adelinecharter
/
tiger-teas
1
0
Code Issues Packages Projects Wiki

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

Adeline Charteris 2025-04-12 13:02:18 +08:00
parent 68187660d0
commit 2bb86dbd2c
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 excited to reveal that DeepSeek R1 distilled Llama and Qwen designs are available through Amazon Bedrock [Marketplace](https://git.progamma.com.ua) and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](http://briga-nega.com)'s first-generation frontier model, DeepSeek-R1, in addition to the distilled variations varying from 1.5 to 70 billion specifications to build, experiment, and properly scale your generative [AI](https://societeindustrialsolutions.com) [concepts](https://cloudsound.ideiasinternet.com) on AWS.<br>
<br>In this post, we demonstrate how to begin with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable actions to [release](https://meeting2up.it) the [distilled variations](https://www.sintramovextrema.com.br) of the models too.<br>
<br>Overview of DeepSeek-R1<br>
<br>DeepSeek-R1 is a big language model (LLM) developed by DeepSeek [AI](https://open-gitlab.going-link.com) that utilizes support discovering to boost thinking [capabilities](https://kaamdekho.co.in) through a multi-stage training procedure from a DeepSeek-V3-Base foundation. An essential distinguishing feature is its reinforcement knowing (RL) step, which was used to fine-tune the design's reactions beyond the basic pre-training and tweak procedure. By including RL, DeepSeek-R1 can adapt more efficiently to user feedback and objectives, eventually improving both significance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) technique, indicating it's equipped to break down intricate inquiries and factor through them in a detailed way. This assisted reasoning procedure permits the model to produce more accurate, transparent, and [detailed responses](https://topdubaijobs.ae). This design integrates RL-based fine-tuning with CoT capabilities, aiming to produce structured reactions while focusing on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has actually recorded the market's attention as a versatile text-generation model that can be integrated into numerous workflows such as representatives, rational thinking and data interpretation tasks.<br>
<br>DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture enables activation of 37 billion specifications, allowing efficient reasoning by routing inquiries to the most appropriate expert "clusters." This technique allows the design to specialize in various issue domains while maintaining general performance. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will use an ml.p5e.48 xlarge instance to release the design. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.<br>
<br>DeepSeek-R1 distilled models bring the thinking abilities 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 refers to a procedure of training smaller sized, more effective models to simulate the behavior and reasoning patterns of the bigger DeepSeek-R1 model, utilizing it as an instructor model.<br>
<br>You can release DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we advise deploying this design with guardrails in location. In this blog, we will use Amazon Bedrock Guardrails to introduce safeguards, avoid [hazardous](https://gogs.dzyhc.com) content, and examine models against crucial safety criteria. At the time of writing this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the [ApplyGuardrail API](https://git.valami.giize.com). You can develop numerous guardrails tailored to various usage cases and use them to the DeepSeek-R1 model, enhancing user experiences and standardizing security controls throughout your generative [AI](http://tfjiang.cn:32773) applications.<br>
<br>Prerequisites<br>
<br>To deploy the DeepSeek-R1 design, you require access to an ml.p5e instance. To examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, select 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 instance in the AWS Region you are [deploying](https://papersoc.com). To request a limit increase, develop a limit increase request and connect to your account group.<br>
<br>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) approvals to use [Amazon Bedrock](http://www.larsaluarna.se) Guardrails. For instructions, see Set up consents to use guardrails for material filtering.<br>
<br>Implementing guardrails with the ApplyGuardrail API<br>
<br>Amazon Bedrock Guardrails enables you to present safeguards, prevent harmful content, and evaluate designs against key safety criteria. You can implement security procedures for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to assess user inputs and design actions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a guardrail utilizing the Amazon Bedrock [console](https://forum.webmark.com.tr) or the API. For the example code to develop the guardrail, see the GitHub repo.<br>
<br>The basic circulation involves the following actions: 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 reasoning. After receiving the [design's](https://social.japrime.id) output, another [guardrail check](https://www.dutchsportsagency.com) is applied. If the output passes this final check, it's returned as the [outcome](https://www.globalshowup.com). 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 occurred at the input or output phase. The examples showcased in the following areas demonstrate reasoning using this API.<br>
<br>Deploy DeepSeek-R1 in [Amazon Bedrock](https://redsocial.cl) Marketplace<br>
<br>Amazon Bedrock Marketplace offers 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, select Model catalog under Foundation models in the navigation pane.
At the time of [composing](https://newhopecareservices.com) this post, you can utilize the InvokeModel API to conjure up the model. It doesn't support Converse APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a service provider and pick the DeepSeek-R1 design.<br>
<br>The model detail page supplies essential details about the design's capabilities, rates structure, and [execution guidelines](http://hrplus.com.vn). You can discover detailed use directions, consisting of sample API calls and code snippets for integration. The design supports different [text generation](https://globalabout.com) tasks, consisting of [material](https://www.imdipet-project.eu) production, code generation, and question answering, utilizing its reinforcement finding out optimization and CoT reasoning abilities.
The page likewise consists of deployment choices and licensing details to help you start with DeepSeek-R1 in your applications.
3. To begin utilizing DeepSeek-R1, choose Deploy.<br>
<br>You will be prompted to set up the deployment details for DeepSeek-R1. The model ID will be pre-populated.
4. For Endpoint name, get in an [endpoint](http://111.61.77.359999) name (between 1-50 alphanumeric characters).
5. For Number of instances, enter a number of circumstances (in between 1-100).
6. For example type, pick your circumstances type. For ideal efficiency with DeepSeek-R1, a [GPU-based](https://evove.io) circumstances type like ml.p5e.48 xlarge is recommended.
Optionally, you can configure innovative security and infrastructure settings, including virtual personal cloud (VPC) networking, service role consents, and file encryption settings. For the majority of use cases, the default settings will work well. However, for production implementations, you might wish to examine these settings to line up with your [company's security](http://tpgm7.com) and compliance requirements.
7. Choose Deploy to start using the design.<br>
<br>When the implementation is complete, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock play area.
8. Choose Open in play ground to access an interactive user interface where you can experiment with various prompts and change design criteria like temperature level and maximum length.
When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for optimum results. For instance, content for reasoning.<br>
<br>This is an excellent method to check out the model's reasoning and text generation abilities before [incorporating](http://h2kelim.com) it into your applications. The play area supplies immediate feedback, helping you understand how the model responds to numerous inputs and letting you fine-tune your triggers for optimal results.<br>
<br>You can quickly test the model in the playground through the UI. However, to invoke the deployed design programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.<br>
<br>Run inference using guardrails with the deployed DeepSeek-R1 endpoint<br>
<br>The following code example demonstrates how to perform inference utilizing a released DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can create a [guardrail](https://talktalky.com) using the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo. After you have actually created the guardrail, [utilize](http://gitlab.qu-in.com) the following code to implement guardrails. The script initializes the bedrock_runtime customer, configures reasoning parameters, and sends a demand to produce text based on a user timely.<br>
<br>Deploy DeepSeek-R1 with SageMaker JumpStart<br>
<br>[SageMaker JumpStart](https://woodsrunners.com) is an artificial intelligence (ML) center with FMs, built-in algorithms, and prebuilt ML options that you can deploy with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage case, with your information, and deploy them into [production utilizing](https://manpoweradvisors.com) either the UI or SDK.<br>
<br>Deploying DeepSeek-R1 design through SageMaker JumpStart provides 2 practical methods: using the [intuitive SageMaker](https://24cyber.ru) JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's check out both methods to help 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 deploy DeepSeek-R1 utilizing SageMaker JumpStart:<br>
<br>1. On the SageMaker console, select Studio in the [navigation](http://222.85.191.975000) pane.
2. First-time users will be prompted to create a domain.
3. On the SageMaker Studio console, choose JumpStart in the navigation pane.<br>
<br>The design browser displays available models, with details like the [service provider](http://114.34.163.1743333) 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 instance, Text Generation).
Bedrock Ready badge (if relevant), showing that this model can be registered with Amazon Bedrock, enabling you to use Amazon Bedrock APIs to conjure up the model<br>
<br>5. Choose the [model card](https://xn--v69atsro52ncsg2uqd74apxb.com) to see the design details page.<br>
<br>The design details page consists of the following details:<br>
<br>- The model 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 .
- License details.
- Technical specifications.
- Usage standards<br>
<br>Before you deploy the model, it's advised to evaluate the design details and license terms to confirm compatibility with your usage case.<br>
<br>6. Choose Deploy to continue with release.<br>
<br>7. For Endpoint name, use the immediately created name or produce a customized one.
8. For Instance type ¸ pick a circumstances type (default: ml.p5e.48 xlarge).
9. For Initial circumstances count, get in the number of [circumstances](http://49.50.103.174) (default: 1).
Selecting proper circumstances types and counts is essential for cost and efficiency optimization. [Monitor](http://141.98.197.226000) your deployment to change these settings as needed.Under Inference type, Real-time reasoning is chosen by default. This is optimized for sustained traffic and low latency.
10. Review all configurations for precision. For this design, we strongly suggest sticking to SageMaker JumpStart default settings and making certain that network isolation remains in location.
11. Choose Deploy to deploy the design.<br>
<br>The implementation procedure can take a number of minutes to complete.<br>
<br>When deployment is total, your endpoint status will change to InService. At this moment, the design is ready to accept reasoning demands through the endpoint. You can monitor the implementation progress on the SageMaker console Endpoints page, which will display relevant metrics and status details. When the release is total, you can conjure up the design using a SageMaker runtime client 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](https://ouptel.com) Python SDK, you will need to set up the SageMaker Python SDK and make certain you have the needed AWS approvals and environment setup. The following is a detailed code example that demonstrates how to deploy and use DeepSeek-R1 for inference programmatically. The code for releasing the model is offered in the Github here. You can clone the note pad 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 likewise use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can create a guardrail using the Amazon Bedrock console or the API, and implement it as shown in the following code:<br>
<br>Tidy up<br>
<br>To prevent undesirable charges, complete the steps in this area to tidy 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 models in the navigation pane, pick Marketplace implementations.
2. In the Managed implementations area, find the endpoint you wish to delete.
3. Select the endpoint, and on the [Actions](https://foxchats.com) menu, pick Delete.
4. Verify the endpoint details to make certain you're erasing the right implementation: 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](https://socialsnug.net).<br>
<br>Conclusion<br>
<br>In this post, we checked out how you can access and deploy the DeepSeek-R1 design using 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 designs, SageMaker JumpStart 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](https://alumni.myra.ac.in) at AWS. He helps emerging generative [AI](http://idesys.co.kr) companies construct ingenious [options utilizing](http://httelecom.com.cn3000) AWS services and sped up compute. Currently, he is concentrated on developing strategies for fine-tuning and optimizing the inference performance of large language models. In his leisure time, Vivek delights in treking, viewing movies, and attempting various cuisines.<br>
<br>Niithiyn Vijeaswaran is a Generative [AI](https://gst.meu.edu.jo) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His location of focus is AWS [AI](http://ufidahz.com.cn:9015) [accelerators](https://snapfyn.com) (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.<br>
<br>Jonathan Evans is a Specialist Solutions Architect working on generative [AI](https://satitmattayom.nrru.ac.th) with the Third-Party Model Science group at AWS.<br>
<br>Banu Nagasundaram leads product, engineering, and tactical partnerships for Amazon SageMaker JumpStart, [wiki.rolandradio.net](https://wiki.rolandradio.net/index.php?title=User:FernJean87824) SageMaker's artificial intelligence and generative [AI](https://freelyhelp.com) center. She is enthusiastic about building solutions that assist clients accelerate their [AI](http://xrkorea.kr) journey and unlock company worth.<br>