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__array__ and __array_wrap__
See original GitHub issueA question about the Numpy array interface. cupy.ndarray implements __array__, but it doesn’t work since np.asarray(cupy_array) raises an exception:
ValueError: object __array__ method not producing an array
So unless you have other plans for this method, it should be changed to return a Numpy array.
Do you want a PR? I’d also like to have the __array_wrap_ method, so I’d implement that one as well.
Issue Analytics
- State:
- Created 6 years ago
- Reactions:1
- Comments:10 (9 by maintainers)
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CuPy uses asynchronous CPU to GPU memory copy by default mode. This is very easy feature because CUDA supports asynchronous execution (kernel launch). But, CuPy does not use asynchronous GPU to CPU memory copy by default. NumPy only supports synchronous execution. So, Python code waits to finish GPU to CPU memory copy. This causes hard to find performance problems.
It is good if the user can select whether to use
__array_wrap__or not by environment variable or global configure flag.We have no plan for it currently, but we will reopen if someone continues the discussion to support it.