a few general questions: 1. with many of the machine-learning tutorials, i notice that the whole workflow is typically wrapped into one task. it seems natural to me that this approach doesn't take advantage of a lot of flyte's features (caching, being able to share tasks across workflows, etc). is there a reason that most of the ML examples are written this way? 2. i often notice that after i tear down a sandbox cluster, running

docker system prune -a --volumes

frees up a sometimes surprising amount of space (i ran the mnist tutorial a few times and pruning the volumes revealed that it had used ~14GB on my machine). is there a way to reduce/mitigate this or is it just a necessary result of saving workflow executions?

@few-animal-62717, 1. Yeah! I think we’ll need to have more tasks, at least for the train and test cases. It just made sense then, not sure why. But if we could have separate tasks for data engineering, train and test, that would be great. Will see if I can modify the examples to have separate tasks for various functionalities. 2. Um, not sure if we could mitigate this. @freezing-airport-6809 @high-accountant-32689, any ideas?

@few-animal-62717 this is docker desktop. It does not automatically prune images. It is safe to prune images. Flyte is not saving them. I think There is a gc that you can enable in docker desktop. Sadly I do not know if a better way of automatically cleaning it

great, thanks for the answers guys