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Under the Anaconda 2023.03 distribution I have installed Lightfm 1.17. The interaction matrix is small, shape (671, 9066) - coming from the small movie lens dataset. When running the code:
from scipy import sparse
from lightfm import LightFM
x = sparse.csr_matrix(interactions.values)
model = LightFM(no_components= 30, loss='warp',k=15)
model.fit(x,epochs=30,num_threads = 4)
in Jupyter, the kernel dies unexpectedly, a pop-up shows-up: Kernel Restarting
The kernel appears to have died. It will restart automatically.
In Spyder I get a similar behaviour, with the kernel restarting. I traced it down to the _run_one_epoch method in lightfm.
Any idea of the potential cause is appreciated.
The text was updated successfully, but these errors were encountered:
sbildea
changed the title
Hello.
Lightfm 1.17 - kernel dies in Jupyter, Spyder when fitting
May 11, 2023
Maybe you can try without the loss='warp' parameter (using default logistic loss).
If it works without the parameter it may be the same issue as I reported in #675.
Microsoft Windows? Then the same thing here. it seems that the 1.17version does not work well on windows, same code runs as expected under ubuntu18(WSL), so I switch my workspace to ubuntu instead of spending more time on solving the window environment issue.
Hello,
Under the Anaconda 2023.03 distribution I have installed Lightfm 1.17. The interaction matrix is small, shape (671, 9066) - coming from the small movie lens dataset. When running the code:
from scipy import sparse
from lightfm import LightFM
x = sparse.csr_matrix(interactions.values)
model = LightFM(no_components= 30, loss='warp',k=15)
model.fit(x,epochs=30,num_threads = 4)
in Jupyter, the kernel dies unexpectedly, a pop-up shows-up: Kernel Restarting
The kernel appears to have died. It will restart automatically.
In Spyder I get a similar behaviour, with the kernel restarting. I traced it down to the _run_one_epoch method in lightfm.
Any idea of the potential cause is appreciated.
The text was updated successfully, but these errors were encountered: