Comments (3)
cc @rjzamora
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Thanks for submitting an issue @Michaw1985 !
I'm not sure if da.linalg.solve
is currently supported for cupy
backed data. I see some errors when I run a simple case locally:
import dask.array as da
import cupy as cp
a = da.asarray(cp.array([[3, 2, 0], [1, -1, 0], [0, 5, 1]]))
b = da.asarray(cp.array([2, 4, -1]))
z = da.linalg.solve(a, b)
z.compute()
...
562 @set_module('numpy')
563 def asarray_chkfinite(a, dtype=None, order=None):
564 """Convert the input to an array, checking for NaNs or Infs.
565
566 Parameters
(...)
624
625 """
--> 626 a = asarray(a, dtype=dtype, order=order)
627 if a.dtype.char in typecodes['AllFloat'] and not np.isfinite(a).all():
628 raise ValueError(
629 "array must not contain infs or NaNs")
File cupy/_core/core.pyx:1475, in cupy._core.core._ndarray_base.__array__()
TypeError: Implicit conversion to a NumPy array is not allowed. Please use `.get()` to construct a NumPy array explicitly.
I can probably look into this error because I can reproduce it locally. However, The error you shared above is clearly a bit different. Are you able to share a minimal reproducer?
from dask.
I tried to get it reproduced with a minimal reproducer, but failed. I tried a simplified version of my code, but only got the error you got. I think it has the same cause, but if you want I can send you my code privately for further review.
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