iolathomson013
/
pixelphotoscript
1
0
Code Issues Packages Projects Wiki

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

Iola Thomson 2025-02-27 00:34:35 +00:00
commit 0e31a16d24
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](https://nerm.club) 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://redsocial.cl)['s first-generation](https://git.jerrita.cn) frontier design, DeepSeek-R1, along with the distilled versions varying from 1.5 to 70 billion criteria to construct, experiment, and properly scale your generative [AI](https://www.applynewjobz.com) ideas on AWS.<br>
<br>In this post, we demonstrate how to begin with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable steps to deploy the distilled versions of the models also.<br>
<br>Overview of DeepSeek-R1<br>
<br>DeepSeek-R1 is a large language model (LLM) developed by DeepSeek [AI](https://www.ayuujk.com) that uses support finding out to boost reasoning abilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. A crucial identifying feature is its support learning (RL) action, which was [utilized](https://git.laser.di.unimi.it) to improve the design's responses beyond the basic pre-training and tweak procedure. By incorporating RL, DeepSeek-R1 can adjust more efficiently to user feedback and objectives, eventually enhancing both significance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) technique, indicating it's geared up to break down complex questions and factor through them in a detailed manner. This guided reasoning process allows the model to produce more accurate, transparent, and detailed answers. This design integrates RL-based fine-tuning with CoT abilities, aiming to produce structured actions while concentrating on interpretability and user interaction. With its comprehensive capabilities DeepSeek-R1 has recorded the industry's attention as a versatile text-generation model that can be integrated into numerous workflows such as agents, logical thinking and information analysis jobs.<br>
<br>DeepSeek-R1 uses a Mix of Experts (MoE) architecture and is 671 billion parameters in size. The MoE architecture enables activation of 37 billion parameters, making it possible for efficient reasoning by routing queries to the most pertinent specialist "clusters." This technique allows the design to specialize in different problem domains while maintaining overall [effectiveness](https://thankguard.com). DeepSeek-R1 requires a minimum of 800 GB of HBM memory in FP8 format for [reasoning](https://estekhdam.in). In this post, we will utilize an ml.p5e.48 xlarge instance to deploy the model. ml.p5e.48 xlarge features 8 Nvidia H200 [GPUs offering](https://prazskypantheon.cz) 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 upon popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a procedure of training smaller sized, more efficient models to simulate the behavior and thinking patterns of the larger DeepSeek-R1 design, utilizing it as a [teacher model](http://gitlab.adintl.cn).<br>
<br>You can deploy DeepSeek-R1 design either through SageMaker JumpStart or [Bedrock Marketplace](https://git.bourseeye.com). Because DeepSeek-R1 is an emerging model, we advise releasing this model with guardrails in place. In this blog site, we will utilize Amazon Bedrock Guardrails to introduce safeguards, avoid hazardous material, and examine models against essential safety requirements. At the time of composing this blog site, for DeepSeek-R1 implementations on [SageMaker JumpStart](https://esvoe.video) and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can produce multiple guardrails [tailored](http://git.baobaot.com) to different use cases and apply them to the DeepSeek-R1 design, enhancing user experiences and standardizing safety controls across your generative [AI](https://www.maisondurecrutementafrique.com) applications.<br>
<br>Prerequisites<br>
<br>To deploy the DeepSeek-R1 design, 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 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 request a limit boost, produce a limit increase demand and reach out to your account group.<br>
<br>Because you will be releasing this model with Amazon Bedrock Guardrails, make certain you have the right AWS Identity and Gain Access To Management (IAM) [permissions](http://40.73.118.158) to use Amazon Bedrock Guardrails. For directions, see Set up approvals to utilize guardrails for content filtering.<br>
<br>Implementing guardrails with the ApplyGuardrail API<br>
<br>Amazon Bedrock Guardrails you to introduce safeguards, prevent hazardous content, and assess designs against key safety requirements. You can carry out precaution for the DeepSeek-R1 design using the Amazon Bedrock ApplyGuardrail API. This permits you to use guardrails to examine user inputs and model responses released on Amazon Bedrock Marketplace and SageMaker JumpStart. You can create a guardrail utilizing the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo.<br>
<br>The basic circulation includes the following actions: First, the system receives 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 model for inference. After getting the model's output, another guardrail check is used. If the output passes this final check, it's returned as the result. However, if either the input or output is stepped in by the guardrail, a message is returned indicating the nature of the intervention and whether it took place 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 provides you access to over 100 popular, [wiki.snooze-hotelsoftware.de](https://wiki.snooze-hotelsoftware.de/index.php?title=Benutzer:Orval41J5492) 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, pick Model catalog under Foundation models in the [navigation](https://community.cathome.pet) pane.
At the time of composing this post, you can use the InvokeModel API to conjure up the design. It does not support Converse APIs and [mediawiki.hcah.in](https://mediawiki.hcah.in/index.php?title=User:TyroneMcCabe) other Amazon Bedrock tooling.
2. Filter for DeepSeek as a supplier and choose the DeepSeek-R1 design.<br>
<br>The model detail page supplies vital details about the design's capabilities, prices structure, and application standards. You can find detailed use instructions, including sample API calls and code snippets for integration. The model supports various text generation jobs, [consisting](http://football.aobtravel.se) of material creation, code generation, and concern answering, using its reinforcement learning optimization and CoT reasoning abilities.
The page likewise includes implementation alternatives and licensing details to help you start with DeepSeek-R1 in your [applications](http://101.200.33.643000).
3. To start utilizing DeepSeek-R1, choose Deploy.<br>
<br>You will be triggered to configure 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 circumstances, get in a variety of [instances](http://120.25.165.2073000) (between 1-100).
6. For example type, choose your circumstances type. For optimum performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is advised.
Optionally, you can set up innovative security and facilities settings, including virtual private cloud (VPC) networking, service role consents, and file encryption settings. For the majority of use cases, the default settings will work well. However, for production releases, you may desire to examine these settings to align with your organization's security and compliance requirements.
7. Choose Deploy to start using the model.<br>
<br>When the deployment is complete, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock playground.
8. Choose Open in play area to access an interactive interface where you can try out various triggers and change design specifications like temperature level and optimum length.
When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for optimal results. For example, content for reasoning.<br>
<br>This is an exceptional method to check out the model's thinking and text generation abilities before incorporating it into your applications. The play area provides immediate feedback, helping you comprehend how the design reacts to different inputs and letting you tweak your triggers for [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11925076) ideal results.<br>
<br>You can quickly check the design in the play ground through the UI. However, to invoke the deployed model 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 shows how to carry out reasoning using a released DeepSeek-R1 design 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 produce the guardrail, see the [GitHub repo](http://39.105.128.46). After you have actually developed the guardrail, use the following code to implement guardrails. The script [initializes](https://epspatrolscv.com) the bedrock_runtime customer, sets up inference criteria, [pediascape.science](https://pediascape.science/wiki/User:DorothyShuman6) and [engel-und-waisen.de](http://www.engel-und-waisen.de/index.php/Benutzer:TaneshaBoland3) sends out a demand to produce text based upon a user prompt.<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 [solutions](https://git.ascarion.org) that you can deploy with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your data, and deploy them into production utilizing either the UI or SDK.<br>
<br>Deploying DeepSeek-R1 model through SageMaker JumpStart offers 2 convenient approaches: using the intuitive SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's check out both approaches to assist you pick the approach that finest matches your needs.<br>
<br>Deploy DeepSeek-R1 through SageMaker JumpStart UI<br>
<br>Complete the following actions to deploy DeepSeek-R1 using SageMaker 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, pick JumpStart in the navigation pane.<br>
<br>The design browser displays available designs, with details like the service provider name and model abilities.<br>
<br>4. Search for [yewiki.org](https://www.yewiki.org/User:AlfonzoMiramonte) DeepSeek-R1 to see the DeepSeek-R1 model card.
Each design card reveals key details, including:<br>
<br>- Model name
- Provider name
- Task category (for example, Text Generation).
Bedrock Ready badge (if relevant), indicating that this design can be signed up with Amazon Bedrock, allowing you to use Amazon Bedrock APIs to invoke the design<br>
<br>5. Choose the model card to view the design details page.<br>
<br>The model details page consists of the following details:<br>
<br>- The model name and provider details.
Deploy button to deploy the design.
About and Notebooks tabs with detailed details<br>
<br>The About [tab consists](https://starttrainingfirstaid.com.au) of important details, such as:<br>
<br>- Model description.
- License details.
- Technical specs.
- Usage standards<br>
<br>Before you release the model, it's recommended to examine the model details and license terms to confirm compatibility with your use case.<br>
<br>6. Choose Deploy to [continue](http://47.103.91.16050903) with release.<br>
<br>7. For Endpoint name, utilize the automatically produced name or develop a custom one.
8. For example type ¸ choose an instance type (default: ml.p5e.48 xlarge).
9. For Initial circumstances count, go into the number of instances (default: 1).
Selecting suitable instance types and counts is essential for cost and efficiency optimization. Monitor your deployment to change these settings as needed.Under Inference type, [wiki.vst.hs-furtwangen.de](https://wiki.vst.hs-furtwangen.de/wiki/User:ArleenBabbidge) Real-time inference is selected by default. This is optimized for [sustained traffic](https://esvoe.video) and low latency.
10. Review all configurations for accuracy. For this design, we strongly suggest adhering to [SageMaker JumpStart](https://esvoe.video) default settings and making certain that network isolation remains in location.
11. Choose Deploy to deploy the model.<br>
<br>The deployment procedure can take several minutes to complete.<br>
<br>When release is complete, your endpoint status will alter to InService. At this point, the design is all set to accept inference demands through the endpoint. You can keep track of the deployment development on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the release is complete, you can invoke the model using a [SageMaker runtime](https://cambohub.com3000) customer and incorporate it with your applications.<br>
<br>Deploy DeepSeek-R1 using the SageMaker Python SDK<br>
<br>To get begun with DeepSeek-R1 utilizing the SageMaker Python SDK, you will require to set up the SageMaker Python SDK and make certain you have the essential AWS consents and environment setup. The following is a detailed code example that demonstrates how to deploy and use DeepSeek-R1 for inference programmatically. The code for deploying 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 also utilize 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 avoid unwanted charges, finish the steps in this area to clean up your resources.<br>
<br>Delete the Amazon Bedrock Marketplace implementation<br>
<br>If you released the design using Amazon Bedrock Marketplace, complete the following actions:<br>
<br>1. On the Amazon [Bedrock](https://raumlaborlaw.com) console, under Foundation designs in the navigation pane, choose Marketplace releases.
2. In the Managed deployments area, locate the endpoint you want to delete.
3. Select the endpoint, and on the Actions menu, [select Delete](http://120.26.79.179).
4. Verify the endpoint details to make certain you're erasing the proper deployment: 1. Endpoint name.
2. Model name.
3. Endpoint status<br>
<br>Delete the SageMaker JumpStart predictor<br>
<br>The SageMaker JumpStart design you deployed 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 and Resources.<br>
<br>Conclusion<br>
<br>In this post, we explored how you can access and release the DeepSeek-R1 design using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to start. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart designs, SageMaker JumpStart pretrained models, Amazon SageMaker [JumpStart Foundation](http://webheaydemo.co.uk) Models, Amazon Bedrock 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 helps emerging generative [AI](https://suomalainennaikki.com) companies build ingenious solutions using AWS services and sped up calculate. Currently, he is focused on developing strategies for [fine-tuning](https://forum.tinycircuits.com) and optimizing the reasoning efficiency of big language models. In his spare time, Vivek enjoys treking, viewing films, and trying different cuisines.<br>
<br>Niithiyn Vijeaswaran is a Generative [AI](https://www.buzzgate.net) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His area of focus is AWS [AI](https://acrohani-ta.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://git.goatwu.com) with the [Third-Party Model](https://jobistan.af) [Science team](https://siman.co.il) at AWS.<br>
<br>Banu Nagasundaram leads item, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](http://1.14.105.160:9211) hub. She is enthusiastic about [constructing services](http://101.42.41.2543000) that help clients accelerate their [AI](http://upleta.rackons.com) journey and unlock organization worth.<br>