# NVIDIA-AI-IOT/torch2trt

An easy to use PyTorch to TensorRT converter

Repository: https://github.com/NVIDIA-AI-IOT/torch2trt
Canonical: https://ross.abutalabs.com/products/torch2trt
Language: Python
License: MIT
License Family: permissive
Topics: jetson-nano, jetson-tx2, jetson-xavier, pytorch, tensorrt, inference, classification
Last push: 2024-08-17T08:59:30+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 2685, "days_push": 746, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 4878, forks 698 (observed 2026-08-28T04:09:01.758079+00:00)

## What it is
torch2trt is a Python library that converts PyTorch models to TensorRT engines using the TensorRT Python API, with a simple single-function conversion interface and extensible custom layer converters. It was created primarily to optimize models for NVIDIA Jetson embedded platforms.

## Use cases
- convert pytorch models to tensorrt for faster inference
- optimize deep learning models on jetson nano or xavier
- speed up resnet or alexnet inference with tensorrt
- run pytorch classification models faster on nvidia gpus
- save and load tensorrt-optimized models as state dicts
- write custom tensorrt layer converters for unsupported ops

## When to choose
- you deploy PyTorch models on NVIDIA Jetson devices and want easy TensorRT acceleration
- you want a minimal, single-call converter for common vision models
- you need to extend conversion with custom Python-registered layer converters

## When to avoid
- you need full coverage of all PyTorch ops or production-grade conversion - use torch-tensorrt or ONNX export
- you are not on NVIDIA hardware with TensorRT available
- your model uses many unsupported layers that you cannot write converters for

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, llm-inference, compiler, gpu-computing
- domain: deep-learning, gpu-computing, developer-tools
- platform: python
- tags: pytorch, tensorrt, model-conversion, inference-optimization, jetson, nvidia, linux, gpu

## Member repositories
- NVIDIA-AI-IOT/torch2trt (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:01.758079+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:18:17.191652+00:00, confidence not recorded.
  - readme: https://github.com/NVIDIA-AI-IOT/torch2trt (fetched 2026-08-28T04:09:01.758079+00:00, sha 11143e4023a0)
- Data as of 2026-08-30T08:39:29.467469+00:00.