johnsondhv629/ivran
1
0
Code Issues Actions Packages Projects Wiki

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

Johnson Salley 2025-02-09 11:16:59 +03:00
commit 38120b82c6
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](http://193.30.123.1883500) to announce 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://ruofei.vip)'s first-generation frontier model, DeepSeek-R1, in addition to the distilled versions ranging from 1.5 to 70 billion parameters to build, experiment, and properly scale your generative [AI](http://touringtreffen.nl) 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 comparable actions to release the distilled versions of the models too.<br>
<br>Overview of DeepSeek-R1<br>
<br>DeepSeek-R1 is a large language design (LLM) established by DeepSeek [AI](http://128.199.161.91:3000) that utilizes reinforcement discovering to improve reasoning capabilities through a multi-stage training procedure from a DeepSeek-V3-Base structure. An essential distinguishing function is its reinforcement knowing (RL) step, which was used to fine-tune the design's actions beyond the basic pre-training and tweak process. By integrating RL, DeepSeek-R1 can adapt more effectively to user feedback and objectives, eventually enhancing both significance and clarity. In addition, DeepSeek-R1 utilizes a chain-of-thought (CoT) method, implying it's geared up to break down complicated questions and [pediascape.science](https://pediascape.science/wiki/User:ChandaRidenour) reason through them in a detailed way. This guided thinking process allows the model to produce more precise, transparent, and [detailed answers](https://studiostilesandtotalfitness.com). This model combines RL-based fine-tuning with CoT capabilities, aiming to create structured responses while focusing on interpretability and user interaction. With its wide-ranging abilities DeepSeek-R1 has caught the industry's attention as a flexible text-generation design that can be integrated into various workflows such as representatives, rational reasoning 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 parameters, allowing efficient inference by routing queries to the most pertinent professional "clusters." This method enables the design to concentrate on different problem domains while maintaining total performance. 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 design. ml.p5e.48 xlarge includes 8 Nvidia H200 GPUs supplying 1128 GB of GPU memory.<br>
<br>DeepSeek-R1 distilled models bring the reasoning abilities of the main R1 design to more efficient architectures based on popular open designs like Qwen (1.5 B, 7B, 14B, and 32B) and Llama (8B and 70B). Distillation refers to a process of smaller sized, more effective models to imitate the habits and reasoning patterns of the bigger DeepSeek-R1 model, [yewiki.org](https://www.yewiki.org/User:LucianaChau79) utilizing it as an instructor [pipewiki.org](https://pipewiki.org/wiki/index.php/User:LawerenceJeanner) design.<br>
<br>You can release DeepSeek-R1 design either through SageMaker JumpStart or Bedrock Marketplace. Because DeepSeek-R1 is an [emerging](https://git.gra.phite.ro) design, we recommend deploying this design with guardrails in location. In this blog site, we will use Amazon Bedrock Guardrails to present safeguards, prevent damaging material, and examine designs against crucial safety criteria. At the time of writing this blog, for DeepSeek-R1 releases on SageMaker JumpStart and Bedrock Marketplace, Bedrock Guardrails supports just the ApplyGuardrail API. You can create several guardrails tailored to various usage cases and use them to the DeepSeek-R1 model, enhancing user experiences and standardizing security controls across your generative [AI](http://94.110.125.250:3000) applications.<br>
<br>Prerequisites<br>
<br>To release the DeepSeek-R1 design, you require access to an ml.p5e circumstances. To examine if you have quotas for P5e, open the Service Quotas console and under AWS Services, choose Amazon SageMaker, and confirm you're using ml.p5e.48 xlarge for endpoint usage. 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, [develop](https://cl-system.jp) a limitation boost request and reach out to your [account team](https://vacaturebank.vrijwilligerspuntvlissingen.nl).<br>
<br>Because you will be deploying this model with [Amazon Bedrock](https://realmadridperipheral.com) Guardrails, make certain you have the appropriate AWS Identity and Gain Access To [Management](https://koubry.com) (IAM) [approvals](https://git.ffho.net) to utilize Amazon Bedrock Guardrails. For instructions, see Set up permissions to use guardrails for material filtering.<br>
<br>Implementing guardrails with the ApplyGuardrail API<br>
<br>Amazon Bedrock Guardrails allows you to introduce safeguards, prevent damaging material, and examine designs against crucial safety criteria. You can execute safety steps for the DeepSeek-R1 design using 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 produce 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 basic 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 to the model for reasoning. After getting the design's output, another guardrail check is applied. If the output passes this final check, it's returned as the 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 happened at the input or output stage. The examples showcased in the following sections demonstrate reasoning 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 foundation designs (FMs) through [Amazon Bedrock](https://centerfairstaffing.com). To gain access to DeepSeek-R1 in Amazon Bedrock, total the following actions:<br>
<br>1. On the Amazon Bedrock console, pick Model catalog under Foundation designs in the navigation pane.
At the time of composing this post, you can use the [InvokeModel API](http://unired.zz.com.ve) to invoke the design. It does not [support Converse](https://accc.rcec.sinica.edu.tw) APIs and other Amazon Bedrock tooling.
2. Filter for DeepSeek as a company and pick the DeepSeek-R1 design.<br>
<br>The design detail page provides vital details about the model's abilities, prices structure, and implementation standards. You can discover detailed usage guidelines, including sample API calls and code snippets for integration. The model supports different text generation tasks, including content development, code generation, and question answering, utilizing its reinforcement discovering optimization and CoT thinking abilities.
The page also consists of implementation choices and licensing details to assist you start with DeepSeek-R1 in your applications.
3. To start utilizing DeepSeek-R1, choose Deploy.<br>
<br>You will be triggered to configure the release 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 Variety of circumstances, get in a variety of circumstances (between 1-100).
6. For example type, select your instance type. For optimal efficiency with DeepSeek-R1, a GPU-based instance type like ml.p5e.48 xlarge is recommended.
Optionally, you can set up sophisticated security and facilities settings, consisting of virtual private cloud (VPC) networking, service function authorizations, and file encryption settings. For a lot of use cases, the default settings will work well. However, for production implementations, you might desire to examine these settings to line up with your organization's security and compliance requirements.
7. Choose Deploy to start utilizing the design.<br>
<br>When the implementation 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 user interface where you can experiment with various prompts and change model parameters like temperature level and optimum length.
When using R1 with Bedrock's InvokeModel and Playground Console, utilize DeepSeek's chat template for optimal results. For example, material for reasoning.<br>
<br>This is an exceptional method to explore the design's thinking and text generation capabilities before incorporating it into your applications. The playground supplies immediate feedback, helping you comprehend how the design reacts to numerous inputs and letting you fine-tune your triggers for optimal outcomes.<br>
<br>You can quickly test the design in the playground through the UI. However, to invoke the released design programmatically with any Amazon Bedrock APIs, you require to get the endpoint ARN.<br>
<br>Run inference using [guardrails](https://svn.youshengyun.com3000) with the deployed DeepSeek-R1 endpoint<br>
<br>The following code example demonstrates how to perform inference using a deployed DeepSeek-R1 model through Amazon Bedrock using the invoke_model and ApplyGuardrail API. You can produce 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 developed the guardrail, use the following code to carry out guardrails. The script initializes the bedrock_runtime customer, sets up inference criteria, and sends out a demand to create text based on 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 options that you can release 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 using either the UI or SDK.<br>
<br>Deploying DeepSeek-R1 model through SageMaker JumpStart uses two practical approaches: [utilizing](http://git.baige.me) the [intuitive SageMaker](https://bootlab.bg-optics.ru) JumpStart UI or implementing programmatically through the SageMaker Python SDK. Let's explore both techniques to assist you choose the method that finest suits your needs.<br>
<br>Deploy DeepSeek-R1 through SageMaker JumpStart UI<br>
<br>Complete the following steps to deploy 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 create a domain.
3. On the SageMaker Studio console, pick JumpStart in the [navigation pane](http://gitlab.dstsoft.net).<br>
<br>The model browser displays available designs, with details like the company name and model capabilities.<br>
<br>4. Search for DeepSeek-R1 to see the DeepSeek-R1 [model card](https://southwales.com).
Each design card reveals crucial details, consisting of:<br>
<br>- Model name
- Provider name
- Task classification (for example, Text Generation).
Bedrock Ready badge (if applicable), showing that this design can be registered with Amazon Bedrock, enabling you to utilize Amazon Bedrock APIs to invoke the design<br>
<br>5. Choose the model card to view the design details page.<br>
<br>The design details page consists of the following details:<br>
<br>- The model name and company details.
Deploy button to release the model.
About and Notebooks tabs with detailed details<br>
<br>The About tab consists of important details, such as:<br>
<br>- Model description.
- License details.
- Technical specifications.
- Usage standards<br>
<br>Before you deploy the model, it's suggested to examine the model details and license terms to validate compatibility with your use case.<br>
<br>6. Choose Deploy to continue with implementation.<br>
<br>7. For Endpoint name, utilize the instantly created name or produce a custom-made one.
8. For example [type ¸](https://vooxvideo.com) select a circumstances type (default: ml.p5e.48 xlarge).
9. For Initial instance count, get in the number of instances (default: 1).
Selecting suitable circumstances types and counts is crucial for cost and performance optimization. Monitor your implementation to change these settings as needed.Under Inference type, [Real-time reasoning](http://sanaldunyam.awardspace.biz) is selected 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 isolation remains in place.
11. Choose Deploy to deploy the model.<br>
<br>The deployment process can take several minutes to finish.<br>
<br>When implementation is total, [trademarketclassifieds.com](https://trademarketclassifieds.com/user/profile/2684771) your endpoint status will change to InService. At this point, the design is all set to accept reasoning requests through the endpoint. You can monitor the release development on the SageMaker console Endpoints page, which will show relevant metrics and status details. When the release is complete, [wiki.lafabriquedelalogistique.fr](https://wiki.lafabriquedelalogistique.fr/Discussion_utilisateur:CliffBresnahan2) you can invoke the model using a SageMaker runtime customer and incorporate it with your applications.<br>
<br>Deploy DeepSeek-R1 using the SageMaker Python SDK<br>
<br>To begin 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 approvals and environment setup. The following is a detailed code example that shows how to deploy and use DeepSeek-R1 for reasoning programmatically. The code for deploying the design is supplied in the Github here. You can clone the notebook and range from SageMaker Studio.<br>
<br>You can run extra requests against the predictor:<br>
<br>Implement guardrails and run reasoning with your SageMaker JumpStart predictor<br>
<br>Similar to Amazon Bedrock, you can likewise utilize 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 revealed in the following code:<br>
<br>Tidy up<br>
<br>To avoid unwanted charges, finish the steps in this area to clean up your [resources](https://thenolugroup.co.za).<br>
<br>Delete the Amazon Bedrock Marketplace release<br>
<br>If you released the design using Amazon Bedrock Marketplace, complete the following actions:<br>
<br>1. On the Amazon Bedrock console, under Foundation [designs](https://soundfy.ebamix.com.br) in the navigation pane, [wiki.whenparked.com](https://wiki.whenparked.com/User:BuddyWager16151) select Marketplace deployments.
2. In the Managed deployments section, locate the [endpoint](https://cl-system.jp) you desire to delete.
3. Select the endpoint, and on the Actions menu, choose Delete.
4. Verify the endpoint details to make certain you're erasing the appropriate deployment: 1. Endpoint name.
2. Model name.
3. Endpoint status<br>
<br>Delete the SageMaker JumpStart predictor<br>
<br>The SageMaker JumpStart model 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 [checked](https://intermilanfansclub.com) 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](http://www.machinekorea.net) now to begin. 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 Getting begun with Amazon SageMaker JumpStart.<br>
<br>About the Authors<br>
<br>Vivek Gangasani is a Lead Specialist [Solutions](http://git.risi.fun) Architect for [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11857434) Inference at AWS. He helps emerging generative [AI](https://talentrendezvous.com) business construct ingenious [options](https://gl.cooperatic.fr) using AWS services and accelerated compute. Currently, he is concentrated on establishing methods for fine-tuning and enhancing the reasoning performance of big language designs. In his leisure time, Vivek delights in hiking, seeing motion pictures, and trying various cuisines.<br>
<br>Niithiyn Vijeaswaran is a Generative [AI](http://121.42.8.157:13000) Specialist Solutions Architect with the Third-Party Model Science team at AWS. His area of focus is AWS [AI](https://talktalky.com) accelerators (AWS Neuron). He holds a Bachelor's degree in Computer [Science](http://www.homeserver.org.cn3000) and Bioinformatics.<br>
<br>Jonathan Evans is a Specialist Solutions Architect dealing with generative [AI](https://job.firm.in) 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](https://gigsonline.co.za) center. She is enthusiastic about developing options that help clients accelerate their [AI](https://deepsound.goodsoundstream.com) journey and unlock business value.<br>