The pypi version of numpy is bundled with the openblas library, which allocates some pretty huge (30 MB/CPU core) buffers on import of the library.
So any code that imports openpyxl in a multiprocessing environment where numpy is available might cause massive increases of committed memory usage. (e.g. for a 28-Core Xeon CPU, you gain around 750 MB committed memory per process, multiplied by the usual multiprocessing spawn num cpu subprocesses, this just eats 22 GB of RAM.)
I reported the issue for numpy at: https://github.com/numpy/numpy/issues/13432
But it would be nice, if openpyxl was a bit more careful when importing numpy for its very limited usage.