Issue with cupyx

[jindalk]

Hi, I'm running the model.fit() command with gpu and am getting the following error:

File /ref/smlab/software/jindalk/.conda/envs/sc_env/lib/python3.10/site-packages/SEACells/core.py:574, in SEACells.fit(self, max_iter, min_iter, initial_archetypes)
    572 if max_iter < min_iter:
    573     raise ValueError("The maximum number of iterations specified is lower than the minimum number of iterations specified.")
--> 574 self._fit(max_iter=max_iter, min_iter=min_iter, initial_archetypes=initial_archetypes, initial_assignments=None)

File /ref/smlab/software/jindalk/.conda/envs/sc_env/lib/python3.10/site-packages/SEACells/core.py:531, in SEACells._fit(self, max_iter, min_iter, initial_archetypes, initial_assignments)
    519 def _fit(self, max_iter: int = 50, min_iter:int=10, initial_archetypes=None, initial_assignments=None):
    520     """
    521     Compute archetypes and loadings given kernel matrix K. Iteratively updates A and B matrices until maximum
    522     number of iterations or convergence has been achieved.
   (...)
    529 
    530     """
--> 531     self.initialize(initial_archetypes=initial_archetypes, initial_assignments=initial_assignments)
    533     converged = False
    534     n_iter = 0

File /ref/smlab/software/jindalk/.conda/envs/sc_env/lib/python3.10/site-packages/SEACells/core.py:172, in SEACells.initialize(self, initial_archetypes, initial_assignments)
    170 A = np.random.random((k, n))
    171 A /= A.sum(0)
--> 172 A = self._updateA(B, A)
    174 if self.verbose:
    175     print('Randomly initialized A matrix.')

File /ref/smlab/software/jindalk/.conda/envs/sc_env/lib/python3.10/site-packages/SEACells/core.py:335, in SEACells._updateA(self, B, A_prev)
    329 t = 0  # current iteration (determine multiplicative update)
    332 if self.gpu:
    333     # Use the GPU version of the update step
--> 335     K = cupyx.scipy.sparse.csc_matrix(self.K)
    337     Ag = cp.array(A)
    338     Kg = K

NameError: name 'cupyx' is not defined

I do have cupyx installed and working, so I think for some reason the model is not loading cupyx despite use_gpu being set as True

Thanks!

[AlinaKurjan]

have you tried importing cupyx separately?

[sitarapersad]

Hi, sorry! The GPU version is still a WIP and gives trouble on some machines. Please let me know if you are able to sucessfully import cupyx, but in the meanwhile the non-GPU version should work, albeit a bit more slowly.

[jaredtumiel]

Importing cupyx and cupy as cp at the top of ../SEACells/core.py seemed to do the trick as a temporary fix

[jindalk]

Yes! We ended up adding the import statements to core.py to fix the issue. Thanks!