def serve(self, config, model_path, gpuid=0):
server_config_path = os.path.join(self._output_dir, "conf.json")
with open(server_config_path, "w") as server_config_file:
json.dump(
{
"models_root":
model_path,
"models": [{
"id": 0,
"model": _RELEASED_MODEL_NAME,
"opt": _trans_options(config, gpuid),
}],
},
server_config_file,
)
port = serving.pick_free_port()
process = utils.run_cmd(
[
"onmt_server",
"--ip",
"127.0.0.1",
"--port",
str(port),
"--url_root",
"/translator-backend",
"--config",
server_config_path,
],
background=True,
)
return process, {"port": port}
def serve(self, config, model_path, gpuid=0):
# Start a new tensorflow_model_server instance.
batching_parameters = self._generate_batching_parameters(config.get('serving'))
port = serving.pick_free_port()
model_name = '%s%s' % (config['source'], config['target'])
cmd = ['tensorflow_model_server',
'--port=%d' % port,
'--model_name=%s' % model_name,
'--model_base_path=%s' % model_path,
'--enable_batching=true',
'--batching_parameters_file=%s' % batching_parameters]
process = utils.run_cmd(cmd, background=True)
info = {'port': port, 'model_name': model_name}
return process, info
def serve(self, config, model_path, gpuid=0):
if isinstance(gpuid, list):
logger.warning('no support of multi-gpu for opennmt_lua serve')
gpuid = gpuid[0]
model_file = os.path.join(model_path, 'model_released.t7')
host_ip = '127.0.0.1'
port = pick_free_port()
options = self._get_translation_options(config, model_file, gpuid=gpuid)
options['host'] = host_ip
options['port'] = port
options['withAttn'] = 'true'
options['mode'] = 'space'
options = _build_cmd_line_options(options)
process = self._run_command(
['th', 'tools/rest_translation_server.lua'] + options, background=True)
info = {'endpoint': 'http://{}:{}/translator/translate'.format(host_ip, port)}
return process, info
def serve(self, config, model_path, gpuid=0):
# Export model (deleting any previously exported models).
export_base_dir = os.path.join(model_path, "export")
if os.path.exists(export_base_dir):
shutile.rmtree(export_base_dir)
export_dir = self._export_model(config, model_path)
# Start a new tensorflow_model_server instance.
batching_parameters = self._generate_batching_parameters(
config.get('serving'))
port = serving.pick_free_port()
model_name = '%s%s' % (config['source'], config['target'])
cmd = [
'tensorflow_model_server',
'--port=%d' % port,
'--model_name=%s' % model_name,
'--model_base_path=%s' % os.path.dirname(export_dir),
'--enable_batching=true',
'--batching_parameters_file=%s' % batching_parameters
]
process = utils.run_cmd(cmd, background=True)
info = {'port': port, 'model_name': model_name}
return process, info
