Roman Feldbauer

Not very actively, though I might find some time to do maintenance tasks like this (which likely only requires changing `np.float` to `np.float32` in the code; PR welcome).

@jolespin Thank you, but now I believe this is in fact not necessary: I have just tested skhubness==0.30 with Python=3.10 and recent numpy, sklearn etc. This runs the example code...

Thx for the feedback. Which line did you change? It is ready enough that I pushed the changes to main. Since the docs are not yet fully updated to reflect...

Last time I tried this, setting this env variable was sufficient to control the number of CPU cores. In general, I'd suggest to train models on GPU rather than CPU...

In this case you might want to try training a small model, that is, not on root or bacteria level, but something like [tax 5794](http://eggnog5.embl.de/download/eggnog_5.0/per_tax_level/5794/). This might finish in finite...

While at it, we might want to let our ` ShuffledProteinIterableDataset` inherit from the [`BufferedShuffleDataset`](https://pytorch.org/docs/stable/_modules/torch/utils/data/dataset.html#BufferedShuffleDataset) newly introduced in PyTorch 1.8. From a first glance, it does exactly the same thing.

To summarize some key points of the recent discussion: Models will receive a metadata field that holds the following information, - UUID as model identifier - Date & timestamp of...

Thanks for the suggestion! We'll look into it.

Hi, I'm not an expert in ARGs, but a few thoughts: 1. From a purely technical perspective, nothing speaks against trying this. Since there's not too much data here, training...