What is exciting about the tools we provided in this competition -- especially the weights and biases leaderboard is the level of transparency you get that you

What is exciting about the tools we provided in this competition -- especially the weights and biases leaderboard is the level of transparency you get that you don't always see in a Kaggle competition (unless it is shared voluntarily). You can see: - All the system logging information including CPU/GPU utilization, with the runtime and type of GPU card used - Extensive logging of model training progression - All of the model artifacts and metadata - A link to the code on GitHub with the code that ran that data. - Anything emitted to stdout (for logging) - etc. This allows for extreme reproducibility and insight that is very helpful. With these tools, the community can see if an "ensemble everything: method is used and how long the model takes to train and what resources are consumed, etc. Good question!