faye33f4403838/grainfather
1
0
Code Issues Actions Packages Projects Wiki

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

Faye Hankinson 2025-02-21 21:53:03 +00:00
commit b23561835e
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 delighted 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 deploy DeepSeek [AI](https://castingnotices.com)'s first-generation frontier model, DeepSeek-R1, in addition to the distilled versions ranging from 1.5 to 70 billion criteria to construct, experiment, and responsibly scale your generative [AI](https://kiwiboom.com) concepts 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 similar steps to release the distilled versions of the models as well.<br>
<br>Overview of DeepSeek-R1<br>
<br>DeepSeek-R1 is a big language model (LLM) established by DeepSeek [AI](http://47.116.130.49) that utilizes reinforcement learning to enhance thinking capabilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. A key distinguishing feature is its support learning (RL) step, which was utilized to refine the model's responses beyond the standard pre-training and fine-tuning process. By including RL, DeepSeek-R1 can adapt better to user feedback and objectives, eventually improving both importance and clarity. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) technique, meaning it's geared up to break down intricate questions and reason through them in a detailed way. This assisted thinking procedure allows the model to produce more accurate, transparent, and detailed answers. This design integrates RL-based [fine-tuning](https://www.unotravel.co.kr) with CoT abilities, aiming to [produce structured](http://1.14.122.1703000) responses while concentrating on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has actually [captured](http://zhandj.top3000) the market's attention as a [flexible](https://git.christophhagen.de) text-generation model that can be incorporated into different workflows such as agents, rational thinking and information interpretation jobs.<br>
<br>DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion criteria in size. The MoE architecture allows activation of 37 billion criteria, enabling effective reasoning by routing inquiries to the most appropriate specialist "clusters." This approach permits the design to focus on different issue domains while maintaining general effectiveness. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will utilize an ml.p5e.48 [xlarge instance](https://gitea.potatox.net) to the model. 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](https://www.womplaz.com) of the main R1 model to more effective architectures based upon [popular](https://cvbankye.com) open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a process of training smaller, more effective designs to imitate the habits and reasoning patterns of the larger DeepSeek-R1 model, using it as a teacher model.<br>
<br>You can release DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we advise releasing this design with guardrails in location. In this blog site, we will use [Amazon Bedrock](http://185.5.54.226) Guardrails to introduce safeguards, avoid harmful content, and assess designs against key security requirements. At the time of composing this blog site, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can develop several guardrails tailored to various usage cases and use them to the DeepSeek-R1 model, improving user experiences and standardizing security controls throughout your generative [AI](https://www.anetastaffing.com) applications.<br>
<br>Prerequisites<br>
<br>To deploy the DeepSeek-R1 design, you need access to an ml.p5e instance. To examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick 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 circumstances in the AWS Region you are releasing. To ask for a limit increase, create a [limitation boost](https://git.phyllo.me) request and [yewiki.org](https://www.yewiki.org/User:Linnie87B672) connect to your account group.<br>
<br>Because you will be deploying this model with Amazon Bedrock Guardrails, make certain you have the proper AWS Identity and Gain Access To Management (IAM) approvals to use Amazon Bedrock Guardrails. For guidelines, see Set up permissions to use guardrails for material filtering.<br>
<br>[Implementing](https://git.fpghoti.com) guardrails with the ApplyGuardrail API<br>
<br>Amazon Bedrock Guardrails permits you to introduce safeguards, prevent harmful content, and evaluate designs against crucial security requirements. You can implement precaution for the DeepSeek-R1 design using the Amazon Bedrock [ApplyGuardrail API](https://cagit.cacode.net). This permits you to use guardrails to examine user inputs and model reactions released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a guardrail using the Amazon Bedrock console or the API. For the example code to produce 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 out to the design for reasoning. After getting the model's output, another guardrail check is applied. If the output passes this final check, it's returned as the result. 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 happened at the input or output phase. The examples showcased in the following areas show reasoning 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 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, pick Model catalog under Foundation designs in the navigation pane.
At the time of composing this post, you can use the InvokeModel API to conjure up the model. It doesn't support Converse APIs and other Amazon Bedrock [tooling](https://sansaadhan.ipistisdemo.com).
2. Filter for [DeepSeek](https://www.youmanitarian.com) as a company and pick the DeepSeek-R1 design.<br>
<br>The model detail page offers vital details about the design's capabilities, pricing structure, and implementation guidelines. You can find detailed use guidelines, including sample API calls and code bits for integration. The model supports various text generation tasks, consisting of material creation, code generation, and concern answering, using its support finding out optimization and CoT reasoning abilities.
The page likewise consists of deployment options and licensing details to assist you get started with DeepSeek-R1 in your applications.
3. To begin using DeepSeek-R1, choose Deploy.<br>
<br>You will be triggered to set up the deployment 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 instances, go into a number of instances (in between 1-100).
6. For Instance type, pick your instance type. For optimal performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is advised.
Optionally, you can set up innovative security and facilities settings, including virtual personal cloud (VPC) networking, service function approvals, and [file encryption](https://aggeliesellada.gr) settings. For a lot of use cases, the default settings will work well. However, for production implementations, you might desire to evaluate these settings to align with your organization's security and compliance requirements.
7. Choose Deploy to begin utilizing the design.<br>
<br>When the implementation is complete, you can check DeepSeek-R1's abilities straight in the Amazon Bedrock play ground.
8. Choose Open in play ground to access an interactive interface where you can try out different triggers and change design parameters like temperature level and optimum length.
When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimum outcomes. For example, content for reasoning.<br>
<br>This is an exceptional way to check out the design's thinking and text generation capabilities before incorporating it into your [applications](http://124.222.181.1503000). The play area provides immediate feedback, assisting you understand how the model reacts to various inputs and letting you tweak your prompts for optimal outcomes.<br>
<br>You can rapidly evaluate the model 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 reasoning utilizing guardrails with the released DeepSeek-R1 endpoint<br>
<br>The following code example shows how to perform reasoning utilizing a deployed DeepSeek-R1 design through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can produce a [guardrail utilizing](http://git.vimer.top3000) the Amazon Bedrock console or the API. For the example code to produce the guardrail, [larsaluarna.se](http://www.larsaluarna.se/index.php/User:BerylTazewell8) see the GitHub repo. After you have developed the guardrail, utilize the following code to implement guardrails. The [script initializes](http://101.132.73.143000) the bedrock_runtime customer, sets up reasoning parameters, and sends a demand to generate 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](http://47.113.115.2393000) ML solutions that you can release with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your use case, with your information, and deploy them into production utilizing either the UI or SDK.<br>
<br>Deploying DeepSeek-R1 model through SageMaker JumpStart offers 2 convenient approaches: using the instinctive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's check out both approaches to assist you choose the technique that finest matches your requirements.<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 triggered to develop a domain.
3. On the SageMaker Studio console, pick JumpStart in the navigation pane.<br>
<br>The design web browser displays available models, with details like the company name and design abilities.<br>
<br>4. Look for DeepSeek-R1 to see the DeepSeek-R1 model card.
Each design card shows key details, consisting of:<br>
<br>- Model name
- Provider name
- Task category (for example, Text Generation).
Bedrock Ready badge (if applicable), suggesting that this design can be signed up with Amazon Bedrock, enabling you to utilize Amazon Bedrock APIs to invoke the model<br>
<br>5. Choose the design card to see the design details page.<br>
<br>The design details page includes the following details:<br>
<br>- The model name and company details.
Deploy button to deploy the model.
About and Notebooks tabs with detailed details<br>
<br>The About tab includes important details, such as:<br>
<br>- Model description.
- License details.
- Technical requirements.
- Usage standards<br>
<br>Before you release the model, it's recommended to review the design details and license terms to validate compatibility with your use case.<br>
<br>6. Choose Deploy to continue with implementation.<br>
<br>7. For Endpoint name, utilize the immediately produced name or produce a custom-made one.
8. For example type ¸ choose an instance type (default: ml.p5e.48 xlarge).
9. For Initial circumstances count, go into the number of circumstances (default: 1).
Selecting proper circumstances types and counts is essential for cost and performance optimization. Monitor your release to adjust these [settings](https://cvbankye.com) 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 precision. For this model, we strongly recommend adhering to SageMaker JumpStart default settings and making certain that network isolation remains in place.
11. Choose Deploy to release the design.<br>
<br>The implementation procedure can take several minutes to finish.<br>
<br>When deployment is complete, your endpoint status will change to InService. At this point, the design is ready to accept reasoning [demands](http://git.cnibsp.com) through the endpoint. You can keep track of the deployment progress on the SageMaker console Endpoints page, which will display appropriate metrics and status details. When the release is total, you can conjure up the [design utilizing](https://hip-hop.id) a SageMaker runtime client and incorporate it with your applications.<br>
<br>Deploy DeepSeek-R1 using the SageMaker Python SDK<br>
<br>To begin with DeepSeek-R1 using the SageMaker Python SDK, you will need to install 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 utilize DeepSeek-R1 for inference programmatically. The code for [deploying](https://avpro.cc) the model is provided in the Github here. You can clone the note pad and run from SageMaker Studio.<br>
<br>You can run additional requests 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 utilizing the Amazon Bedrock console or the API, and execute it as shown in the following code:<br>
<br>Clean up<br>
<br>To avoid undesirable charges, complete the actions in this section to clean up your resources.<br>
<br>Delete the Amazon Bedrock Marketplace deployment<br>
<br>If you released the model using Amazon Bedrock Marketplace, total the following steps:<br>
<br>1. On the Amazon Bedrock console, under Foundation models in the navigation pane, select Marketplace deployments.
2. In the Managed implementations section, locate the endpoint you want to erase.
3. Select the endpoint, and on the Actions menu, pick Delete.
4. Verify the endpoint details to make certain you're deleting the [correct](https://testgitea.cldevops.de) implementation: 1. [Endpoint](http://193.200.130.1863000) 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 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 deploy the DeepSeek-R1 model using Bedrock Marketplace and SageMaker JumpStart. 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 designs, SageMaker JumpStart pretrained designs, 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](http://api.cenhuy.com:3000) business construct innovative options using AWS services and accelerated compute. Currently, he is concentrated on [establishing methods](http://huaang6688.gnway.cc3000) for fine-tuning and optimizing the reasoning performance of large language models. In his complimentary time, Vivek delights in hiking, seeing motion pictures, and trying different foods.<br>
<br>Niithiyn Vijeaswaran is a Generative [AI](https://abileneguntrader.com) Specialist Solutions Architect with the Third-Party Model [Science team](http://www.chinajobbox.com) at AWS. His location of focus is AWS [AI](http://152.136.126.252:3000) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.<br>
<br>Jonathan Evans is a Professional Solutions Architect dealing with generative [AI](https://www.jr-it-services.de:3000) with the Third-Party Model Science group at AWS.<br>
<br>Banu Nagasundaram leads product, engineering, and tactical collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://umindconsulting.com) center. She is passionate about building solutions that help customers accelerate their [AI](http://lty.co.kr) journey and unlock service value.<br>