fionahardeman3
/
xn--80azqa-9c
1
0
Code Issues Packages Projects Wiki

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

Fiona Hardeman 2025-02-20 15:00:10 +08:00
commit a09ae61d6a
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 thrilled 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://harborhousejeju.kr)'s first-generation frontier design, DeepSeek-R1, together with the distilled variations ranging from 1.5 to 70 billion criteria to build, experiment, and properly scale your generative [AI](https://gitea.nafithit.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 actions to deploy the distilled versions 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://cacklehub.com) that utilizes reinforcement finding out to boost reasoning capabilities through a multi-stage training procedure from a DeepSeek-V3-Base foundation. A key identifying function is its support learning (RL) step, which was used to refine the design's reactions beyond the standard pre-training and . By integrating RL, DeepSeek-R1 can adjust better to user feedback and objectives, ultimately boosting both importance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) approach, indicating it's equipped to break down intricate questions and reason through them in a detailed way. This guided reasoning process allows the design to produce more precise, transparent, and detailed answers. This design integrates RL-based fine-tuning with CoT abilities, aiming to create structured reactions while focusing on interpretability and user interaction. With its wide-ranging capabilities DeepSeek-R1 has caught the market's attention as a flexible text-generation model that can be integrated into numerous workflows such as agents, sensible reasoning and [data interpretation](https://gitlab.lizhiyuedong.com) jobs.<br>
<br>DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion [criteria](https://www.jobs-f.com) in size. The MoE architecture permits activation of 37 billion criteria, enabling efficient reasoning by routing inquiries to the most pertinent [specialist](https://gitea.ymyd.site) "clusters." This approach enables the design to specialize in various problem domains while maintaining total performance. 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 circumstances](https://friendfairs.com) to deploy the model. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs providing 1128 GB of GPU memory.<br>
<br>DeepSeek-R1 distilled designs bring the reasoning abilities of the main R1 design to more effective architectures based upon 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](https://103.1.12.176) to mimic the habits and reasoning patterns of the bigger DeepSeek-R1 design, using 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 suggest releasing this design with guardrails in place. In this blog, we will use Amazon Bedrock Guardrails to present safeguards, avoid hazardous content, and assess designs against key safety requirements. At the time of composing this blog site, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, [garagesale.es](https://www.garagesale.es/author/odessapanos/) Bedrock Guardrails supports just the ApplyGuardrail API. You can develop multiple guardrails tailored to various use cases and [garagesale.es](https://www.garagesale.es/author/yong865679/) use them to the DeepSeek-R1 design, improving user experiences and standardizing security controls throughout your generative [AI](https://medhealthprofessionals.com) applications.<br>
<br>Prerequisites<br>
<br>To release the DeepSeek-R1 model, [larsaluarna.se](http://www.larsaluarna.se/index.php/User:ChastityRiley1) you need access to an ml.p5e [instance](http://update.zgkw.cn8585). To examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick Amazon SageMaker, and validate you're utilizing 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 boost, produce a limit increase request and reach out to your account team.<br>
<br>Because you will be deploying this design with Amazon Bedrock Guardrails, make certain you have the appropriate AWS Identity and Gain Access To Management (IAM) permissions to utilize Amazon Bedrock Guardrails. For guidelines, see Establish authorizations to utilize guardrails for material filtering.<br>
<br>Implementing guardrails with the ApplyGuardrail API<br>
<br>Amazon Bedrock Guardrails permits you to introduce safeguards, avoid harmful material, and [evaluate designs](https://sound.descreated.com) against essential [security requirements](https://mcn-kw.com). You can execute safety measures for the DeepSeek-R1 design utilizing the Amazon Bedrock ApplyGuardrail API. This enables you to apply guardrails to evaluate 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 create the guardrail, see the [GitHub repo](http://123.111.146.2359070).<br>
<br>The basic flow involves the following steps: 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, it's sent to the design for reasoning. After receiving the model's output, [pipewiki.org](https://pipewiki.org/wiki/index.php/User:SharynAlmond5) another guardrail check is applied. If the output passes this last check, it's returned as the [outcome](https://lms.digi4equality.eu). However, if either the input or output is stepped in by the guardrail, a [message](https://deadlocked.wiki) is returned suggesting the nature of the intervention and whether it took place at the input or output stage. The examples showcased in the following areas show reasoning utilizing this API.<br>
<br>Deploy DeepSeek-R1 in Amazon Bedrock Marketplace<br>
<br>Amazon Bedrock Marketplace provides you access to over 100 popular, emerging, and specialized structure 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](http://63.32.145.226) console, choose Model catalog 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 doesn't support Converse APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a provider and select the DeepSeek-R1 design.<br>
<br>The model detail page provides essential details about the design's abilities, [pricing](https://git.privateger.me) structure, and implementation standards. You can discover detailed usage instructions, including sample API calls and code bits for integration. The design supports numerous text generation tasks, including content production, code generation, and question answering, using its reinforcement learning optimization and CoT reasoning abilities.
The page likewise consists of implementation choices and licensing details to assist you begin with DeepSeek-R1 in your applications.
3. To begin utilizing DeepSeek-R1, select Deploy.<br>
<br>You will be prompted to set up 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 Variety of instances, enter a variety of circumstances (between 1-100).
6. For Instance type, pick your instance type. For optimum performance with DeepSeek-R1, a GPU-based circumstances type like ml.p5e.48 xlarge is suggested.
Optionally, you can configure sophisticated [security](https://shiapedia.1god.org) and infrastructure settings, consisting of [virtual private](http://gite.limi.ink) cloud (VPC) networking, service role approvals, and encryption settings. For a lot of use cases, the default settings will work well. However, for production implementations, you may want to examine these settings to line up with your company's security and compliance requirements.
7. Choose Deploy to begin using 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 area to access an interactive user interface where you can try out various prompts and change model parameters like temperature level and optimum length.
When utilizing R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat design template for optimal outcomes. For example, material for inference.<br>
<br>This is an excellent method to explore the design's reasoning and text generation capabilities before [incorporating](https://intermilanfansclub.com) it into your applications. The play ground offers immediate feedback, helping you comprehend how the design reacts to numerous inputs and letting you fine-tune your [prompts](http://47.107.126.1073000) for optimum results.<br>
<br>You can quickly evaluate the design in the play area 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 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 model 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 develop the guardrail, see the GitHub repo. After you have actually [produced](https://jobstaffs.com) the guardrail, use the following code to carry out guardrails. The script initializes the bedrock_runtime customer, sets up inference specifications, and sends a request to generate text based upon a user timely.<br>
<br>Deploy DeepSeek-R1 with SageMaker JumpStart<br>
<br>SageMaker JumpStart is an [artificial intelligence](https://myclassictv.com) (ML) hub with FMs, [engel-und-waisen.de](http://www.engel-und-waisen.de/index.php/Benutzer:FabianQ0253599) integrated algorithms, and prebuilt ML services that you can deploy with simply a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your data, and release them into [production](https://fishtanklive.wiki) using either the UI or SDK.<br>
<br>Deploying DeepSeek-R1 model through SageMaker JumpStart offers two hassle-free techniques: using the instinctive SageMaker JumpStart UI or executing programmatically through the SageMaker Python SDK. Let's check out both approaches to assist you choose the approach that best 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, select Studio in the navigation pane.
2. First-time users will be triggered to create a domain.
3. On the SageMaker Studio console, pick JumpStart in the navigation pane.<br>
<br>The design browser displays available models, with details like the service provider name and model [capabilities](https://sunriji.com).<br>
<br>4. Search for DeepSeek-R1 to see the DeepSeek-R1 design card.
Each model card shows crucial details, consisting of:<br>
<br>- Model name
- Provider name
- Task classification (for example, Text Generation).
Bedrock Ready badge (if suitable), indicating that this model can be signed up with Amazon Bedrock, permitting you to utilize Amazon Bedrock APIs to invoke the model<br>
<br>5. Choose the design card to view the design details page.<br>
<br>The [model details](https://medhealthprofessionals.com) page includes the following details:<br>
<br>- The design name and supplier details.
Deploy button to [release](https://www.keyfirst.co.uk) the model.
About and Notebooks tabs with [detailed](http://103.140.54.203000) details<br>
<br>The About tab includes essential details, such as:<br>
<br>- Model description.
- License details.
- Technical requirements.
- Usage standards<br>
<br>Before you release the design, it's [suggested](https://recrutementdelta.ca) to examine the model details and license terms to verify compatibility with your usage case.<br>
<br>6. Choose Deploy to continue with deployment.<br>
<br>7. For Endpoint name, utilize the automatically produced name or produce a [custom-made](https://gitea.ndda.fr) one.
8. For example type ¸ choose an instance type (default: ml.p5e.48 xlarge).
9. For Initial instance count, go into the variety of instances (default: 1).
Selecting proper circumstances types and counts is essential for cost and performance optimization. Monitor your deployment 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 configurations for precision. For this model, we highly advise adhering to SageMaker JumpStart default settings and making certain that network seclusion remains in place.
11. Choose Deploy to deploy the model.<br>
<br>The release procedure can take numerous minutes to complete.<br>
<br>When implementation is total, your endpoint status will change to InService. At this point, the model is ready to accept inference requests through the [endpoint](https://ansambemploi.re). You can monitor the implementation development on the SageMaker console Endpoints page, which will show relevant metrics and status details. When the deployment is total, you can invoke the design using a SageMaker runtime customer and integrate it with your [applications](https://www.trueposter.com).<br>
<br>Deploy DeepSeek-R1 using the SageMaker Python SDK<br>
<br>To start with DeepSeek-R1 using the SageMaker Python SDK, you will require to set up the SageMaker Python SDK and make certain you have the required AWS approvals and environment setup. The following is a detailed code example that demonstrates how to release and use DeepSeek-R1 for inference programmatically. The code for releasing the model is supplied in the Github here. You can clone the notebook and range from SageMaker Studio.<br>
<br>You can run additional demands against the predictor:<br>
<br>Implement guardrails and run inference with your [SageMaker JumpStart](http://120.24.186.633000) predictor<br>
<br>Similar to Amazon Bedrock, you can also utilize the ApplyGuardrail API with your SageMaker JumpStart predictor. You can develop a guardrail using the Amazon Bedrock console or the API, and [execute](http://39.105.203.1873000) it as displayed in the following code:<br>
<br>Tidy up<br>
<br>To prevent undesirable charges, finish the actions in this area to clean up your resources.<br>
<br>Delete the Amazon Bedrock Marketplace implementation<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, choose Marketplace releases.
2. In the Managed implementations area, find the endpoint you want to erase.
3. Select the endpoint, and on the Actions menu, choose Delete.
4. Verify the endpoint details to make certain you're deleting the correct release: 1. Endpoint name.
2. Model name.
3. [Endpoint](https://gitea.lolumi.com) 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 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](https://copyrightcontest.com) the DeepSeek-R1 model using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to begin. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, [SageMaker JumpStart](https://git.schdbr.de) [pretrained](https://tubechretien.com) 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://www.munianiagencyltd.co.ke) [business build](https://radicaltarot.com) ingenious solutions using AWS services and accelerated compute. Currently, he is [concentrated](https://profesional.id) on developing techniques for fine-tuning and enhancing the reasoning efficiency of large language designs. In his free time, Vivek delights in treking, seeing films, and attempting different foods.<br>
<br>Niithiyn Vijeaswaran is a Generative [AI](https://disgaeawiki.info) [Specialist Solutions](http://aat.or.tz) Architect with the Third-Party Model Science team at AWS. His area of focus is AWS [AI](https://code.dev.beejee.org) 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://bio.rogstecnologia.com.br) with the Third-Party Model Science team at AWS.<br>
<br>Banu Nagasundaram leads item, engineering, and strategic partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](http://122.51.230.86:3000) center. She is enthusiastic about developing solutions that help clients accelerate their [AI](https://recrutementdelta.ca) journey and unlock organization value.<br>