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After an investigation of this problem, I found that this failure is caused by scikit-optimize library used by Bagua. In detail, scikit-optimize library currently uses an old builtin type of numpy (i.e., numpy.int), which is deprecated by numpy>=1.24.0.
To avoid this problem, users can choose to use numpy<1.24.0.
In the meantime, scikit-optimize is trying to fix this problem (PR: scikit-optimize/scikit-optimize#1123). After that, users can use the updated scikit-optimize with numpy>=1.24.0 in Bagua.
see this:Lightning-AI/pytorch-lightning#16225
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