angelitafarber/freesoftwareservers
1
0
Code Issues Actions Packages Projects Wiki

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

Angelita Farber 2025-02-16 09:04:05 +00:00
parent e3c25f1be6
commit e82bbee2a7
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 delighted 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://repos.ubtob.net)'s first-generation frontier design, DeepSeek-R1, along with the distilled variations ranging from 1.5 to 70 billion specifications to build, experiment, and responsibly scale your [generative](https://git.dev-store.xyz) [AI](https://nse.ai) ideas 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 similar steps to deploy the distilled variations of the designs also.<br>
<br>Overview of DeepSeek-R1<br>
<br>DeepSeek-R1 is a big language design (LLM) established by DeepSeek [AI](https://radiothamkin.com) that utilizes support finding out to enhance reasoning capabilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A crucial identifying feature is its reinforcement knowing (RL) action, which was utilized to improve the model's actions beyond the standard pre-training and fine-tuning procedure. By incorporating RL, DeepSeek-R1 can adapt more successfully to user feedback and goals, eventually enhancing both importance and clearness. In addition, DeepSeek-R1 employs a chain-of-thought (CoT) method, indicating it's [equipped](https://careers.express) to break down intricate queries and factor through them in a detailed manner. This assisted reasoning procedure allows the model to produce more precise, transparent, and detailed answers. This design combines RL-based fine-tuning with CoT abilities, aiming to create structured responses while concentrating on interpretability and user interaction. With its [comprehensive abilities](https://memorial-genweb.org) DeepSeek-R1 has captured the market's attention as a versatile text-generation design that can be incorporated into numerous workflows such as representatives, rational thinking and information [analysis](http://wecomy.co.kr) 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 parameters, [enabling effective](https://bpx.world) reasoning by routing queries to the most relevant specialist "clusters." This technique enables the model to specialize in various problem domains while maintaining overall 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 circumstances to deploy the model. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.<br>
<br>DeepSeek-R1 [distilled designs](http://www.zeil.kr) bring the thinking abilities of the main R1 design to more efficient architectures based on popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a process of training smaller, more effective designs to simulate the habits and reasoning patterns of the bigger DeepSeek-R1 model, using it as an instructor design.<br>
<br>You can deploy DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging design, we suggest deploying this model with guardrails in location. In this blog site, we will use Amazon Bedrock Guardrails to present safeguards, avoid hazardous content, and examine designs against key security requirements. At the time of writing this blog, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can develop numerous guardrails tailored to different usage cases and use them to the DeepSeek-R1 design, enhancing user experiences and standardizing security controls across your generative [AI](https://diskret-mote-nodeland.jimmyb.nl) applications.<br>
<br>Prerequisites<br>
<br>To release the DeepSeek-R1 design, you require access to an ml.p5e [instance](https://careerportals.co.za). To examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and confirm you're utilizing ml.p5e.48 xlarge for endpoint use. Make certain that you have at least one ml.P5e.48 xlarge instance in the AWS Region you are releasing. To ask for a limitation boost, produce a limitation boost demand and [connect](https://git.bloade.com) to your account team.<br>
<br>Because you will be releasing this design with Amazon Bedrock Guardrails, make certain you have the appropriate AWS Identity and Gain Access To Management (IAM) authorizations to use Amazon Bedrock Guardrails. For instructions, see Set up consents to use guardrails for material filtering.<br>
<br>Implementing guardrails with the ApplyGuardrail API<br>
<br>Amazon Bedrock Guardrails enables you to present safeguards, avoid hazardous content, and assess models against key safety criteria. You can carry out [safety steps](http://116.204.119.1713000) for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This permits you to apply 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 create the guardrail, see the GitHub repo.<br>
<br>The general circulation includes the following steps: 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 [receiving](https://lovematch.vip) the [model's](https://www.ayurjobs.net) output, another guardrail check is used. If the output passes this last check, it's returned as the final outcome. However, if either the input or output is stepped in 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 show reasoning 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 structure models (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following steps:<br>
<br>1. On the Amazon Bedrock console, pick Model brochure under Foundation models in the navigation pane.
At the time of composing this post, you can use the InvokeModel API to conjure up the model. It does not support Converse APIs and other Amazon Bedrock tooling.
2. Filter for [DeepSeek](https://www.usbstaffing.com) as a service provider and select the DeepSeek-R1 model.<br>
<br>The design detail page supplies essential details about the design's capabilities, rates structure, and implementation guidelines. You can discover detailed use directions, consisting of sample API calls and code snippets for combination. The design supports numerous text generation jobs, consisting of content creation, code generation, and question answering, utilizing its reinforcement finding out optimization and CoT thinking abilities.
The page likewise includes release choices and licensing details to assist you get going with DeepSeek-R1 in your applications.
3. To start using DeepSeek-R1, choose Deploy.<br>
<br>You will be triggered to set up the 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 circumstances, enter a number of circumstances (between 1-100).
6. For example type, pick your circumstances type. For optimum performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised.
Optionally, you can set up advanced security and facilities settings, including virtual private cloud (VPC) networking, service function consents, and file encryption settings. For most utilize cases, the default settings will work well. However, for production implementations, you might wish to examine these settings to line up with your organization's security and compliance requirements.
7. Choose Deploy to start using the model.<br>
<br>When the release is complete, you can check DeepSeek-R1's capabilities straight in the Amazon Bedrock play area.
8. Choose Open in play area to access an interactive interface where you can explore different triggers and adjust design parameters like temperature level and optimum length.
When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimal results. For example, material for reasoning.<br>
<br>This is an excellent method to explore the design's thinking and text generation abilities before incorporating it into your applications. The play ground provides immediate feedback, assisting you understand how the model reacts to various inputs and letting you fine-tune your triggers for ideal results.<br>
<br>You can quickly check the design in the play ground through the UI. However, [wiki.rolandradio.net](https://wiki.rolandradio.net/index.php?title=User:RYUDarell4) to conjure up the released design programmatically with any Amazon Bedrock APIs, you need 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 [deployed](https://baitshepegi.co.za) DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and [ApplyGuardrail API](http://120.77.67.22383). You can produce a guardrail utilizing the Amazon Bedrock [console](https://www.aspira24.com) or the API. For the example code to create the guardrail, see the GitHub repo. After you have created the guardrail, utilize the following code to implement guardrails. The script initializes the bedrock_runtime customer, configures reasoning parameters, and sends a demand 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) center with FMs, integrated algorithms, and prebuilt ML services that you can deploy with simply a few clicks. With [SageMaker](https://neoshop365.com) 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 provides 2 convenient techniques: using the user-friendly SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's explore both methods to assist you choose the approach that finest matches your needs.<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](http://123.60.67.64) a domain.
3. On the SageMaker Studio console, choose JumpStart in the navigation pane.<br>
<br>The model internet browser displays available models, with details like the provider name and [design abilities](https://support.mlone.ai).<br>
<br>4. Look for DeepSeek-R1 to see the DeepSeek-R1 design card.
Each design card shows essential details, [trademarketclassifieds.com](https://trademarketclassifieds.com/user/profile/2672496) including:<br>
<br>- Model name
- Provider name
- Task category (for example, Text Generation).
Bedrock Ready badge (if applicable), [indicating](https://www.remotejobz.de) that this design can be signed up with Amazon Bedrock, permitting you to utilize Amazon Bedrock APIs to invoke the model<br>
<br>5. Choose the model card to view the model details page.<br>
<br>The design details page includes the following details:<br>
<br>- The model name and service provider 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](https://git.xinstitute.org.cn) description.
- License details.
- Technical specifications.
- Usage guidelines<br>
<br>Before you deploy the design, it's recommended to review the design details and license terms to [validate compatibility](https://thebigme.cc3000) with your use case.<br>
<br>6. Choose Deploy to proceed with release.<br>
<br>7. For Endpoint name, use the immediately produced name or create a custom one.
8. For Instance type ¸ select a circumstances type (default: ml.p5e.48 xlarge).
9. For Initial circumstances count, enter the variety of circumstances (default: 1).
Selecting suitable circumstances types and counts is important for expense and efficiency optimization. Monitor your [deployment](https://links.gtanet.com.br) to adjust these settings as needed.Under Inference type, Real-time inference is picked by default. This is enhanced for sustained traffic and low latency.
10. Review all setups for accuracy. For this model, [higgledy-piggledy.xyz](https://higgledy-piggledy.xyz/index.php/User:WinstonNajera5) we strongly recommend adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in location.
11. Choose Deploy to deploy the model.<br>
<br>The implementation process can take several minutes to finish.<br>
<br>When deployment is total, your endpoint status will alter to InService. At this moment, the model is ready to accept reasoning requests through the endpoint. You can keep an eye on the deployment development on the SageMaker console Endpoints page, which will show appropriate metrics and status details. When the deployment is total, you can conjure up the design utilizing a SageMaker runtime client and incorporate 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 set up the SageMaker Python SDK and make certain you have the necessary AWS permissions 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](https://git.brodin.rocks) 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 reasoning with your SageMaker JumpStart predictor<br>
<br>Similar to Amazon Bedrock, you can likewise use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can produce a guardrail utilizing the Amazon [Bedrock console](https://gl.vlabs.knu.ua) or the API, and implement it as displayed in the following code:<br>
<br>Clean up<br>
<br>To avoid undesirable charges, finish the steps in this area to clean up your resources.<br>
<br>Delete the Amazon Bedrock Marketplace release<br>
<br>If you released the design using Amazon Bedrock Marketplace, total the following steps:<br>
<br>1. On the Amazon Bedrock console, under Foundation models in the navigation pane, pick Marketplace releases.
2. In the Managed deployments area, find the endpoint you desire to delete.
3. Select the endpoint, [forum.altaycoins.com](http://forum.altaycoins.com/profile.php?id=1073734) and on the Actions menu, [pick Delete](http://www.boot-gebraucht.de).
4. Verify the endpoint details to make certain you're erasing the appropriate release: 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](https://www.cbl.aero).<br>
<br>Conclusion<br>
<br>In this post, we explored how you can access and release the DeepSeek-R1 model utilizing Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to start. 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 Marketplace, and Getting started with Amazon SageMaker JumpStart.<br>
<br>About the Authors<br>
<br>Vivek Gangasani is a Lead Specialist Solutions Architect for [Inference](https://vtuvimo.com) at AWS. He helps emerging [generative](https://code.52abp.com) [AI](http://www.hakyoun.co.kr) companies construct innovative options using AWS services and sped up calculate. Currently, he is focused on developing strategies for fine-tuning and enhancing the reasoning efficiency of big language designs. In his downtime, Vivek takes pleasure in hiking, enjoying motion pictures, and attempting different foods.<br>
<br>Niithiyn Vijeaswaran is a Generative [AI](http://git.taokeapp.net:3000) Specialist Solutions Architect with the Third-Party Model [Science team](http://201.17.3.963000) at AWS. His area of focus is AWS [AI](https://kryza.network) [accelerators](http://www.jedge.top3000) (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.<br>
<br>Jonathan Evans is a Specialist Solutions Architect dealing with generative [AI](https://www.gotonaukri.com) with the Third-Party Model Science group at AWS.<br>
<br>Banu Nagasundaram leads product, engineering, and tactical collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](https://gogs.yaoxiangedu.com) hub. She is [passionate](http://president-park.co.kr) about developing solutions that help consumers accelerate their [AI](http://git.twopiz.com:8888) journey and unlock business value.<br>