Mattia Cerrato presents a tutorial about the use of Weights & Biases platform useful to keep track of results, hyperparameters and random seeds in ML experiments.
Title: Experiment tracking with Weights & Biases
Performing experiments is perhaps the most time consuming activity in ML research, especially at the junior level. Often too little effort is spent in understanding how to optimize this process. The Weights & Biases (W&B) platform provides a simple Python interface which may be used to keep track of results, hyperparameters and random seeds. It has intuitive visualization utilities which may be used to write experimental reports starting from raw performance metric data. Furthermore, it provides an easy way to perform hyperparameter search (random, grid and even Bayesian search strategies are available) and even some light training orchestration capabilities. In this talk, we will see how to extend our experimental scripts so that W&B can help us keep our sanity during the experimental phase of a project.
When: On 4th June at 11.30