augustvaughn03/153
1
0
Code Issues Actions Packages Projects Wiki

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

August Vaughn 2025-02-16 11:09:51 +00:00
commit b83be96280
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 announce that DeepSeek R1 distilled Llama and Qwen models are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](http://artpia.net)'s first-generation frontier design, [89u89.com](https://www.89u89.com/author/sole7081199/) DeepSeek-R1, in addition to the distilled versions ranging from 1.5 to 70 billion criteria to develop, experiment, and properly scale your generative [AI](http://106.55.3.105:20080) concepts on AWS.<br>
<br>In this post, we show how to begin with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable steps to release the distilled variations of the models as well.<br>
<br>Overview of DeepSeek-R1<br>
<br>DeepSeek-R1 is a large language model (LLM) developed by DeepSeek [AI](http://158.160.20.3:3000) that uses support discovering to boost thinking abilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. A key identifying feature is its support knowing (RL) action, which was used to refine the model's reactions beyond the basic pre-training and tweak process. By integrating RL, DeepSeek-R1 can adapt better to user feedback and objectives, ultimately enhancing both importance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) approach, meaning it's equipped to break down complex queries and reason through them in a detailed manner. This assisted thinking process permits the model to produce more accurate, transparent, and detailed answers. This design combines RL-based fine-tuning with CoT capabilities, aiming to produce structured responses while [concentrating](https://learninghub.fulljam.com) on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has actually recorded the industry's attention as a flexible text-generation model that can be incorporated into numerous workflows such as representatives, logical reasoning and information analysis jobs.<br>
<br>DeepSeek-R1 uses a Mix of Experts (MoE) [architecture](https://nurseportal.io) and is 671 billion parameters in size. The MoE architecture enables activation of 37 billion specifications, making it possible for efficient inference by routing inquiries to the most pertinent specialist "clusters." This technique allows the design to focus on different problem domains while maintaining total effectiveness. DeepSeek-R1 needs 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 deploy the design. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.<br>
<br>DeepSeek-R1 distilled designs bring the thinking capabilities of the main R1 design to more [efficient architectures](https://wiki.contextgarden.net) based upon [popular](https://glhwar3.com) 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 efficient designs to mimic the habits and thinking patterns of the bigger DeepSeek-R1 model, using it as an instructor model.<br>
<br>You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we recommend deploying this model with guardrails in place. In this blog, we will use Amazon Bedrock Guardrails to introduce safeguards, avoid harmful content, and assess designs against key security requirements. At the time of writing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails [supports](https://profesional.id) just the ApplyGuardrail API. You can create multiple guardrails tailored to various usage cases and use them to the DeepSeek-R1 design, improving user experiences and [standardizing security](https://git.thetoc.net) controls throughout your generative [AI](https://e-sungwoo.co.kr) applications.<br>
<br>Prerequisites<br>
<br>To release the DeepSeek-R1 model, you need access to an ml.p5e instance. To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and verify 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. To ask for a limitation increase, develop a limitation boost demand and reach out to your account group.<br>
<br>Because you will be deploying this design with Amazon Bedrock Guardrails, make certain you have the right AWS Identity and [trademarketclassifieds.com](https://trademarketclassifieds.com/user/profile/2672496) Gain Access To Management (IAM) authorizations to use Amazon Bedrock Guardrails. For instructions, see Establish permissions to utilize guardrails for content filtering.<br>
<br>Implementing guardrails with the ApplyGuardrail API<br>
<br>Amazon Bedrock [Guardrails permits](https://cvmira.com) you to introduce safeguards, avoid damaging content, and evaluate designs against essential safety requirements. You can implement safety measures for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to examine user inputs and model actions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can create a guardrail using the Amazon Bedrock [console](https://gitlab.dituhui.com) or [trademarketclassifieds.com](https://trademarketclassifieds.com/user/profile/2684771) the API. For the example code to develop the guardrail, see the GitHub repo.<br>
<br>The general circulation includes the following steps: First, the system gets 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 inference. After receiving the model's output, another guardrail check is used. If the output passes this last check, it's returned as the result. 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 show [reasoning utilizing](https://git.hichinatravel.com) this API.<br>
<br>Deploy DeepSeek-R1 in Amazon Bedrock Marketplace<br>
<br>Amazon Bedrock [Marketplace](https://nmpeoplesrepublick.com) provides you access to over 100 popular, emerging, and specialized foundation models (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 composing this post, you can utilize the [InvokeModel API](https://spaceballs-nrw.de) to invoke the design. It doesn't support Converse APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a provider and select the DeepSeek-R1 design.<br>
<br>The model detail page offers essential details about the model's abilities, rates structure, and implementation standards. You can find [detailed](https://copyright-demand-letter.com) usage instructions, including sample API calls and code snippets for integration. The design supports different text generation jobs, consisting of content creation, code generation, and question answering, utilizing its reinforcement finding out optimization and CoT reasoning capabilities.
The page likewise includes release options and to assist you begin with DeepSeek-R1 in your applications.
3. To start utilizing DeepSeek-R1, pick Deploy.<br>
<br>You will be prompted to configure the release details for DeepSeek-R1. The model ID will be pre-populated.
4. For Endpoint name, get in an endpoint name (in between 1-50 alphanumeric characters).
5. For Variety of instances, go into a number of circumstances (between 1-100).
6. For example type, pick your [circumstances type](https://joydil.com). For ideal performance with DeepSeek-R1, a GPU-based [circumstances type](http://39.98.79.181) like ml.p5e.48 xlarge is advised.
Optionally, you can configure advanced security and infrastructure settings, including virtual personal cloud (VPC) networking, service role consents, and encryption settings. For most utilize cases, the default settings will work well. However, for production implementations, you may desire to review these [settings](http://81.71.148.578080) to align with your company's security and compliance requirements.
7. Choose Deploy to begin utilizing the design.<br>
<br>When the release is complete, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock playground.
8. Choose Open in play ground to access an interactive user interface where you can explore various triggers and adjust model specifications like temperature level and maximum length.
When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for optimum results. For instance, material for reasoning.<br>
<br>This is an excellent method to explore the [design's reasoning](http://162.55.45.543000) and text generation abilities before incorporating it into your applications. The playground offers immediate feedback, assisting you understand how the design reacts to different inputs and letting you tweak your prompts for [ideal outcomes](https://projobs.dk).<br>
<br>You can rapidly check the model in the playground through the UI. However, to invoke the released design 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 inference utilizing a deployed DeepSeek-R1 model through Amazon Bedrock using 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 actually created the guardrail, utilize the following code to execute guardrails. The script initializes the bedrock_runtime client, sets up reasoning parameters, and sends a request to [generate text](https://skilling-india.in) based on a user prompt.<br>
<br>Deploy DeepSeek-R1 with SageMaker JumpStart<br>
<br>SageMaker JumpStart is an artificial intelligence (ML) center with FMs, built-in algorithms, and prebuilt ML options that you can release with simply 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 model through SageMaker JumpStart offers two convenient techniques: utilizing the intuitive SageMaker JumpStart UI or implementing programmatically through the [SageMaker Python](http://grainfather.co.uk) SDK. Let's [explore](https://repo.myapps.id) both approaches to assist you choose the [approach](https://barokafunerals.co.za) that finest suits your needs.<br>
<br>Deploy DeepSeek-R1 through SageMaker JumpStart UI<br>
<br>Complete the following actions to release DeepSeek-R1 using SageMaker JumpStart:<br>
<br>1. On the SageMaker console, pick 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 browser displays available designs, with details like the provider name and [design capabilities](https://git.goatwu.com).<br>
<br>4. Look for DeepSeek-R1 to view the DeepSeek-R1 design card.
Each model card shows essential details, including:<br>
<br>- Model name
- Provider name
- Task classification (for example, Text Generation).
Bedrock Ready badge (if relevant), suggesting that this design can be registered with Amazon Bedrock, enabling you to use Amazon Bedrock APIs to invoke the model<br>
<br>5. Choose the model card to view the model details page.<br>
<br>The model details page consists of the following details:<br>
<br>- The design name and 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 specs.
- Usage guidelines<br>
<br>Before you release the design, it's suggested to evaluate the design details and license terms to verify compatibility with your use case.<br>
<br>6. Choose Deploy to continue with release.<br>
<br>7. For Endpoint name, utilize the automatically produced name or create a customized one.
8. For Instance type ¸ select a circumstances type (default: [wiki.whenparked.com](https://wiki.whenparked.com/User:Steffen5509) ml.p5e.48 xlarge).
9. For [Initial instance](https://thisglobe.com) count, get in the variety of circumstances (default: 1).
Selecting proper circumstances types and counts is important for expense and efficiency optimization. Monitor your deployment to change these settings as needed.Under Inference type, Real-time inference is chosen by default. This is enhanced for sustained traffic and low latency.
10. Review all configurations for accuracy. For this model, we strongly advise adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in location.
11. Choose Deploy to release the design.<br>
<br>The implementation process can take numerous minutes to complete.<br>
<br>When release is complete, your endpoint status will change to InService. At this moment, the model is ready to accept reasoning demands through the endpoint. You can monitor the implementation progress on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the deployment is complete, you can invoke the design utilizing a SageMaker runtime customer and integrate 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 needed AWS consents and environment setup. The following is a detailed code example that demonstrates how to deploy and utilize DeepSeek-R1 for inference programmatically. The code for [releasing](https://www.loupanvideos.com) the model is provided in the Github here. You can clone the notebook and run from SageMaker Studio.<br>
<br>You can run additional requests against the predictor:<br>
<br>Implement guardrails and run [inference](https://careers.webdschool.com) 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 produce a guardrail using the Amazon Bedrock console or the API, and execute it as shown in the following code:<br>
<br>Clean up<br>
<br>To prevent undesirable charges, complete the actions in this area to clean up your resources.<br>
<br>Delete the Amazon Bedrock Marketplace deployment<br>
<br>If you deployed the design using Amazon Bedrock Marketplace, total the following steps:<br>
<br>1. On the Amazon Bedrock console, under Foundation models in the navigation pane, choose Marketplace releases.
2. In the Managed releases section, locate the endpoint you desire to delete.
3. Select the endpoint, and on the Actions menu, choose Delete.
4. Verify the endpoint details to make certain you're erasing the proper 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 costs if you leave it running. Use the following code to delete 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 explored how you can access and release the DeepSeek-R1 model using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get going. For more details, refer to 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 at AWS. He helps emerging generative [AI](http://119.3.29.177:3000) business build ingenious solutions using AWS services and accelerated calculate. Currently, he is focused on developing methods for fine-tuning and optimizing the reasoning performance of big language designs. In his downtime, Vivek enjoys hiking, viewing motion pictures, and attempting different foods.<br>
<br>Niithiyn Vijeaswaran is a Generative [AI](http://121.36.37.70:15501) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His location of focus is AWS [AI](https://gitea.umrbotech.com) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.<br>
<br>Jonathan Evans is a Professional Solutions Architect dealing with generative [AI](https://gitea.itskp-odense.dk) with the Third-Party Model Science group at AWS.<br>
<br>Banu Nagasundaram leads product, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and [generative](https://www.atlantistechnical.com) [AI](https://video.invirtua.com) center. She is enthusiastic about constructing options that help customers accelerate their [AI](https://gogs.les-refugies.fr) journey and unlock service worth.<br>