erdgarfield17/twoplustwoequal
1
0
Code Issues Projects Wiki

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

Garfield Blodgett 2025-02-08 23:12:01 +00:00
commit 5460985cd2
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 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 deploy DeepSeek [AI](http://tian-you.top:7020)'s first-generation frontier design, 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://surreycreepcatchers.ca) ideas on AWS.<br>
<br>In this post, we show how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow similar actions to deploy the distilled variations of the models too.<br>
<br>Overview of DeepSeek-R1<br>
<br>DeepSeek-R1 is a big language design (LLM) developed by DeepSeek [AI](https://hesdeadjim.org) that uses support discovering to enhance thinking abilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A key differentiating feature is its support learning (RL) step, which was utilized to refine the model's responses beyond the basic pre-training and fine-tuning process. By incorporating RL, DeepSeek-R1 can adjust better to user feedback and objectives, eventually boosting both importance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) technique, suggesting it's equipped to break down complex questions and reason through them in a detailed way. This assisted thinking process permits the design to produce more precise, transparent, and detailed answers. This model combines RL-based fine-tuning with CoT capabilities, aiming to generate structured reactions while focusing on interpretability and user interaction. With its extensive capabilities DeepSeek-R1 has caught the [market's attention](https://www.gabeandlisa.com) as a versatile text-generation model that can be incorporated into numerous workflows such as agents, sensible reasoning and data analysis jobs.<br>
<br>DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture permits activation of 37 billion specifications, making it possible for effective inference by routing queries to the most pertinent specialist "clusters." This approach allows the design to specialize in 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 use an ml.p5e.48 [xlarge instance](https://wiki.whenparked.com) to deploy the model. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.<br>
<br>DeepSeek-R1 distilled designs bring the thinking abilities of the main R1 model to more effective architectures based upon popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a process of training smaller, more effective models to imitate the behavior and reasoning patterns of the bigger DeepSeek-R1 model, using it as an instructor design.<br>
<br>You can release DeepSeek-R1 model either through [SageMaker JumpStart](https://jobsportal.harleysltd.com) or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we recommend releasing this model with guardrails in place. In this blog site, we will utilize Amazon Bedrock Guardrails to present safeguards, avoid harmful material, and examine designs against essential security criteria. At the time of composing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can create multiple guardrails tailored to different use cases and apply them to the DeepSeek-R1 design, enhancing user experiences and standardizing security controls throughout your generative [AI](https://iesoundtrack.tv) applications.<br>
<br>Prerequisites<br>
<br>To release the DeepSeek-R1 model, you require access to an ml.p5e circumstances. To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and confirm you're utilizing ml.p5e.48 xlarge for endpoint usage. 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 boost demand [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:CharleyRudall29) and connect to your account group.<br>
<br>Because you will be releasing this model with Amazon Bedrock Guardrails, make certain you have the correct AWS Identity and Gain Access To Management (IAM) authorizations to utilize Amazon Bedrock Guardrails. For instructions, see Set up approvals 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 damaging content, and assess designs against key safety criteria. You can implement precaution for the DeepSeek-R1 model using the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to assess user inputs and model responses deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can create a guardrail using the Amazon Bedrock [console](https://repo.amhost.net) or the API. For the example code to produce the guardrail, see the GitHub repo.<br>
<br>The general flow includes the following steps: First, the system [receives](http://39.100.93.1872585) 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 model for inference. After getting the model's output, another guardrail check is used. 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 occurred at the input or output phase. The [examples](https://isourceprofessionals.com) showcased in the following areas demonstrate reasoning [utilizing](https://git.kimcblog.com) this API.<br>
<br>Deploy DeepSeek-R1 in Amazon Bedrock Marketplace<br>
<br>Amazon Bedrock Marketplace provides 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, choose Model brochure under Foundation models in the navigation pane.
At the time of composing this post, you can utilize the InvokeModel API to invoke the model. It doesn't support Converse APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a company and pick the DeepSeek-R1 model.<br>
<br>The design detail page provides vital details about the design's capabilities, rates structure, and application guidelines. You can find detailed usage guidelines, including sample API calls and code snippets for [combination](https://git.rtd.one). The model supports various text generation jobs, including material production, code generation, and [question](https://travel-friends.net) answering, utilizing its support discovering [optimization](http://47.93.156.1927006) and CoT reasoning abilities.
The page likewise consists of implementation choices and licensing details to help you get going with DeepSeek-R1 in your applications.
3. To begin using DeepSeek-R1, select Deploy.<br>
<br>You will be triggered to configure the implementation details for DeepSeek-R1. The model ID will be [pre-populated](http://okna-samara.com.ru).
4. For Endpoint name, get in an endpoint name (in between 1-50 alphanumeric characters).
5. For Number of circumstances, enter a number of instances (between 1-100).
6. For Instance type, pick your instance type. For ideal efficiency with DeepSeek-R1, a GPU-based instance 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 settings. For most use cases, the default settings will work well. However, for production implementations, you may desire to review these settings to line up with your company's security and compliance requirements.
7. Choose Deploy to begin utilizing the design.<br>
<br>When the deployment is total, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock play area.
8. Choose Open in playground to access an interactive interface where you can try out different triggers and adjust design criteria like temperature level and maximum length.
When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for optimum outcomes. For instance, material for inference.<br>
<br>This is an exceptional method to explore the design's thinking and text generation capabilities before incorporating it into your applications. The playground provides instant feedback, assisting you comprehend how the model reacts to numerous inputs and letting you tweak your triggers for optimal outcomes.<br>
<br>You can rapidly test the model in the [playground](http://git.zltest.com.tw3333) through the UI. However, to conjure up the released design [programmatically](https://groupeudson.com) with any Amazon Bedrock APIs, you need to get the endpoint ARN.<br>
<br>Run reasoning utilizing guardrails with the deployed DeepSeek-R1 endpoint<br>
<br>The following code example demonstrates how to carry out reasoning using a released DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. 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. After you have created the guardrail, use the following code to implement guardrails. The script initializes the bedrock_runtime customer, sets up reasoning criteria, and sends a demand to create 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 algorithms, and prebuilt 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 release them into production utilizing either the UI or SDK.<br>
<br>Deploying DeepSeek-R1 design through SageMaker JumpStart uses 2 practical approaches: using the instinctive SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's check out both techniques to assist you select the approach that [finest suits](https://www.eadvisor.it) your needs.<br>
<br>Deploy DeepSeek-R1 through SageMaker JumpStart UI<br>
<br>Complete the following steps to deploy DeepSeek-R1 using SageMaker JumpStart:<br>
<br>1. On the SageMaker console, [pick Studio](http://116.198.225.843000) in the navigation pane.
2. First-time users will be prompted to develop a domain.
3. On the SageMaker Studio console, select JumpStart in the [navigation pane](http://113.177.27.2002033).<br>
<br>The model 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 design card.
Each design card shows crucial details, including:<br>
<br>- Model name
- Provider name
- Task category (for instance, Text Generation).
Bedrock Ready badge (if appropriate), indicating that this design can be registered 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 design details page includes the following details:<br>
<br>- The model name and supplier details.
Deploy button to deploy the design.
About and Notebooks tabs with detailed details<br>
<br>The About tab includes crucial details, such as:<br>
<br>- Model .
- License details.
- Technical specs.
- Usage standards<br>
<br>Before you release the design, it's advised to review the model details and license terms to validate compatibility with your use case.<br>
<br>6. Choose Deploy to continue with deployment.<br>
<br>7. For Endpoint name, use the automatically created name or produce a custom-made one.
8. For Instance type ¸ select an instance type (default: ml.p5e.48 xlarge).
9. For Initial circumstances count, get in the number of instances (default: 1).
Selecting proper instance types and counts is crucial for cost and performance optimization. Monitor your deployment to change these settings as needed.Under Inference type, Real-time inference is picked by default. This is enhanced for sustained traffic and low [latency](http://111.9.47.10510244).
10. Review all configurations for precision. For this design, we highly recommend sticking to SageMaker JumpStart default settings and making certain that [network isolation](https://git.chocolatinie.fr) remains in place.
11. Choose Deploy to deploy the model.<br>
<br>The release process can take numerous minutes to finish.<br>
<br>When release is complete, your endpoint status will change to InService. At this point, the design is all set to accept inference requests through the endpoint. You can keep track of the implementation development on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the deployment is complete, you can invoke the design using a SageMaker runtime customer and incorporate it with your [applications](http://154.64.253.773000).<br>
<br>Deploy DeepSeek-R1 utilizing the SageMaker Python SDK<br>
<br>To get begun with DeepSeek-R1 utilizing 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 example that demonstrates how to deploy and use DeepSeek-R1 for inference programmatically. The code for releasing 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](https://gitlab.freedesktop.org) 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 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 unwanted charges, finish the steps in this area to clean up your [resources](https://edge1.co.kr).<br>
<br>Delete the Amazon Bedrock Marketplace deployment<br>
<br>If you released the design using Amazon Bedrock Marketplace, total the following actions:<br>
<br>1. On the Amazon Bedrock console, under Foundation designs in the [navigation](https://git.thatsverys.us) pane, pick Marketplace deployments.
2. In the Managed implementations section, find 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 proper implementation: 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 delete 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 explored how you can access and release the DeepSeek-R1 design using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker [JumpStart](https://radiothamkin.com) in SageMaker Studio or Amazon Bedrock Marketplace now to start. For more details, refer to Use Amazon Bedrock tooling with [Amazon SageMaker](https://careers.midware.in) JumpStart designs, SageMaker JumpStart pretrained models, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Getting going with Amazon SageMaker JumpStart.<br>
<br>About the Authors<br>
<br>Vivek Gangasani is a [Lead Specialist](https://ospitalierii.ro) Solutions Architect for Inference at AWS. He helps emerging generative [AI](http://www.hxgc-tech.com:3000) companies develop innovative services using AWS services and sped up compute. Currently, he is concentrated on developing methods for fine-tuning and optimizing the reasoning performance of large language models. In his downtime, Vivek takes pleasure in treking, enjoying films, and trying different foods.<br>
<br>Niithiyn Vijeaswaran is a Generative [AI](https://git.programming.dev) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His location of focus is AWS [AI](http://168.100.224.79:3000) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.<br>
<br>Jonathan Evans is an Expert Solutions Architect working on generative [AI](https://repo.correlibre.org) with the Third-Party Model Science group at AWS.<br>
<br>Banu Nagasundaram leads item, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://tv.360climatechange.com) hub. She is passionate about constructing solutions that assist customers accelerate their [AI](https://repo.correlibre.org) journey and unlock business worth.<br>