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Your error output indicates problems with mpi. You are using mpich, which seems to have trouble with our default MPI-command used to initiate the training.
A quick workaround should be to change this line

aml/aml/mlp.py

Line 364 in f8adf1a

mode = 'OpenMPI-single'

to

mode = 'MPI'

Other recommendations would be to check that n2p2 was compiled with mpich and that the number of cores accessible to the QbC run does not exceed n_tasks*n_core_task, which is 24 cores in the example.

Another workaround is to set n_core_task=1, which would trigger a serial n2p2 run. You can still parallelize up to the number of committee members with n_tasks.

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Answer selected by cschran
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@cenwanglai
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@adilalam45
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