speed

[bjstewart1]

I'm trying to train topic models on gene expression and ATAC data.
Even with GPU, I'm finding this very slow particularly for the ATAC data. - for this i've slimmed the data down to ~10K peaks from 50K cells, but ideally would like to use closer to 100K peaks.
The tutorial suggests caching data to disk
model.write_ondisk_dataset(train, dirname = './....' is taking several hours,
equally model.get_learning_rate_bounds is taking ~5 hours.
that's before we even get to .fit()

the output of import torch torch.cuda.is_available()
is True

Is this expected behaviour?
Do you have suggestions for speedup?