Build Scale and deploy Generative AI models using NVIDIA NeMo On AWS EKS | AWS OnAir-S05

AWS Events
13 Jun 202421:22

TLDRThe AWS OnAir episode showcases NVIDIA NeMo, a platform for creating custom generative AI applications. Product Marketing Manager Nal explains NeMo's capabilities, emphasizing its end-to-end approach from data acquisition to deployment. A live demo by Anker and Wien illustrates deploying NeMo on AWS EKS for distributed training of large language models, highlighting the efficiency of using NVIDIA GPUs and the ease of orchestration with Kubernetes. The discussion underlines the transformative impact of generative AI on various industries and the importance of investing in such technologies for business growth.

Takeaways

  • 😀 NVIDIA NeMo is an end-to-end platform for creating custom generative AI applications, which can be deployed on premise, cloud, or hybrid environments.
  • 🌟 Generative AI is transforming businesses by generating content such as text, design, images, and videos, impacting various fields including text summarization, translation, and coding assistance.
  • 📈 Companies investing in AI are 2.6 times more likely to see a revenue increase of 10% or more, highlighting the significant impact of AI on business growth.
  • 🔧 NeMo facilitates the entire AI model development process, from data acquisition and curation to model customization, information retrieval, and governance.
  • 🚀 NVIDIA NeMo accelerates performance on NVIDIA GPUs, making it easier to build and deploy generative AI applications efficiently.
  • 🔍 NeMo's data preparation involves cleaning and curating data to ensure quality and relevance for training AI models.
  • 🛠️ Model customization in NeMo allows for tailoring AI models for specific tasks such as question answering or summarization.
  • 📚 Information retrieval in NeMo ensures that AI models have access to up-to-date data, enabling accurate and relevant responses.
  • 🏢 Governance in NeMo involves setting guidelines to ensure AI models operate within their intended scope and do not deviate from their training.
  • 🔗 NVIDIA NeMo integrates with NVIDIA Magnum, providing a pre-optimized environment for easy deployment of AI applications.
  • 📚 The introduction of NVIDIA microservices for different stages of AI model development, such as data curation and customization, is in Early Access and offers a modular approach to building AI applications.

Q & A

  • What is NVIDIA NeMo and what does it stand for?

    -NVIDIA NeMo stands for 'Neural Modules' and it is an end-to-end platform for creating custom generative AI applications that can be deployed on premises, in the cloud, or in a hybrid environment.

  • How does generative AI transform businesses?

    -Generative AI transforms businesses by automating tasks such as text generation, summarizing emails, writing marketing copies, and even translating languages, which can lead to increased efficiency and revenue growth.

  • What is the significance of investing in generative AI for companies?

    -Companies that invest in generative AI are 2.6 times more likely to increase their revenue by 10% or more, indicating the potential for significant business growth.

  • What are some of the applications of generative AI mentioned in the script?

    -Some applications of generative AI mentioned include text generation, summarization, translation, coding assistance, and visual content creation such as images and videos.

  • What is the meaning of 'end-to-end' in the context of AI model development?

    -In AI model development, 'end-to-end' refers to the complete process from data acquisition to model customization, information retrieval, and deployment, which NVIDIA NeMo supports.

  • What is the role of data curation in training generative AI models?

    -Data curation involves cleaning and preparing the data for training generative AI models. It includes removing duplicates and sensitive information to ensure the quality and safety of the training data.

  • What is the purpose of model customization in AI?

    -Model customization tailors a pre-trained AI model to specific needs, such as question answering, summarization, or other specialized use cases, making the model more relevant and accurate for its intended application.

  • Why is information retrieval important for generative AI models?

    -Information retrieval is important because it allows generative AI models to access current and relevant data that may not be included in their original training data, ensuring the model's responses are up-to-date and accurate.

  • What is the significance of governance in deploying generative AI models?

    -Governance ensures that generative AI models operate within defined parameters and do not deviate from their intended use, protecting against misuse and maintaining the integrity of the model's output.

  • How does NVIDIA NeMo support deployment of generative AI applications?

    -NVIDIA NeMo supports deployment through NVIDIA Magnum, which provides optimized and pre-configured environments for running generative AI applications, making it easy to deploy on various platforms.

  • What is the role of microservices in NVIDIA NeMo?

    -Microservices in NVIDIA NeMo represent different stages of the AI development process, such as data curation and model customization. They are currently in Early Access and allow for modular and flexible development of AI applications.

Outlines

00:00

🌟 Introduction to NVIDIA Nemo and Generative AI

In this segment, the host welcomes a guest named Nal, a product marketing manager for NVIDIA Nemo, a platform for creating generative AI applications. They discuss Nal's background, their shared connection to Pittsburgh, and Nal's role at NVIDIA. Nal provides a brief on what Nemo is, mentioning it as an end-to-end platform for creating custom generative AI applications. The conversation touches on the transformative impact of generative AI on various industries, including text generation, translation, coding, and visual content creation. The host encourages audience interaction by asking about their experiences with generative AI.

05:02

📈 Breakdown of AI Model Development with NVIDIA Nemo

Nal elaborates on the process of AI model development, focusing on the steps involved from data acquisition to deployment. He explains the importance of data curation and cleaning, the concept of model customization, and the necessity of information retrieval to ensure models are up-to-date. Nal introduces the term 'end-to-end' to describe Nemo's comprehensive approach, which includes data preparation, model training, and deployment stages. He also highlights the benefits of NVIDIA's platform, emphasizing its ability to accelerate performance on NVIDIA GPUs and its flexibility in deployment across various environments.

10:03

🤖 Live Demo of Distributed Training with NVIDIA Nemo on Amazon EKS

The script transitions to a live demonstration by Aner and Wien, who introduce themselves as NVIDIA and AWS team members, respectively. They discuss the process of setting up an EKS (Elastic Kubernetes Service) cluster for distributed training of large language models, emphasizing the need for high computational power. Aner explains the configuration for creating an EKS cluster with GPU instances, while Wien provides insights into Amazon EKS as a managed Kubernetes platform. The demonstration includes creating a shared EFS (Elastic File System) for storage and highlights the use of the NVIDIA GPU operator for seamless setup.

15:05

🚀 Executing Distributed Training with NVIDIA Nemo

Continuing the live demo, the presenters guide the audience through the process of executing distributed training using NVIDIA Nemo. They detail the steps for setting up the EKS cluster, configuring the shared file system, and launching the training job. The script mentions the use of the 'eksctl' command-line tool for cluster creation and the importance of an EFA (Elastic Fabric Adapter) for optimized data sharing. The demonstration concludes with the successful launch of the training job, showing the Nemo application in action.

20:06

🔗 Conclusion and Resources for NVIDIA Nemo

In the final segment, the host wraps up the discussion by summarizing the key points of the demo and emphasizing the ease of setting up distributed training with NVIDIA Nemo. The presenters provide links for further information and resources, encouraging the audience to explore and try out Nemo for their AI applications. The conversation highlights the accessibility and support provided for users interested in leveraging Nemo for generative AI development.

Mindmap

Keywords

💡NVIDIA NeMo

NVIDIA NeMo, which stands for 'Neural Modules,' is an open-source platform developed by NVIDIA for building and training generative AI models. It is designed to facilitate the creation of custom generative AI applications, such as text, image, and video generation. In the video, NeMo is highlighted as an end-to-end platform that supports the development process from data acquisition to model deployment, optimized for performance on NVIDIA GPUs.

💡Generative AI

Generative AI refers to artificial intelligence systems that can generate new content, such as text, images, or videos, that are not simply alterations of existing content. In the script, the transformative impact of generative AI on various industries is discussed, including its use in summarizing emails, writing marketing copies, and even aiding in coding by suggesting fixes and functions.

💡AWS EKS

AWS EKS, or Amazon Elastic Kubernetes Service, is a managed service provided by Amazon Web Services that makes it easy to run Kubernetes, an open-source container orchestration system. The script mentions EKS in the context of deploying and scaling generative AI models using NVIDIA NeMo, emphasizing its role in facilitating distributed training across multiple nodes with GPU support.

💡Distributed Training

Distributed training is a method of training machine learning models across multiple processors or nodes to accelerate the process. In the video, distributed training is used to train large-scale generative AI models more efficiently by leveraging the computational power of multiple GPUs, which is particularly relevant when using NVIDIA NeMo on AWS EKS.

💡Elastic Fabric Adapter (EFA)

Elastic Fabric Adapter is a network interface that is designed to improve the performance of high-bandwidth, low-latency applications, such as distributed machine learning training. The script mentions enabling EFA in the EKS cluster to enhance the data sharing process during distributed training of generative AI models.

💡Inference

Inference in the context of AI refers to the process of making predictions or decisions based on a trained model. While the script does not explicitly define inference, it is implied in the discussion of deploying and running AI models after they have been trained, which is a critical step in applying generative AI in real-world applications.

💡NVIDIA Microservices

NVIDIA Microservices, as mentioned in the script, are a collection of modular services designed to handle different stages of AI model development, such as data curation and model customization. These microservices are part of NVIDIA's strategy to streamline the process of building and deploying AI applications.

💡Data Acquisition

Data acquisition is the process of gathering and collecting data required for training AI models. In the video, it is one of the initial steps in the AI model development process, where data from various sources, such as public internet data or proprietary company data, is obtained and prepared for training generative AI models.

💡Model Customization

Model customization involves tailoring a pre-trained AI model to specific needs or use cases. In the script, it is discussed as a part of the AI development process where a model is fine-tuned for tasks such as question answering or summarization, making it suitable for particular applications.

💡Information Retrieval

Information retrieval is the process of accessing and incorporating up-to-date data into an AI model to ensure its responses are current and accurate. The script discusses this as a crucial step for models like LLMs (Large Language Models) to provide relevant and updated information to end-users.

💡Gating

Gating in the context of AI models refers to the implementation of restrictions or guidelines to ensure the model's responses are appropriate and within the intended use case. The script mentions gating as a critical step to prevent AI models from accessing or revealing sensitive or irrelevant information.

Highlights

Introduction to NVIDIA NeMo, an end-to-end generative AI platform.

Nal, a Product Marketing Manager for NVIDIA NeMo, discusses the platform's capabilities.

The impact of generative AI on various industries, including text generation and summarization.

Personal anecdotes about the transformative effect of AI translation on non-English speakers.

The potential of generative AI in coding, making it easier to debug and create functions.

The rise of visual content creation using generative AI for images and videos.

Statistical data showing companies investing in AI are more likely to see significant revenue increases.

Explanation of the term 'end-to-end' in AI model development, covering data preparation to deployment.

Details on data acquisition and curation for training generative AI models.

Customization of AI models for specific tasks such as question answering or summarization.

Information retrieval methods to keep AI models updated with current data.

The importance of governance in AI to ensure models operate within their intended parameters.

NVIDIA NeMo's support for deploying generative AI applications on various platforms, including on-premise and cloud.

Introduction of NVIDIA's microservices for different stages of AI model development.

A live demo of deploying NVIDIA NeMo on AWS EKS for distributed training of generative AI models.

Explanation of Amazon EKS as a fully managed Kubernetes service for orchestrating containerized jobs.

Technical demonstration of setting up an EKS cluster and EFS file system for distributed training.

How to configure and initiate a distributed training session using NVIDIA NeMo on EKS.

Summary of the process for setting up and running distributed training with NVIDIA NeMo on AWS EKS.

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