jeanninecaffyn
/
nebulun
1
0
Code Issues Packages Projects Wiki

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

Jeannine Caffyn 2025-04-05 04:06:03 +08:00
commit 383a3f886b
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 designs are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now release DeepSeek [AI](https://www.ggram.run)'s first-generation frontier design, DeepSeek-R1, in addition to the distilled variations ranging from 1.5 to 70 billion parameters to build, experiment, and responsibly scale your generative [AI](http://118.195.204.252:8080) concepts on AWS.<br>
<br>In this post, we demonstrate how to start with DeepSeek-R1 on Amazon Bedrock [Marketplace](http://acs-21.com) and SageMaker JumpStart. You can follow similar steps to release the distilled versions of the models also.<br>
<br>[Overview](https://ttemployment.com) of DeepSeek-R1<br>
<br>DeepSeek-R1 is a large language design (LLM) established by DeepSeek [AI](https://enitajobs.com) that uses reinforcement finding out to boost [reasoning abilities](http://194.67.86.1603100) through a multi-stage training process from a DeepSeek-V3-Base foundation. An essential distinguishing feature is its reinforcement knowing (RL) action, which was used to fine-tune the [design's responses](https://givebackabroad.org) beyond the basic pre-training and fine-tuning process. By including RL, DeepSeek-R1 can adapt more effectively to user feedback and objectives, eventually enhancing both relevance and [disgaeawiki.info](https://disgaeawiki.info/index.php/User:DollyFlockhart) clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) method, suggesting it's geared up to break down intricate inquiries and reason through them in a detailed way. This directed reasoning [process permits](https://blackfinn.de) the model to produce more precise, transparent, and detailed responses. This design integrates [RL-based fine-tuning](https://iraqitube.com) with CoT capabilities, aiming to create structured actions while focusing on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has captured the industry's attention as a flexible text-generation design that can be incorporated into numerous workflows such as agents, rational thinking and data analysis tasks.<br>
<br>DeepSeek-R1 utilizes a Mix of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture permits activation of 37 billion criteria, enabling efficient reasoning by routing inquiries to the most pertinent professional "clusters." This technique enables the model to concentrate on different issue 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 utilize 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.<br>
<br>DeepSeek-R1 distilled models bring the thinking [abilities](https://farmjobsuk.co.uk) of the main R1 model to more effective 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 sized, more effective models to simulate the behavior and thinking patterns of the bigger DeepSeek-R1 model, utilizing it as an instructor model.<br>
<br>You can deploy DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we recommend releasing this design with guardrails in place. In this blog site, we will utilize Amazon Bedrock Guardrails to introduce safeguards, prevent hazardous material, and evaluate designs against key security requirements. At the time of composing this blog, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only 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 across your generative [AI](https://jobsubscribe.com) applications.<br>
<br>Prerequisites<br>
<br>To deploy the DeepSeek-R1 design, you need access to an ml.p5e instance. To [inspect](https://volunteering.ishayoga.eu) if you have quotas for P5e, open the Service Quotas console and under AWS Services, select 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 circumstances in the you are releasing. To ask for a limitation boost, [develop](http://8.139.7.16610880) a limit boost request and connect to your account team.<br>
<br>Because you will be releasing this design with Amazon Bedrock Guardrails, make certain you have the proper AWS Identity and Gain Access To Management (IAM) approvals to utilize Amazon Bedrock Guardrails. For directions, see Establish permissions to use guardrails for material filtering.<br>
<br>Implementing guardrails with the ApplyGuardrail API<br>
<br>Amazon Bedrock Guardrails permits you to present safeguards, avoid damaging content, and assess models against key security requirements. You can carry out precaution for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This enables you to apply guardrails to evaluate user inputs and design actions released on [Amazon Bedrock](https://redebrasil.app) Marketplace and SageMaker JumpStart. You can develop a guardrail using the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo.<br>
<br>The general circulation involves the following steps: First, the system gets an input for the model. This input is then processed through the ApplyGuardrail API. If the input passes the guardrail check, it's sent to the model for reasoning. After getting the design's output, another guardrail check is applied. If the [output passes](http://www.szkis.cn13000) this last check, it's returned as the [outcome](https://bocaiw.in.net). 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 took place at the input or output phase. The examples showcased in the following areas show [inference](http://123.111.146.2359070) using this API.<br>
<br>Deploy DeepSeek-R1 in Amazon Bedrock Marketplace<br>
<br>Amazon Bedrock Marketplace offers you access to over 100 popular, [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:MathewCutlack) emerging, and specialized foundation 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, select Model brochure under Foundation designs in the navigation pane.
At the time of writing this post, you can use the InvokeModel API to conjure up 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 design.<br>
<br>The model detail page provides important details about the design's abilities, pricing structure, and [execution standards](https://mssc.ltd). You can discover detailed use guidelines, including sample API calls and code bits for integration. The design supports various text generation jobs, consisting of content creation, code generation, and question answering, using its support learning optimization and CoT thinking capabilities.
The page also includes [deployment choices](https://www.imdipet-project.eu) and licensing details to help you get started with DeepSeek-R1 in your applications.
3. To start using DeepSeek-R1, pick Deploy.<br>
<br>You will be triggered to configure the release 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 [genbecle.com](https://www.genbecle.com/index.php?title=Utilisateur:QHKGerman64153) Variety of instances, get in a number of instances (between 1-100).
6. For Instance type, pick your instance type. For ideal efficiency with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is recommended.
Optionally, you can set up advanced security and infrastructure settings, consisting of virtual private cloud (VPC) networking, service function permissions, and encryption settings. For many use cases, the default settings will work well. However, for [wavedream.wiki](https://wavedream.wiki/index.php/User:DelorasHqu) production implementations, you might want to evaluate these settings to align with your company's security and compliance requirements.
7. Choose Deploy to begin utilizing the model.<br>
<br>When the release is total, 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 various prompts and adjust model parameters like temperature level and optimum length.
When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for ideal outcomes. For instance, content for reasoning.<br>
<br>This is an outstanding way to explore the model's thinking and text generation capabilities before integrating it into your applications. The playground supplies instant feedback, assisting you [understand](https://estekhdam.in) how the model responds to different inputs and letting you fine-tune your prompts for optimal results.<br>
<br>You can quickly evaluate the model in the [playground](https://www.klaverjob.com) through the UI. However, [raovatonline.org](https://raovatonline.org/author/jennax25174/) to conjure up the deployed design programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.<br>
<br>Run reasoning using guardrails with the deployed DeepSeek-R1 endpoint<br>
<br>The following code example demonstrates how to perform inference using a deployed DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can develop a guardrail utilizing the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo. After you have created the guardrail, use the following code to execute guardrails. The script [initializes](https://source.lug.org.cn) the bedrock_runtime client, configures inference specifications, and sends out a demand to produce 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 services that you can release with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your use case, with your data, and deploy them into production utilizing either the UI or SDK.<br>
<br>Deploying DeepSeek-R1 model through [SageMaker JumpStart](https://x-like.ir) offers two hassle-free approaches: using the user-friendly SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's explore both techniques to help you choose the approach that finest suits 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, choose 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 shows available designs, with details like the provider name and model capabilities.<br>
<br>4. Look for DeepSeek-R1 to view the DeepSeek-R1 design card.
Each design card reveals key details, including:<br>
<br>- Model name
- Provider name
- Task category (for example, Text Generation).
[Bedrock Ready](http://82.146.58.193) badge (if appropriate), suggesting that this design can be registered with Amazon Bedrock, enabling you to utilize Amazon Bedrock APIs to conjure up the design<br>
<br>5. Choose the [model card](http://63.32.145.226) to view the design details page.<br>
<br>The design details page includes the following details:<br>
<br>- The design name and supplier details.
Deploy button to deploy the model.
About and Notebooks tabs with detailed details<br>
<br>The About tab includes essential details, such as:<br>
<br>- Model description.
- License details.
- Technical specs.
- Usage standards<br>
<br>Before you release 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 implementation.<br>
<br>7. For Endpoint name, utilize the immediately generated name or create a custom one.
8. For example type ¸ select a circumstances type (default: ml.p5e.48 xlarge).
9. For Initial instance count, get in the number of circumstances (default: 1).
Selecting proper circumstances types and counts is crucial for expense and efficiency optimization. Monitor your implementation to adjust these settings as needed.Under [Inference](http://git.permaviat.ru) type, Real-time reasoning is chosen by default. This is optimized for sustained traffic and low latency.
10. Review all configurations for accuracy. For this model, we highly advise adhering to SageMaker JumpStart default settings and making certain that network isolation remains in place.
11. Choose Deploy to deploy the model.<br>
<br>The implementation process can take numerous minutes to complete.<br>
<br>When [implementation](https://micircle.in) is total, your endpoint status will alter to InService. At this moment, the design is prepared to accept inference requests through the endpoint. You can keep track of the release progress on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the deployment is complete, you can invoke the design 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 SDK, you will require to install the SageMaker Python SDK and make certain you have the necessary AWS consents and environment setup. The following is a detailed code example that demonstrates how to deploy and utilize DeepSeek-R1 for inference programmatically. The code for releasing the design is offered in the Github here. You can clone the notebook and range from SageMaker Studio.<br>
<br>You can run extra demands 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 create a guardrail using the Amazon Bedrock console or the API, and execute it as displayed in the following code:<br>
<br>Tidy up<br>
<br>To prevent unwanted charges, complete the steps in this section to clean up your resources.<br>
<br>Delete the Amazon Bedrock Marketplace release<br>
<br>If you released the design utilizing Amazon Bedrock Marketplace, total the following steps:<br>
<br>1. On the Amazon Bedrock console, under Foundation [designs](http://www.mouneyrac.com) in the navigation pane, select Marketplace implementations.
2. In the Managed deployments area, locate the endpoint you desire to erase.
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](https://www.koumii.com) JumpStart design you deployed will sustain costs if you leave it running. Use the following code to erase the endpoint if you want to stop sustaining charges. For more details, see Delete Endpoints and Resources.<br>
<br>Conclusion<br>
<br>In this post, we [checked](https://edge1.co.kr) out how you can access and deploy the DeepSeek-R1 design using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in [SageMaker Studio](http://www.szkis.cn13000) or Amazon Bedrock Marketplace now to start. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker [JumpStart](https://gitea.sitelease.ca3000) models, SageMaker JumpStart pretrained designs, 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](http://112.48.22.1963000) Solutions Architect for Inference at AWS. He helps emerging generative [AI](http://xintechs.com:3000) business build ingenious options utilizing AWS services and sped up calculate. Currently, he is focused on establishing strategies for fine-tuning and optimizing the reasoning performance of big language models. In his spare time, Vivek takes pleasure in treking, watching films, and attempting various foods.<br>
<br>Niithiyn Vijeaswaran is a Generative [AI](https://eastcoastaudios.in) [Specialist Solutions](https://gigen.net) Architect with the Third-Party Model Science group at AWS. His area of focus is AWS [AI](https://sudanre.com) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer technology and Bioinformatics.<br>
<br>Jonathan Evans is an Expert Solutions Architect dealing with generative [AI](https://www.lokfuehrer-jobs.de) with the Third-Party Model Science group at AWS.<br>
<br>Banu Nagasundaram leads product, [wiki.rolandradio.net](https://wiki.rolandradio.net/index.php?title=User:TimAmbrose899) engineering, and strategic collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://git.bourseeye.com) hub. She is enthusiastic about developing options that assist customers accelerate their [AI](https://dev-members.writeappreviews.com) journey and unlock business value.<br>