  The goal of hyperparameter tuning (or hyperparameter optimization) is to
optimize the hyperparameters to improve the performance of the machine or deep
learning model. spotPython (``Sequential Parameter Optimization Toolbox in
Python'') is the Python version of the well-known hyperparameter tuner SPOT,
which has been developed in the R programming environment for statistical
analysis for over a decade. PyTorch is an optimized tensor library for deep
learning using GPUs and CPUs. This document shows how to integrate the
spotPython hyperparameter tuner into the PyTorch training workflow. As an
example, the results of the CIFAR10 image classifier are used. In addition to
an introduction to spotPython, this tutorial also includes a brief comparison
with Ray Tune, a Python library for running experiments and tuning
hyperparameters. This comparison is based on the PyTorch hyperparameter tuning
tutorial. The advantages and disadvantages of both approaches are discussed. We
show that spotPython achieves similar or even better results while being more
flexible and transparent than Ray Tune.
