That's honestly no way to get anything done... Unless you're replicating someone else's thing exactly, You can't really get by with one training process. You w

That's honestly no way to get anything done... Unless you're replicating someone else's thing exactly, You can't really get by with one training process. You want to be trying different things, and running a few samples of each configuration to account for random variation. I'm not even talking about decadent hyper-parameter sweeps to fine-tune. I'm talking like, how wide do I need my layers to be, what optimizers are good, how deep should I make the network, etc. I want to be training 5-10 models at a time minimum. 20-30 would be much more productive. If I can only train one model at once, it's not really worth the effort — it's better to work on one of the other tickets for the library.