Frequently asked questions
How to use the GPU?
Using a GPU may significantly speed-up the training or inference of Novae.
If you have a valid GPU for PyTorch, you can set the accelerator argument (e.g., one of ["cpu", "gpu", "tpu", "hpu", "mps", "auto"]) in the following methods: model.fit(), model.fine_tune(), model.compute_representations().
When using a GPU, we also highly recommend setting multiple workers to speed up the dataset __getitem__. For that, you'll need to set the num_workers argument in the previous methods, according to the number of CPUs available (num_workers=8 is usually a good value).
For more details, refer to the API of the PyTorch Lightning Trainer and to the API of the PyTorch DataLoader.
How to load a pretrained model?
We highly recommend to load a pretrained Novae model instead of re-training from scratch. For that, choose an available Novae model name on our HuggingFace collection, and provide this name to the model.save_pretrained() method:
from novae import Novae
model = Novae.from_pretrained("MICS-Lab/novae-human-0") # or any valid model name
How to save my own model?
If you have trained or fine-tuned your own Novae model, you can save it for later use. For that, use the model.save_pretrained() method as below:
Then, you can load this model back via the model.from_pretrained() method:
How to disable or enable lazy loading?
By default, lazy loading is used only on large datasets. To enforce a specific behaviour, you can do the following:
# never use lazy loading
novae.settings.disable_lazy_loading()
# always use lazy loading
novae.settings.enable_lazy_loading()
# use lazy loading only for AnnData objects with 1M+ cells
novae.settings.enable_lazy_loading(n_obs_threshold=1_000_000)
How to update the logging level?
The logging level can be update as below:
import logging
from novae import log
log.setLevel(logging.ERROR) # or any other level, e.g. logging.DEBUG
How to contribute?
If you want to contribute, check our contributing guide.
How to resolve any other issue?
If you have any bug/question/suggestion, don't hesitate to open a new issue.