consuelocowley
/
chancefinders
1
0
Code Issues Packages Projects Wiki

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

Consuelo Cowley 2025-04-04 05:40:09 +08:00
commit a23ab88b24
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 reveal that DeepSeek R1 distilled Llama and Qwen models are available through Amazon Bedrock Marketplace and [Amazon SageMaker](https://forum.elaivizh.eu) JumpStart. With this launch, you can now deploy DeepSeek [AI](https://virnal.com)'s first-generation frontier model, DeepSeek-R1, in addition to the distilled versions varying from 1.5 to 70 billion criteria to build, experiment, and properly scale your generative [AI](https://git.aaronmanning.net) 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](https://www.punajuaj.com) actions to release the distilled variations of the models as well.<br>
<br>Overview of DeepSeek-R1<br>
<br>DeepSeek-R1 is a large language design (LLM) established by DeepSeek [AI](https://codeh.genyon.cn) that uses reinforcement discovering to improve reasoning abilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. A key identifying function is its support learning (RL) step, which was utilized to refine the design's actions beyond the basic pre-training and fine-tuning process. By incorporating RL, DeepSeek-R1 can adjust more efficiently to user feedback and objectives, eventually improving both relevance and clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) method, meaning it's geared up to break down complicated inquiries and factor through them in a detailed way. This assisted thinking procedure allows the model to produce more precise, transparent, and detailed answers. This design integrates RL-based fine-tuning with CoT capabilities, aiming to create [structured responses](https://xnxxsex.in) while concentrating on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has captured the market's attention as a flexible text-generation design that can be integrated into different workflows such as agents, sensible thinking 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 enables activation of 37 billion specifications, allowing effective inference by routing questions to the most pertinent expert "clusters." This approach enables the model to specialize in different problem domains while maintaining total efficiency. DeepSeek-R1 requires at least 800 GB of HBM memory in FP8 format for inference. In this post, we will use an ml.p5e.48 xlarge instance to deploy the model. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs offering 1128 GB of [GPU memory](https://church.ibible.hk).<br>
<br>DeepSeek-R1 distilled designs bring the reasoning abilities of the main R1 design to more effective architectures based upon popular open designs like Qwen (1.5 B, 7B, 14B, and [trademarketclassifieds.com](https://trademarketclassifieds.com/user/profile/2672496) 32B) and Llama (8B and 70B). Distillation refers to a procedure of training smaller sized, more efficient designs to simulate the behavior and reasoning patterns of the larger DeepSeek-R1 model, utilizing it as a teacher design.<br>
<br>You can deploy DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we advise releasing this design with [guardrails](https://www.9iii9.com) in place. In this blog, we will utilize Amazon Bedrock Guardrails to introduce safeguards, prevent harmful material, and evaluate models against crucial security requirements. At the time of writing this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails [supports](https://git.xantxo-coquillard.fr) just the ApplyGuardrail API. You can create multiple guardrails tailored to different use cases and use them to the DeepSeek-R1 model, improving user experiences and standardizing security controls across your generative [AI](https://hellovivat.com) applications.<br>
<br>Prerequisites<br>
<br>To deploy the DeepSeek-R1 design, you need access to an ml.p5e instance. To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and validate you're using ml.p5e.48 xlarge for endpoint usage. Make certain that you have at least one ml.P5e.48 xlarge instance in the AWS Region you are [releasing](https://aji.ghar.ku.jaldi.nai.aana.ba.tume.dont.tach.me). To request a limit increase, develop a limitation boost request and connect to your account group.<br>
<br>Because you will be releasing this design with Amazon Bedrock Guardrails, make certain you have the proper [AWS Identity](https://www.tvcommercialad.com) and Gain Access To Management (IAM) permissions to utilize Amazon Bedrock Guardrails. For guidelines, see Set up consents to use guardrails for content filtering.<br>
<br>Implementing guardrails with the ApplyGuardrail API<br>
<br>Amazon Bedrock Guardrails enables you to introduce safeguards, prevent damaging content, and assess designs against crucial security criteria. You can carry out [security procedures](http://8.138.140.943000) for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to evaluate user inputs and model responses deployed 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](https://eurosynapses.giannistriantafyllou.gr) the guardrail, see the GitHub repo.<br>
<br>The basic circulation 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 receiving the design's output, another guardrail check is used. If the output passes this final check, it's returned as the [final result](http://39.108.93.0). However, if either the input or output is intervened by the guardrail, a message is returned showing the nature of the intervention and whether it happened at the input or output stage. The examples showcased in the following sections demonstrate inference utilizing this API.<br>
<br>Deploy DeepSeek-R1 in Amazon Bedrock Marketplace<br>
<br>Amazon Bedrock Marketplace gives you access to over 100 popular, emerging, and specialized foundation designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, complete 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 writing this post, you can use the InvokeModel API to invoke the model. It doesn't support Converse APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a [provider](https://gitlab.henrik.ninja) and choose the DeepSeek-R1 design.<br>
<br>The model detail page supplies essential details about the design's abilities, prices structure, and application guidelines. You can discover detailed usage directions, consisting of sample API calls and code snippets for combination. The design supports numerous text generation tasks, including material development, code generation, and question answering, using its reinforcement discovering [optimization](https://sc.e-path.cn) and CoT reasoning capabilities.
The page likewise includes release options and licensing details to assist you start with DeepSeek-R1 in your applications.
3. To start utilizing DeepSeek-R1, select Deploy.<br>
<br>You will be triggered to configure the release details for DeepSeek-R1. The model ID will be pre-populated.
4. For Endpoint name, go into an endpoint name (between 1-50 alphanumeric characters).
5. For Variety of instances, enter a variety of instances (in between 1-100).
6. For example type, select your circumstances type. For ideal performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is recommended.
Optionally, you can set up advanced security and infrastructure settings, including virtual private cloud (VPC) networking, service function authorizations, and file encryption settings. For the majority of use cases, the default settings will work well. However, for production implementations, you may wish to evaluate these settings to line up with your organization's security and compliance requirements.
7. Choose Deploy to begin utilizing the model.<br>
<br>When the deployment is complete, you can evaluate DeepSeek-R1's abilities straight in the Amazon Bedrock playground.
8. Choose Open in play ground to access an interactive interface where you can try out various prompts and adjust model specifications like temperature level and maximum length.
When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for optimum outcomes. For instance, material for reasoning.<br>
<br>This is an outstanding method to explore the design's thinking and text generation capabilities before [incorporating](https://www.virsocial.com) it into your applications. The play ground offers immediate feedback, helping you understand how the model reacts to numerous inputs and letting you fine-tune your triggers for ideal results.<br>
<br>You can [rapidly evaluate](https://smaphofilm.com) the model in the playground through the UI. However, to conjure up the released design programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.<br>
<br>Run reasoning utilizing guardrails with the deployed DeepSeek-R1 endpoint<br>
<br>The following code example shows how to carry out reasoning using a released DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can develop a guardrail using the Amazon Bedrock console or the API. For the example code to create the guardrail, see the GitHub repo. After you have [produced](http://116.62.159.194) the guardrail, use the following code to [implement guardrails](https://bdenc.com). The script initializes the bedrock_runtime customer, configures inference specifications, and sends out a request to produce text based upon a user timely.<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 services that you can deploy with just a couple of 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 uses two convenient techniques: using the instinctive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's explore both approaches to assist you pick the approach that best fits your requirements.<br>
<br>Deploy DeepSeek-R1 through SageMaker JumpStart UI<br>
<br>Complete the following actions to deploy DeepSeek-R1 utilizing SageMaker JumpStart:<br>
<br>1. On the SageMaker console, select Studio in the navigation pane.
2. First-time users will be prompted to produce a domain.
3. On the SageMaker Studio console, pick JumpStart in the navigation pane.<br>
<br>The design web browser shows available designs, with [details](http://39.105.203.1873000) like the supplier name and model capabilities.<br>
<br>4. Search for DeepSeek-R1 to view the DeepSeek-R1 model card.
Each model card shows key details, including:<br>
<br>- Model name
- [Provider](https://japapmessenger.com) name
- Task (for instance, Text Generation).
Bedrock Ready badge (if relevant), [indicating](https://sondezar.com) that this design can be signed up with Amazon Bedrock, permitting you to utilize Amazon Bedrock APIs to invoke the design<br>
<br>5. Choose the [design card](https://jobsantigua.com) to view the design details page.<br>
<br>The model details page consists of the following details:<br>
<br>- The design name and service provider details.
Deploy button to release the design.
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 specs.
- Usage standards<br>
<br>Before you deploy the design, it's suggested to review the model details and license terms to verify compatibility with your usage case.<br>
<br>6. Choose Deploy to continue with release.<br>
<br>7. For Endpoint name, use the immediately created name or produce a custom-made one.
8. For [Instance type](https://local.wuanwanghao.top3000) ¸ pick an instance type (default: ml.p5e.48 xlarge).
9. For Initial instance count, get in the [variety](https://storymaps.nhmc.uoc.gr) of circumstances (default: 1).
Selecting appropriate [instance types](https://degroeneuitzender.nl) and counts is vital for expense and performance optimization. Monitor your deployment to adjust these settings as needed.Under Inference type, Real-time reasoning is chosen by default. This is optimized for sustained traffic and low latency.
10. Review all configurations for precision. For this model, we highly suggest adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in place.
11. Choose Deploy to release the model.<br>
<br>The deployment procedure can take several minutes to complete.<br>
<br>When implementation is complete, your endpoint status will change to InService. At this point, the design is all set to accept reasoning demands through the endpoint. You can keep an eye on the release progress on the [SageMaker](https://lovelynarratives.com) console Endpoints page, which will show appropriate metrics and status details. When the deployment is total, you can conjure up the model using a SageMaker runtime customer and incorporate it with your applications.<br>
<br>Deploy DeepSeek-R1 using the SageMaker Python SDK<br>
<br>To get going with DeepSeek-R1 utilizing the SageMaker Python SDK, you will need to install the SageMaker Python SDK and make certain you have the essential AWS permissions and environment setup. The following is a detailed code example that shows how to release and use DeepSeek-R1 for inference programmatically. The code for deploying the design is supplied in the Github here. You can clone the note pad and range from SageMaker Studio.<br>
<br>You can run [additional](http://47.122.66.12910300) requests 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 create a guardrail utilizing the Amazon Bedrock console or the API, and implement it as displayed in the following code:<br>
<br>Tidy up<br>
<br>To avoid unwanted charges, complete the steps 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 actions:<br>
<br>1. On the Amazon Bedrock console, under Foundation models in the navigation pane, select Marketplace releases.
2. In the Managed implementations section, find the endpoint you desire to delete.
3. Select the endpoint, and on the Actions menu, select Delete.
4. Verify the endpoint details to make certain you're erasing the appropriate deployment: 1. Endpoint name.
2. Model name.
3. Endpoint status<br>
<br>Delete the SageMaker JumpStart predictor<br>
<br>The SageMaker JumpStart design you released 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 deploy the DeepSeek-R1 model utilizing 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 models, SageMaker JumpStart pretrained models, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Starting 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](https://neejobs.com) business develop innovative services utilizing AWS services and sped up compute. Currently, he is focused on establishing strategies for fine-tuning and enhancing the reasoning efficiency of big language designs. In his leisure time, Vivek takes pleasure in treking, viewing films, and attempting various cuisines.<br>
<br>Niithiyn Vijeaswaran is a Generative [AI](http://a21347410b.iask.in:8500) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His location of focus is AWS [AI](http://101.132.73.14:3000) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.<br>
<br>[Jonathan Evans](https://wacari-git.ru) is a Professional Solutions Architect working on generative [AI](http://park8.wakwak.com) with the Third-Party Model Science team at AWS.<br>
<br>Banu Nagasundaram leads product, engineering, and tactical collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://gitcq.cyberinner.com) hub. She is enthusiastic about [developing solutions](https://social.updum.com) that assist customers accelerate their [AI](http://maitri.adaptiveit.net) journey and unlock service worth.<br>