Creating Custom AI Models Using NVIDIA TAO Toolkit with Azure Machine Learning

Learn how to accelerate your vision AI model development using NVIDIA TAO Toolkit and deploy it for inference with NVIDIA Triton Inference Server—all on the…

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Chintan Shah
•8 min read•advanced•
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Overview

The article discusses the process of creating custom AI models using the NVIDIA TAO Toolkit and deploying them with the NVIDIA Triton Inference Server on the Azure Machine Learning platform. It outlines an end-to-end workflow that includes model training, optimization, and deployment, emphasizing the benefits of a cloud-native approach to AI applications.

What You'll Learn

1

How to install the NGC Azure ML Quick Launch Toolkit for model training

2

How to train and optimize an object detection model using NVIDIA TAO Toolkit

3

How to deploy a model using NVIDIA Triton Inference Server on Azure ML

4

How to create an Azure ML endpoint for model inference

Prerequisites & Requirements

  • Basic understanding of AI/ML concepts
  • Familiarity with Azure Machine Learning(optional)
  • Experience with Python and Jupyter notebooks

Key Questions Answered

What is the workflow for creating custom AI models using NVIDIA TAO Toolkit?
The workflow involves installing the NGC Azure ML Quick Launch Toolkit, training and optimizing a pretrained object detection model, and deploying the optimized model on Azure ML with NVIDIA Triton Inference Server. This process streamlines the AI model lifecycle from preparation to deployment.
How do you register a model for inference in Azure ML?
To register a model for inference, navigate to the Azure ML workspace, select 'Models' from the left menu, and click 'Register' from datastore. Choose the Triton model type and specify the model path to complete the registration process.
What are the steps to deploy a model using NVIDIA Triton Inference Server?
Deploying a model with NVIDIA Triton Inference Server involves uploading the trained model from the Datastore, pushing the NVIDIA Triton container to the Azure Container Registry, and creating an inference endpoint for testing with sample images.
What are the recommended Azure VM types for GPU compute resources?
The recommended Azure VM types for GPU compute resources include NCsv3, NDv2, NC A100 v4, and ND A100 v4 series, which are suitable for running AI/ML workloads effectively.

Technologies & Tools

AI/ML
Nvidia Tao Toolkit
Used for training and optimizing AI models.
AI/ML
Nvidia Triton Inference Server
Used for deploying models for inference.
Cloud Service
Azure Machine Learning
Used for managing the machine learning project lifecycle.

Key Actionable Insights

1
Utilizing the NVIDIA TAO Toolkit can significantly reduce the time and expertise required to develop AI models. By leveraging pretrained models, developers can focus on fine-tuning rather than starting from scratch.
This approach is particularly beneficial for teams with limited AI expertise, allowing them to deploy sophisticated models more quickly.
2
Deploying models using NVIDIA Triton Inference Server standardizes the inference process, ensuring that models can be served efficiently and at scale.
This is crucial for production environments where performance and scalability are key considerations.
3
Creating an Azure ML endpoint allows for easy integration of AI models into applications, enabling real-time inference capabilities.
This is essential for applications that require immediate responses, such as image recognition or anomaly detection.

Common Pitfalls

1
One common pitfall is not properly configuring the Azure resources, which can lead to deployment failures.
It's essential to ensure that the configuration files are correctly set up with the right Azure subscription ID and resource group details to avoid these issues.
2
Another issue is neglecting to optimize the model before deployment, which can result in poor performance in production.
Optimizing the model through techniques like pruning and quantization is crucial for maintaining efficiency and accuracy.

Related Concepts

AI/ML Model Training
Cloud-native Application Development
Model Deployment Strategies