dorrissample16/vibestream
1
0
Code Issues Actions Packages Projects Wiki

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

Dorris Sample 2025-04-04 10:47:26 +08:00
commit ea056a8811
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](https://www.jobexpertsindia.com) to reveal 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](https://familytrip.kr)'s first-generation frontier design, DeepSeek-R1, along with the distilled variations ranging from 1.5 to 70 billion parameters to develop, experiment, and responsibly scale your generative [AI](http://git.meloinfo.com) concepts on AWS.<br>
<br>In this post, we show how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and [SageMaker JumpStart](http://154.64.253.773000). You can follow similar actions to release the distilled versions of the designs also.<br>
<br>Overview of DeepSeek-R1<br>
<br>DeepSeek-R1 is a big language design (LLM) developed by DeepSeek [AI](https://investsolutions.org.uk) that uses support finding out to improve reasoning capabilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. An essential differentiating feature is its support knowing (RL) action, which was utilized to improve the design's responses beyond the basic pre-training and tweak process. By integrating RL, DeepSeek-R1 can adapt better to user feedback and goals, eventually enhancing both significance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) approach, suggesting it's geared up to break down complicated questions and factor through them in a detailed manner. This guided reasoning procedure allows the design to produce more accurate, transparent, and detailed answers. This [design combines](https://nurseportal.io) RL-based fine-tuning with CoT abilities, aiming to produce structured actions while focusing on interpretability and user [interaction](http://62.234.217.1373000). With its wide-ranging capabilities DeepSeek-R1 has caught the market's attention as a versatile text-generation model that can be incorporated into different workflows such as agents, sensible thinking and data analysis tasks.<br>
<br>DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture enables activation of 37 billion criteria, enabling efficient reasoning by [routing questions](http://damoa8949.com) to the most relevant expert "clusters." This method permits the model to focus on different problem domains while maintaining general effectiveness. DeepSeek-R1 requires a minimum of 800 GB of HBM memory in FP8 format for reasoning. In this post, we will utilize an ml.p5e.48 xlarge circumstances to release the design. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.<br>
<br>DeepSeek-R1 distilled models bring the thinking [abilities](http://www.xn--80agdtqbchdq6j.xn--p1ai) of the main R1 model to more efficient architectures based on popular open designs 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 habits and reasoning patterns of the larger DeepSeek-R1 design, utilizing 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 model, we advise deploying this design with guardrails in place. In this blog, we will use Amazon Bedrock Guardrails to introduce safeguards, prevent hazardous material, and examine designs against essential security requirements. At the time of writing this blog, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can develop multiple 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](http://wiki.faramirfiction.com) applications.<br>
<br>Prerequisites<br>
<br>To deploy the DeepSeek-R1 design, you require access to an ml.p5e circumstances. To check if you have quotas for P5e, open the Service Quotas console and [wiki.dulovic.tech](https://wiki.dulovic.tech/index.php/User:KarolynMcelroy1) 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 instance in the AWS Region you are releasing. To ask for a limit increase, produce a limitation increase demand and connect to your account team.<br>
<br>Because you will be deploying this model with Amazon Bedrock Guardrails, make certain you have the right AWS Identity and Gain Access To Management (IAM) approvals to utilize Amazon [Bedrock Guardrails](https://jp.harmonymart.in). For guidelines, see Establish authorizations to use [guardrails](https://eurosynapses.giannistriantafyllou.gr) for material filtering.<br>
<br>Implementing guardrails with the ApplyGuardrail API<br>
<br>Amazon Bedrock Guardrails allows you to present safeguards, avoid hazardous material, and examine models against key safety criteria. You can execute safety procedures for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This enables you to use guardrails to evaluate user inputs and model actions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce 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 basic circulation involves the following steps: First, the system receives an input for the model. This input is then processed through the [ApplyGuardrail API](http://hrplus.com.vn). If the input passes the guardrail check, it's sent to the design for inference. After receiving the model's output, another guardrail check is applied. If the output passes this final check, it's returned as the final result. However, if either the input or output is stepped in by the guardrail, a message is returned indicating the nature of the intervention and whether it occurred at the input or output stage. The examples showcased in the following areas demonstrate inference using this API.<br>
<br>Deploy DeepSeek-R1 in Amazon Bedrock 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 brochure under Foundation models in the navigation pane.
At the time of writing this post, you can utilize the InvokeModel API to invoke the design. It doesn't support Converse APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a supplier and select the DeepSeek-R1 model.<br>
<br>The model detail page offers vital details about the design's capabilities, pricing structure, and implementation guidelines. You can find detailed usage directions, including sample API calls and code bits for integration. The model supports different text generation jobs, including material creation, code generation, and question answering, utilizing its reinforcement finding out optimization and CoT reasoning abilities.
The page likewise consists of implementation alternatives and licensing details to help you get going with DeepSeek-R1 in your applications.
3. To start utilizing DeepSeek-R1, choose Deploy.<br>
<br>You will be prompted to configure the implementation details for DeepSeek-R1. The design ID will be pre-populated.
4. For Endpoint name, go into an endpoint name (between 1-50 alphanumeric characters).
5. For Variety of circumstances, [surgiteams.com](https://surgiteams.com/index.php/User:EarnestineCurrey) enter a number of circumstances (between 1-100).
6. For example type, choose your circumstances type. For ideal performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is suggested.
Optionally, you can configure advanced security and facilities settings, consisting of [virtual personal](https://www.roednetwork.com) cloud (VPC) networking, service role consents, and file encryption settings. For most [utilize](https://www.com.listatto.ca) cases, the default settings will work well. However, for production implementations, you may want to examine these settings to line up with your company's security and compliance requirements.
7. Choose Deploy to begin using the model.<br>
<br>When the implementation is complete, you can test DeepSeek-R1's abilities straight in the Amazon Bedrock play ground.
8. Choose Open in play area to access an interactive user interface where you can try out different triggers and adjust design specifications like temperature and optimum length.
When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for optimal results. For instance, material for reasoning.<br>
<br>This is an excellent way to check out the design's thinking and text generation capabilities before integrating it into your applications. The play ground supplies immediate feedback, helping you understand how the model reacts to various inputs and letting you tweak your triggers for ideal outcomes.<br>
<br>You can quickly test the design in the playground through the UI. However, to conjure up the deployed design programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.<br>
<br>Run reasoning using guardrails with the deployed DeepSeek-R1 endpoint<br>
<br>The following code example demonstrates how to carry out inference utilizing a released DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can produce a guardrail utilizing the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo. After you have produced the guardrail, [gratisafhalen.be](https://gratisafhalen.be/author/lienpipkin/) use the following code to execute guardrails. The script initializes the bedrock_runtime customer, sets up reasoning specifications, and sends a request to generate 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 simply a few clicks. With SageMaker JumpStart, [surgiteams.com](https://surgiteams.com/index.php/User:MoniqueMcCabe0) you can tailor pre-trained models to your usage case, with your information, and release them into [production utilizing](https://upmasty.com) either the UI or SDK.<br>
<br>Deploying DeepSeek-R1 model through SageMaker JumpStart provides 2 practical methods: using the user-friendly SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's check out both approaches to help you select the technique that best fits 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, select Studio in the navigation pane.
2. First-time users will be prompted to produce a domain.
3. On the [SageMaker Studio](http://119.45.49.2123000) console, pick JumpStart in the navigation pane.<br>
<br>The design internet browser shows available designs, with details like the supplier name and model capabilities.<br>
<br>4. Search for DeepSeek-R1 to see the DeepSeek-R1 model card.
Each model card reveals [crucial](https://justhired.co.in) details, including:<br>
<br>- Model name
- Provider name
- Task [classification](https://maarifatv.ng) (for example, Text Generation).
[Bedrock Ready](http://getthejob.ma) badge (if appropriate), suggesting that this design can be signed up with Amazon Bedrock, allowing you to use Amazon Bedrock APIs to conjure up the model<br>
<br>5. Choose the model card to view the model details page.<br>
<br>The model details page includes the following details:<br>
<br>- The design name and provider details.
Deploy button to release the design.
About and [Notebooks tabs](https://koubry.com) with detailed details<br>
<br>The About tab includes crucial details, such as:<br>
<br>- Model description.
- License details.
- Technical requirements.
- Usage guidelines<br>
<br>Before you release the design, it's advised to examine the model details and license terms to confirm compatibility with your usage case.<br>
<br>6. Choose Deploy to proceed with deployment.<br>
<br>7. For Endpoint name, use the immediately generated name or produce a custom one.
8. For Instance type ¸ choose a circumstances type (default: ml.p5e.48 xlarge).
9. For Initial instance count, get in the variety of instances (default: 1).
Selecting suitable circumstances types and counts is vital for cost and efficiency optimization. Monitor your deployment to change these settings as needed.Under Inference type, Real-time inference is selected by default. This is enhanced for sustained traffic and low latency.
10. Review all setups for precision. For this design, we highly recommend to SageMaker JumpStart default settings and making certain that network isolation remains in location.
11. Choose Deploy to release the design.<br>
<br>The release procedure can take numerous minutes to complete.<br>
<br>When deployment is total, your endpoint status will alter to [InService](http://www.hakyoun.co.kr). At this moment, the design is ready to accept inference demands through the endpoint. You can keep an eye on the [release progress](https://gitea.star-linear.com) on the SageMaker console Endpoints page, which will display pertinent metrics and status details. When the implementation is total, you can invoke the model using a SageMaker runtime customer and integrate it with your applications.<br>
<br>Deploy DeepSeek-R1 utilizing the SageMaker Python SDK<br>
<br>To get going with DeepSeek-R1 using the SageMaker Python SDK, you will need to set up the [SageMaker Python](https://ashawo.club) SDK and make certain you have the essential AWS authorizations and environment setup. The following is a detailed code example that shows how to deploy and use DeepSeek-R1 for inference programmatically. The code for deploying the design is provided in the Github here. You can clone the notebook and [wiki.myamens.com](http://wiki.myamens.com/index.php/User:JOXTheresa) range from SageMaker Studio.<br>
<br>You can run additional demands against the predictor:<br>
<br>Implement guardrails and run [reasoning](http://git.nikmaos.ru) 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 [produce](http://182.92.163.1983000) a guardrail utilizing the Amazon Bedrock console or the API, [hb9lc.org](https://www.hb9lc.org/wiki/index.php/User:JewellMoffett6) and execute it as shown in the following code:<br>
<br>Clean up<br>
<br>To avoid unwanted charges, complete the actions in this section to tidy up your resources.<br>
<br>Delete the Amazon Bedrock Marketplace implementation<br>
<br>If you deployed the design using Amazon Bedrock Marketplace, complete the following actions:<br>
<br>1. On the Amazon Bedrock console, under Foundation models in the navigation pane, pick Marketplace deployments.
2. In the Managed deployments section, find the endpoint you desire to delete.
3. Select the endpoint, and on the Actions menu, [pick Delete](https://gitlab.dituhui.com).
4. Verify the endpoint details to make certain you're deleting 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 deployed 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 deploy the DeepSeek-R1 design utilizing Bedrock Marketplace and [SageMaker](https://igazszavak.info) JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon [Bedrock Marketplace](https://gitea.shoulin.net) now to get started. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained models, [trademarketclassifieds.com](https://trademarketclassifieds.com/user/profile/2672496) Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting begun 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://www.bakicicepte.com) business build innovative options utilizing AWS services and sped up compute. Currently, he is concentrated on developing methods for [fine-tuning](https://cruzazulfansclub.com) and enhancing the inference performance of large language models. In his totally free time, Vivek enjoys hiking, watching motion pictures, and attempting different foods.<br>
<br>Niithiyn Vijeaswaran is a Generative [AI](http://154.64.253.77:3000) Specialist Solutions Architect with the Third-Party Model [Science](http://shiningon.top) group at AWS. His area of focus is AWS [AI](https://dinle.online) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.<br>
<br>Jonathan Evans is a Specialist Solutions Architect working on generative [AI](https://wino.org.pl) 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](http://www.grainfather.de) center. She is passionate about developing options that assist clients accelerate their [AI](https://zidra.ru) journey and unlock service worth.<br>