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Not all environments support /dev/shm. It then crashes #42

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140 changes: 111 additions & 29 deletions hypermapper/local_search.py
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Original file line number Diff line number Diff line change
Expand Up @@ -616,6 +616,69 @@ def parallel_optimization_function(
for key, column in col_of_keys.items()
}

def sequential_local_search(
input_list,
output_list,
input_params,
param_space,
optimization_function_parameters,
proc,
):
sys.stdout.write_to_logfile("Running sequential_local_search \n")
while len(input_list) > 0:
config = input_list.pop(0)
if config is None:
break
iteration_data_array = {}
configuration = config["config"]
idx = config["idx"]
sys.stdout.write_to_logfile("Starting local search on configuration: " + str(configuration) + "\n")
while configuration is not None:
neighbors = get_neighbors(configuration, param_space)
neighbors = data_tuples_to_dict_list(neighbors, input_params)
function_values, feasibility_indicators = optimization_function(
configurations=neighbors, **optimization_function_parameters
)
function_values_size = len(function_values)
new_data_array = concatenate_list_of_dictionaries(
neighbors[:function_values_size]
)
new_data_array[scalarization_key] = function_values
if enable_feasible_predictor:
new_data_array[feasible_parameter] = feasibility_indicators
iteration_data_array = concatenate_data_dictionaries(
iteration_data_array, new_data_array
)
"""
The out of budget stopping criterion is removed for now, since it's not deemed relevant for this algorithm!
"""
if enable_feasible_predictor:
best_neighbor = get_min_feasible_configurations(
new_data_array, 1, scalarization_key, feasible_parameter
)
else:
best_neighbor = get_min_configurations(
new_data_array, 1, scalarization_key
)

for key in configuration:
configuration[key] = [configuration[key]]
if are_configurations_equal(best_neighbor, configuration, input_params):
sys.stdout.write_to_logfile("Local minimum found: " + str(best_neighbor) + "\n")
output_list.append(
{"data_array": iteration_data_array}
)
configuration = None
else:
sys.stdout.write_to_logfile((
"Replacing configuration by best neighbor: "
+ str(best_neighbor)
+ "\n"
))
configuration = {
key: value[0] for key, value in best_neighbor.items()
}

def parallel_multistart_local_search(
input_queue,
output_queue,
Expand Down Expand Up @@ -692,33 +755,53 @@ def parallel_multistart_local_search(
+ str(number_of_configurations)
+ "\n"
)
input_queue = JoinableQueue()
output_queue = Queue()
# puts each configuration in a queue to be evaluated in parallel
for idx in range(number_of_configurations):
input_queue.put(
{
"config": get_single_configuration(local_search_configurations, idx),
"idx": idx,
}
)
sys.stdout.write_to_logfile((f"{idx}, \n"))

for i in range(number_of_cpus):
input_queue.put(None)

result_array = {}
if number_of_cpus == 1:
parallel_multistart_local_search(
input_queue,
output_queue,
input_list = []
output_list = []
# puts each configuration in a list to be evaluated in sequence
for idx in range(number_of_configurations):
input_list.append(
{
"config": get_single_configuration(local_search_configurations, idx),
"idx": idx,
}
)
sys.stdout.write_to_logfile((f"{idx}, \n"))

sequential_local_search(
input_list,
output_list,
input_params,
param_space,
optimization_function_parameters,
0,
)
input_queue.join()

for i in range(number_of_configurations):
result = output_list.pop(0)
result_array = concatenate_data_dictionaries(result_array, result["data_array"])
data_array = concatenate_data_dictionaries(result_array, data_array)

else:

input_queue = JoinableQueue()
output_queue = Queue()
# puts each configuration in a queue to be evaluated in parallel
for idx in range(number_of_configurations):
input_queue.put(
{
"config": get_single_configuration(local_search_configurations, idx),
"idx": idx,
}
)
sys.stdout.write_to_logfile((f"{idx}, \n"))

for i in range(number_of_cpus):
input_queue.put(None)


processes = [
Process(
target=parallel_multistart_local_search,
Expand All @@ -739,19 +822,18 @@ def parallel_multistart_local_search(
process.start()
input_queue.join()

result_array = {}
for i in range(number_of_configurations):
result = output_queue.get()
sys.stdout.write_to_logfile(result["logstring"])
result_array = concatenate_data_dictionaries(result_array, result["data_array"])
data_array = concatenate_data_dictionaries(result_array, data_array)
for i in range(number_of_configurations):
result = output_queue.get()
sys.stdout.write_to_logfile(result["logstring"])
result_array = concatenate_data_dictionaries(result_array, result["data_array"])
data_array = concatenate_data_dictionaries(result_array, data_array)

input_queue.close()
output_queue.close()
input_queue.close()
output_queue.close()

if number_of_cpus != 1:
for i in range(len(processes)):
processes[i].join()
if number_of_cpus != 1:
for i in range(len(processes)):
processes[i].join()

local_search_time = datetime.datetime.now()
sys.stdout.write_to_logfile(
Expand Down