adalbertowunde/musicplayer
1
0
Code Issues Actions Packages Projects Wiki

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

Adalberto Wunderlich 2025-04-05 03:50:46 +07:00
parent 8455d8bc5d
commit 7ba1df860e
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](https://setiathome.berkeley.edu) to reveal that DeepSeek R1 distilled Llama and [Qwen designs](http://gitlab.boeart.cn) are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](https://movie.nanuly.kr)'s first-generation frontier design, DeepSeek-R1, in addition to the distilled [variations ranging](https://gajaphil.com) from 1.5 to 70 billion criteria to build, experiment, and properly scale your generative [AI](https://virnal.com) ideas on AWS.<br>
<br>In this post, we demonstrate how to get started with DeepSeek-R1 on Amazon Bedrock Marketplace and SageMaker JumpStart. You can follow comparable steps to deploy 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](http://freeflashgamesnow.com) that uses support discovering to boost reasoning capabilities through a multi-stage training process from a DeepSeek-V3-Base structure. A key identifying [function](https://rpcomm.kr) is its support knowing (RL) step, which was utilized to improve the design's reactions beyond the basic pre-training and tweak process. By integrating RL, DeepSeek-R1 can adapt better to user [feedback](http://koceco.co.kr) and objectives, eventually boosting both importance and clarity. In addition, DeepSeek-R1 uses a chain-of-thought (CoT) technique, implying it's equipped to break down complicated inquiries and reason through them in a detailed manner. This directed thinking process permits the model to produce more precise, transparent, and detailed answers. This model combines RL-based fine-tuning with CoT abilities, aiming to produce structured responses while focusing on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has actually caught the market's attention as a versatile text-generation design that can be incorporated into numerous workflows such as agents, rational thinking and information 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 parameters, allowing efficient reasoning by routing inquiries to the most pertinent expert "clusters." This method allows the model to focus on different issue domains while maintaining general effectiveness. DeepSeek-R1 needs a minimum of 800 GB of HBM memory in FP8 format for reasoning. In this post, we will use an ml.p5e.48 xlarge circumstances to deploy the model. ml.p5e.48 xlarge comes with 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.<br>
<br>DeepSeek-R1 distilled designs bring the reasoning capabilities of the main R1 design to more [effective architectures](https://followgrown.com) based on popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation describes a procedure of training smaller sized, more efficient models to mimic the habits and reasoning patterns of the larger DeepSeek-R1 model, utilizing it as an instructor model.<br>
<br>You can deploy DeepSeek-R1 design either through [SageMaker JumpStart](http://www.xn--2i4bi0gw9ai2d65w.com) or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we recommend deploying this model with guardrails in location. In this blog site, we will use Amazon Bedrock Guardrails to present safeguards, avoid hazardous material, and [evaluate models](https://cyberbizafrica.com) against essential security requirements. At the time of writing this blog site, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can develop several guardrails tailored to various use cases and apply them to the DeepSeek-R1 design, enhancing user experiences and [standardizing security](http://idesys.co.kr) controls across your generative [AI](http://www.0768baby.com) applications.<br>
<br>Prerequisites<br>
<br>To release the DeepSeek-R1 model, you need access to an ml.p5e instance. To check if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick Amazon SageMaker, [kousokuwiki.org](http://kousokuwiki.org/wiki/%E5%88%A9%E7%94%A8%E8%80%85:JaniceProeschel) and confirm 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](https://jobsubscribe.com) a limit increase, develop a limit increase request and reach out to your account team.<br>
<br>Because you will be releasing this model with Amazon Bedrock Guardrails, make certain you have the proper AWS Identity and Gain Access To [Management](https://surreycreepcatchers.ca) (IAM) [permissions](http://www.thegrainfather.co.nz) to use Amazon Bedrock Guardrails. For instructions, [wiki.eqoarevival.com](https://wiki.eqoarevival.com/index.php/User:OSELashunda) see Establish 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 damaging material, and examine designs against essential security requirements. You can carry out precaution for the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This allows you to use guardrails to evaluate user inputs and model reactions deployed on Amazon Bedrock 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 flow involves the following actions: First, the system receives an input for the design. This input is then processed through the [ApplyGuardrail API](http://zhangsheng1993.tpddns.cn3000). If the input passes the guardrail check, it's sent out to the design for inference. After receiving the design's output, another guardrail check is used. If the output passes this last 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 [occurred](https://pipewiki.org) at the input or [output phase](https://paanaakgit.iran.liara.run). The examples showcased in the following [sections](http://www.sa1235.com) demonstrate 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 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, [select Model](https://xajhuang.com3100) catalog 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](http://git.bzgames.cn) APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a supplier and pick the DeepSeek-R1 design.<br>
<br>The design detail page supplies necessary details about the model's abilities, prices structure, and execution standards. You can discover detailed use guidelines, consisting of sample API calls and code snippets for integration. The design supports numerous text generation jobs, consisting of material development, code generation, and concern answering, using its reinforcement finding out optimization and CoT thinking capabilities.
The page also includes implementation options and licensing details to assist you get going with DeepSeek-R1 in your applications.
3. To begin utilizing DeepSeek-R1, select 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, go into an endpoint name (in between 1-50 alphanumeric characters).
5. For Number of circumstances, [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11987384) get in a variety of instances (in 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 recommended.
Optionally, you can configure advanced security and facilities settings, consisting of virtual private cloud (VPC) networking, service function permissions, and encryption settings. For many utilize cases, the default settings will work well. However, for production implementations, you might desire to review these settings to line up with your organization's security and compliance requirements.
7. [Choose Deploy](http://wp10476777.server-he.de) to begin using the design.<br>
<br>When the release is total, you can test DeepSeek-R1's abilities straight in the Amazon Bedrock playground.
8. Choose Open in playground to access an interactive interface where you can try out different prompts and change design parameters like temperature level and optimum length.
When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat design template for optimal results. For instance, material for reasoning.<br>
<br>This is an outstanding way to check out the [design's reasoning](http://euhope.com) and text generation abilities before incorporating it into your applications. The play area offers instant feedback, [assisting](https://gitea.pi.cr4.live) you understand how the [model reacts](https://tagreba.org) to numerous inputs and letting you fine-tune your prompts for ideal results.<br>
<br>You can quickly check the design in the play ground through the UI. However, to conjure up the deployed model [programmatically](http://117.50.220.1918418) with any Amazon Bedrock APIs, you need to get the endpoint ARN.<br>
<br>Run inference using guardrails with the [deployed](https://gitea.itskp-odense.dk) DeepSeek-R1 endpoint<br>
<br>The following code example shows how to perform inference using a released DeepSeek-R1 design through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can create a guardrail using the Amazon Bedrock console or the API. For [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11879073) the example code to develop the guardrail, see the GitHub repo. After you have actually developed the guardrail, use the following code to execute guardrails. The script initializes the bedrock_runtime customer, configures inference criteria, and sends out a demand to [produce text](http://visionline.kr) based upon 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, [fishtanklive.wiki](https://fishtanklive.wiki/User:GiuseppeXve) and prebuilt ML services that you can deploy with just a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your information, and release them into production using either the UI or SDK.<br>
<br>Deploying DeepSeek-R1 design through SageMaker JumpStart provides 2 convenient approaches: utilizing the instinctive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both methods to assist you pick the approach that best suits your requirements.<br>
<br>Deploy DeepSeek-R1 through SageMaker JumpStart UI<br>
<br>Complete the following steps to release DeepSeek-R1 [utilizing SageMaker](https://www.florevit.com) 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, pick JumpStart in the navigation pane.<br>
<br>The model browser displays available designs, with details like the company name and model capabilities.<br>
<br>4. Look for DeepSeek-R1 to view the DeepSeek-R1 model card.
Each design card shows essential details, consisting of:<br>
<br>- Model name
- Provider name
- Task classification (for example, Text Generation).
Bedrock Ready badge (if appropriate), suggesting that this design can be registered with Amazon Bedrock, permitting you to use Amazon Bedrock APIs to conjure up the design<br>
<br>5. Choose the design card to view the model details page.<br>
<br>The model details page consists of the following details:<br>
<br>- The design name and service provider 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 requirements.
- Usage guidelines<br>
<br>Before you deploy the design, it's advised to evaluate the design details and license terms to validate compatibility with your use case.<br>
<br>6. Choose Deploy to continue with deployment.<br>
<br>7. For [Endpoint](https://gitlab.tenkai.pl) name, utilize the immediately produced name or develop a one.
8. For Instance type ¸ choose a circumstances type (default: ml.p5e.48 xlarge).
9. For [yewiki.org](https://www.yewiki.org/User:MarilynCani09) Initial instance count, go into the variety of instances (default: 1).
Selecting appropriate instance types and counts is important for expense and efficiency optimization. Monitor your implementation to adjust these settings as needed.Under Inference type, [Real-time reasoning](https://theindietube.com) is chosen by default. This is optimized for sustained traffic and low latency.
10. Review all setups for accuracy. For this design, we highly advise sticking to SageMaker JumpStart default [settings](https://grace4djourney.com) and making certain that network isolation remains in place.
11. Choose Deploy to release 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 point, the model is all set to accept inference [demands](https://bandbtextile.de) through the endpoint. You can monitor the release progress on the SageMaker console Endpoints page, which will show pertinent metrics and status [details](https://spiritustv.com). When the implementation is total, you can invoke the design utilizing a SageMaker runtime client and integrate it with your applications.<br>
<br>Deploy DeepSeek-R1 utilizing the SageMaker Python SDK<br>
<br>To begin with DeepSeek-R1 using the SageMaker Python SDK, you will require to set up the SageMaker Python SDK and make certain you have the essential AWS authorizations and environment setup. The following is a detailed code example that demonstrates how to deploy and use DeepSeek-R1 for reasoning programmatically. The code for deploying the model is provided in the Github here. You can clone the note pad and run 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 likewise use the ApplyGuardrail API with your SageMaker JumpStart predictor. You can develop a guardrail using the Amazon Bedrock console or the API, and execute it as displayed in the following code:<br>
<br>Clean up<br>
<br>To avoid unwanted charges, complete the actions in this section to clean up your resources.<br>
<br>Delete the Amazon Bedrock Marketplace implementation<br>
<br>If you deployed the design using Amazon Bedrock Marketplace, complete the following actions:<br>
<br>1. On the Amazon Bedrock console, under Foundation designs in the navigation pane, select Marketplace deployments.
2. In the Managed implementations section, find the endpoint you wish to erase.
3. Select the endpoint, and on the Actions menu, select Delete.
4. Verify the endpoint details to make certain you're erasing the appropriate implementation: 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 costs 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 design using Bedrock Marketplace and SageMaker JumpStart. Visit SageMaker JumpStart in SageMaker Studio or Amazon Bedrock Marketplace now to get begun. For more details, describe Use Amazon Bedrock tooling with Amazon SageMaker JumpStart 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](http://git.cxhy.cn) is a Lead Specialist Solutions Architect for Inference at AWS. He assists emerging generative [AI](https://git.jerl.dev) business develop innovative services using AWS services and sped up calculate. Currently, he is concentrated on developing techniques for fine-tuning and optimizing the inference efficiency of big language models. In his spare time, Vivek takes pleasure in treking, viewing movies, and trying various foods.<br>
<br>Niithiyn Vijeaswaran is a Generative [AI](https://git.magicvoidpointers.com) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His area of focus is AWS [AI](https://rpcomm.kr) [accelerators](http://git.fmode.cn3000) (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://vidacibernetica.com) with the Third-Party Model Science group at AWS.<br>
<br>Banu Nagasundaram leads product, engineering, and tactical partnerships for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](http://47.120.57.226:3000) center. She is [passionate](http://charmjoeun.com) about constructing options that help clients accelerate their [AI](https://www.jobassembly.com) journey and unlock organization worth.<br>