ggfthaddeus65
/
remote-life
1
0
Code Issues Packages Projects Wiki

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

master
Thaddeus Stricklin 2025-02-06 21:04:25 +00:00
commit c14505efd2
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 announce that DeepSeek R1 distilled Llama and Qwen designs are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](http://b-ways.sakura.ne.jp)'s first-generation [frontier](https://git.ipmake.me) model, DeepSeek-R1, together with the distilled versions ranging from 1.5 to 70 billion specifications to construct, experiment, and responsibly scale your generative [AI](https://www.sportpassionhub.com) concepts on AWS.<br>
<br>In this post, we demonstrate how to get started with DeepSeek-R1 on [Amazon Bedrock](http://gamebizdev.ru) Marketplace and [SageMaker JumpStart](https://git.unicom.studio). You can follow similar actions to release the distilled versions of the designs as well.<br>
<br>Overview of DeepSeek-R1<br>
<br>DeepSeek-R1 is a big language design (LLM) established by DeepSeek [AI](https://gitea.pi.cr4.live) that uses reinforcement learning to boost thinking capabilities through a multi-stage training process from a DeepSeek-V3[-Base foundation](https://www.ieo-worktravel.com). A crucial distinguishing feature is its support knowing (RL) step, which was utilized to refine the model's actions beyond the standard pre-training and fine-tuning procedure. By integrating RL, DeepSeek-R1 can adjust better to user feedback and goals, ultimately improving both importance and clearness. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) approach, [meaning](http://47.242.77.180) it's equipped to break down intricate inquiries and reason through them in a detailed manner. This assisted reasoning process permits the design to produce more accurate, transparent, and detailed answers. This [design integrates](https://activitypub.software) RL-based fine-tuning with CoT abilities, aiming to produce structured actions while concentrating on interpretability and user [interaction](http://114.111.0.1043000). With its wide-ranging capabilities DeepSeek-R1 has recorded the industry's attention as a flexible text-generation design that can be integrated into various workflows such as representatives, rational thinking and information analysis jobs.<br>
<br>DeepSeek-R1 utilizes a Mixture of Experts (MoE) architecture and is 671 billion specifications in size. The MoE architecture permits activation of 37 billion specifications, enabling effective inference by routing inquiries to the most relevant specialist "clusters." This technique allows the model to specialize in various problem domains while maintaining total efficiency. DeepSeek-R1 needs at least 800 GB of HBM memory in FP8 format for reasoning. In this post, we will use an ml.p5e.48 xlarge circumstances to release the design. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.<br>
<br>DeepSeek-R1 distilled designs bring the thinking abilities of the main R1 design to more effective architectures based on popular open models like Qwen (1.5 B, 7B, 14B, and 32B) and [wiki.vst.hs-furtwangen.de](https://wiki.vst.hs-furtwangen.de/wiki/User:DomingaEspinoza) Llama (8B and 70B). Distillation refers to a process of training smaller sized, more efficient designs to mimic the behavior and thinking patterns of the bigger DeepSeek-R1 design, using it as an instructor design.<br>
<br>You can release DeepSeek-R1 design either through SageMaker JumpStart or [Bedrock](http://47.107.80.2363000) Marketplace. Because DeepSeek-R1 is an [emerging](https://chumcity.xyz) model, we advise deploying this design with guardrails in location. In this blog site, we will utilize Amazon Bedrock Guardrails to introduce safeguards, prevent damaging content, and examine designs against essential security criteria. At the time of composing this blog, for DeepSeek-R1 deployments on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can create multiple guardrails tailored to various use cases and use them to the DeepSeek-R1 design, [improving](http://git.attnserver.com) user experiences and standardizing security controls throughout your generative [AI](http://www.evmarket.co.kr) applications.<br>
<br>Prerequisites<br>
<br>To deploy the DeepSeek-R1 design, you need access to an ml.p5e [instance](https://newborhooddates.com). To inspect 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 deploying. To request a limitation boost, produce a limitation boost demand and connect to your account group.<br>
<br>Because you will be deploying this design with Amazon Bedrock Guardrails, make certain you have the right AWS Identity and Gain Access To Management (IAM) authorizations to utilize Amazon Bedrock Guardrails. For directions, see Set up [consents](https://www.suntool.top) to use guardrails for material filtering.<br>
<br>Implementing guardrails with the ApplyGuardrail API<br>
<br>Amazon Bedrock Guardrails enables you to introduce safeguards, avoid damaging material, and examine models against essential safety requirements. You can execute safety procedures for [fishtanklive.wiki](https://fishtanklive.wiki/User:DouglasWhitney) the DeepSeek-R1 model utilizing the Amazon Bedrock ApplyGuardrail API. This permits you to apply guardrails to examine user inputs and model responses released on Amazon Bedrock Marketplace and SageMaker JumpStart. 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.<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 design for reasoning. After getting the model's output, another guardrail check is applied. If the output passes this final check, it's returned as the [outcome](https://avpro.cc). However, if either the input or output is intervened by the guardrail, a message is returned suggesting the nature of the intervention and whether it occurred at the input or output stage. The examples showcased in the following areas demonstrate 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, emerging, and specialized structure designs (FMs) through Amazon Bedrock. To gain access to DeepSeek-R1 in Amazon Bedrock, total the following steps:<br>
<br>1. On the [Amazon Bedrock](http://ncdsource.kanghehealth.com) console, pick Model brochure under Foundation designs in the navigation pane.
At the time of composing this post, you can use the InvokeModel API to invoke the model. It does not support Converse APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a supplier and select the DeepSeek-R1 model.<br>
<br>The design detail page provides essential details about the design's abilities, rates structure, and implementation guidelines. You can discover detailed use directions, consisting of sample API calls and code snippets for integration. The design supports different text generation jobs, including content development, code generation, and concern answering, utilizing its support discovering optimization and CoT thinking capabilities.
The page likewise includes implementation choices and licensing details to assist you get begun with DeepSeek-R1 in your applications.
3. To begin [utilizing](https://superappsocial.com) DeepSeek-R1, [select Deploy](https://pl.velo.wiki).<br>
<br>You will be triggered to set up the deployment details for DeepSeek-R1. The design ID will be pre-populated.
4. For Endpoint name, enter an endpoint name (in between 1-50 [alphanumeric](http://stream.appliedanalytics.tech) characters).
5. For Number of instances, enter a number of instances (in between 1-100).
6. For Instance type, pick your instance type. For optimum performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is recommended.
Optionally, you can configure sophisticated security and facilities settings, including virtual private cloud (VPC) networking, service role approvals, and file encryption settings. For many use cases, the default settings will work well. However, for production releases, you might want to review these settings to line up with your company's security and compliance requirements.
7. Choose Deploy to begin utilizing the design.<br>
<br>When the release is total, you can test DeepSeek-R1's capabilities straight in the Amazon Bedrock playground.
8. Choose Open in playground to access an interactive interface where you can experiment with different prompts and change model specifications like temperature and maximum length.
When utilizing R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for optimum results. For instance, content for inference.<br>
<br>This is an exceptional way to explore the model's reasoning and text generation capabilities before integrating it into your applications. The playground offers immediate feedback, helping you comprehend how the model reacts to numerous inputs and letting you tweak your prompts for ideal results.<br>
<br>You can quickly check the design in the play area through the UI. However, to conjure up the released model programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.<br>
<br>Run inference using with the released DeepSeek-R1 endpoint<br>
<br>The following code example shows how to carry out inference using a released DeepSeek-R1 model 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. After you have actually produced the guardrail, use the following code to execute guardrails. The script initializes the bedrock_runtime customer, sets up reasoning parameters, 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 (ML) hub with FMs, built-in algorithms, and prebuilt ML services that you can release with simply a few clicks. With SageMaker JumpStart, you can tailor pre-trained designs to your usage case, with your information, and deploy them into production utilizing either the UI or SDK.<br>
<br>Deploying DeepSeek-R1 design through SageMaker JumpStart offers 2 convenient methods: using the instinctive SageMaker JumpStart UI or carrying out programmatically through the SageMaker Python SDK. Let's check out both approaches to help you choose the method that best matches your requirements.<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, select 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 internet browser displays available models, with details like the company name and model capabilities.<br>
<br>4. Search for DeepSeek-R1 to see the DeepSeek-R1 design card.
Each design card reveals key details, consisting of:<br>
<br>- Model name
- [Provider](https://hitechjobs.me) name
- Task category (for example, Text Generation).
Bedrock Ready badge (if applicable), showing that this design can be signed up with Amazon Bedrock, allowing you to utilize Amazon Bedrock APIs to conjure up the model<br>
<br>5. Choose the model card to view the model details page.<br>
<br>The model details page includes the following details:<br>
<br>- The model name and service provider details.
[Deploy button](http://49.235.130.76) to deploy the model.
About and Notebooks tabs with detailed details<br>
<br>The About tab consists of crucial details, such as:<br>
<br>- Model description.
- License details.
- Technical specifications.
- Usage standards<br>
<br>Before you release the model, it's suggested to examine the model details and license terms to validate compatibility with your usage case.<br>
<br>6. Choose Deploy to continue with deployment.<br>
<br>7. For Endpoint name, utilize the immediately produced name or create a custom one.
8. For example type ¸ pick a circumstances type (default: ml.p5e.48 xlarge).
9. For Initial circumstances count, go into the number of circumstances (default: 1).
Selecting proper circumstances types and counts is important for cost and performance optimization. Monitor your release to adjust these settings as needed.Under Inference type, Real-time inference is picked by default. This is optimized for [sustained traffic](https://www.cbl.aero) and low latency.
10. Review all setups for precision. For this model, we highly suggest sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in place.
11. Choose Deploy to release the model.<br>
<br>The implementation process can take a number of minutes to complete.<br>
<br>When implementation is total, your endpoint status will alter to InService. At this moment, the model is all set to accept inference demands through the [endpoint](https://thedatingpage.com). You can keep an eye on the release progress on the SageMaker console Endpoints page, which will show relevant metrics and status details. When the release is total, you can conjure up the model utilizing a [SageMaker runtime](https://becalm.life) customer and incorporate it with your applications.<br>
<br>Deploy DeepSeek-R1 utilizing the SageMaker Python SDK<br>
<br>To get going with DeepSeek-R1 using the SageMaker Python SDK, you will need to set up the SageMaker Python SDK and make certain you have the needed AWS consents and environment setup. The following is a detailed code example that shows how to release and utilize DeepSeek-R1 for reasoning programmatically. The code for releasing the model is [offered](https://picturegram.app) in the Github here. You can clone the note pad and range from SageMaker Studio.<br>
<br>You can run additional requests against the predictor:<br>
<br>Implement guardrails and run reasoning with your SageMaker JumpStart predictor<br>
<br>Similar to Amazon Bedrock, [hb9lc.org](https://www.hb9lc.org/wiki/index.php/User:Margareta19E) 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 implement it as [displayed](http://94.130.182.1543000) in the following code:<br>
<br>Clean up<br>
<br>To prevent unwanted charges, complete the steps in this section to clean up your [resources](https://git.privateger.me).<br>
<br>Delete the Amazon Bedrock Marketplace implementation<br>
<br>If you deployed the model using Amazon Bedrock Marketplace, complete the following actions:<br>
<br>1. On the Amazon Bedrock console, under Foundation models in the navigation pane, pick Marketplace releases.
2. In the Managed implementations section, find the endpoint you desire to erase.
3. Select the endpoint, and on the Actions menu, select Delete.
4. Verify the endpoint details to make certain you're deleting 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 deployed will sustain costs 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.<br>
<br>Conclusion<br>
<br>In this post, we explored how you can access and deploy the DeepSeek-R1 design utilizing 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 designs, SageMaker JumpStart pretrained designs, Amazon SageMaker JumpStart Foundation Models, Amazon Bedrock Marketplace, and Beginning 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](http://www.chinajobbox.com) companies build ingenious options using AWS services and accelerated calculate. Currently, he is focused on developing methods for [wavedream.wiki](https://wavedream.wiki/index.php/User:LanSeyler65095) fine-tuning and optimizing the inference efficiency of large language designs. In his totally free time, Vivek enjoys hiking, seeing films, and trying different cuisines.<br>
<br>Niithiyn Vijeaswaran is a Generative [AI](https://git.alexhill.org) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His area of focus is AWS [AI](http://154.9.255.198:3000) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.<br>
<br>Jonathan Evans is an Expert Solutions Architect working on generative [AI](https://wp.nootheme.com) with the Third-Party Model Science group at AWS.<br>
<br>Banu Nagasundaram leads item, engineering, and strategic partnerships for Amazon [SageMaker](https://www.grandtribunal.org) JumpStart, SageMaker's artificial intelligence and generative [AI](http://162.14.117.234:3000) center. She is passionate about [developing services](http://47.242.77.180) that assist consumers accelerate their [AI](https://g.6tm.es) journey and unlock service value.<br>