def _create_tfs_config(self): models = tfs_utils.find_models() if not models: raise ValueError('no SavedModel bundles found!') if self._tfs_default_model_name == 'None': default_model = os.path.basename(models[0]) if default_model: self._tfs_default_model_name = default_model log.info('using default model name: {}'.format( self._tfs_default_model_name)) else: log.info('no default model detected') # config (may) include duplicate 'config' keys, so we can't just dump a dict config = 'model_config_list: {\n' for m in models: config += ' config: {\n' config += ' name: "{}",\n'.format(os.path.basename(m)) config += ' base_path: "{}",\n'.format(m) config += ' model_platform: "tensorflow"\n' config += ' }\n' config += '}\n' log.info('tensorflow serving model config: \n%s\n', config) with open('/sagemaker/model-config.cfg', 'w') as f: f.write(config)
def _create_tfs_config(self): models = tfs_utils.find_models() if not models: raise ValueError("no SavedModel bundles found!") if self._tfs_default_model_name == "None": default_model = os.path.basename(models[0]) if default_model: self._tfs_default_model_name = default_model log.info("using default model name: {}".format( self._tfs_default_model_name)) else: log.info("no default model detected") # config (may) include duplicate 'config' keys, so we can't just dump a dict config = "model_config_list: {\n" for m in models: config += " config: {\n" config += " name: '{}'\n".format(os.path.basename(m)) config += " base_path: '{}'\n".format(m) config += " model_platform: 'tensorflow'\n" config += " model_version_policy: {\n" config += " specific: {\n" for version in tfs_utils.find_model_versions(m): config += " versions: {}\n".format(version) config += " }\n" config += " }\n" config += " }\n" config += "}\n" log.info("tensorflow serving model config: \n%s\n", config) with open(self._tfs_config_path, "w") as f: f.write(config)