Deploying Models from TensorFlow Model Zoo Using NVIDIA DeepStream and NVIDIA Triton Inference Server

If you’re building unique AI/DL application, you are constantly looking to train and deploy AI models from various frameworks like TensorFlow, PyTorch, TensorRT…

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NVIDIA
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Dhruv Singal
•9 min read•advanced•
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Overview

This article provides a comprehensive guide on deploying AI models from the TensorFlow Model Zoo using NVIDIA DeepStream and Triton Inference Server. It covers the integration of these technologies for efficient model deployment, including detailed steps for setting up object detection models and optimizing performance.

What You'll Learn

1

How to deploy a TensorFlow model using NVIDIA DeepStream and Triton Inference Server

2

How to create configuration files for Triton and DeepStream applications

3

How to optimize AI models using TensorFlow-TensorRT for better performance

Prerequisites & Requirements

  • DeepStream SDK installed on an NVIDIA GPU
  • Basic understanding of AI model deployment and configuration(optional)

Key Questions Answered

How do you deploy a FasterRCNN model using DeepStream?
To deploy a FasterRCNN model using DeepStream, you need to download the model, create configuration files for Triton and DeepStream, build a custom parser, and finally run the DeepStream application. This process involves specific steps such as setting up the model directory and defining input/output parameters.
What are the performance benefits of using TensorRT with TensorFlow models?
Using TensorRT with TensorFlow models can significantly enhance performance by optimizing the model through techniques like layer fusion and mixed precision. The article shows that the optimized FP16 model on an NVIDIA T4 achieved 32.36 fps compared to 12.80 fps for the unoptimized FP32 model.
What are the key steps to optimize a TensorFlow model for deployment?
Key steps to optimize a TensorFlow model include using TensorFlow-TensorRT for model optimization, adjusting batch sizes, and utilizing mixed precision. The article provides specific commands and configurations to achieve these optimizations effectively.

Key Statistics & Figures

FPS with FP32 model
12.80 fps
Performance of the base FasterRCNN-InceptionV2 model running in native TensorFlow.
FPS with optimized FP16 model on NVIDIA T4
32.36 fps
Performance of the optimized model with NMS on CPU for multiple streams.
FPS with optimized FP16 model on NVIDIA Jetson NX
14.92 fps
Performance of the optimized model with NMS on CPU for multiple streams.

Technologies & Tools

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Framework
Nvidia Deepstream
Used for building scalable AI solutions for streaming video.
Backend
Nvidia Triton Inference Server
Enables deployment of trained models from various frameworks.
Framework
Tensorflow
Source of the FasterRCNN model used in the deployment example.
Optimization Tool
Tensorrt
Used for optimizing TensorFlow models to improve inference performance.

Key Actionable Insights

1
Leverage the native integration of Triton with DeepStream to streamline model deployment.
This integration allows for quick prototyping and deployment of models from various frameworks, enhancing productivity for developers working on AI applications.
2
Utilize TensorRT optimizations to improve model inference speed and efficiency.
Optimizing models with TensorRT can lead to significant performance gains, as demonstrated in the article with a nearly doubled frame rate for the FasterRCNN model.

Common Pitfalls

1
Failing to correctly configure the Triton and DeepStream configuration files can lead to deployment issues.
It's essential to ensure that all parameters, such as input/output tensor specifications, are accurately defined to avoid runtime errors.
2
Neglecting to optimize the model before deployment may result in suboptimal performance.
Without optimizations like TensorRT, models may run slower than necessary, impacting the application's responsiveness and efficiency.

Related Concepts

AI Model Deployment Strategies
Performance Optimization Techniques
Integration Of Multiple AI Frameworks