Welcome to the "Getting Started with TensorRT" GitHub repository! This project aims to provide a comprehensive introduction to TensorRT, a powerful deep learning inference optimizer and runtime library developed by NVIDIA.
TensorRT is an essential tool for optimizing and accelerating deep learning models, making them highly efficient for deployment on NVIDIA GPUs. By leveraging TensorRT, developers can achieve significantly faster inference times while maintaining high accuracy levels.
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Layer Fusion: TensorRT combines multiple layers in a neural network into a single, optimized layer, reducing memory usage and enhancing inference speed.
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Precision Calibration: TensorRT allows the calibration of models to lower numerical precision, like INT8 or FP16, without sacrificing accuracy. This results in faster inference, reduced memory consumption, and optimized GPU utilization.
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Dynamic Tensor Memory: With TensorRT, memory allocation and reuse for tensors are optimized, leading to a reduction in overall memory usage.
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Kernel Auto-tuning: TensorRT automatically tunes GPU kernels for specific GPU architectures, ensuring optimal performance across different hardware configurations.
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Multi-GPU Support: TensorRT can take advantage of multiple GPUs to further accelerate inference, making it an excellent choice for high-performance computing environments.
This repository is a comprehensive guide to getting started with TensorRT. It contains practical examples, code snippets, and step-by-step tutorials to help you grasp the fundamentals and unlock the full potential of TensorRT for your deep learning projects.
We encourage contributions from the open-source community to enrich this repository further. Whether it's bug fixes, new features, or additional examples, your input is highly valued.
Let's embark on an exciting journey together to explore the cutting-edge world of deep learning optimization with TensorRT. Happy learning!
Find the detailed documentation and instructions on how to get started with TensorRT in the wiki section of this repository.
License:
This project is licensed under the MIT License. Feel free to use, modify, and distribute the code, keeping the appropriate attribution and license terms.