Yes, there are binary utilities available for working with TensorRT Engine. TensorRT is a deep learning inference optimizer and runtime library developed by NVIDIA. It is commonly used to optimize and deploy deep learning models for inference on NVIDIA GPUs.
TensorRT provides several binary utilities that can be useful in different stages of the TensorRT workflow. Here are a few notable ones:
trtexec: This utility allows you to run TensorRT engines from the command line. It provides options to specify the network model, input data, precision modes, batch sizes, and other runtime configurations.
trtconvertpy: This utility allows you to convert trained models in various popular deep learning frameworks (such as TensorFlow and ONNX) to TensorRT engines. It simplifies the process of optimizing the models for efficient inference on NVIDIA GPUs.
trtexec (with --export) and trtoptimize: These utilities enable you to serialize a TensorRT engine to a binary file (usually with the .engine extension). This binary representation can be later loaded and executed directly without the need for re-optimization. This is particularly useful when deploying the model in production environments.
trtserver: TensorRT Inference Server is a scalable, production-ready inference serving solution from NVIDIA. It provides a server infrastructure for hosting and serving TensorRT engines over the network, allowing multiple clients to make inference requests simultaneously.