TensorRT is a high-performance optimizer and runtime engine for deep learning reasoning, aiming at improving the reasoning speed and efficiency of deep learning model.
TensorRT accelerates the reasoning process of deep learning model by using various optimization techniques. These optimization techniques include network pruning, quantization, layer fusion and concurrent execution. Network pruning can reduce the calculation of the model, and optimize the storage and calculation efficiency of the model by removing redundant parameters and connections.
Quantization can convert floating-point parameters and activation values into low-order numbers, thus reducing the storage requirements of the model and improving the calculation speed. Layer fusion is to merge multiple layers into a larger layer to reduce memory access and computational overhead. Concurrent execution allows multiple operations to be executed in parallel in different GPU streams, which further improves the reasoning speed.
TensorRT also supports various deep learning frameworks, such as TensorFlow, PyTorch and ONNX, which can be seamlessly integrated with these frameworks to facilitate developers to deploy and optimize their models. TensorRT provides easy-to-use APIs and tools that developers can use to optimize and deploy models to achieve efficient reasoning performance.
TensorRT also provides some other functions.
For example, it supports dynamic shape input, which means that the model can accept different sizes of input in the reasoning process. Some tools of quantitative perception training are also provided, which can predict the quantization error in training and reduce the precision loss in quantitative reasoning.
Generally speaking, TRT(TensorRT) is an optimizer and runtime engine developed by NVIDIA for high-performance deep learning reasoning. It uses optimization technology to support various deep learning frameworks and improves the reasoning speed and efficiency of deep learning model. Developers can use TensorRT to optimize, deploy and run their deep learning models and get better performance.