def wait_for_model()

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

• 32 lines of code
• 5 McCabe index (conditional complexity)

def wait_for_model(rest_port, model_name, timeout_seconds, pid=None):
    """
    Notice:
    The calculation for retry count based on timeout_seconds might introduce a small delta (0.1s) for each retry
    which might cause total timeout longer than timeout_seconds
    """
    tfs_url = "http://localhost:{}/v1/models/{}".format(rest_port, model_name)
    start = time.time()
    try:
        session = requests.Session()
        backoff_factor = 0.1
        # sleep = {backoff factor} * (2 ^ ({number of retries so far} - 1))
        retry_count = retry_from_timeout(timeout_seconds, backoff_factor)
        retries = Retry(total=retry_count, backoff_factor=backoff_factor)
        session.mount("http://", requests.adapters.HTTPAdapter(max_retries=retries))
        log.info(
            "Trying to connect with model server: {} with timeout : {} and retry : {}".format(
                tfs_url, timeout_seconds, retry_count
            )
        )
        response = session.get(tfs_url, timeout=0.1)
        log.info(
            f"tfs response status_code: {response.status_code} with content : {json.loads(response.content)}"
        )
        end = time.time()
        if response.status_code == 200:
            if is_model_ready(response):
                return
            elif wait_for_model_ready(tfs_url, timeout_seconds - int(end - start)):
                return

        raise MultiModelException(408, "Timed out after {} seconds".format(timeout_seconds), pid)
    except (
        ConnectionRefusedError,
        NewConnectionError,
        MaxRetryError,
        requests.exceptions.ConnectionError,
    ):
        raise MultiModelException(408, "Timed out after {} seconds".format(timeout_seconds), pid)