This repo has examples for using ONNX Runtime (ORT) for accelerating training of Transformer models. These examples focus on large scale model training and achieving the best performance in Azure Machine Learning service and NVIDIA DGX-2. ONNX Runtime has the capability to train existing PyTorch models (implemented using torch.nn.Module) through its optimized backend. The examples in this repo demonstrate how ORTTrainer* API can be used to switch the training backend for such models to ONNX Runtime with just a few changes to the existing training code.
*ORTTrainer API is experimental and expected to see significant changes in the near future. A new version of the API is under active development. The improvements to the API will provide a more seamless integration with PyTorch training that requires minimal changes in users’ training code.
Outline the examples in the repository.
| Example | Description |
|---|---|
| getting-started | Get started with ONNX Runtime with a simple PyTorch transformer model |
| nvidia-bert | Using ONNX Runtime Training with BERT pretraining implementation in PyTorch maintained by nvidia |
| huggingface-gpt2 | Using ONNX Runtime Training with GPT2 finetuning for Language Modeling in PyTorch maintained by huggingface |
The examples in this repo depend on a Docker image that includes ONNX Runtime for training. The docker image is available at mcr.microsoft.com/azureml/onnxruntime-training. The Docker image is tested in AzureML and DGX-2 environments. For running the examples in other environments, building a new Docker image may be necessary.
Readme files for every example listed above provide a step-by-step instructions to execute the example.
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