candrabenn5514
/
together
1
0
Code Issues Packages Projects Wiki

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

Candra Benn 2025-05-28 01:59:59 +00:00
commit 795d81f8ed
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 models are available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy DeepSeek [AI](https://thathwamasijobs.com)'s first-generation frontier model, DeepSeek-R1, in addition to the distilled variations ranging from 1.5 to 70 billion parameters to develop, experiment, and properly scale your generative [AI](https://ourehelp.com) concepts 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 similar actions to [release](http://212.64.10.1627030) the distilled variations of the designs as well.<br>
<br>Overview of DeepSeek-R1<br>
<br>DeepSeek-R1 is a big language design (LLM) developed by DeepSeek [AI](https://www.pickmemo.com) that utilizes reinforcement finding out to improve thinking capabilities through a multi-stage training process from a DeepSeek-V3-Base foundation. A [crucial distinguishing](http://8.137.58.203000) feature is its support knowing (RL) step, which was used to improve the design's actions beyond the basic pre-training and tweak process. By incorporating RL, DeepSeek-R1 can adjust more efficiently to user feedback and objectives, ultimately enhancing both significance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) method, suggesting it's [equipped](https://rrallytv.com) to break down intricate questions and reason through them in a detailed manner. This directed reasoning process allows the design to [produce](http://150.158.93.1453000) more accurate, transparent, and detailed responses. This model integrates RL-based fine-tuning with CoT abilities, aiming to produce structured responses while focusing on interpretability and user interaction. With its extensive abilities DeepSeek-R1 has actually captured the market's attention as a versatile text-generation model that can be incorporated into different workflows such as representatives, logical thinking and information analysis jobs.<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 specifications, making it possible for effective inference by routing queries to the most appropriate specialist "clusters." This method allows the design to [specialize](https://git.on58.com) in various problem domains while maintaining general efficiency. 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 instance to release the design. ml.p5e.48 xlarge features 8 Nvidia H200 GPUs offering 1128 GB of GPU memory.<br>
<br>DeepSeek-R1 distilled models bring the reasoning 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 Llama (8B and 70B). Distillation refers to a process of training smaller sized, more efficient models to imitate the habits and reasoning patterns of the larger DeepSeek-R1 design, utilizing it as a teacher design.<br>
<br>You can deploy DeepSeek-R1 model either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an emerging model, we [recommend releasing](https://ozoms.com) this model with guardrails in place. In this blog, we will utilize Amazon Bedrock Guardrails to introduce safeguards, avoid hazardous content, and [assess designs](http://media.nudigi.id) against key safety requirements. At the time of writing this blog, for DeepSeek-R1 implementations on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports only the ApplyGuardrail API. You can produce numerous guardrails tailored to different use cases and apply them to the DeepSeek-R1 model, improving user experiences and standardizing security controls across your generative [AI](http://jobs.freightbrokerbootcamp.com) applications.<br>
<br>Prerequisites<br>
<br>To release the DeepSeek-R1 design, [systemcheck-wiki.de](https://systemcheck-wiki.de/index.php?title=Benutzer:MarilynPrice497) you require access to an ml.p5e instance. To inspect if you have quotas for P5e, open the Service Quotas console and under AWS Services, pick Amazon SageMaker, and verify you're utilizing ml.p5e.48 xlarge for [endpoint](https://www.olindeo.net) use. Make certain that you have at least one ml.P5e.48 xlarge circumstances in the AWS Region you are deploying. To ask for a limitation increase, develop a limitation boost request and connect to your account group.<br>
<br>Because you will be releasing this model with [Amazon Bedrock](https://zidra.ru) Guardrails, make certain you have the right AWS Identity and Gain Access To Management (IAM) consents to use Amazon Bedrock Guardrails. For guidelines, see Establish approvals to utilize guardrails for material filtering.<br>
<br>Implementing guardrails with the ApplyGuardrail API<br>
<br>Amazon Bedrock Guardrails permits you to introduce safeguards, prevent harmful material, and [evaluate models](http://git.jishutao.com) against crucial security criteria. You can implement security [measures](https://www.eticalavoro.it) for the DeepSeek-R1 [design utilizing](https://rassi.tv) the Amazon Bedrock [ApplyGuardrail](http://gs1media.oliot.org) API. This enables you to apply guardrails to evaluate user inputs and model responses released 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 includes the following steps: First, the system gets an input for the model. This input is then processed through the [ApplyGuardrail API](https://talentmatch.somatik.io). If the input passes the guardrail check, it's sent out to the design for inference. After getting the model's output, another guardrail check is applied. If the output passes this last check, it's returned as the final outcome. 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 took place at the input or output phase. The examples showcased in the following areas show inference using this API.<br>
<br>Deploy DeepSeek-R1 in [Amazon Bedrock](http://kiwoori.com) 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 console, [select Model](https://heli.today) catalog under Foundation models in the navigation pane.
At the time of writing this post, you can utilize the [InvokeModel API](http://139.9.50.1633000) to invoke the design. It does not support Converse APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a supplier and pick the DeepSeek-R1 model.<br>
<br>The model detail page supplies essential details about the model's abilities, pricing structure, and execution standards. You can find detailed use guidelines, consisting of sample API calls and code bits for integration. The design supports various text generation jobs, content creation, code generation, and concern answering, utilizing its reinforcement discovering optimization and [CoT thinking](https://git.markscala.org) capabilities.
The page likewise includes deployment choices and licensing details to assist you begin with DeepSeek-R1 in your applications.
3. To begin using DeepSeek-R1, choose Deploy.<br>
<br>You will be prompted to set up the deployment details for DeepSeek-R1. The model ID will be pre-populated.
4. For Endpoint name, enter an endpoint name (in between 1-50 alphanumeric characters).
5. For Variety of circumstances, enter a variety of circumstances (in between 1-100).
6. For example type, choose your instance type. For optimum performance with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is advised.
Optionally, you can set up innovative security and infrastructure settings, including virtual private cloud (VPC) networking, service role approvals, and file encryption settings. For many utilize cases, the default settings will work well. However, for [surgiteams.com](https://surgiteams.com/index.php/User:FerdinandYgl) production releases, you might want to examine these settings to line up with your company's security and compliance requirements.
7. Choose Deploy to begin [utilizing](https://git.sunqida.cn) the design.<br>
<br>When the deployment is complete, you can test DeepSeek-R1's abilities straight in the Amazon Bedrock play area.
8. Choose Open in playground to access an interactive interface where you can experiment with different prompts and adjust design criteria like temperature and maximum length.
When using R1 with Bedrock's InvokeModel and Playground Console, use DeepSeek's chat template for optimum outcomes. For example, material for inference.<br>
<br>This is an excellent way to check out the model's thinking and text generation abilities before incorporating it into your applications. The playground offers immediate feedback, assisting you comprehend how the [model responds](https://git.xutils.co) to different inputs and letting you tweak your prompts for ideal outcomes.<br>
<br>You can quickly test the design in the playground through the UI. However, to conjure up the released design programmatically with any Amazon Bedrock APIs, you need to get the endpoint ARN.<br>
<br>Run reasoning utilizing guardrails with the deployed DeepSeek-R1 endpoint<br>
<br>The following code example shows how to perform inference utilizing a deployed DeepSeek-R1 model through Amazon Bedrock utilizing the invoke_model and ApplyGuardrail API. You can produce a guardrail utilizing the Amazon Bedrock console or the API. For the example code to produce the guardrail, see the GitHub repo. After you have actually created the guardrail, utilize the following code to carry out guardrails. The [script initializes](http://101.43.151.1913000) the bedrock_runtime client, configures reasoning parameters, and sends out a demand to create 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](https://ayjmultiservices.com) ML services that you can release with just a couple of clicks. With SageMaker JumpStart, you can tailor pre-trained models to your usage case, with your information, and deploy them into production using either the UI or SDK.<br>
<br>Deploying DeepSeek-R1 design through SageMaker JumpStart provides 2 convenient techniques: utilizing the instinctive SageMaker JumpStart UI or carrying out [programmatically](https://iklanbaris.id) through the SageMaker Python SDK. Let's explore both approaches to assist you pick the technique that finest fits 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, pick Studio in the navigation pane.
2. First-time users will be prompted to create a domain.
3. On the SageMaker Studio console, select JumpStart in the navigation pane.<br>
<br>The model browser shows available designs, with details like the service provider name and design capabilities.<br>
<br>4. Search for DeepSeek-R1 to view the DeepSeek-R1 design card.
Each design 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 design can be signed up with Amazon Bedrock, allowing you to use Amazon Bedrock APIs to conjure up the design<br>
<br>5. Choose the model card to see the design details page.<br>
<br>The model details page consists of the following details:<br>
<br>- The model name and [provider details](http://gs1media.oliot.org).
Deploy button to deploy the design.
About and Notebooks tabs with detailed details<br>
<br>The About tab includes crucial details, such as:<br>
<br>- Model description.
- License details.
- Technical specifications.
- Usage guidelines<br>
<br>Before you deploy the design, it's advised to examine the design details and license terms to validate compatibility with your usage case.<br>
<br>6. Choose Deploy to [continue](https://54.165.237.249) with implementation.<br>
<br>7. For Endpoint name, use the automatically produced name or create a customized one.
8. For Instance type ¸ select a circumstances type (default: ml.p5e.48 xlarge).
9. For Initial instance count, enter the number of instances (default: 1).
Selecting appropriate instance types and counts is crucial for cost and efficiency optimization. Monitor your release to adjust these settings as needed.Under Inference type, Real-time inference is chosen by default. This is enhanced for sustained traffic and low latency.
10. Review all setups for precision. For this design, we strongly recommend sticking to SageMaker JumpStart default settings and making certain that network seclusion remains in place.
11. Choose Deploy to [release](http://124.16.139.223000) the design.<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 moment, the design is ready to accept inference requests through the endpoint. You can keep track of the release development on the SageMaker console Endpoints page, which will display appropriate metrics and status details. When the implementation is complete, you can invoke the design utilizing a SageMaker runtime customer and incorporate it with your applications.<br>
<br>Deploy DeepSeek-R1 using the [SageMaker Python](http://famedoot.in) SDK<br>
<br>To start with DeepSeek-R1 utilizing the SageMaker Python SDK, you will need to install the SageMaker Python SDK and make certain you have the [essential AWS](https://cello.cnu.ac.kr) approvals and [wiki.dulovic.tech](https://wiki.dulovic.tech/index.php/User:MaryanneShumack) environment setup. The following is a detailed code example that demonstrates how to deploy and utilize DeepSeek-R1 for inference programmatically. The code for deploying the design is provided in the Github here. You can clone the notebook and range from SageMaker Studio.<br>
<br>You can run additional requests against the predictor:<br>
<br>Implement guardrails and run inference 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 or the API, and [execute](http://51.75.64.148) it as shown in the following code:<br>
<br>Clean up<br>
<br>To avoid unwanted charges, finish the actions in this area to tidy up your resources.<br>
<br>Delete the Amazon Bedrock Marketplace release<br>
<br>If you deployed the model utilizing Amazon Bedrock Marketplace, total the following actions:<br>
<br>1. On the Amazon Bedrock console, under Foundation models in the navigation pane, select Marketplace deployments.
2. In the [Managed releases](https://adventuredirty.com) area, locate the endpoint you desire to erase.
3. Select the endpoint, and on the Actions menu, choose Delete.
4. Verify the endpoint details to make certain you're erasing the correct 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.<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 get going. For more details, refer to Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models, SageMaker JumpStart pretrained models, Amazon SageMaker JumpStart Foundation 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 assists emerging generative [AI](https://wiki.ragnaworld.net) business build ingenious solutions utilizing AWS services and accelerated calculate. Currently, he is focused on developing techniques for fine-tuning and optimizing the reasoning efficiency of big [language designs](https://app.zamow-kontener.pl). In his downtime, Vivek delights in hiking, seeing motion pictures, and attempting various cuisines.<br>
<br>Niithiyn Vijeaswaran is a Generative [AI](https://vieclam.tuoitrethaibinh.vn) Specialist Solutions Architect with the Third-Party Model Science group at AWS. His area of focus is AWS [AI](http://123.249.110.128:5555) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer Science and Bioinformatics.<br>
<br>Jonathan Evans is a Professional Solutions Architect working on generative [AI](http://personal-view.com) with the Third-Party Model Science group at AWS.<br>
<br>Banu Nagasundaram leads item, engineering, and strategic collaborations for Amazon SageMaker JumpStart, SageMaker's artificial intelligence and generative [AI](http://minority2hire.com) center. She is [enthusiastic](http://8.139.7.16610880) about constructing options that help customers accelerate their [AI](https://jobs.campus-party.org) journey and unlock business value.<br>