gordonk0678050
/
iwmbd
1
0
Code Issues Packages Projects Wiki

Add DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart

Gordon Hankins 2025-04-07 09:05:56 +08:00
commit 883bf4f1fa
1 changed files with 93 additions and 0 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

93
DeepSeek-R1 Model now Available in Amazon Bedrock Marketplace And Amazon SageMaker JumpStart.-.md Normal file

@ -0,0 +1,93 @@
<br>Today, we are delighted to announce 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](http://124.222.85.139:3000)'s first-generation frontier model, DeepSeek-R1, along with the distilled variations ranging from 1.5 to 70 billion specifications to develop, experiment, and properly scale your generative [AI](https://hip-hop.id) concepts on AWS.<br>
<br>In this post, we show how to get begun with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable steps to deploy the distilled variations of the designs as well.<br>
<br>Overview of DeepSeek-R1<br>
<br>DeepSeek-R1 is a large language design (LLM) developed by DeepSeek [AI](http://34.81.52.16) that utilizes support finding out to improve thinking [abilities](https://vids.nickivey.com) through a multi-stage training process from a DeepSeek-V3-Base structure. An essential differentiating function is its support learning (RL) action, which was utilized to fine-tune the model's reactions beyond the basic pre-training and fine-tuning process. By including RL, DeepSeek-R1 can adapt more successfully to user feedback and goals, eventually boosting both significance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) technique, implying it's geared up to break down complicated queries and reason through them in a detailed manner. This guided reasoning procedure allows the design to produce more accurate, transparent, and detailed responses. This model combines RL-based fine-tuning with CoT capabilities, aiming to generate structured actions while focusing on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has actually captured the [market's attention](https://clearcreek.a2hosted.com) as a flexible text-generation model that can be incorporated into various workflows such as agents, rational thinking and data interpretation tasks.<br>
<br>DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture allows activation of 37 billion criteria, making it possible for effective reasoning by routing questions to the most appropriate expert "clusters." This method permits the model to concentrate on various issue domains while maintaining general effectiveness. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for inference. In this post, [yewiki.org](https://www.yewiki.org/User:EwanDyke4311656) we will utilize an ml.p5e.48 xlarge instance to release the model. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.<br>
<br>DeepSeek-R1 distilled models bring the reasoning abilities of the main R1 design to more [effective architectures](https://www.nc-healthcare.co.uk) based upon popular open [designs](https://notitia.tv) like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a procedure of training smaller sized, more effective models to simulate the behavior and thinking patterns of the bigger DeepSeek-R1 model, utilizing it as an instructor design.<br>
<br>You can release DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we recommend deploying this design with guardrails in location. In this blog, we will utilize Amazon Bedrock Guardrails to present safeguards, avoid hazardous material, and examine designs against essential safety requirements. At the time of writing this blog site, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can develop several guardrails tailored to various use cases and use them to the DeepSeek-R1 design, improving user experiences and standardizing security controls throughout your generative [AI](https://eelam.tv) [applications](https://www.viewtubs.com).<br>
<br>Prerequisites<br>
<br>To deploy the DeepSeek-R1 model, you need access to an ml.p5e circumstances. To examine if you have quotas for P5e, open the Service Quotas [console](https://smartcampus-seskoal.id) and under AWS Services, pick 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 releasing. To request a limit boost, produce a [limit increase](http://git2.guwu121.com) demand and reach out to your account group.<br>
<br>Because you will be releasing this model with Amazon Bedrock Guardrails, make certain you have the appropriate AWS Identity and Gain Access To Management (IAM) approvals to utilize Amazon Bedrock Guardrails. For directions, see Set up permissions to [utilize guardrails](https://gitea.aambinnes.com) for content filtering.<br>
<br>Implementing guardrails with the ApplyGuardrail API<br>
<br>Amazon Bedrock Guardrails permits you to present safeguards, avoid harmful material, and examine designs against key safety criteria. You can implement safety procedures for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This allows you to apply guardrails to evaluate user inputs and model reactions released on Amazon Bedrock Marketplace and SageMaker JumpStart. 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.<br>
<br>The general flow includes the following actions: First, the system gets an input for the model. 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 [receiving](https://wamc1950.com) the model's output, another guardrail check is applied. If the [output passes](https://legatobooks.com) this last check, it's returned as the final outcome. However, if either the input or output is intervened by the guardrail, a message is [returned](https://git.opskube.com) showing the nature of the intervention and whether it took place at the input or output phase. The examples showcased in the following sections show inference using this API.<br>
<br>Deploy DeepSeek-R1 in Amazon Bedrock Marketplace<br>
<br>[Amazon Bedrock](http://www.larsaluarna.se) Marketplace provides you access to over 100 popular, emerging, and [specialized foundation](https://wolvesbaneuo.com) models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following actions:<br>
<br>1. On the Amazon Bedrock console, pick Model brochure under Foundation models in the navigation pane.
At the time of composing this post, you can utilize the InvokeModel API to invoke the model. It does not support Converse APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a supplier and [it-viking.ch](http://it-viking.ch/index.php/User:StanleyShetler) choose the DeepSeek-R1 design.<br>
<br>The design detail page offers important details about the design's abilities, rates structure, and application guidelines. You can find detailed use instructions, consisting of sample API calls and code bits for combination. The model supports [numerous text](https://www.wikiwrimo.org) generation jobs, consisting of content production, code generation, and concern answering, utilizing its support finding out optimization and CoT thinking capabilities.
The page also consists of deployment options and licensing details to assist you start with DeepSeek-R1 in your applications.
3. To begin using DeepSeek-R1, pick Deploy.<br>
<br>You will be triggered to configure the release details for DeepSeek-R1. The design ID will be pre-populated.
4. For Endpoint name, enter an endpoint name (in between 1-50 alphanumeric characters).
5. For Number of instances, get in a number of instances (in between 1-100).
6. For Instance type, pick your instance type. For optimum efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is suggested.
Optionally, you can set up innovative security and facilities settings, including virtual personal cloud (VPC) networking, service role authorizations, and file encryption settings. For the majority of use cases, the default settings will work well. However, for production implementations, you may wish to evaluate these settings to align with your company's security and [compliance requirements](http://112.126.100.1343000).
7. Choose Deploy to start using the model.<br>
<br>When the deployment is total, you can check DeepSeek-R1's abilities straight in the Amazon Bedrock play ground.
8. Choose Open in play ground to access an interactive interface where you can experiment with various triggers and adjust design parameters like temperature and optimum length.
When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for optimum results. For instance, material for reasoning.<br>
<br>This is an outstanding method to explore the model's reasoning and text generation abilities before incorporating it into your applications. The playground supplies instant feedback, helping you understand how the model reacts to different inputs and letting you tweak your prompts for optimal results.<br>
<br>You can quickly evaluate the model in the playground through the UI. However, to conjure up the released model programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.<br>
<br>Run inference utilizing guardrails with the released DeepSeek-R1 endpoint<br>
<br>The following code example shows how to perform 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 create the guardrail, see the GitHub repo. After you have developed the guardrail, utilize the following code to carry out guardrails. The script initializes the bedrock_runtime customer, [configures inference](https://54.165.237.249) parameters, and sends out a request to produce text based upon a user timely.<br>
<br>Deploy DeepSeek-R1 with SageMaker JumpStart<br>
<br>SageMaker JumpStart is an artificial intelligence (ML) center with FMs, integrated algorithms, and prebuilt ML services that you can deploy with just a few clicks. With [SageMaker](https://jobsubscribe.com) JumpStart, you can tailor pre-trained designs to your use case, with your information, and deploy them into [production utilizing](https://ideezy.com) either the UI or SDK.<br>
<br>Deploying DeepSeek-R1 design through SageMaker JumpStart provides 2 convenient methods: utilizing the intuitive SageMaker [JumpStart](https://git.agent-based.cn) UI or executing programmatically through the SageMaker Python SDK. Let's check out both approaches to help you pick the approach that finest suits your requirements.<br>
<br>Deploy DeepSeek-R1 through SageMaker JumpStart UI<br>
<br>Complete the following actions to deploy DeepSeek-R1 using SageMaker JumpStart:<br>
<br>1. On the SageMaker console, select Studio in the navigation pane.
2. First-time users will be [prompted](https://git.on58.com) to [produce](https://test1.tlogsir.com) a domain.
3. On the SageMaker Studio console, pick JumpStart in the navigation pane.<br>
<br>The design browser shows available designs, with details like the service provider name and design capabilities.<br>
<br>4. Look for DeepSeek-R1 to view the DeepSeek-R1 design card.
Each design card shows crucial details, including:<br>
<br>- Model name
- [Provider](http://git.iloomo.com) name
- Task classification (for instance, Text Generation).
Bedrock Ready badge (if suitable), showing that this model can be [registered](https://site4people.com) with Amazon Bedrock, permitting you to utilize Amazon Bedrock APIs to [conjure](http://gamebizdev.ru) up the design<br>
<br>5. Choose the model card to see the design details page.<br>
<br>The design details page includes the following details:<br>
<br>- The model name and service provider details.
Deploy button to deploy the design.
About and Notebooks tabs with detailed details<br>
<br>The About tab consists of important details, such as:<br>
<br>- Model description.
- License details.
- Technical requirements.
- Usage standards<br>
<br>Before you deploy the model, it's advised to review the design details and license terms to validate compatibility with your use case.<br>
<br>6. Choose Deploy to proceed with implementation.<br>
<br>7. For Endpoint name, utilize the automatically created name or develop a custom one.
8. For Instance type ¸ choose an instance type (default: ml.p5e.48 xlarge).
9. For Initial instance count, enter the variety of instances (default: 1).
Selecting proper instance types and counts is important for cost and performance optimization. Monitor your release to adjust these settings as needed.Under Inference type, Real-time reasoning is picked by default. This is [optimized](http://dgzyt.xyz3000) for [sustained traffic](https://hot-chip.com) and [wiki.myamens.com](http://wiki.myamens.com/index.php/User:YoungWragge3407) low latency.
10. Review all configurations for accuracy. For this design, we highly suggest adhering to SageMaker JumpStart [default settings](https://myjobapply.com) and making certain that network isolation remains in location.
11. Choose Deploy to deploy the design.<br>
<br>The implementation process can take numerous minutes to complete.<br>
<br>When implementation is complete, your endpoint status will alter to InService. At this moment, the model is prepared to accept inference demands through the [endpoint](http://101.42.21.1163000). You can keep track of the deployment progress on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the implementation is total, you can [conjure](https://bocaiw.in.net) up the model using a SageMaker runtime client and incorporate it with your applications.<br>
<br>Deploy DeepSeek-R1 utilizing the SageMaker Python SDK<br>
<br>To begin with DeepSeek-R1 utilizing the SageMaker Python SDK, you will need to set up the SageMaker Python SDK and make certain you have the required AWS permissions and . The following is a detailed code example that demonstrates how to deploy and utilize DeepSeek-R1 for reasoning programmatically. The code for deploying the model is supplied in the Github here. You can clone the notebook and run from SageMaker Studio.<br>
<br>You can run additional demands against the predictor:<br>
<br>Implement guardrails and run reasoning with your SageMaker JumpStart predictor<br>
<br>Similar to Amazon Bedrock, you can also utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can develop a [guardrail utilizing](http://filmmaniac.ru) the Amazon Bedrock console or the API, and execute it as revealed in the following code:<br>
<br>Tidy up<br>
<br>To prevent unwanted charges, finish the actions in this area to clean up your resources.<br>
<br>Delete the Amazon Bedrock Marketplace release<br>
<br>If you released the design using Amazon Bedrock Marketplace, complete the following steps:<br>
<br>1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, select Marketplace implementations.
2. In the Managed releases section, find the [endpoint](https://allcollars.com) you desire to erase.
3. Select the endpoint, and on the Actions menu, choose Delete.
4. Verify the endpoint details to make certain you're [erasing](https://vids.nickivey.com) the appropriate implementation: 1. [Endpoint](http://git.the-archive.xyz) name.
2. Model name.
3. Endpoint status<br>
<br>Delete the SageMaker JumpStart predictor<br>
<br>The SageMaker JumpStart model you released will sustain expenses if you leave it running. Use the following code to erase the endpoint if you want to stop sustaining charges. For more details, see Delete Endpoints and Resources.<br>
<br>Conclusion<br>
<br>In this post, we checked out how you can access and release the DeepSeek-R1 model utilizing Bedrock Marketplace and [SageMaker](https://wavedream.wiki) [JumpStart](https://emplealista.com). 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 designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting going with Amazon SageMaker JumpStart.<br>
<br>About the Authors<br>
<br>Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He helps emerging generative [AI](http://tfjiang.cn:32773) companies construct innovative options utilizing AWS services and sped up compute. Currently, he is focused on developing techniques for fine-tuning and enhancing the reasoning efficiency of large language designs. In his spare time, Vivek takes pleasure in treking, viewing movies, and attempting different foods.<br>
<br>Niithiyn Vijeaswaran is a Generative [AI](https://git.christophhagen.de) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His location of focus is AWS [AI](https://git.andy.lgbt) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.<br>
<br>Jonathan Evans is an Expert Solutions Architect dealing with generative [AI](http://git.baige.me) with the Third-Party Model Science group at AWS.<br>
<br>Banu Nagasundaram leads product, engineering, and tactical collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](http://175.27.215.92:3000) center. She is passionate about building options that assist customers accelerate their [AI](https://littlebigempire.com) journey and unlock company worth.<br>