GPU and CPU usage about contextualized-topic-models HOT 2 CLOSED

can you share something for me to reproduce this error? e.g., a sample of the npy for example.

My current best guess would be that after

ctm.load("/home/username/saved_topic_model/", 49)

the weights are not on cuda and so you might need to manually do something like ctm.model.to("device")

from contextualized-topic-models.

Hi,

1. Yes you're right, when I first initialized CTM, it's in GPU but after I loaded trained model, it moves to cpu. Is there any pointers how I can fix this?

Update: I trained CTM on CPU and load it in GPU, that's why it causes this problem. The way I solve it is changing "USE_CUDA" and "device" to true and "cuda" respectively in the for loop here

2. Another problem I am struggling with is the workers problem. I keep getting this warning and when I run topic model along with BART in the same program, it causes CUDA issue. I tried to divide it by two (i.e. int(mp.cpu_count/2) ) but the warning still says I am using 80.

Update: This is caused by the same problem, trained and tested on different machine. The num_data_loader_workers was set to 80 because mp.cpu_count was 80 on my previous device. I modified code here such that if k == "num_data_loader_workers": v = mp.cpu_count()

Thanks a lot!

from contextualized-topic-models.