Introduction to ONNX

There are a variety of DL frameworks you can use to train a DL model. However, one of the major difficulties in DL model deployment arises from the lack of interoperability among these frameworks. For example, conversion between PyTorch and TensorFlow (TF) introduces many difficulties.

In many cases, DL models are augmented further for the deployment environment to increase accuracy and reduce inference latency, utilizing the acceleration provided by the underlying hardware. Unfortunately, this requires a broad knowledge of software as well as hardware because each type of hardware provides different accelerations for the running application. Hardware that is commonly used for DL includes the Central Processing Unit (CPU), Graphical Processing Unit (GPU), Associative Processing Unit (APU), Tensor Processing Unit (TPU), Field Programmable Gate Array (FPGA), Vision Processing Unit (VPU), Neural Processing Unit (NPU), and JetsonBoard.

This process is not a...