Building and Deploying a Face Mask Detection Application Using NGC Collections

AI workflows are complex. Building an AI application is no trivial task, as it takes various stakeholders with domain expertise to develop and deploy the…

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NVIDIA
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Akhil Docca
•16 min read•advanced•
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DockerHelmResNetTransfer Learning

Overview

This article details the process of building and deploying a face mask detection application using NVIDIA's NGC Collections. It covers the necessary components, workflows, and tools required to create an AI application from development to deployment.

What You'll Learn

1

How to use NGC Collections to streamline AI application development

2

How to fetch and configure the TLT container for model training

3

How to prepare datasets for training a face mask detection model

4

How to prune and retrain a model for optimized performance

5

How to deploy a mask detection application using Helm charts

Prerequisites & Requirements

  • Docker runtime installed and configured
  • NGC CLI installed and configured
  • Basic understanding of AI model training and deployment(optional)

Key Questions Answered

What steps are involved in building a face mask detection application?
The article outlines a comprehensive workflow that includes fetching the TLT container, preparing datasets, training the model using transfer learning, pruning the model, and deploying it using Helm charts. Each step is crucial for ensuring the application is effective and efficient.
How can I prepare datasets for training with TLT?
Datasets must be converted to KITTI format using helper scripts from the GitHub repository. The article provides specific commands to download and prepare the datasets, ensuring they meet the required structure for training.
What is the process for pruning a trained model?
Pruning involves removing unnecessary weights from the model to optimize it for low-latency inference. The article details the parameters needed for pruning and provides commands to execute this process effectively.
How do I publish a model to an NGC private registry?
The article explains the steps to upload the final model to the NGC private registry, allowing team members to access it for their use cases. It emphasizes the importance of this step for collaborative development.

Technologies & Tools

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AI/ML Framework
Ngc Collections
Used to access GPU-optimized software for building AI applications.
Containerization
Docker
Used to run the TLT container for model training.
Deployment Tool
Helm
Used to package and deploy the mask detection application.
Video Analytics
Deepstream
Used for inference in the mask detection application.

Key Actionable Insights

1
Utilizing NGC Collections can significantly reduce the time and effort needed to build AI applications.
By consolidating necessary components like models and containers in one place, developers can focus more on application logic rather than infrastructure setup.
2
Pruning models is essential for optimizing performance, especially in real-time applications like video analytics.
This step ensures that the model runs efficiently on limited resources, which is critical for applications that require low latency.
3
Deploying applications using Helm charts simplifies the management of complex deployments.
Helm allows for easy updates and rollbacks, making it a powerful tool for maintaining production-grade applications.

Common Pitfalls

1
Failing to properly configure the Docker runtime can lead to issues when fetching and running containers.
Ensure that Docker is correctly installed and configured to avoid runtime errors that can halt the development process.
2
Not following the required dataset structure can result in training failures.
It's crucial to adhere to the specified data tree structure for the datasets to ensure compatibility with the training scripts.

Related Concepts

Transfer Learning
Model Pruning
AI Model Deployment
Containerization With Docker