Who is this for?
This is an advanced topic for ML developers who want to reduce latency and memory bandwidth by exporting INT8 models to the `.vgf` file format using the ExecuTorch Arm backend.
What will you learn?
Upon completion of this Learning Path, you will be able to:
- Explain when to use post-training quantization (PTQ) vs quantization-aware training (QAT)
- Prepare and quantize a PyTorch model using TorchAO PT2E quantization APIs
- Export the quantized model to TOSA and generate a model artifact with the ExecuTorch Arm backend
- Validate the exported graph by visualizing it using Google's Model Explorer
Prerequisites
Before starting, you will need the following:
- Basic PyTorch model training and evaluation experience
- A development machine with Python 3.10+ and PyTorch installed that runs ExecuTorch