mpiexec -n <num_nodes> python -m mpi4py.futures mpi\mpi_bagging.py
will create 1 dispatcher node with rank 0 and num_node-1 workers for the pool
"""
## generate Training set, Test set & V_0s
X_train, y_train = generate_train_set(Config.N_train, Config.Delta,
Config.d)
X_test, y_test = generate_test_set(Config.N_test, Config.Delta, Config.d)
V_0_train = generate_V_0(Config.N_train, Config.Delta, Config.d)
V_0_test = generate_V_0(Config.N_test, Config.Delta, Config.d)
logger.info(f"V_0_test = {V_0_test}")
reference = create_GPR(Config.N_train)
reference.fit(X_train, y_train)
f_X = reference.predict(X_test)
reference_error = normalized_error_VT(f_X, y_test, V_0_test)
logger.info(f"reference error : {reference_error}")
## MPI execute
results = []
with MPIPoolExecutor() as executor:
futures = []
# evaluate model for all points in grid by creating new mpi node
for r in Config.Ratios:
logger.info(f"starting evaluation for ratio {r}")
future = executor.submit(evaluate_boosting,
X_train.copy(),
## create logger
logger = generate_logger_MPI(LOGFILE, LOGLEVEL, rank)
logger.info(f"node with rank {rank} started")
if rank == 0:
"""
executed by main MPI process
mpiexec -n <num_nodes> python -m mpi4py.futures mpi\mpi_bagging.py
will create 1 dispatcher node with rank 0 and num_node-1 workers for the pool
"""
## generate model
gpr = create_GPR(Config.N_train)
model = SimpleBagger(1, 1, gpr)
## generate Training set & V_0
X_train, y_train = generate_train_set(Config.N_train, Config.Delta,
Config.d)
V_0 = generate_V_0(100000, Config.Delta, Config.d)
logger.info(f"V_0 = {V_0}")
## MPI execute
results = []
with MPIPoolExecutor() as executor:
futures = []
# evaluate model for all points in grid by creating new mpi node
for m in Config.M_grid:
for alpha in Config.alpha_grid:
