trtutils

A high-level Python interface for TensorRT inference, providing a simple and unified way to run arbitrary TensorRT engines. This library abstracts away the complexity of CUDA memory management, binding management, and engine execution, making it easy to perform inference with any TensorRT engine.

Features

• Simple, high-level interface for TensorRT inference
• Automatic CUDA memory management
• Support for arbitrary TensorRT engines
• Built-in preprocessing and postprocessing capabilities
• Comprehensive type hints and documentation
• Support for both basic engine execution and end-to-end model inference

Quick Start

Basic Engine Usage

The TRTEngine class provides a simple interface for running any TensorRT engine:

from trtutils import TRTEngine

# Load your TensorRT engine
engine = TRTEngine("path_to_engine")

# Get input specifications
print(engine.input_shapes)  # Expected input shapes
print(engine.input_dtypes)  # Expected input data types

# Run inference
inputs = read_your_data()
outputs = engine.execute(inputs)

Installation

pip install trtutils

For additional features, you can install optional dependencies:

# For JIT compiler
pip install "trtutils[jit]"

# For development
pip install "trtutils[dev]"

Documentation

For detailed documentation, including advanced usage, examples, and API reference, visit our documentation site.

Examples

Check out our examples directory for more detailed usage examples, including:

• Basic engine usage
• End-to-end model inference
• YOLO model implementation
• Benchmarking utilities

Performance

Device	YOLOv8m	YOLOv8n
OrinAGX-64GB
OrinAGX-32GB
OrinNX-16GB
OrinNano-8GB
XavierNX-8GB
3080Ti
TitanRTX

License

This project is licensed under the MIT License - see the LICENSE file for details.