Accelerate PyTorch models with ONNX Runtime

ONNX Runtime for PyTorch accelerates PyTorch model training using ONNX Runtime.

It is available via the torch-ort python package.

This repository contains the source code for the package, as well as instructions for running the package.

Pre-requisites

You need a machine with at least one NVIDIA or AMD GPU to run ONNX Runtime for PyTorch.

You can install and run torch-ort in your local environment, or with Docker.

Install in a local Python environment

Default dependencies

By default, torch-ort depends on PyTorch 1.9.0, ONNX Runtime 1.9.0 and CUDA 10.2.

1. Install CUDA 10.2

2. Install CuDNN 7.6

3. Install torch-ort

   • pip install torch-ort

4. Run post-installation script for ORTModule

   • python -m torch_ort.configure

Get install instructions for other combinations in the Get Started Easily section at https://www.onnxruntime.ai/ under the Optimize Training tab.

Verify your installation

1. Clone this repo

   • git clone git@github.com:pytorch/ort.git

2. Install extra dependencies

   • pip install wget pandas sklearn transformers

3. Run the training script

   • python ./ort/tests/bert_for_sequence_classification.py

Add ONNX Runtime for PyTorch to your PyTorch training script

from torch_ort import ORTModule
model = ORTModule(model)

# PyTorch training script follows

Samples

To see torch-ort in action, see https://github.com/microsoft/onnxruntime-training-examples, which shows you how to train the most popular HuggingFace models.

License

This project has an MIT license, as found in the LICENSE file.