/ˈtɛnsər-ɑːr-ti/
n. “A high-performance deep learning inference library for NVIDIA GPUs.”
TensorRT is a platform developed by NVIDIA that optimizes and accelerates the inference of neural networks on GPUs. Unlike training-focused frameworks, TensorRT is designed specifically for deploying pre-trained deep learning models efficiently, minimizing latency and maximizing throughput in production environments.
TensorRT supports a wide range of neural network architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer-based models. It performs optimizations such as layer fusion, precision calibration (FP32, FP16, INT8), and kernel auto-tuning to achieve peak performance on NVIDIA hardware.
Key characteristics of TensorRT include:
- High Performance: Optimizes GPU execution for low-latency inference.
- Precision Calibration: Supports mixed-precision computing (FP32, FP16, INT8) for faster inference with minimal accuracy loss.
- Cross-Framework Support: Imports models from frameworks like TensorFlow, PyTorch, and ONNX.
- Layer and Kernel Optimization: Fuses layers and selects the most efficient GPU kernels automatically.
- Deployment Ready: Designed for production inference on edge devices, servers, and cloud GPUs.
Conceptual example of TensorRT usage:
import tensorrt as trt
TRT_LOGGER = trt.Logger(trt.Logger.WARNING)
builder = trt.Builder(TRT_LOGGER)
network = builder.create_network()
parser = trt.OnnxParser(network, TRT_LOGGER)
with open("model.onnx", "rb") as f:
parser.parse(f.read())
engine = builder.build_cuda_engine(network)
# Use engine to run optimized inference on GPUConceptually, TensorRT is like giving a pre-trained neural network a turbo boost, carefully reconfiguring it to run as fast as possible on NVIDIA GPUs without retraining. It is essential for applications where real-time AI inference is critical, such as autonomous vehicles, robotics, and video analytics.