluate the design details and license terms to [verify compatibility](https://asesordocente.com) with your usage case.
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6. [Choose Deploy](http://ieye.xyz5080) to proceed with deployment.
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7. For Endpoint name, utilize the instantly generated 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, get in the variety of instances (default: 1). +Selecting appropriate [circumstances types](http://git.attnserver.com) and counts is essential for cost and performance optimization. Monitor your [implementation](http://188.68.40.1033000) to change 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 accuracy. For this model, we [highly advise](https://git.polycompsol.com3000) sticking to SageMaker JumpStart default settings and making certain that network isolation remains in location. +11. Choose Deploy to release the model.
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The release process can take several minutes to finish.
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When release is complete, your endpoint status will change to InService. At this point, the design is ready to accept reasoning demands through the endpoint. You can keep an eye on the implementation progress on the SageMaker console Endpoints page, which will display appropriate metrics and status details. When the implementation is total, you can invoke the model utilizing a SageMaker runtime client and incorporate it with your applications.
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Deploy DeepSeek-R1 utilizing the SageMaker Python SDK
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To start with DeepSeek-R1 utilizing the SageMaker Python SDK, you will require to install the SageMaker Python SDK and make certain you have the essential AWS [authorizations](https://taelimfwell.com) 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 design is supplied 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 predictor
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Similar to Amazon Bedrock, you can likewise use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a guardrail utilizing the Amazon Bedrock console or the API, and execute it as shown in the following code:
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Clean up
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To avoid [undesirable](https://flexwork.cafe24.com) charges, complete the steps in this area to clean up your resources.
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Delete the Amazon Bedrock Marketplace deployment
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If you released the design using Amazon Bedrock Marketplace, complete the following steps:
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1. On the Amazon Bedrock console, under Foundation models in the navigation pane, select Marketplace releases. +2. In the Managed deployments section, locate the [endpoint](https://git.ivabus.dev) you wish to delete. +3. Select the endpoint, and on the Actions menu, [choose Delete](http://git.keliuyun.com55676). +4. Verify the endpoint details 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 design you released will sustain expenses if you leave it running. Use the following code to delete 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](http://123.56.247.1933000) how you can access and deploy the DeepSeek-R1 model utilizing 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 designs, [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11860868) SageMaker JumpStart pretrained models, [Amazon SageMaker](https://bvbborussiadortmundfansclub.com) JumpStart Foundation Models, Amazon Bedrock Marketplace, and Beginning 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 at AWS. He assists emerging generative [AI](https://www.linkedaut.it) business develop ingenious solutions using AWS services and accelerated calculate. Currently, he is focused on developing techniques for fine-tuning and optimizing the reasoning efficiency of large language designs. In his leisure time, Vivek delights in hiking, seeing motion pictures, and attempting different cuisines.
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Niithiyn Vijeaswaran is a Generative [AI](https://138.197.71.160) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His area of focus is AWS [AI](http://tfjiang.cn:32773) [accelerators](https://git.corp.xiangcms.net) (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.
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Jonathan Evans is a Specialist Solutions Architect dealing with generative [AI](https://rootsofblackessence.com) with the Third-Party Model Science team at AWS.
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Banu Nagasundaram leads item, engineering, and tactical collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://www.rozgar.site) center. She is passionate about developing solutions that assist consumers accelerate their [AI](https://strimsocial.net) journey and unlock service value.
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