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