def main():
# Extract the trunk directory and append to PYTHONPATH cause we need
# the specific operation process class
# backends, environments, pySPACE are the parts to go back
file_path = os.path.dirname(os.path.abspath(__file__))
pyspace_path = file_path[:file_path.rfind('pySPACE') - 1]
if pyspace_path not in sys.path:
sys.path.append(pyspace_path)
# Get the file name of the process to unpickle and call
proc_file_name = sys.argv[1]
# Unpickle the process
with open(proc_file_name, 'rb') as proc_file:
proc = pickle.load(proc_file)
# noinspection PyBroadException
try:
# Do the actual call
proc()
except Exception:
logging.exception("Error while executing the process:")
finally:
# Deleted the proc_file since we don't need it any more
try:
os.unlink(proc_file_name)
except IOError:
logging.exception("Error while unlinking the process file:")
# Inform the listener socket in the Backend that this job has finished
send_sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
host = sys.argv[2]
port = int(sys.argv[3])
send_sock.connect((host, port))
try:
LoadLevelerComHandler.send_message(
send_sock, host, port, LoadLevelerComHandler.MESSAGES.FINISHED)
finally:
send_sock.shutdown(socket.SHUT_RDWR)
send_sock.close()
def main():
# Extract the trunk directory and append to PYTHONPATH cause we need
# the specific operation process class
# backends, environments, pySPACE are the parts to go back
file_path = os.path.dirname(os.path.abspath(__file__))
pyspace_path = file_path[:file_path.rfind('pySPACE')-1]
if pyspace_path not in sys.path:
sys.path.append(pyspace_path)
# Get the file name of the process to unpickle and call
proc_file_name = sys.argv[1]
# Unpickle the process
with open(proc_file_name, 'rb') as proc_file:
proc = pickle.load(proc_file)
# noinspection PyBroadException
try:
# Do the actual call
proc()
except Exception:
logging.exception("Error while executing the process:")
finally:
# Deleted the proc_file since we don't need it any more
try:
os.unlink(proc_file_name)
except IOError:
logging.exception("Error while unlinking the process file:")
# Inform the listener socket in the Backend that this job has finished
send_sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
host = sys.argv[2]
port = int(sys.argv[3])
send_sock.connect((host, port))
try:
LoadLevelerComHandler.send_message(send_sock, host, port, LoadLevelerComHandler.MESSAGES.FINISHED)
finally:
send_sock.shutdown(socket.SHUT_RDWR)
send_sock.close()
def main():
# Parse input
dir_path = sys.argv[1]
train_instances_file_name = os.path.join(dir_path,
'subflow_data.pickle')
runs = eval(sys.argv[4])
# construct socket to communicate with backend
send_sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
send_sock.connect((sys.argv[2], int(sys.argv[3])))
try:
flow_ids = eval(sys.argv[5])
for flow_id in flow_ids:
flow_file_name = os.path.join(dir_path, flow_id + '.pickle')
# Unpickle the flow and train_instances
with open(flow_file_name, 'rb') as flow_file:
flow = pickle.load(flow_file)
with open(train_instances_file_name, 'rb') as train_instances_file:
train_instances = pickle.load(train_instances_file)
try:
# Execute the flow
# .. note:: the here executed flows can not store anything
# meta data of result collection is NOT updated!
_, result_collection = flow(train_instances=train_instances, runs=runs)
# Store results
result_file_name = flow_file_name.replace('.pickle', '_result.pickle')
with open(result_file_name, 'wb') as result_file:
pickle.dump(result_collection, result_file, protocol=pickle.HIGHEST_PROTOCOL)
finally:
# Delete the flow_file since we don't need it any more
# training_file is may be used by other flows so don't delete it
os.remove(flow_file_name)
# Inform Backend that this flow has finished
send_sock = LoadLevelerComHandler(send_sock, sys.argv[2], int(sys.argv[3]),
LoadLevelerComHandler.MESSAGES.FINISHED, flow_id)
# give backend some time to get information
time.sleep(1)
finally:
send_sock.shutdown(socket.SHUT_RDWR)
send_sock.close()
def main():
# Parse input
dir_path = sys.argv[1]
train_instances_file_name = os.path.join(dir_path, 'subflow_data.pickle')
runs = eval(sys.argv[4])
# construct socket to communicate with backend
send_sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
send_sock.connect((sys.argv[2], int(sys.argv[3])))
try:
flow_ids = eval(sys.argv[5])
for flow_id in flow_ids:
flow_file_name = os.path.join(dir_path, flow_id + '.pickle')
# Unpickle the flow and train_instances
with open(flow_file_name, 'rb') as flow_file:
flow = pickle.load(flow_file)
with open(train_instances_file_name, 'rb') as train_instances_file:
train_instances = pickle.load(train_instances_file)
try:
# Execute the flow
# .. note:: the here executed flows can not store anything
# meta data of result collection is NOT updated!
_, result_collection = flow(train_instances=train_instances,
runs=runs)
# Store results
result_file_name = flow_file_name.replace(
'.pickle', '_result.pickle')
with open(result_file_name, 'wb') as result_file:
pickle.dump(result_collection,
result_file,
protocol=pickle.HIGHEST_PROTOCOL)
finally:
# Delete the flow_file since we don't need it any more
# training_file is may be used by other flows so don't delete it
os.remove(flow_file_name)
# Inform Backend that this flow has finished
send_sock = LoadLevelerComHandler(
send_sock, sys.argv[2], int(sys.argv[3]),
LoadLevelerComHandler.MESSAGES.FINISHED, flow_id)
# give backend some time to get information
time.sleep(1)
finally:
send_sock.shutdown(socket.SHUT_RDWR)
send_sock.close()