3 posts tagged with "PyTorch"

Have you ever wondered 🤔 how PyTorch nn.Module works? I was always curious to understand how the internals work too. Recently I was reading Fast.ai's Deep learning for coders book's 19th chapter, where we learn how to build minimal versions of PyTorch and FastAI modules like

• Dataset, Dataloaders
• Modules
• FastAI Learner

This intrigued 🤔 me to take a look at the PyTorch source code for nn.Module. The code for nn.Module is 1000+ lines 😮. After a few cups of coffee ☕☕, I was able to make sense of what is happening inside. Hopefully, by end of this post, you would have an understanding of what goes insidenn.Module without those cups of coffee 😄.

One of the least taught skill in machine learning is how to manage and track machine learning experiments effectively. Once you get out of the shell of beginner-level projects and get into some serious projects/research, experiment tracking and management become one of the most crucial parts of your project.

However, no course teaches you how to manage your experiments in-depth, so here I am trying to fill in the gap and share my experience on how I track and manage my experiments effectively for all my projects and Kaggle competitions.

In this post, I would like to share knowledge gained from working on several ML and DL projects.

• Need for experiment tracking
• Conventional ways for experiment tracking and configuration management
• Trackables in a machine learning project
• Experiment tracking using Weights and Biases
• Configuration Management with Hydra

While designing DL modules like a classification head, it is required to calculate the input features. PyTorch Lazy modules comes to the rescue by helping us automate it.

In this post, we will explore how we can use PyTorch Lazy modules to re-write PyTorch models used for

• Image classifiers
• Unet