2 and 3 - I want to manage access control over the RestAPI

Long story short, put a load-balancer in front of the entire thing (see the k8s setup), and have the load-balancer verify JWT token as authentication (this is usually the easiest)

1- Exactly, custom code

Yes, we need to add a custom example there (somehow forgotten)
Could you open an Issue for that?
in the meantime:
` #

Preprocess class Must be named "Preprocess"

No need to inherit or to implement all methods

lass Preprocess(object):
"""
Preprocess class Must be named "Preprocess"
Otherwise there are No limitations, No need to inherit or to implement all methods
Notice! This is not thread safe! the same instance may be accessed from multiple threads simultaneously
"""

def __init__(self):
    # set internal state, this will be called only once. (i.e. not per request)
    # it will also set the internal model_endpoint to reference the specific model endpoint object being served
    self.model_endpoint = None  # type: clearml_serving.serving.endpoints.ModelEndpoint
    self._model = None

def load(self, local_file_name: str) -> Optional[Any]:  # noqa
    """
    Optional: provide loading method for the model
    useful if we need to load a model in a specific way for the prediction engine to work
    :param local_file_name: file name / path to read load the model from
    :return: Object is stored on self._model
    """
    pass

def preprocess(
        self,
        body: dict,
        state: dict,
        collect_custom_statistics_fn: Optional[Callable[[dict], None]],
) -> Any:  # noqa
    """
    Optional: do something with the request data, return any type of object.
    The returned object will be passed as is to the inference engine
    :param body: dictionary as recieved from the RestAPI
    :param state: Use state dict to store data passed to the post-processing function call.
        This is a per-request state dict (meaning a new empty dict will be passed per request)
        Usage example:
        >>> def preprocess(..., state):
                state['preprocess_aux_data'] = [1,2,3]
        >>> def postprocess(..., state):
                print(state['preprocess_aux_data'])
    :param collect_custom_statistics_fn: Optional, if provided allows to send a custom set of key/values
        to the statictics collector servicd.
        None is passed if statiscs collector is not configured, or if the current request should not be collected
        Usage example:
        >>> print(body)
        {"x0": 1, "x1": 2}
        >>> if collect_custom_statistics_fn:
        >>>   collect_custom_statistics_fn({"x0": 1, "x1": 2})
    :return: Object to be passed directly to the model inference
    """
    return body

def postprocess(
        self,
        data: Any,
        state: dict,
        collect_custom_statistics_fn: Optional[Callable[[dict], None]],
) -> dict:  # noqa
    """
    Optional: post process the data returned from the model inference engine
    returned dict will be passed back as the request result as is.
    :param data: object as recieved from the inference model function
    :param state: Use state dict to store data passed to the post-processing function call.
        This is a per-request state dict (meaning a dict instance per request)
        Usage example:
        >>> def preprocess(..., state):
                state['preprocess_aux_data'] = [1,2,3]
        >>> def postprocess(..., state):
                print(state['preprocess_aux_data'])
    :param collect_custom_statistics_fn: Optional, if provided allows to send a custom set of key/values
        to the statictics collector servicd.
        None is passed if statiscs collector is not configured, or if the current request should not be collected
        Usage example:
        >>> if collect_custom_statistics_fn:
        >>>   collect_custom_statistics_fn({"y": 1})
    :return: Dictionary passed directly as the returned result of the RestAPI
    """
    return data

def process(
        self,
        data: Any,
        state: dict,
        collect_custom_statistics_fn: Optional[Callable[[dict], None]],
) -> Any:  # noqa
    """
    Optional: do something with the actual data, return any type of object.
    The returned object will be passed as is to the postprocess function engine
    :param data: object as recieved from the preprocessing function
    :param state: Use state dict to store data passed to the post-processing function call.
        This is a per-request state dict (meaning a dict instance per request)
        Usage example:
        >>> def preprocess(..., state):
                state['preprocess_aux_data'] = [1,2,3]
        >>> def postprocess(..., state):
                print(state['preprocess_aux_data'])
    :param collect_custom_statistics_fn: Optional, if provided allows to send a custom set of key/values
        to the statictics collector servicd.
        None is passed if statiscs collector is not configured, or if the current request should not be collected
        Usage example:
        >>> if collect_custom_statistics_fn:
        >>>   collect_custom_statistics_fn({"type": "classification"})
    :return: Object to be passed tp the post-processing function
    """
    return data `

Does that help?