def dummy(input, i):
global counter
counter += 1
logger = generate_logger_MPI(LOGFILE, LOGLEVEL, rank)
logger.info(f"input={input},i= {i}, rank={MPI.COMM_WORLD.Get_rank()}")
logger.info(f"counter value = {counter}")
return [counter] * 2
def evaluate_model_MPI(*args, **kwargs):
"""
wrapper around the evaluate_model to allow for creating a logger with the MPI rank
adds logger to the kwargs and calls the evaluate model function
:return: evaluate_model(*args,**kwargs)
:rtype: list(float)
"""
logger = generate_logger_MPI(LOGFILE, LOGLEVEL, rank)
kwargs["logger"] = logger
return evaluate_boosting(*args, **kwargs)
def train_and_evaluate_wrapper(model, train_indices,alpha,m,i):
"""
wrapper around the evaluate_model to allow for creating a logger with the MPI rank
adds logger to the kwargs and calls the evaluate model function
:return: evaluate_model(*args,**kwargs)
:rtype: list(float)
"""
logger = generate_logger_MPI(LOGFILE,LOGLEVEL,rank)
logger.info(f"executing{i}-th train_evaluate for M,alpha= {m},{alpha} ")
X_train, y_train = DataContainer.X_train[train_indices],DataContainer.y_train[train_indices]
return train_and_evaluate(model, X_train, y_train, DataContainer.X_test_list)
adds logger to the kwargs and calls the evaluate model function
:return: evaluate_model(*args,**kwargs)
:rtype: list(float)
"""
logger = generate_logger_MPI(LOGFILE, LOGLEVEL, rank)
kwargs["logger"] = logger
return evaluate_boosting(*args, **kwargs)
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
## 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 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)
