Cainã Max Couto da Silva – PyTorch Workflow Mastery: A Guide to Track and Optimize Model Performance
www.pydata.org
This tutorial empowers deep learning practitioners to master the entire PyTorch workflow, from efficient model creation to advanced tracking and optimization techniques. We’ll begin by exploring a practical PyTorch workflow, then delve into integrating popular experiment tracking tools like MLFlow and Weights & Biases. You’ll learn to log custom metrics, artifacts, and interactive visualizations, enhancing your model development process. Finally, we’ll tackle hyperparameter optimization using Optuna’s Bayesian search, all while maintaining meticulous experiment tracking for easy comparison and reproducibility.
By the end of the session, you’ll have constructed a robust, modular pipeline for managing experiments and optimizing model performance. Whether you’re new to PyTorch or an experienced data scientist looking to improve your workflow, this hands-on tutorial offers immediately applicable insights and techniques to enhance your deep learning projects across diverse domains.
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