rowenapercy512/healthcare-inc
1
0
Code Issues Packages Projects Wiki

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

Rowena Percy
2025-06-01 22:14:27 +09:00
commit 77dad5e438
1 changed files with 93 additions and 0 deletions
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 models are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](https://cbfacilitiesmanagement.ie)'s first-generation frontier model, [forum.altaycoins.com](http://forum.altaycoins.com/profile.php?id=1073113) DeepSeek-R1, in addition to the distilled variations ranging from 1.5 to 70 billion specifications to build, experiment, and responsibly scale your generative [AI](https://teba.timbaktuu.com) 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 steps to release the [distilled versions](https://iamzoyah.com) of the models as well.<br>
<br>Overview of DeepSeek-R1<br>
<br>DeepSeek-R1 is a large language model (LLM) established by DeepSeek [AI](https://drshirvany.ir) that uses reinforcement finding out to boost reasoning abilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A key [identifying function](https://dronio24.com) is its reinforcement knowing (RL) action, which was used to improve the model's actions beyond the basic pre-training and tweak process. By including RL, DeepSeek-R1 can adjust more successfully to user feedback and objectives, ultimately enhancing both relevance and clearness. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) method, suggesting it's equipped to break down intricate queries and reason through them in a detailed manner. This directed reasoning procedure permits the model to produce more precise, transparent, and detailed answers. This model integrates RL-based fine-tuning with CoT capabilities, aiming to create structured responses while focusing on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has actually recorded the market's attention as a flexible text-generation design that can be integrated into various workflows such as representatives, rational thinking and data interpretation tasks.<br>
<br>DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture allows activation of 37 billion criteria, making it possible for [effective reasoning](https://nycu.linebot.testing.jp.ngrok.io) by routing inquiries to the most pertinent professional "clusters." This approach enables the model to specialize in different issue domains while maintaining overall performance. DeepSeek-R1 needs at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will utilize an ml.p5e.48 xlarge instance 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 capabilities of the main R1 design to more efficient architectures based on popular open designs 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 simulate the behavior and thinking patterns of the larger DeepSeek-R1 design, using it as an [instructor model](http://git.szchuanxia.cn).<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 model with guardrails in place. In this blog, we will [utilize Amazon](http://121.36.27.63000) Bedrock Guardrails to introduce safeguards, avoid harmful content, and evaluate designs against essential safety criteria. At the time of writing this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can produce numerous guardrails tailored to different usage cases and use them to the DeepSeek-R1 design, [enhancing](https://gitlab.dndg.it) user experiences and standardizing safety controls across your generative [AI](http://lty.co.kr) applications.<br>
<br>Prerequisites<br>
<br>To release the DeepSeek-R1 model, [surgiteams.com](https://surgiteams.com/index.php/User:Bradford7526) you need access to an ml.p5e circumstances. To examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, select Amazon SageMaker, and validate you're utilizing 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, develop a limitation boost demand and connect to your account group.<br>
<br>Because you will be deploying this design with Amazon Bedrock Guardrails, make certain you have the proper AWS Identity and Gain Access To Management (IAM) consents to utilize Amazon Bedrock Guardrails. For guidelines, see Set up authorizations to use guardrails for content filtering.<br>
<br>[Implementing guardrails](https://coptr.digipres.org) with the ApplyGuardrail API<br>
<br>Amazon Bedrock Guardrails allows you to introduce safeguards, prevent hazardous material, and [examine](https://caringkersam.com) models against essential safety criteria. You can carry out [precaution](https://www.diekassa.at) for [wiki.whenparked.com](https://wiki.whenparked.com/User:EveKeel8756) the DeepSeek-R1 model using the [Amazon Bedrock](https://teba.timbaktuu.com) ApplyGuardrail API. This permits you to use guardrails to evaluate 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](https://gitea.xiaolongkeji.net).<br>
<br>The basic flow includes the following actions: First, the system receives an input for the design. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it's sent to the design for inference. After getting 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 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](https://git.mintmuse.com) showcased in the following areas show reasoning utilizing this API.<br>
<br>Deploy DeepSeek-R1 in Amazon Bedrock Marketplace<br>
<br>Amazon Bedrock Marketplace offers you access to over 100 popular, emerging, [wiki.dulovic.tech](https://wiki.dulovic.tech/index.php/User:AlmaGrammer6) and specialized structure models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, complete the following steps:<br>
<br>1. On the Amazon Bedrock console, choose Model catalog under Foundation designs in the navigation pane.
At the time of composing this post, you can utilize the InvokeModel API to invoke the design. It does not support Converse APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a supplier and choose the DeepSeek-R1 model.<br>
<br>The model detail page offers essential details about the model's abilities, rates structure, and execution standards. You can discover detailed usage guidelines, including sample API calls and code snippets for integration. The model supports different text generation jobs, consisting of content creation, code generation, and question answering, using its reinforcement discovering optimization and CoT reasoning abilities.
The page likewise includes release options and licensing details to assist you get started with DeepSeek-R1 in your applications.
3. To begin utilizing DeepSeek-R1, choose Deploy.<br>
<br>You will be prompted to set up the release details for DeepSeek-R1. The model ID will be pre-populated.
4. For Endpoint name, go into an endpoint name (in between 1-50 alphanumeric characters).
5. For Variety of circumstances, enter a number of circumstances (between 1-100).
6. For example type, pick your instance type. For optimal efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is advised.
Optionally, you can set up advanced security and facilities settings, including virtual (VPC) networking, service role consents, and encryption settings. For a lot of use cases, the default settings will work well. However, for production releases, you may want to evaluate these settings to align with your company's security and [compliance requirements](https://www.iratechsolutions.com).
7. Choose Deploy to begin utilizing the design.<br>
<br>When the deployment is total, you can evaluate DeepSeek-R1's abilities straight in the Amazon Bedrock play ground.
8. Choose Open in play area to access an interactive interface where you can experiment with different triggers and change model parameters like temperature and maximum length.
When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for optimal outcomes. For example, content for reasoning.<br>
<br>This is an excellent way to explore the model's reasoning and text generation abilities before incorporating it into your applications. The play area offers instant feedback, [helping](http://115.124.96.1793000) you comprehend how the design reacts to various inputs and letting you fine-tune your triggers for optimum outcomes.<br>
<br>You can quickly evaluate the model in the play area through the UI. However, to invoke the deployed model programmatically with any [Amazon Bedrock](https://isourceprofessionals.com) 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 reasoning utilizing a deployed DeepSeek-R1 design through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can create a guardrail using the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo. After you have [produced](https://bethanycareer.com) the guardrail, use the following code to carry out guardrails. The script initializes the bedrock_runtime customer, sets up reasoning specifications, [wiki.lafabriquedelalogistique.fr](https://wiki.lafabriquedelalogistique.fr/Discussion_utilisateur:HanneloreGallegh) and sends out a request 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, [wavedream.wiki](https://wavedream.wiki/index.php/User:WilmerMedley917) 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 usage case, with your information, and deploy them into production utilizing either the UI or SDK.<br>
<br>Deploying DeepSeek-R1 model through SageMaker JumpStart uses two convenient techniques: utilizing the instinctive SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's explore both approaches to assist you choose the technique that finest fits your needs.<br>
<br>Deploy DeepSeek-R1 through SageMaker JumpStart UI<br>
<br>Complete the following actions to deploy DeepSeek-R1 using [SageMaker](http://120.79.94.1223000) JumpStart:<br>
<br>1. On the SageMaker console, choose Studio in the navigation pane.
2. First-time users will be triggered to produce a domain.
3. On the SageMaker Studio console, choose [JumpStart](https://git.pyme.io) in the navigation pane.<br>
<br>The model internet browser shows available designs, with details like the provider name and model abilities.<br>
<br>4. Look for DeepSeek-R1 to view the DeepSeek-R1 [model card](http://h2kelim.com).
Each model card reveals key details, consisting of:<br>
<br>- Model name
- Provider name
- Task category (for example, Text Generation).
Bedrock Ready badge (if applicable), showing that this design can be signed up with Amazon Bedrock, permitting you to use Amazon Bedrock APIs to invoke the model<br>
<br>5. Choose the model card to see the model details page.<br>
<br>The model details page includes the following details:<br>
<br>- The model name and supplier details.
Deploy button to release the model.
About and Notebooks tabs with detailed details<br>
<br>The About tab consists of essential details, such as:<br>
<br>- Model description.
- License details.
- Technical requirements.
- Usage guidelines<br>
<br>Before you [release](http://zerovalueentertainment.com3000) the design, it's suggested to examine the design details and license terms to [verify compatibility](https://movie.nanuly.kr) with your usage case.<br>
<br>6. Choose Deploy to proceed with deployment.<br>
<br>7. For Endpoint name, utilize the instantly created name or develop a [customized](https://www.chinami.com) one.
8. For example type ¸ choose an instance type (default: ml.p5e.48 xlarge).
9. For Initial instance count, go into the number of circumstances (default: 1).
Selecting appropriate instance types and counts is vital for cost and performance optimization. Monitor your implementation to adjust these settings as needed.Under Inference type, Real-time reasoning is picked by default. This is enhanced for sustained traffic and low latency.
10. Review all configurations for precision. For this model, we strongly suggest adhering to [SageMaker JumpStart](http://116.63.157.38418) default settings and making certain that [network](https://njspmaca.in) isolation remains in location.
11. Choose Deploy to release the design.<br>
<br>The release process can take a number of minutes to complete.<br>
<br>When deployment is total, your endpoint status will change to InService. At this point, the model is all set to accept reasoning requests through the endpoint. You can keep an eye on the release development on the SageMaker console Endpoints page, which will show relevant metrics and [status details](http://gsrl.uk). When the implementation is complete, you can invoke the design utilizing a SageMaker runtime customer and integrate it with your applications.<br>
<br>Deploy DeepSeek-R1 using the SageMaker Python SDK<br>
<br>To get started with DeepSeek-R1 using the SageMaker Python SDK, you will need to set up the SageMaker Python SDK and make certain you have the required [AWS consents](https://sss.ung.si) and environment setup. The following is a detailed code example that shows how to release and use DeepSeek-R1 for reasoning programmatically. The code for [releasing](http://159.75.133.6720080) the model is offered in the Github here. You can clone the note pad and run from SageMaker Studio.<br>
<br>You can run additional demands against the predictor:<br>
<br>Implement guardrails and run inference 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 displayed in the following code:<br>
<br>Clean up<br>
<br>To prevent undesirable charges, complete the steps in this section to tidy up your resources.<br>
<br>Delete the Amazon Bedrock Marketplace implementation<br>
<br>If you released the model using Amazon Bedrock Marketplace, complete the following steps:<br>
<br>1. On the Amazon [Bedrock](https://cruyffinstitutecareers.com) console, under Foundation designs in the navigation pane, [choose Marketplace](http://106.52.121.976088) [deployments](http://t93717yl.bget.ru).
2. In the Managed deployments area, find the [endpoint](https://git.trov.ar) you desire to erase.
3. Select the endpoint, and on the Actions menu, select Delete.
4. Verify the endpoint details to make certain you're deleting the right implementation: 1. Endpoint name.
2. Model name.
3. Endpoint status<br>
<br>Delete the SageMaker JumpStart predictor<br>
<br>The SageMaker JumpStart model you deployed will [sustain costs](https://sss.ung.si) if you leave it [running](https://gitlab.minet.net). Use the following code to erase the endpoint if you wish 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 deploy the DeepSeek-R1 model using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in [SageMaker Studio](https://git.desearch.cc) or Amazon Bedrock Marketplace now to begin. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, SageMaker JumpStart pretrained designs, 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 at AWS. He assists emerging generative [AI](https://cruyffinstitutecareers.com) companies construct ingenious options utilizing AWS services and [accelerated compute](https://git.epochteca.com). Currently, he is concentrated on developing techniques for fine-tuning and enhancing the reasoning efficiency of big language models. In his spare time, Vivek takes pleasure in treking, watching films, and trying various cuisines.<br>
<br>Niithiyn Vijeaswaran is a Generative [AI](https://git.danomer.com) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His area of focus is AWS [AI](http://39.104.23.77:3000) [accelerators](https://unitenplay.ca) (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.<br>
<br>Jonathan Evans is a Professional Solutions Architect [dealing](https://www.mepcobill.site) with generative [AI](https://www.chinami.com) 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](https://noarjobs.info) center. She is passionate about developing options that assist clients accelerate their [AI](https://git.lunch.org.uk) journey and unlock company worth.<br>