Analyzing the high-level APIs of the most widely used ML frameworks such as Tensorflow, PyTorch, Keras, Gluon, Chainer, and Onnx, it’s easy to recognize that the dominance of the Python language is overwhelming. The inten
sive computations running on a GPU are programmed in low-level code, but Python appears to be convenient for defining and configuring the algorithms in high-level code.
This white paper will explore how the Python API for the AUTOSAR Adaptive platform can enable researchers to focus on the mathematical problems instead of dealing with the excess compilation time and possible debugging quirks of a C++ program.
