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Welcome to the Getting Started with TensorRT GitHub repository! This repository serves as a comprehensive guide and resource for developers who want to learn how to optimize deep learning models for deployment using NVIDIA TensorRT. TensorRT is a high-performance deep learning inference optimizer and runtime library developed by NVIDIA that provides accelerated inference for deep learning models on NVIDIA GPUs.
Whether you are new to TensorRT or have some experience with it, this repository is designed to help you quickly grasp the fundamentals and best practices of using TensorRT for efficient and fast inference of deep learning models.
- Overview
- Table of Contents
- What is TensorRT?
- Getting Started
- Installation
- Usage
- Examples
- Contributing
- License
TensorRT is an NVIDIA software library that optimizes and deploys deep learning models for inference on NVIDIA GPUs. It is designed to provide high performance and low latency inference for deep learning applications in production environments. TensorRT can take trained neural network models from popular deep learning frameworks, such as TensorFlow and PyTorch, and optimize them for deployment.
Key features of TensorRT include:
- Precision Calibration: TensorRT can automatically calibrate the precision of network weights to improve inference accuracy.
- Layer Fusion: TensorRT fuses multiple layers of a deep learning model into a single layer to reduce memory consumption and improve performance.
- Dynamic Tensor Memory: TensorRT optimizes memory allocation and reuses tensors to minimize memory overhead.
- Kernel Auto-Tuning: TensorRT dynamically selects the best kernel implementation for each layer based on the target GPU and other configurations.
- INT8/FP16 Inference: TensorRT supports INT8 and mixed precision (FP16/FP32) inference, which can significantly speed up inference while maintaining acceptable accuracy levels.
- Multi-Platform Support: TensorRT supports deployment on various NVIDIA GPUs, including Jetson, Tesla, and NVIDIA's data center GPUs.
If you are new to TensorRT and want to get started quickly, you can follow these steps to begin:
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Getting Started : Check out the blog from NVIDIA for additional information about Tensorrt
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Installation: Check out the Installation section for instructions on how to install TensorRT on your system.
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Usage: Once you have TensorRT installed, the Usage section will guide you through the process of optimizing and running your first deep learning model.
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Examples: Explore the Examples section to find practical examples and use cases of using TensorRT with different deep learning frameworks and models.
In this section, we provide step-by-step instructions to help you install TensorRT on your system. Ensure that you meet the necessary prerequisites before proceeding with the installation.
To install TensorRT, follow the instructions outlined in the Installation Guide.
Once you have TensorRT successfully installed on your system, this section will guide you through the process of optimizing and running deep learning models using TensorRT. You will learn how to take a pre-trained model from popular deep learning frameworks and deploy it with TensorRT for fast and efficient inference.
Refer to the Usage Guide for detailed instructions on how to use TensorRT effectively.
This section provides examples showcasing how to use TensorRT with different deep-learning frameworks and models. Each example includes detailed explanations and code samples to facilitate easy understanding and implementation.
Explore the Examples directory to access the various use cases.
We welcome contributions from the community to improve and expand this repository. If you have suggestions, bug fixes, or want to add new examples, please check out our Contribution Guidelines to get started.
This repository is licensed under the MIT License, allowing you to freely use, modify, and distribute the code and examples. Please review the license terms before using the repository.
We hope this repository is valuable for your journey into using TensorRT for efficient and accelerated deep learning inference on NVIDIA GPUs. Happy optimizing and deploying!