judithrios651/famenest
1
0
Code Issues Actions Packages Projects Wiki

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

Judith Rios 2025-02-12 20:05:49 +01:00
commit db74947b60
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 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 deploy DeepSeek [AI](https://gitlab.buaanlsde.cn)'s first-generation frontier model, DeepSeek-R1, along with the distilled variations varying from 1.5 to 70 billion specifications to build, experiment, and responsibly scale your generative [AI](https://asw.alma.cl) ideas on AWS.<br>
<br>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 variations of the designs too.<br>
<br>Overview of DeepSeek-R1<br>
<br>DeepSeek-R1 is a big language design (LLM) established by DeepSeek [AI](https://gitea-working.testrail-staging.com) that utilizes support learning to enhance thinking capabilities through a multi-stage training process from a DeepSeek-V3-Base structure. A key distinguishing function is its support knowing (RL) step, which was utilized to improve the design's reactions beyond the basic pre-training and fine-tuning process. By including RL, DeepSeek-R1 can adapt better to user feedback and objectives, eventually boosting both significance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) technique, [archmageriseswiki.com](http://archmageriseswiki.com/index.php/User:JeannetteI75) suggesting it's geared up to break down complicated inquiries and factor through them in a detailed manner. This assisted reasoning procedure permits the design to produce more accurate, transparent, and detailed responses. This design integrates [RL-based](https://welcometohaiti.com) [fine-tuning](http://31.184.254.1768078) with CoT abilities, aiming to produce structured responses while concentrating on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has caught the market's attention as a flexible text-generation design that can be incorporated into different workflows such as agents, [rational reasoning](https://www.nairaland.com) and information analysis jobs.<br>
<br>DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture allows activation of 37 billion specifications, making it possible for efficient reasoning by routing questions to the most pertinent professional "clusters." This technique permits the model to [specialize](http://120.237.152.2188888) in various issue domains while maintaining overall 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 model. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs supplying 1128 GB of [GPU memory](http://test-www.writebug.com3000).<br>
<br>DeepSeek-R1 distilled designs bring the thinking [capabilities](http://yezhem.com9030) 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 of training smaller, more effective models to simulate the habits and thinking patterns of the larger DeepSeek-R1 model, using it as an instructor model.<br>
<br>You can release DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we recommend releasing this model with guardrails in location. In this blog site, we will use Amazon Bedrock Guardrails to introduce safeguards, prevent damaging material, and assess designs against crucial security requirements. At the time of composing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create several guardrails tailored to different use cases and apply them to the DeepSeek-R1 design, improving user [experiences](https://git.newpattern.net) and standardizing safety controls throughout your generative [AI](https://git.andrewnw.xyz) applications.<br>
<br>Prerequisites<br>
<br>To deploy the DeepSeek-R1 design, you need access to an ml.p5e [circumstances](https://archie2429263902267.bloggersdelight.dk). To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and confirm you're utilizing ml.p5e.48 xlarge for [endpoint usage](http://39.101.179.1066440). Make certain that you have at least one ml.P5e.48 xlarge circumstances in the AWS Region you are releasing. To ask for a limitation boost, develop a limit increase request and reach out to your account team.<br>
<br>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) authorizations to use Amazon Bedrock Guardrails. For directions, see Establish authorizations to use guardrails for material filtering.<br>
<br>Implementing guardrails with the ApplyGuardrail API<br>
<br>Amazon Bedrock Guardrails enables you to introduce safeguards, avoid damaging material, and evaluate designs against essential security requirements. You can execute precaution for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to apply guardrails to evaluate user inputs and model reactions [deployed](https://speeddating.co.il) on Amazon Bedrock Marketplace and SageMaker JumpStart. You can develop a guardrail using the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo.<br>
<br>The basic flow includes 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 check, it's sent to the model for reasoning. After getting the model's output, another guardrail check is applied. 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 suggesting the nature of the intervention and whether it took place at the input or output stage. The examples showcased in the following areas show reasoning utilizing this API.<br>
<br>Deploy DeepSeek-R1 in Amazon Bedrock Marketplace<br>
<br>Amazon [Bedrock Marketplace](https://gitlab.ujaen.es) provides you access to over 100 popular, emerging, and specialized structure designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following actions:<br>
<br>1. On the Amazon Bedrock console, choose Model catalog under [Foundation models](https://www.buzzgate.net) in the navigation pane.
At the time of composing this post, you can utilize the InvokeModel API to invoke the design. It does not [support Converse](http://oj.algorithmnote.cn3000) APIs and other Amazon Bedrock tooling.
2. Filter for [wiki.snooze-hotelsoftware.de](https://wiki.snooze-hotelsoftware.de/index.php?title=Benutzer:KristeenBurkhart) DeepSeek as a supplier and pick the DeepSeek-R1 model.<br>
<br>The design detail page provides essential details about the model's capabilities, rates structure, and application standards. You can find detailed usage directions, including sample API calls and code snippets for combination. The model supports various text generation tasks, including content creation, code generation, and question answering, utilizing its reinforcement discovering optimization and CoT thinking capabilities.
The page also consists of implementation alternatives and licensing details to help you start with DeepSeek-R1 in your applications.
3. To begin using DeepSeek-R1, select Deploy.<br>
<br>You will be triggered to set up the implementation details for DeepSeek-R1. The design ID will be pre-populated.
4. For Endpoint name, get in an endpoint name (in between 1-50 [alphanumeric](http://13.213.171.1363000) characters).
5. For Number of circumstances, enter a number of instances (in between 1-100).
6. For example type, pick your circumstances type. For optimum performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is recommended.
Optionally, you can set up advanced security and infrastructure settings, including virtual private cloud (VPC) networking, service function authorizations, and encryption settings. For many use cases, the default settings will work well. However, for production deployments, you may desire to review these settings to line up with your organization's security and compliance requirements.
7. Choose Deploy to begin utilizing the design.<br>
<br>When the release is total, you can evaluate DeepSeek-R1's abilities straight in the Amazon Bedrock play area.
8. Choose Open in play area to access an interactive interface where you can try out different triggers and change model parameters like temperature level and optimum length.
When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for optimum outcomes. For instance, content for reasoning.<br>
<br>This is an exceptional method to check out the design's reasoning and text generation capabilities before integrating it into your applications. The playground offers immediate feedback, helping you comprehend how the to different inputs and letting you tweak your prompts for optimal outcomes.<br>
<br>You can quickly test the design in the play area through the UI. However, to conjure up the deployed design programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.<br>
<br>Run inference utilizing guardrails with the deployed DeepSeek-R1 endpoint<br>
<br>The following code example shows how to carry out reasoning utilizing a deployed DeepSeek-R1 design through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can develop a guardrail using the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo. After you have created the guardrail, utilize the following code to [execute guardrails](http://www.chinajobbox.com). The script initializes the bedrock_runtime customer, configures reasoning criteria, and sends out a request to create text based on 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 solutions that you can release with just a few clicks. With SageMaker JumpStart, you can tailor [disgaeawiki.info](https://disgaeawiki.info/index.php/User:EvangelineSingle) pre-trained designs to your usage case, with your data, and deploy them into [production](https://ransomware.design) using either the UI or SDK.<br>
<br>Deploying DeepSeek-R1 design through SageMaker JumpStart uses two convenient approaches: utilizing the instinctive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's check out both methods to help you select the method that best matches your requirements.<br>
<br>Deploy DeepSeek-R1 through SageMaker JumpStart UI<br>
<br>Complete the following actions to release DeepSeek-R1 utilizing SageMaker JumpStart:<br>
<br>1. On the SageMaker console, pick Studio in the navigation pane.
2. First-time users will be prompted to develop a domain.
3. On the SageMaker Studio console, choose JumpStart in the navigation pane.<br>
<br>The design browser displays available designs, with details like the provider name and design capabilities.<br>
<br>4. Search for DeepSeek-R1 to see the DeepSeek-R1 design card.
Each model card reveals crucial details, consisting of:<br>
<br>- Model name
- Provider name
- Task category (for example, Text Generation).
Bedrock Ready badge (if suitable), showing that this design can be signed up with Amazon Bedrock, enabling you to use Amazon Bedrock APIs to invoke the model<br>
<br>5. Choose the model card to see the design details page.<br>
<br>The model details page consists of the following details:<br>
<br>- The model name and company details.
Deploy button to deploy the model.
About and Notebooks tabs with [detailed](http://47.100.3.2093000) details<br>
<br>The About tab consists of crucial details, such as:<br>
<br>- Model description.
- License details.
- Technical specs.
- Usage standards<br>
<br>Before you deploy the design, it's recommended to review the design details and license terms to verify compatibility with your usage case.<br>
<br>6. Choose Deploy to continue with [release](https://git.schdbr.de).<br>
<br>7. For Endpoint name, use the instantly created name or produce a customized one.
8. For example type ¸ pick an instance type (default: ml.p5e.48 xlarge).
9. For Initial circumstances count, get in the variety of instances (default: 1).
Selecting appropriate [circumstances types](http://47.109.24.444747) and counts is crucial for expense and performance optimization. Monitor your implementation to change these settings as needed.Under Inference type, Real-time reasoning is chosen by default. This is enhanced for sustained traffic and low latency.
10. Review all configurations for accuracy. For this model, [yewiki.org](https://www.yewiki.org/User:MarieGxx43) we highly suggest sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in place.
11. Choose Deploy to deploy the design.<br>
<br>The implementation procedure can take several minutes to finish.<br>
<br>When implementation 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 deployment development on the SageMaker console Endpoints page, which will display appropriate metrics and status details. When the deployment is complete, you can conjure up the design using a SageMaker runtime customer and incorporate it with your applications.<br>
<br>Deploy DeepSeek-R1 utilizing the SageMaker Python SDK<br>
<br>To get started with DeepSeek-R1 using the SageMaker Python SDK, you will require to set up the SageMaker Python SDK and make certain you have the needed AWS consents and [pediascape.science](https://pediascape.science/wiki/User:EdytheIvory959) environment setup. The following is a detailed code example that shows how to deploy and [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11862161) use DeepSeek-R1 for inference programmatically. The code for releasing the model is supplied in the Github here. You can clone the note pad and range from SageMaker Studio.<br>
<br>You can run additional demands against the predictor:<br>
<br>Implement guardrails and run inference with your SageMaker JumpStart predictor<br>
<br>Similar to Amazon Bedrock, you can likewise utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can develop a guardrail using the Amazon Bedrock console or the API, and [implement](http://sujongsa.net) it as displayed in the following code:<br>
<br>Clean up<br>
<br>To prevent unwanted charges, complete the steps in this area to tidy up your resources.<br>
<br>Delete the Amazon Bedrock Marketplace implementation<br>
<br>If you deployed the model utilizing Amazon Bedrock Marketplace, total the following actions:<br>
<br>1. On the Amazon Bedrock console, under [Foundation](http://gitlab.lvxingqiche.com) designs in the navigation pane, pick Marketplace implementations.
2. In the Managed releases section, locate the endpoint you want to erase.
3. Select the endpoint, and on the Actions menu, choose Delete.
4. Verify the endpoint details to make certain you're deleting the appropriate deployment: 1. Endpoint name.
2. Model name.
3. [Endpoint](https://reckoningz.com) status<br>
<br>Delete the SageMaker JumpStart predictor<br>
<br>The SageMaker JumpStart design you [deployed](http://www.maxellprojector.co.kr) will sustain expenses 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.<br>
<br>Conclusion<br>
<br>In this post, we checked out how you can access and deploy the DeepSeek-R1 model using Bedrock Marketplace and [SageMaker JumpStart](https://raisacanada.com). Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to start. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, [Amazon Bedrock](http://www.dahengsi.com30002) Marketplace, and Beginning with Amazon SageMaker JumpStart.<br>
<br>About the Authors<br>
<br>Vivek Gangasani is a Lead Specialist [Solutions](https://www.friend007.com) Architect for Inference at AWS. He helps emerging generative [AI](https://marcosdumay.com) companies build ingenious solutions utilizing AWS services and [it-viking.ch](http://it-viking.ch/index.php/User:AngelicaSnowball) accelerated calculate. Currently, he is concentrated on establishing methods for fine-tuning and enhancing the reasoning efficiency of large language designs. In his [complimentary](https://wiki.atlantia.sca.org) time, Vivek delights in treking, watching motion pictures, and attempting various foods.<br>
<br>[Niithiyn Vijeaswaran](https://trabaja.talendig.com) is a Generative [AI](https://cielexpertise.ma) Specialist Solutions Architect with the [Third-Party Model](http://kyeongsan.co.kr) [Science](http://travelandfood.ru) group at AWS. His area of focus is AWS [AI](https://gitea.sprint-pay.com) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.<br>
<br>Jonathan Evans is an Expert Solutions Architect working on generative [AI](https://voggisper.com) with the Third-Party Model Science group at AWS.<br>
<br>Banu Nagasundaram leads product, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://git.skyviewfund.com) hub. She is passionate about developing services that assist clients accelerate their [AI](http://git.nuomayun.com) journey and unlock service value.<br>