def main():
if len(sys.argv) != 2:
print('USAGE: run_model.py [param_file]')
sys.exit(-1)
log_keras_version_info()
param_file = sys.argv[1]
param_dict = pyhocon.ConfigFactory.parse_file(param_file)
params = Params(replace_none(param_dict))
log_dir = params.get("model_serialization_prefix", None) # pylint: disable=no-member
if log_dir is not None:
sys.stdout = TeeLogger(log_dir + "_stdout.log", sys.stdout)
sys.stderr = TeeLogger(log_dir + "_stderr.log", sys.stderr)
handler = logging.FileHandler(log_dir + "_python_logging.log")
handler.setLevel(logging.INFO)
handler.setFormatter(
logging.Formatter(
'%(asctime)s - %(levelname)s - %(name)s - %(message)s'))
logging.getLogger().addHandler(handler)
shutil.copyfile(param_file, log_dir + "_model_params.json")
model_type = params.pop_choice('model_class', concrete_models.keys())
model_class = concrete_models[model_type]
model = model_class(params)
if model.can_train():
logger.info("Training model")
model.train()
else:
logger.info(
"Not enough training inputs. Assuming you wanted to load a model instead."
)
# TODO(matt): figure out a way to specify which epoch you want to load a model from.
model.load_model()
K.clear_session()
def serve(port: int, config_file: str):
# read in the Typesafe-style config file
solver_params = ConfigFactory.parse_file(config_file)
params = Params(replace_none(solver_params))
model_type = params.pop_choice('model_class', concrete_models.keys())
solver_class = concrete_models[model_type]
solver = solver_class(params)
global mySolver
mySolver = SolverServer(solver)
# start the server on the specified port
print("starting server")
app.run(host='0.0.0.0')