Simplifying AI Application Development: A Guide to NVIDIA Cloud Native Stack
Summary
Developing AI applications can be a complex and time-consuming process. To address this challenge, NVIDIA has created the Cloud Native Stack (CNS), an open-source reference architecture designed to simplify AI application development. This article explores the key features and benefits of CNS, including its ability to run and test containerized GPU-accelerated applications, and its compatibility with enterprise Kubernetes-based platforms.
Understanding the Challenges of AI Application Development
Developing AI applications requires scalable, efficient, and flexible infrastructure. Traditional infrastructure often struggles to meet the demands of modern AI workloads, leading to bottlenecks in development and deployment processes. To overcome these challenges, organizations are turning to cloud-native technologies.
Introducing NVIDIA Cloud Native Stack
NVIDIA Cloud Native Stack (CNS) is an open-source reference architecture designed to simplify AI application development. CNS provides a validated software stack that includes various versioned software components, such as Kubernetes, Helm, Containerd, NVIDIA GPU Operator, and NVIDIA Network Operator. This stack enables developers to run and test containerized GPU-accelerated applications orchestrated by Kubernetes.
Key Features of CNS
- Kubernetes: CNS includes Kubernetes, a container orchestration system that automates the deployment, scaling, and management of containerized applications.
- Helm: Helm is a package manager for Kubernetes that simplifies the installation and management of applications.
- Containerd: Containerd is a container runtime that provides a lightweight and efficient way to run containers.
- NVIDIA GPU Operator: The NVIDIA GPU Operator simplifies the ability to run AI workloads on cloud-native technologies, providing an easy way to experience the latest NVIDIA features.
- NVIDIA Network Operator: The NVIDIA Network Operator provides a simple way to manage and configure network resources for AI workloads.
Optional Add-On Tools
CNS also includes optional add-on tools, such as:
- microK8s: A lightweight, fast, and secure way to run Kubernetes.
- Storage: Provides persistent storage for AI applications.
- LoadBalancer: Enables load balancing for AI applications.
- Monitoring: Provides monitoring capabilities for AI applications.
- KServe: A Kubernetes-based platform for serving machine learning models.
Benefits of Using CNS
Using CNS provides several benefits, including:
- Simplified AI Application Development: CNS abstracts away much of the complexity involved in setting up and maintaining AI environments, enabling developers to focus on prototyping and testing AI applications.
- Compatibility with Enterprise Kubernetes-Based Platforms: Applications developed on CNS are assured to be compatible with deployments based on NVIDIA AI Enterprise, providing a smooth transition from development to production.
- Flexibility: CNS can be deployed on bare metal, cloud, or VM-based environments.
Deploying NVIDIA NIM with KServe
Deploying NVIDIA NIM on CNS with KServe simplifies the development process and ensures that AI workflows are scalable, resilient, and easy to manage. By using Kubernetes and KServe, developers can seamlessly integrate NVIDIA NIM with other microservices, creating a robust and efficient AI application development platform.
Steps to Deploy NIM on KServe
- Install CNS with KServe: Follow the instructions to install CNS with KServe.
- Enable Storage and Monitoring: Enable the storage and monitoring options to monitor the performance of the deployed model and scale the services as needed.
- Deploy NIM on KServe: Follow the steps for deploying NIM on KServe.
Table: Key Features of CNS
| Feature | Description |
|---|---|
| Kubernetes | Container orchestration system |
| Helm | Package manager for Kubernetes |
| Containerd | Container runtime |
| NVIDIA GPU Operator | Simplifies running AI workloads on cloud-native technologies |
| NVIDIA Network Operator | Manages and configures network resources for AI workloads |
| microK8s | Lightweight, fast, and secure way to run Kubernetes |
| Storage | Provides persistent storage for AI applications |
| LoadBalancer | Enables load balancing for AI applications |
| Monitoring | Provides monitoring capabilities for AI applications |
| KServe | Kubernetes-based platform for serving machine learning models |
Table: Benefits of Using CNS
| Benefit | Description |
|---|---|
| Simplified AI Application Development | Abstracts away complexity, enabling developers to focus on prototyping and testing AI applications |
| Compatibility with Enterprise Kubernetes-Based Platforms | Assures compatibility with deployments based on NVIDIA AI Enterprise |
| Flexibility | Can be deployed on bare metal, cloud, or VM-based environments |
Conclusion
NVIDIA Cloud Native Stack is a powerful tool for simplifying AI application development. By providing a validated software stack and abstracting away complexity, CNS enables developers to focus on driving innovation in their AI initiatives. With its flexibility, scalability, and ease of use, CNS is an ideal choice for organizations of all sizes looking to accelerate AI innovation.