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NVIDIA-AI-IOT avatar

NVIDIA-AI-IOT/torch2trt

0
View on GitHub↗
4,877 stars·699 forks·Python·MIT·19 views

Torch2trt

torch2trt is a tool for transforming PyTorch model modules into optimized TensorRT engines to improve inference performance on NVIDIA GPUs. It functions as a deep learning model optimizer and engine generator that converts neural network layers into high-performance runtime formats for hardware-accelerated graphics processors.

The project features a custom layer conversion tool that allows users to define and register Python-based conversion logic to handle specialized operations not supported by default. This extensibility is paired with a registry-based system for mapping specific layer types to user-defined conversion functions.

The system covers GPU inference acceleration through deep learning model quantization and quantization aware training to reduce memory usage and increase throughput. It also includes capabilities for model persistence, allowing the state of optimized engines to be stored and reloaded.

Features

  • TensorRT Framework Integrations - Integrates TensorRT optimization into PyTorch by translating deep learning operations into optimized graphs.
  • Custom Neural Network Layers - Supports the implementation of specialized conversion logic for non-standard neural network operations.
  • GPU-Accelerated Inference - Accelerates GPU inference by transforming deep learning models into highly optimized hardware-specific formats.
  • Deep Learning Optimization - Reduces model latency and memory usage through precision optimization and computational graph refinement.
  • ONNX-to-TensorRT Conversions - Transforms model modules into optimized TensorRT engines to improve inference performance on GPUs.
  • PyTorch-to-TensorRT Converters - Transforms PyTorch model modules into optimized TensorRT engines to improve GPU inference performance.
  • TensorRT Engine Generators - Converts neural network layers into high-performance runtime engines for hardware-accelerated graphics processors.
  • Model Quantization - Reduces the precision of model weights to decrease memory footprint and increase GPU throughput.
  • Precision Quantization - Implements precision quantization to reduce memory usage and accelerate GPU inference.
  • PyTorch Model Optimizations - Optimizes PyTorch models by converting them into TensorRT engines for lower latency on NVIDIA GPUs.
  • Model Conversion Utilities - Provides a utility for defining and registering Python-based conversion logic for model transformation.
  • Quantization-Aware Training - Implements training techniques that simulate quantization noise to optimize model precision.
  • Recursive Module Operations - Provides utilities for recursively traversing the PyTorch model hierarchy to transform modules into TensorRT representations.
  • Layer Conversion Registries - Features a registry-based system to map specific layer types to user-defined conversion functions.
  • Scripting Extension Layers - Exposes a scripting extension layer using Python to extend the functionality of the conversion process.

Star history

Star history chart for nvidia-ai-iot/torch2trtStar history chart for nvidia-ai-iot/torch2trt

How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.

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Frequently asked questions

What does nvidia-ai-iot/torch2trt do?

torch2trt is a tool for transforming PyTorch model modules into optimized TensorRT engines to improve inference performance on NVIDIA GPUs. It functions as a deep learning model optimizer and engine generator that converts neural network layers into high-performance runtime formats for hardware-accelerated graphics processors.

What are the main features of nvidia-ai-iot/torch2trt?

The main features of nvidia-ai-iot/torch2trt are: TensorRT Framework Integrations, Custom Neural Network Layers, GPU-Accelerated Inference, Deep Learning Optimization, ONNX-to-TensorRT Conversions, PyTorch-to-TensorRT Converters, TensorRT Engine Generators, Model Quantization.

What are some open-source alternatives to nvidia-ai-iot/torch2trt?

Open-source alternatives to nvidia-ai-iot/torch2trt include: wang-xinyu/tensorrtx — tensorrtx is a computer vision inference engine and model implementation library designed for graphics processor… pytorch/executorch — ExecuTorch is a lightweight C++ runtime for deploying PyTorch models on mobile, embedded, and edge hardware. It… tingsongyu/pytorch_tutorial — This project is a comprehensive collection of educational examples and reference implementations for building vision… tingsongyu/pytorch-tutorial-2nd — This project is a comprehensive instructional resource and course for building neural networks using PyTorch. It… abetlen/llama-cpp-python — llama-cpp-python provides a Python interface for the llama.cpp library, enabling the execution of large language… nvidia/isaac-gr00t.