Introduction

Has it ever happened to you when you run experiments, change some things, run it again, then, few hours or days later, you have worst performances than before, but you don't remember what you changed? Your search for hours before finding it, or even worse, never find it again?

This is why I created Deepboard, a modern experiment tracking tool to help you organise your deep learning / machine learning research. We currently find ourselves overwhelmed by a lot of experiment tracking tools such as Weights & Biases, ClearML, MLFlow, Tensorboard, etc., so why a new one? I believe these tools can be split into two main categories: full ML lifecycle management and lightweight experiment tracking. Popular proprietary tools such as Weights & Biases and ClearML and the open source MLFlow are designed to manage the full ML lifecycle. On the other side, tensorboard is a lightweight experiment tracking tool as it focuses on logging and visualizing experiment results, and comparing them. Tensorboard have been introduced quite some time ago, and while it is still a great tool, I believe it lacks some useful features. Deepboard aims to replace tensorboard as a lightweight experiment tracking tool, while providing a modern and user-friendly interface, and some useful features missing in the classic Tensorboard.

With deepboard, all your logs are stored locally in a single database file, similar to tensorboard that stores the logs in binary files. This mean you do not have to be connected to internet, or send your data to a cloud service, everything is local. Deepboard uses a sqlite3 database, which makes its format transparent easy to work with. In addition, it comes with a public api to read the logs programmatically, which is not the case with tensorboard. It also comes with a beautiful and modern web interface to visualize and compare your experiments.

However, where deepboard really separates itself from others is with its unique designed philosophy around reproducibility. Deepboard is designed not only to track and visualize your experiments, but also to allow you to reproduce the results of any previously run experiment.