At RSNA 2019 NVIDIA announced updates to the Clara Application Framework that takes healthcare AI to the edge.
Overview
NVIDIA's Clara Application Framework is designed to enhance healthcare AI by bringing it to edge devices. The framework includes various SDKs, such as Clara Train SDK, Clara Deploy SDK, and Clara AGX SDK, enabling developers to build, adapt, and deploy AI-powered workflows efficiently.
What You'll Learn
1
How to utilize the Clara Train SDK for federated learning in healthcare AI applications
2
Why Automatic Mixed Precision (AMP) can enhance training performance significantly
3
How to implement AI-assisted annotation for medical imaging workflows
4
When to use the Clara Pipeline Orchestrator for multi-container environments
Prerequisites & Requirements
- Understanding of AI/ML concepts and healthcare applications
- Familiarity with NVIDIA EGX edge computing platform(optional)
Key Questions Answered
What features does the Clara Train SDK offer for AI development?
The Clara Train SDK provides features like federated learning for privacy-preserving collaboration, Automatic Mixed Precision (AMP) for performance boosts up to 55x faster, and deterministic training for reproducibility. It also includes a Python-based API for advanced users to create custom functions.
How does the Clara Deploy SDK enhance AI deployment?
The Clara Deploy SDK introduces a Pipeline Orchestrator for improved performance in multi-container environments, gRPC-based platform APIs for extensibility, and efficient memory handling to accelerate I/O. These features allow developers to monitor and optimize their AI workflows effectively.
What is the Clara AGX SDK and its purpose?
The Clara AGX SDK is a domain-optimized developer kit designed for NVIDIA's Jetson compute platform. It includes end-to-end samples for modalities like endoscopy and ultrasound, enabling developers to quickly start building applications for smart devices in healthcare.
Key Statistics & Figures
Performance improvement with Automatic Mixed Precision
55x faster
This performance boost applies to training workflows using the Clara Train SDK.
Technologies & Tools
Framework
Clara Application Framework
Used for building, adapting, and deploying AI-powered workflows in healthcare.
Edge Computing Platform
Nvidia Egx
The platform on which the Clara Application Framework operates.
Developer Kit
Clara Agx SDK
A domain-optimized toolkit for developing applications on NVIDIA's Jetson platform.
Key Actionable Insights
1Leverage the federated learning capabilities of the Clara Train SDK to collaborate on AI models without compromising patient data privacy.This is particularly important in healthcare settings where data sensitivity is paramount. By using federated learning, teams can build robust AI models while adhering to privacy regulations.
2Utilize Automatic Mixed Precision (AMP) to significantly speed up your AI training workflows.AMP can provide performance improvements of up to 55x, making it a critical feature for developers looking to optimize their training processes, especially when working with large datasets.
3Implement the Clara Pipeline Orchestrator to manage complex AI workflows efficiently.This tool is essential for developers working in environments with multiple containers and pipelines, as it helps streamline operations and improve overall system performance.
Common Pitfalls
1
Neglecting to implement privacy-preserving techniques in AI workflows can lead to data breaches.
In healthcare, maintaining patient confidentiality is critical. Developers should ensure they utilize features like federated learning to safeguard sensitive data.
Related Concepts
Federated Learning
Automatic Mixed Precision
Ai-assisted Annotation
Nvidia Jetson Platform