def _generate_ts_config_properties()

in src/sagemaker_pytorch_serving_container/torchserve.py [0:0]

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

def _generate_ts_config_properties():
    env = environment.Environment()
    user_defined_configuration = {
        "default_response_timeout": env.model_server_timeout,
        "default_workers_per_model": env.model_server_workers,
        "inference_address": "http://0.0.0.0:{}".format(env.inference_http_port),
        "management_address": "http://0.0.0.0:{}".format(env.management_http_port),
    }

    ts_env = ts_environment.TorchServeEnvironment()

    if ts_env.is_env_set() and not ENABLE_MULTI_MODEL:
        models_string = f'''{{\\
        "{DEFAULT_TS_MODEL_NAME}": {{\\
            "1.0": {{\\
                "defaultVersion": true,\\
                "marName": "{DEFAULT_TS_MODEL_NAME}.mar",\\
                "minWorkers": {ts_env._min_workers},\\
                "maxWorkers": {ts_env._max_workers},\\
                "batchSize": {ts_env._batch_size},\\
                "maxBatchDelay": {ts_env._max_batch_delay},\\
                "responseTimeout": {ts_env._response_timeout}\\
                }}\\
            }}\\
        }}'''
        user_defined_configuration["models"] = models_string
        logger.warn("Sagemaker TS environment variables have been set and will be used "
                    "for single model endpoint.")

    custom_configuration = str()

    for key in user_defined_configuration:
        value = user_defined_configuration.get(key)
        if value:
            custom_configuration += "{}={}\n".format(key, value)

    if ENABLE_MULTI_MODEL:
        default_configuration = utils.read_file(MME_TS_CONFIG_FILE)
    else:
        default_configuration = utils.read_file(DEFAULT_TS_CONFIG_FILE)

    return default_configuration + custom_configuration