TensorRT

Introduction to TensorRT

TensorRT is NVIDIA's high-performance deep learning inference platform. As open-source software, it includes crucial components like plugins and an ONNX parser, along with sample applications that showcase its capabilities and utilization. TensorRT is a subset of NVIDIA's full General Availability (GA) release, but stands distinct with added extensions and bug fixes. This article dives into the elements and processes of building and using TensorRT.

What is TensorRT?

TensorRT is designed to optimize deep learning models, making them run faster and more efficiently. It achieves this by providing a comprehensive software development kit (SDK) that allows for the low-latency and high-throughput inference of trained deep learning models, primarily across NVIDIA GPUs.

Features of TensorRT

1. Open Source Nature: The open-source repository of TensorRT contains vital components such as the TensorRT plugins and an ONNX parser. It allows developers to customize the tools according to their needs and contribute back to the community.

2. Sample Applications: TensorRT provides sample applications that demonstrate its usage. These examples are instrumental in helping developers understand and leverage the capabilities of TensorRT in real-world scenarios.

3. Continuous Updates: Frequent updates and bug fixes ensure that users are equipped with the latest advancements and improvements available within the TensorRT framework.

Prerequisites and Installation

To start using TensorRT, especially for development purposes, certain system packages are required:

• CUDA and cuDNN: These facilitate parallel computing on NVIDIA GPUs, essential for deploying TensorRT applications.
• GNU Make and CMake: For building and compiling the TensorRT components.
• Python and Pip: Python is the primary language for TensorRT, and packages can be installed via pip.
• Essential Utilities: Tools such as Git and pkg-config are necessary for downloading and configuring the project resources.

For many users, installing the prebuilt TensorRT Python package is the easiest way to get started. This can be quickly done using a pip command:

pip install tensorrt

This command installs the TensorRT package, skipping the need for manual build processes and allowing users to immediately begin integrating TensorRT within Python environments.

Building TensorRT

TensorRT can be built from source, which allows for more control over its configuration and customization. The build process involves:

1. Setting Up the Build Environment: This is often done using Docker containers tailored for TensorRT builds, saving configuration time and ensuring consistency across environments.

2. Downloading the TensorRT Sources: Using Git to clone the necessary repositories and initiate the submodules.

3. Compiling: On various platforms, including Linux and Windows, the sources are compiled using CMake to generate the required Makefiles, followed by the make command to build.

Community and Support

NVIDIA provides extensive support through various platforms including:

• Official Documentation: Comprehensive guides and notes on how to maximize TensorRT's potential.
• Discussion Forums: A community forum where developers can ask questions, share insights, and collaborate on TensorRT projects.

For enterprise-level support, TensorRT comes under the NVIDIA AI Enterprise software suite, offering a professional level of assistance and resources.

Conclusion

TensorRT is a robust, open-source platform for deep learning inference, particularly optimized for NVIDIA's GPU architecture. It offers both ease of use through prebuilt Python packages and customization through source builds. With a supportive community and thorough documentation, TensorRT continues to be a powerful tool for AI practitioners looking to deploy efficient and fast deep learning models.