Discover how to enhance your machine learning training process using PyTorch Lightning and Weights & Biases in this informative 33-minute video tutorial. Learn to build ML pipelines, track experiments, and utilize callbacks effectively. Follow along with a provided Colab notebook as Deep Learning Educator Charles Frye guides you through the process. Explore topics such as setting up the environment, constructing models, advanced logging techniques, implementing Lightning Callbacks, creating LightningDataModules, and configuring the Trainer. Gain insights into reviewing the W&B Dashboard, analyzing logs, and understanding metadata, system metrics, and model topology. Perfect for machine learning practitioners looking to streamline their workflow and improve experiment tracking.
Supercharge Your Training With PyTorch Lightning and Weights & Biases
Overview
Syllabus
- What is Weights & Biases? What is PyTorch Lightning?
- Installs, imports, and setup
- Building a model with Lightning and W&B
- Next-level logging: model files, logits, and more
- Lightning Callbacks for logging media to W&B
- Writing a LightningDataModule
- Putting it all together with the Trainer
- Reviewing the W&B Dashboard
- Logs in the standard out
- Metadata, system metrics, and model topology in the W&B Dashboard
- Outro
Taught by
Weights & Biases
