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args = np.array([arg.ctypes.data for arg in args], dtype=np.uint64)
```
Now the kernel can be launched:
Specifically, how to pass typed pointers and scalars is really unclear.
This also raises the question: It seems NumPy has to be a run-time dependency of cuda-python (which is completely fine, since it is impossible that users have GPU projects installed to an environment where NumPy does not exist, we just need to ensure this intent is expressed as a package dependency).
@gigony complained the following example is unclear compared to CuPy's RawKernel, and I agree.
cuda-python/docs_src/source/overview.md
Lines 208 to 223 in e1e3325
Specifically, how to pass typed pointers and scalars is really unclear.
This also raises the question: It seems NumPy has to be a run-time dependency of cuda-python (which is completely fine, since it is impossible that users have GPU projects installed to an environment where NumPy does not exist, we just need to ensure this intent is expressed as a package dependency).
For comparison, here's
cupy.RawKernel
's documentationhttps://docs.cupy.dev/en/stable/user_guide/kernel.html#kernel-arguments
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