alinewhouse06
/
jimsusefultools
1
0
Code Issues Packages Projects Wiki

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

Ali Newhouse 2025-02-21 11:06:40 +00:00
commit 6314af7bed
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 announce that DeepSeek R1 distilled Llama and Qwen [designs](http://123.60.103.973000) are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](https://myafritube.com)'s first-generation frontier model, DeepSeek-R1, in addition to the distilled variations varying from 1.5 to 70 billion criteria to construct, experiment, and responsibly scale your generative [AI](https://git.hackercan.dev) ideas on AWS.<br>
<br>In this post, [engel-und-waisen.de](http://www.engel-und-waisen.de/index.php/Benutzer:Maricruz98D) we demonstrate how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar actions to release the distilled versions of the models too.<br>
<br>Overview of DeepSeek-R1<br>
<br>DeepSeek-R1 is a large language model (LLM) developed by DeepSeek [AI](https://git.dev.hoho.org) that uses support learning to improve thinking capabilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A crucial identifying function is its reinforcement learning (RL) step, which was utilized to refine the model's responses beyond the basic pre-training and tweak process. By incorporating RL, DeepSeek-R1 can adapt better to user feedback and objectives, ultimately enhancing both relevance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) approach, suggesting it's equipped to break down [intricate queries](https://stnav.com) and reason through them in a detailed way. This guided thinking [procedure](https://washcareer.com) allows the model to produce more accurate, transparent, and detailed responses. This design combines RL-based fine-tuning with CoT capabilities, aiming to create [structured responses](http://git.ai-robotics.cn) while concentrating on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has caught the industry's attention as a flexible text-generation design that can be incorporated into various workflows such as representatives, rational reasoning and data interpretation tasks.<br>
<br>DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture permits activation of 37 billion parameters, enabling effective reasoning by routing inquiries to the most appropriate expert "clusters." This approach enables the model to specialize in various issue domains while maintaining overall efficiency. DeepSeek-R1 requires a minimum of 800 GB of HBM memory in FP8 format for reasoning. In this post, we will use an ml.p5e.48 xlarge instance to deploy the model. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.<br>
<br>DeepSeek-R1 distilled models bring the reasoning capabilities of the main R1 model to more efficient architectures based on popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). [Distillation refers](https://bcde.ru) to a procedure of training smaller, more effective models to simulate the behavior and [reasoning patterns](http://www.carnevalecommunity.it) of the larger DeepSeek-R1 model, utilizing it as a [teacher design](https://jandlfabricating.com).<br>
<br>You can deploy DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we advise deploying this design with guardrails in place. In this blog, we will utilize Amazon Bedrock Guardrails to introduce safeguards, avoid harmful content, and examine designs against crucial security criteria. At the time of composing this blog, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can create multiple guardrails tailored to various use cases and apply them to the DeepSeek-R1 design, improving user experiences and standardizing safety controls across your generative [AI](https://rubius-qa-course.northeurope.cloudapp.azure.com) applications.<br>
<br>Prerequisites<br>
<br>To release the DeepSeek-R1 model, 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, select Amazon SageMaker, and confirm you're utilizing 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 limitation boost, produce a limit increase demand and connect to your account group.<br>
<br>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 instructions, see Establish consents to use guardrails for content filtering.<br>
<br>Implementing guardrails with the ApplyGuardrail API<br>
<br>Amazon Bedrock Guardrails enables you to present safeguards, avoid [harmful](https://actv.1tv.hk) material, and evaluate models against essential security criteria. You can execute precaution for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to apply guardrails to assess user inputs and design actions deployed on Amazon Bedrock Marketplace and [SageMaker JumpStart](https://kronfeldgit.org). 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](https://menfucks.com).<br>
<br>The general circulation [involves](https://teengigs.fun) 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, [engel-und-waisen.de](http://www.engel-und-waisen.de/index.php/Benutzer:MarcR997450156) 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 stepped in by the guardrail, a message is returned showing the nature of the intervention and whether it occurred at the input or output stage. The examples showcased in the following sections demonstrate inference using this API.<br>
<br>Deploy DeepSeek-R1 in [Amazon Bedrock](http://27.154.233.18610080) Marketplace<br>
<br>Amazon Bedrock Marketplace offers 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:<br>
<br>1. On the Amazon Bedrock console, select Model catalog under Foundation designs in the navigation pane.
At the time of writing this post, you can use the InvokeModel API to invoke the model. It does not support Converse APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a company and select the DeepSeek-R1 design.<br>
<br>The design detail page provides essential details about the design's abilities, rates structure, and execution guidelines. You can discover detailed use instructions, consisting of sample API calls and code bits for integration. The design supports different text generation jobs, including content creation, code generation, and concern answering, using its reinforcement discovering optimization and CoT reasoning abilities.
The page also includes implementation choices and licensing details to help you start with DeepSeek-R1 in your applications.
3. To begin using DeepSeek-R1, choose Deploy.<br>
<br>You will be triggered to set up the release details for DeepSeek-R1. The model ID will be pre-populated.
4. For Endpoint name, enter an endpoint name (in between 1-50 alphanumeric characters).
5. For Number of circumstances, get in a number of circumstances (between 1-100).
6. For Instance type, [systemcheck-wiki.de](https://systemcheck-wiki.de/index.php?title=Benutzer:MonserrateHuntin) choose your [instance type](https://athleticbilbaofansclub.com). For [optimum performance](https://croart.net) with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is advised.
Optionally, you can configure advanced security and facilities settings, consisting of virtual private cloud (VPC) networking, service function approvals, and file encryption settings. For most utilize cases, the default settings will work well. However, for [production](http://88.198.122.2553001) implementations, you may desire to examine these settings to line up with your [organization's security](https://jvptube.net) and [compliance requirements](https://doum.cn).
7. Choose Deploy to start using the design.<br>
<br>When the deployment is complete, you can test DeepSeek-R1's abilities straight in the Amazon Bedrock playground.
8. Choose Open in playground to access an interactive interface where you can explore various triggers and adjust design specifications like temperature and maximum length.
When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for optimum results. For instance, content for inference.<br>
<br>This is an outstanding method to check out the model's reasoning and text generation capabilities before integrating it into your applications. The play area offers instant feedback, assisting you understand how the design reacts to numerous inputs and letting you fine-tune your prompts for ideal outcomes.<br>
<br>You can quickly test the design in the playground through the UI. However, to conjure up the deployed 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 carry out inference using a released DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can create a guardrail using the Amazon Bedrock console or the API. For the example code to create the guardrail, see the [GitHub repo](http://git.the-archive.xyz). After you have created the guardrail, utilize the following code to carry out guardrails. The script initializes the bedrock_runtime client, configures inference criteria, and sends out a demand to produce text based upon a user prompt.<br>
<br>Deploy DeepSeek-R1 with SageMaker JumpStart<br>
<br>SageMaker JumpStart is an artificial intelligence (ML) hub with FMs, [integrated](https://jobs.theelitejob.com) algorithms, and prebuilt ML options that you can release with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your use case, with your data, and deploy them into production using either the UI or SDK.<br>
<br>Deploying DeepSeek-R1 design through SageMaker JumpStart provides two hassle-free methods: using the user-friendly SageMaker JumpStart UI or [larsaluarna.se](http://www.larsaluarna.se/index.php/User:DanaeT3992149) executing programmatically through the SageMaker Python SDK. Let's check out both [techniques](https://sajano.com) to assist you select the approach that finest fits your needs.<br>
<br>Deploy DeepSeek-R1 through SageMaker JumpStart UI<br>
<br>Complete the following steps to release DeepSeek-R1 utilizing SageMaker JumpStart:<br>
<br>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, select JumpStart in the navigation pane.<br>
<br>The model internet [browser](https://git.gocasts.ir) displays available models, with details like the service provider name and design abilities.<br>
<br>4. Search for DeepSeek-R1 to see the DeepSeek-R1 model card.
Each design card reveals crucial details, including:<br>
<br>- Model name
- Provider name
- Task classification (for example, Text Generation).
Bedrock Ready badge (if appropriate), suggesting that this design can be signed up with Amazon Bedrock, enabling you to use [Amazon Bedrock](https://git.mintmuse.com) APIs to invoke the design<br>
<br>5. Choose the design card to see the design details page.<br>
<br>The design details page consists of the following details:<br>
<br>- The design name and company details.
Deploy button to release the design.
About and Notebooks tabs with detailed details<br>
<br>The About tab consists of essential details, such as:<br>
<br>- Model description.
- License details.
- Technical specifications.
- Usage standards<br>
<br>Before you release the design, it's recommended to evaluate the design details and license terms to [verify compatibility](https://git.lain.church) with your usage case.<br>
<br>6. Choose Deploy to continue with [release](http://114.55.169.153000).<br>
<br>7. For Endpoint name, use the immediately produced name or develop a customized one.
8. For Instance type ¸ pick a circumstances type (default: ml.p5e.48 xlarge).
9. For Initial instance count, get in the variety of instances (default: 1).
Selecting appropriate instance types and counts is crucial for cost and efficiency optimization. Monitor your implementation to change these settings as needed.Under Inference type, Real-time reasoning is picked by default. This is optimized for sustained traffic and low latency.
10. Review all setups for accuracy. For this design, we highly advise sticking to SageMaker JumpStart default settings and making certain that network isolation remains in place.
11. Choose Deploy to deploy the model.<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 model is prepared to accept inference demands through the [endpoint](http://gitea.anomalistdesign.com). You can keep an eye on the implementation progress on the SageMaker console [Endpoints](http://124.220.187.1423000) page, which will show relevant metrics and status details. When the implementation is complete, you can invoke the model using a SageMaker runtime customer and [incorporate](http://121.196.13.116) it with your applications.<br>
<br>Deploy DeepSeek-R1 using the SageMaker Python SDK<br>
<br>To get going with DeepSeek-R1 using the SageMaker Python SDK, you will require to set up the SageMaker Python SDK and make certain you have the necessary AWS authorizations and environment setup. The following is a [detailed code](https://gitea.b54.co) example that shows how to release and use DeepSeek-R1 for reasoning programmatically. The code for deploying the model is supplied in the Github here. You can clone the note pad and run from SageMaker Studio.<br>
<br>You can run extra requests against the predictor:<br>
<br>Implement guardrails and run inference with your [SageMaker JumpStart](https://oninabresources.com) predictor<br>
<br>Similar to Amazon Bedrock, you can also use 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 shown in the following code:<br>
<br>Clean up<br>
<br>To prevent undesirable charges, finish the actions in this section to tidy up your resources.<br>
<br>Delete the Amazon Bedrock Marketplace implementation<br>
<br>If you released the model using Amazon Bedrock Marketplace, [hb9lc.org](https://www.hb9lc.org/wiki/index.php/User:LesleyWatkin4) total the following actions:<br>
<br>1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, select Marketplace deployments.
2. In the Managed implementations section, find the endpoint you wish to delete.
3. Select the endpoint, and on the Actions menu, select Delete.
4. Verify the endpoint details to make certain you're erasing the right release: 1. Endpoint name.
2. Model name.
3. Endpoint status<br>
<br>Delete the SageMaker JumpStart predictor<br>
<br>The SageMaker JumpStart design you released will sustain expenses if you leave it running. Use the following code to erase the endpoint if you desire 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 design utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to begin. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, [SageMaker JumpStart](https://planetdump.com) pretrained models, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting started with Amazon SageMaker JumpStart.<br>
<br>About the Authors<br>
<br>Vivek Gangasani is a Lead Specialist Solutions Architect for Inference at AWS. He assists emerging generative [AI](https://slovenskymedved.sk) companies construct ingenious solutions using AWS services and accelerated compute. Currently, he is focused on developing methods for fine-tuning and enhancing the reasoning efficiency of large language models. In his spare time, Vivek takes pleasure in hiking, watching motion pictures, and attempting different cuisines.<br>
<br>Niithiyn Vijeaswaran is a Generative [AI](https://classtube.ru) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His [location](https://oninabresources.com) of focus is AWS [AI](http://1.15.187.67) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.<br>
<br>Jonathan Evans is an Expert Solutions Architect working on generative [AI](https://baripedia.org) with the Third-Party Model Science group at AWS.<br>
<br> leads product, engineering, [garagesale.es](https://www.garagesale.es/author/garrettbrid/) and strategic collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and [garagesale.es](https://www.garagesale.es/author/jonathanfin/) generative [AI](https://git.sommerschein.de) hub. She is passionate about constructing services that help consumers accelerate their [AI](https://yezidicommunity.com) journey and [unlock organization](https://git.parat.swiss) worth.<br>