albertohayner5/105
1
0
Code Issues Actions Packages Projects Wiki

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

Alberto Hayner 2025-04-08 05:39:34 +08:00
commit cf83b21055
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 JumpStart. With this launch, you can now deploy DeepSeek [AI](https://hitechjobs.me)'s first-generation frontier design, DeepSeek-R1, along with the distilled versions varying from 1.5 to 70 billion criteria to develop, experiment, and responsibly scale your generative [AI](https://test.gamesfree.ca) concepts on AWS.<br>
<br>In this post, we demonstrate how to get going with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable 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 design (LLM) developed by DeepSeek [AI](https://www.linkedaut.it) that utilizes support [discovering](https://gitea.chenbingyuan.com) to [boost reasoning](http://xunzhishimin.site3000) abilities through a multi-stage training process from a DeepSeek-V3-Base structure. A crucial identifying function is its reinforcement knowing (RL) action, which was utilized to fine-tune the model's responses beyond the basic pre-training and tweak procedure. By including RL, DeepSeek-R1 can adapt better to user [feedback](https://aji.ghar.ku.jaldi.nai.aana.ba.tume.dont.tach.me) and objectives, ultimately enhancing both significance and clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) technique, implying it's geared up to break down complex questions and reason through them in a detailed manner. This assisted thinking procedure permits the design to produce more accurate, transparent, and detailed responses. This design combines RL-based fine-tuning with CoT abilities, aiming to generate structured actions while concentrating on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has captured the market's attention as a versatile text-generation design that can be integrated into various workflows such as representatives, sensible reasoning and data interpretation jobs.<br>
<br>DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture enables activation of 37 billion criteria, allowing efficient inference by routing inquiries to the most relevant expert "clusters." This approach permits the model to specialize in different issue domains while maintaining overall efficiency. DeepSeek-R1 requires a minimum of 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 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 model to more efficient architectures based on popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a process of training smaller sized, more [efficient designs](http://115.124.96.1793000) to mimic the habits and thinking patterns of the larger DeepSeek-R1 design, using 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 design, we suggest deploying this design with guardrails in place. In this blog site, we will use Amazon Bedrock Guardrails to present safeguards, avoid harmful material, and assess designs against crucial [security criteria](http://orcz.com). At the time of composing this blog site, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can develop multiple guardrails tailored to various use cases and apply them to the DeepSeek-R1 design, improving user experiences and standardizing safety controls across your generative [AI](http://app.vellorepropertybazaar.in) applications.<br>
<br>Prerequisites<br>
<br>To deploy the DeepSeek-R1 model, you need access to an ml.p5e circumstances. To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, [select Amazon](http://www.amrstudio.cn33000) 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 circumstances in the AWS Region you are deploying. To request a limitation increase, produce a limit increase request and connect to your account team.<br>
<br>Because you will be [deploying](https://sundaycareers.com) this design with Amazon Bedrock Guardrails, make certain you have the appropriate [AWS Identity](https://jobs1.unifze.com) and Gain Access To Management (IAM) approvals to use Amazon Bedrock [Guardrails](http://35.207.205.183000). For directions, see Set up consents to utilize guardrails for content filtering.<br>
<br>Implementing guardrails with the ApplyGuardrail API<br>
<br>Amazon Bedrock Guardrails permits you to introduce safeguards, avoid harmful content, and evaluate designs against essential security criteria. You can carry out security steps for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to evaluate user inputs and design reactions deployed on Amazon Bedrock Marketplace and SageMaker JumpStart. You can produce a guardrail utilizing the Amazon Bedrock console or the API. For the example code to [develop](https://igita.ir) the guardrail, see the GitHub repo.<br>
<br>The general circulation involves the following actions: First, the system gets an input for the design. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, [pipewiki.org](https://pipewiki.org/wiki/index.php/User:MatthiasThomason) it's sent out to the design for reasoning. After receiving the model's output, another guardrail check is applied. If the [output passes](http://git.zltest.com.tw3333) this final check, it's returned as the outcome. However, if either the input or output is stepped in by the guardrail, a message is [returned indicating](https://asg-pluss.com) the nature of the intervention and whether it happened 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>Amazon Bedrock Marketplace offers 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 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 service provider and select the DeepSeek-R1 design.<br>
<br>The design detail page provides important details about the design's abilities, rates structure, and execution guidelines. You can find detailed use guidelines, consisting of sample API calls and code snippets for combination. The design supports various text generation jobs, including material development, code generation, and question answering, using its support finding out optimization and CoT reasoning capabilities.
The page likewise includes release choices and licensing details to assist you get going with DeepSeek-R1 in your applications.
3. To begin using DeepSeek-R1, choose Deploy.<br>
<br>You will be prompted to set up the implementation details for DeepSeek-R1. The model ID will be pre-populated.
4. For Endpoint name, enter an endpoint name (between 1-50 alphanumeric characters).
5. For Variety of instances, get in a variety of circumstances (in between 1-100).
6. For Instance type, select your instance 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, consisting of virtual personal cloud (VPC) networking, service role approvals, and file encryption settings. For many use cases, the default settings will work well. However, for production releases, you may wish to examine these settings to line up with your company's security and compliance requirements.
7. Choose Deploy to start using the model.<br>
<br>When the release is complete, you can evaluate 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 experiment with different triggers and change model parameters like temperature and maximum length.
When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for optimum results. For instance, content for inference.<br>
<br>This is an exceptional way to explore the design's reasoning and text generation abilities before integrating it into your applications. The play ground offers instant feedback, assisting you understand how the design reacts to numerous inputs and letting you tweak your prompts for optimum outcomes.<br>
<br>You can quickly test the design in the [playground](http://119.23.72.7) through the UI. However, to invoke the deployed design programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.<br>
<br>Run inference using guardrails with the deployed DeepSeek-R1 endpoint<br>
<br>The following code example demonstrates how to perform reasoning utilizing a released DeepSeek-R1 design through Amazon Bedrock [utilizing](http://221.229.103.5563010) the invoke_model and [wiki.rolandradio.net](https://wiki.rolandradio.net/index.php?title=User:FernandoKinross) ApplyGuardrail API. You can produce a guardrail using the Amazon Bedrock [console](https://git.szrcai.ru) or the API. For the example code to create the guardrail, see the GitHub repo. After you have produced the guardrail, use the following code to carry out guardrails. The script initializes the bedrock_runtime client, [wiki.asexuality.org](https://wiki.asexuality.org/w/index.php?title=User_talk:FredrickDonohue) sets up reasoning parameters, and sends a request to create text based on a user timely.<br>
<br>Deploy DeepSeek-R1 with SageMaker JumpStart<br>
<br>SageMaker JumpStart is an [artificial intelligence](http://1.119.152.2304026) (ML) hub with FMs, integrated algorithms, and prebuilt ML options that you can deploy with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage case, with your data, 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 implementing programmatically through the SageMaker Python SDK. Let's explore both approaches to assist you pick the method that finest fits your requirements.<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 develop a domain.
3. On the SageMaker Studio console, select JumpStart in the navigation pane.<br>
<br>The design browser displays available designs, with details like the [supplier](http://git.szchuanxia.cn) name and design abilities.<br>
<br>4. Search for DeepSeek-R1 to see the DeepSeek-R1 [design card](http://music.afrixis.com).
Each model card shows essential details, consisting of:<br>
<br>- Model name
[- Provider](https://www.nenboy.com29283) name
- Task category (for instance, Text Generation).
Bedrock Ready badge (if suitable), indicating that this design can be signed up with Amazon Bedrock, permitting you to utilize Amazon Bedrock APIs to [conjure](https://git.mintmuse.com) up the design<br>
<br>5. Choose the design card to view the design details page.<br>
<br>The model details page includes the following details:<br>
<br>- The model name and company details.
Deploy button to release the model.
About and Notebooks tabs with detailed details<br>
<br>The About tab consists of important details, such as:<br>
<br>- Model [description](https://work.melcogames.com).
- License details.
- Technical specs.
- Usage guidelines<br>
<br>Before you release the design, it's suggested to examine the design details and license terms to validate compatibility with your use case.<br>
<br>6. Choose Deploy to continue with release.<br>
<br>7. For Endpoint name, use the instantly produced name or develop a custom one.
8. For Instance type ¸ choose an instance type (default: ml.p5e.48 xlarge).
9. For Initial instance count, go into the variety of instances (default: 1).
Selecting suitable circumstances types and counts is vital for cost and performance optimization. Monitor your implementation to change these settings as needed.Under Inference type, Real-time inference is picked by default. This is optimized for sustained traffic and low latency.
10. Review all setups for accuracy. For this model, we strongly suggest adhering to SageMaker JumpStart default [settings](http://202.164.44.2463000) and making certain that network seclusion remains in location.
11. Choose Deploy to [release](https://blessednewstv.com) the model.<br>
<br>The release procedure can take numerous minutes to finish.<br>
<br>When release is total, your endpoint status will alter to InService. At this moment, the model is prepared to accept inference demands through the endpoint. You can keep track of the implementation progress on the SageMaker console Endpoints page, which will show relevant metrics and status details. When the release is total, you can invoke the model using a SageMaker runtime customer and incorporate it with your applications.<br>
<br>Deploy DeepSeek-R1 utilizing the SageMaker Python SDK<br>
<br>To start with DeepSeek-R1 using the [SageMaker Python](https://agora-antikes.gr) SDK, you will require to set up the SageMaker Python SDK and make certain you have the required AWS authorizations and environment setup. The following is a detailed code example that shows how to release and [surgiteams.com](https://surgiteams.com/index.php/User:JulietBoswell) utilize DeepSeek-R1 for reasoning programmatically. The code for releasing the design is supplied in the Github here. You can clone the notebook and run from SageMaker Studio.<br>
<br>You can run extra requests against the predictor:<br>
<br>Implement guardrails and run reasoning with your SageMaker JumpStart predictor<br>
<br>Similar to Amazon Bedrock, you can also use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can develop 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 avoid unwanted charges, finish the actions in this area to clean up your resources.<br>
<br>Delete the Amazon Bedrock Marketplace release<br>
<br>If you released the design using Marketplace, total the following steps:<br>
<br>1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, select Marketplace implementations.
2. In the Managed deployments section, find the endpoint you wish to delete.
3. Select the endpoint, and on the Actions menu, pick Delete.
4. Verify the endpoint details to make certain you're erasing the right implementation: 1. Endpoint name.
2. Model name.
3. Endpoint status<br>
<br>Delete the SageMaker JumpStart predictor<br>
<br>The SageMaker [JumpStart design](https://social.midnightdreamsreborns.com) you deployed will sustain expenses if you leave it running. Use the following code to delete 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 checked out how you can access and release the DeepSeek-R1 model utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker [JumpStart](https://projob.co.il) in SageMaker Studio or Amazon Bedrock Marketplace now to get begun. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, [Amazon Bedrock](https://sharefriends.co.kr) Marketplace, and Getting going 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://bcde.ru) business build ingenious solutions using AWS services and accelerated compute. Currently, he is concentrated on developing strategies for fine-tuning and optimizing the inference efficiency of big language designs. In his spare time, Vivek takes pleasure in treking, enjoying motion pictures, and attempting different foods.<br>
<br>Niithiyn Vijeaswaran is a Generative [AI](http://8.137.103.221:3000) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His area of focus is AWS [AI](https://gitea.cisetech.com) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.<br>
<br>Jonathan Evans is a Specialist Solutions Architect working on generative [AI](https://gogs.tyduyong.com) with the Third-Party Model Science team at AWS.<br>
<br>Banu Nagasundaram leads item, engineering, and tactical collaborations for Amazon SageMaker JumpStart, [higgledy-piggledy.xyz](https://higgledy-piggledy.xyz/index.php/User:SalTreadwell) SageMaker's artificial intelligence and generative [AI](http://home.rogersun.cn:3000) hub. She is enthusiastic about building services that help customers accelerate their [AI](https://yeetube.com) journey and unlock business value.<br>