...

On the computer used for training, we are using an RTX A4000. Its full datasheet can be found here.

ONNX

In order to fully utilize GPUs, much work needs to be done to optimize code. Fortunately, there are packages that can do almost all of the work for us.ONNX

ONNX (the The Open Neural Network eXchange) takes trained neural networks from other frameworks and converts them into low-level instructions.Exchange) is an AI ecosystem that created standards for representing machine learning algorithms. These standards allow ONNX machine learning algorithms to be run on a wide variety of machines (unfortunately excluding Apple).

ONNX supports a wide variety of frameworks, including Matlab, Keras, TensorFlow, and PyTorch. For a full list and detailed instructions on converting to and from ONNX models, see this page: For more on ONNX, see https://onnx.ai/about.htmlsupported-tools.html

Instructions for converting a TensorFlow model to ONNX:

1. Install ONNX convertor:
   pip install git+https://github.com/onnx/tensorflow-onnx
2. To convert a model from TensorFlow to ONNX, run the following.
   python -m tf2onnx.convert --saved-model tensorflow-model-path --output model.onnx
3. The model is now ready to be run on ONNX Runtime.

ONNX Runtime

ONNX does not itself perform inference. To do that, you need to install a runtime that can run ONNX. Use the following website for installation and operation instructions: https://onnxruntime.ai/index.html#getStartedTable

Using this, you can perform inference up to 17x faster. ONNX Runtime also allows you to accelerate PyTorch training (only PyTorch so far) up to 40% faster.