def _create_tfs_config()

in tensorflow/inference/docker/build_artifacts/sagemaker/serve.py [0:0]

• 28 lines of code
• 6 McCabe index (conditional complexity)

    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", encoding="utf8") as f:
            f.write(config)