Introduction

Running TensorFlow models with the NVIDIA TensorRT inference engine significantly enhances performance by accelerating deep learning model inference. This integration is particularly beneficial in real-time applications where latency is critical, such as autonomous vehicles, robotics, and interactive applications. In this article, we explore the integration process, demonstrate its technical aspects, and show performance gains through a Python example.

What is TensorRT?

NVIDIA TensorRT is a high-performance deep learning inference optimizer and runtime that delivers low latency and high throughput for deep learning applications. TensorRT is designed to leverage GPU capabilities fully, and this is achieved primarily by optimizing the trained model to make the most efficient use of resources.

Key features of TensorRT include:

• Weight and activation precision calibration
• Layer and tensor optimizations
• Kernel auto-tuning & fusion
• Dynamic Tensor Memory (DALI) usage
• Targeted optimizations for specific hardware

Why Use TensorRT with TensorFlow?

TensorFlow is a versatile deep learning library. When used in combination with TensorRT, the inference performance can be improved significantly. This is especially important when deploying models to production environments where resource efficiency and response times are crucial.

Benefits of Using TensorRT with TensorFlow:

1. Reduced Latency: Optimize runtime and reduce execution time for neural networks.
2. Improved Throughput: Enhanced performance allows for processing more requests per second.
3. Resource Efficiency: Efficiently utilize GPU resources by optimizing memory and processing power.
4. Precision Calibration: Enable FP16 and INT8 optimizations for a balance between speed and accuracy.

Integration Process

To integrate TensorFlow with TensorRT, the critical step is converting the TensorFlow frozen graph to a TensorRT inference graph. Below, we detail a step-by-step technical explanation.

Requirements

Ensure the following are installed on your system:

• NVIDIA GPU with CUDA support
• TensorFlow (v1.7.0 or newer recommended)
• TensorRT (compatible version with TensorFlow)
• cuDNN Library compatible with TensorRT

Conversion and Execution

Here's a code example demonstrating the integration: