artvalle57090
/
199
1
0
Code Issues Packages Projects Wiki

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

Art Valle 2025-05-31 04:57:00 +08:00
commit 215458fa66
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 designs are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](https://git.cacpaper.com)'s first-generation frontier model, DeepSeek-R1, in addition to the distilled versions varying from 1.5 to 70 billion specifications to build, experiment, and properly scale your [generative](https://job-maniak.com) [AI](http://43.142.132.208:18930) 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 comparable actions to release the distilled variations of the designs too.<br>
<br>Overview of DeepSeek-R1<br>
<br>DeepSeek-R1 is a big language design (LLM) developed by DeepSeek [AI](https://git.fanwikis.org) that utilizes reinforcement finding out to enhance reasoning abilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. An essential differentiating feature is its reinforcement learning (RL) action, which was utilized to improve the design's responses beyond the standard pre-training and fine-tuning procedure. By integrating RL, DeepSeek-R1 can adjust better to user feedback and goals, eventually boosting both relevance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) approach, suggesting it's geared up to break down [complex queries](https://meetcupid.in) and [systemcheck-wiki.de](https://systemcheck-wiki.de/index.php?title=Benutzer:LesleeDzq5655094) factor through them in a detailed way. This assisted reasoning [procedure](https://kahkaham.net) enables the design to produce more precise, transparent, and detailed responses. This model integrates RL-based fine-tuning with CoT abilities, aiming to produce structured actions while concentrating on interpretability and user interaction. With its [comprehensive capabilities](https://apkjobs.com) DeepSeek-R1 has actually caught the industry's attention as a flexible text-generation model that can be incorporated into different workflows such as representatives, [logical thinking](http://git.zhiweisz.cn3000) and information interpretation tasks.<br>
<br>DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture [permits](https://www.teacircle.co.in) activation of 37 billion specifications, allowing efficient inference by routing questions to the most pertinent [specialist](https://gogs.greta.wywiwyg.net) "clusters." This method allows the design to focus on different problem domains while maintaining total effectiveness. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for inference. 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 providing 1128 GB of GPU memory.<br>
<br>DeepSeek-R1 distilled models bring the reasoning capabilities of the main R1 model to more efficient architectures based upon popular 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 mimic the behavior and reasoning patterns of the bigger DeepSeek-R1 model, using it as an instructor design.<br>
<br>You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we suggest deploying this design with guardrails in location. In this blog site, we will use Amazon Bedrock Guardrails to present safeguards, avoid damaging content, and assess models against essential security requirements. At the time of writing this blog site, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create several guardrails tailored to different usage cases and apply them to the DeepSeek-R1 design, improving user experiences and standardizing security controls throughout your generative [AI](https://gitlab.lizhiyuedong.com) applications.<br>
<br>Prerequisites<br>
<br>To release 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](https://academia.tripoligate.com) and under AWS Services, select 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 deploying. To ask for a limitation boost, develop a limit increase request and reach out to your account team.<br>
<br>Because you will be releasing this design with Amazon Bedrock Guardrails, make certain you have the right AWS Identity and Gain Access To Management (IAM) consents to utilize Amazon Bedrock Guardrails. For guidelines, see Establish consents to use guardrails for material filtering.<br>
<br>Implementing guardrails with the ApplyGuardrail API<br>
<br>Amazon Bedrock Guardrails allows you to present safeguards, prevent damaging material, and examine designs against crucial safety requirements. You can execute security steps for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to examine user inputs and model responses 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 develop the guardrail, see the GitHub repo.<br>
<br>The basic circulation involves the following steps: First, the system [receives](http://omkie.com3000) 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 design's output, another guardrail check is applied. If the output passes this last check, it's returned as the final 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 stage. The examples showcased in the following areas show inference using this API.<br>
<br>Deploy DeepSeek-R1 in Amazon Bedrock Marketplace<br>
<br> Marketplace gives 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 designs in the navigation pane.
At the time of writing this post, you can use the InvokeModel API to invoke the model. It does not support Converse APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a supplier and pick the DeepSeek-R1 design.<br>
<br>The model detail page supplies necessary details about the model's capabilities, rates structure, and application guidelines. You can discover detailed usage guidelines, consisting of sample API calls and code snippets for combination. The model supports various text generation tasks, consisting of content production, code generation, and concern answering, using its support finding out [optimization](https://complexityzoo.net) and CoT reasoning abilities.
The page also includes release choices and licensing details to help you begin with DeepSeek-R1 in your applications.
3. To begin using DeepSeek-R1, choose Deploy.<br>
<br>You will be triggered to configure the implementation details for DeepSeek-R1. The design ID will be pre-populated.
4. For Endpoint name, enter an endpoint name (in between 1-50 alphanumeric characters).
5. For Number of circumstances, get in a variety of instances (in between 1-100).
6. For example type, select your [circumstances type](http://gitlab.hupp.co.kr). For optimum efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is recommended.
Optionally, you can set up advanced security and facilities settings, consisting of virtual private cloud (VPC) networking, service function consents, and encryption settings. For the majority of use cases, the default settings will work well. However, for production implementations, you might desire to review these settings to align with your organization's security and compliance requirements.
7. Choose Deploy to start using the design.<br>
<br>When the deployment is complete, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock playground.
8. Choose Open in play area to access an interactive interface where you can experiment with different triggers and adjust model specifications like temperature level and optimum length.
When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for ideal outcomes. For example, material for reasoning.<br>
<br>This is an exceptional way to explore the model's thinking and text generation abilities before incorporating it into your applications. The playground supplies immediate feedback, assisting you comprehend how the model responds to numerous inputs and letting you fine-tune your prompts for optimum results.<br>
<br>You can [rapidly evaluate](https://meta.mactan.com.br) the model in the play area through the UI. However, to invoke the [deployed design](http://profilsjob.com) programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.<br>
<br>Run reasoning using guardrails with the deployed DeepSeek-R1 endpoint<br>
<br>The following code example shows how to perform inference using a released DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and [ApplyGuardrail API](https://oldgit.herzen.spb.ru). You can produce a guardrail utilizing the Amazon Bedrock console or the API. For the example code to develop the guardrail, see the GitHub repo. After you have developed the guardrail, use the following code to carry out guardrails. The script initializes the bedrock_runtime customer, configures reasoning specifications, and sends a demand to generate text 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 solutions that you can deploy with just a few 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 design through SageMaker JumpStart offers two hassle-free techniques: using the user-friendly SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's explore both methods to help you pick the approach that finest suits your needs.<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, pick Studio in the navigation pane.
2. First-time users will be triggered to create a domain.
3. On the SageMaker Studio console, pick JumpStart in the navigation pane.<br>
<br>The design browser displays available models, with details like the company name and model abilities.<br>
<br>4. Search for DeepSeek-R1 to view the DeepSeek-R1 design card.
Each model card shows key details, consisting of:<br>
<br>- Model name
- Provider name
- Task classification (for instance, Text Generation).
Bedrock Ready badge (if appropriate), indicating that this model can be signed up with Amazon Bedrock, permitting you to use Amazon Bedrock APIs to conjure up 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 design name and service provider 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 specifications.
- Usage guidelines<br>
<br>Before you deploy the design, it's suggested to review the model details and license terms to confirm compatibility with your usage case.<br>
<br>6. Choose Deploy to continue with implementation.<br>
<br>7. For Endpoint name, use the automatically produced name or produce a custom-made one.
8. For Instance type ¸ select an instance type (default: ml.p5e.48 xlarge).
9. For Initial instance count, enter the variety of instances (default: 1).
Selecting appropriate circumstances types and counts is important for expense and efficiency optimization. Monitor your implementation 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.
10. Review all setups for precision. For this design, we strongly advise sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in place.
11. Choose Deploy to release the model.<br>
<br>The release procedure can take numerous minutes to finish.<br>
<br>When deployment is complete, your endpoint status will change to InService. At this moment, the model is prepared to accept reasoning demands through the endpoint. You can keep track of the release development on the SageMaker console Endpoints page, which will display appropriate metrics and [status details](https://tricityfriends.com). When the release is total, you can conjure up the model utilizing a SageMaker runtime client and integrate it with your applications.<br>
<br>Deploy DeepSeek-R1 using the SageMaker Python SDK<br>
<br>To start with DeepSeek-R1 utilizing the [SageMaker Python](https://giftconnect.in) SDK, you will require to set up the SageMaker Python SDK and make certain you have the needed AWS authorizations and environment setup. The following is a detailed code example that shows how to release and utilize DeepSeek-R1 for inference programmatically. The code for releasing the design is supplied in the Github here. You can clone the notebook and range from SageMaker Studio.<br>
<br>You can run extra requests against the predictor:<br>
<br>Implement guardrails and run inference with your [SageMaker JumpStart](https://video.etowns.ir) predictor<br>
<br>Similar to Amazon Bedrock, you can also use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a guardrail utilizing the Amazon Bedrock console or the API, and execute it as shown in the following code:<br>
<br>Tidy up<br>
<br>To avoid unwanted charges, complete the steps in this section to clean up your [resources](https://www.lokfuehrer-jobs.de).<br>
<br>Delete the Amazon Bedrock Marketplace release<br>
<br>If you released the model utilizing Amazon Bedrock Marketplace, complete the following actions:<br>
<br>1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, pick Marketplace [deployments](https://tylerwesleywilliamson.us).
2. In the Managed deployments area, locate the endpoint you want 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 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 [released](https://manchesterunitedfansclub.com) will sustain expenses if you leave it running. Use the following code to erase the endpoint if you wish to stop sustaining charges. For more details, see [Delete Endpoints](https://git.fracturedcode.net) 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](https://git.easytelecoms.fr) in SageMaker Studio or Amazon Bedrock Marketplace now to begin. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker [JumpStart](https://prosafely.com) designs, SageMaker JumpStart pretrained models, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Beginning with Amazon SageMaker JumpStart.<br>
<br>About the Authors<br>
<br>Vivek Gangasani is a Lead Specialist Solutions Architect for [Inference](http://www.iilii.co.kr) at AWS. He helps emerging generative [AI](http://101.200.181.61) companies build innovative solutions utilizing AWS services and accelerated compute. Currently, he is focused on [developing methods](https://makestube.com) for fine-tuning and optimizing the reasoning efficiency of big language models. In his spare time, [Vivek enjoys](http://116.198.225.843000) treking, watching movies, and attempting different foods.<br>
<br>Niithiyn Vijeaswaran is a Generative [AI](https://www.ejobsboard.com) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His location of focus is AWS [AI](http://159.75.133.67:20080) 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://gitlab.isc.org) with the [Third-Party Model](https://talktalky.com) Science team at AWS.<br>
<br>Banu Nagasundaram leads product, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](http://tian-you.top:7020) hub. She is enthusiastic about developing services that assist consumers accelerate their [AI](https://www.pakalljobz.com) journey and unlock company worth.<br>