trt-torch-quickstart

Convert PyTorch models to TensorRT engines using torch_tensorrt.

13.2k|2.4k|Updated May 2, 2019

Search Tags:#PyTorch #AI inference #TensorRT #CUDA #model conversion

Ready to run

npx skills add https://github.com/NVIDIA/TensorRT --skill trt-torch-quickstart

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: trt-torch-quickstart
Source: https://github.com/NVIDIA/TensorRT/tree/main/.agents/skills/trt-torch-quickstart
Command: npx skills add https://github.com/NVIDIA/TensorRT --skill trt-torch-quickstart

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires torch, torch_tensorrt, tensorrt, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the issue of converting PyTorch models to TensorRT engines efficiently using the torch_tensorrt library, providing a streamlined process for AI inference acceleration.

Core Features & Use Cases

PyTorch to TensorRT Conversion: Offers both AOT (Ahead-of-Time) and JIT (Just-In-Time) compilation methods for PyTorch models.
Model Prerequisites: Provides clear instructions for model requirements and environment setup to ensure successful compilation.
Use Case: Ideal for users who wish to compile PyTorch models to TensorRT engines for AI inference without the need for an ONNX intermediate file.

Quick Start

Run the command: trt-torch-quickstart compile --model-path /path/to/your/model.py.

Frequently Asked Questions about trt-torch-quickstart

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I convert a PyTorch model to a TensorRT engine for AI inference?▼

You can convert a PyTorch model to a TensorRT engine using the torch_tensorrt library, which supports both AOT and JIT compilation methods to accelerate AI inference without requiring an ONNX intermediate file.

What is the difference between AOT and JIT compilation for TensorRT engines?▼

TensorRT engine compilation offers AOT (Ahead-of-Time) for pre-compiled deployment readiness and JIT (Just-In-Time) for runtime execution, both streamlining PyTorch model conversion for efficient AI inference acceleration.

Do I need CUDA and specific environment versions to compile PyTorch models with TensorRT?▼

Yes, compiling PyTorch models to TensorRT engines requires a CUDA environment along with specific version constraints for PyTorch, TensorRT, and CUDA to ensure successful compilation and inference acceleration.

Can I compile a PyTorch model to TensorRT without exporting an ONNX file?▼

Yes, you can bypass the ONNX intermediate file entirely by using torch_tensorrt to directly compile PyTorch models into TensorRT engines through AOT or JIT compilation methods.

What is the best way to start compiling a PyTorch model to TensorRT?▼

The quickest way to start compiling a PyTorch model to TensorRT is by running the command `trt-torch-quickstart compile --model-path /path/to/your/model.py` after ensuring your environment meets the prerequisites.

When should I use torch_tensorrt over ONNX for PyTorch model deployment?▼

Use torch_tensorrt for end-to-end AI deployment workflows when you want to skip the ONNX intermediate step and directly compile PyTorch models to TensorRT engines for inference acceleration.