code for multi-gpu training

[Adasunnylily]

hi，it seems like the code provided is only for one GPU training, could you please provide the code for multi-gpu training?

[YueYANG1996]

We didn't implement the multi-gpu training, since we just train a single linear layer, the training of LaBo is very fast (one epoch of entire ImageNet only takes around 10 minutes). The time-consuming part of the code could be the feature precompute, but it will only run once. Feel free to implement multi-gpu by yourself and I believe it is easy to do with pytorch-lightning.

[Adasunnylily]

Many thanks for your help, I will try it later.
I have met another problem with using apricot when I implement experiments on cub. I follow the cfg provided and in it the parameter concept_select_fn = "submodular". But I got this error

File "/opt/conda/lib/python3.7/site-packages/apricot/functions/base.py", line 202, in fit
raise ValueError("X cannot contain negative values or must be entirely "
ValueError: X cannot contain negative values or must be entirely negative values

when execute the code
"selected = selector.fit(augmented_concept_features).ranking"
in /home/zhangyx3/LaBo/models/select_concept/select_algo.py,
I print the augumented_concept_features which is composed of text features and mi_score and found it indeed has both positive and negative values, but the fit func in apricot only support all positive or all negative values,
I couldn't find which part is wrong so I tried to solve this error by adding the opposite of the minimum value then changing all values in the "augumented_concept_features" into positive values, it can run then, but the result I got is about 2 point decrease compared to the results provided in the paper (table 10) on cub dataset almost in all settings(4、8、16、all-shots).
Could you please tell me where probably this error comes (I haven't change anything of the code), and is there any missing preprocess for augmented_concept_features that I can help improve my results?
Many Thanks!!!

[YueYANG1996]

check this: #1 (comment)