Inference with TensorRT .engine File on Python

NVIDIA TensorRT is a high-performance deep learning inference library for deployment on NVIDIA GPUs. TensorRT optimizes the usage of GPU and CPU resources, ensuring high throughput and low latency for inference workloads. When dealing with deep learning models, TensorRT converts ML models from popular deep learning frameworks like TensorFlow, PyTorch, and ONNX into highly optimized `.engine` files, which are GPU-specific for fast inference. This article will provide a detailed guide to performing inference using TensorRT `.engine` files in Python.

Overview of TensorRT

1. TensorRT Optimization: TensorRT optimizes neural networks by applying kernel auto-tuning, precision calibration (FP32, FP16, INT8), and layer fusion to enhance performance.
2. Serialization: Once a model is optimized, TensorRT serializes this optimized model into a `.engine` file which can be later deserialized for inference.
3. Deployment: The `.engine` file created is optimized for the GPU on which it was generated, providing the best runtime performance.

Key Components of TensorRT Inference

• Builder: Used for creating the `ICudaEngine`.
• Network Definition: Represents the structure of the neural network.
• Parser: Converts models from different formats into TensorRT format.
• Engine: Serialized version of the optimized model.
• Execution Context: Facilitates binding of inputs/outputs and performs the inference.

Setting Up TensorRT in Python

To run inference using TensorRT, the necessary CUDA, cuDNN, and TensorRT libraries must be installed. Python APIs provided by TensorRT facilitate better integration and ease of scripting.

Example: Inference with TensorRT `.engine` File

Below is a simple example illustrating how TensorRT's Python API is used to execute inference from an existing `.engine` file:

• FP32: Standard 32-bit floating-point operations.
• FP16: Half-precision that utilizes Tensor Cores for faster computation while reducing memory footprint.
• INT8: Statistical post-training calibration leads to the smallest models with faster computation but requires calibration.