OK, if participants don’t have the input data, I assume you are setting up machines for them to train and evaluate their models?



 turian:

In Docker, I’ve found that shared mounts sometimes have slow (particularly write) performance. So if the dataset lives outside Docker, reads might be slow?


I haven’t seen this issue in docker and am heavily using it for my workloads.
A slow data loading can of course be met, if you are using a mounted folder from a network drive, but that’s not depending on docker but the network speed, so I would recommend to copy the data to a fast local storage.