  We report the development of a python-based auxiliary-field quantum Monte
Carlo (AFQMC) program, ipie, with preliminary timing benchmarks and new AFQMC
results on the isomerization of [Cu$_2$O$_2$$]^{2+}$. We demonstrate how
implementations for both central and graphical processing units (CPUs and GPUs)
are achieved in ipie. We show an interface of ipie with PySCF as well as a
straightforward template for adding new estimators to ipie. Our timing
benchmarks against other C++ codes, QMCPACK and Dice, suggest that ipie is
faster or similarly performing for all chemical systems considered on both CPUs
and GPUs. Our results on [Cu$_2$O$_2$$]^{2+}$ using selected configuration
interaction trials show that it is possible to converge the ph-AFQMC
isomerization energy between bis($\mu$-oxo) and $\mu$-$\eta^2$:$\eta^2$ peroxo
configurations to the exact known results for small basis sets with $10^5$ to
$10^6$ determinants. We also report the isomerization energy with a
quadruple-zeta basis set with an estimated error less than a kcal/mol, which
involved 52 electrons and 290 orbitals with $10^6$ determinants in the trial
wavefunction. These results highlight the utility of ph-AFQMC and ipie for
systems with modest strong correlation and large-scale dynamic correlation.
