TensorFlow Serving export signature without arguments - tensorflow

Question

I would like to add extra signature to SavadModel, which will return business description and serve it with TensorFlow Serving.
#tf.function
def info():
return json.dumps({
'name': 'My model',
'description': 'This is model description.',
'project': 'Product ABCD',
'type': 'some_type',
...
})
As is written in TensorFlow Core manual https://www.tensorflow.org/guide/saved_model#identifying_a_signature_to_export, I can easily export signature which accepts arguments providing tf.TensorSpec.
Is it possible to export signature without arguments and call it on server?
Added after #EricMcLachlan comments:
When I try to call a function without defined signature (input_signature=[]) with a code like this:
data = json.dumps({"signature_name": "info", "inputs": None})
headers = {"content-type": "application/json"}
json_response = requests.post('http://localhost:8501/v1/models/my_model:predict', data=data, headers=headers)
I get next error in the response:
'_content': b'{ "error": "Failed to get input map for signature: info" }'

Answer 1

Defining the Signature:
I was going to write my own example, but here's such a great example provided by #AntPhitlok in another StackOverflow post:
class MyModule(tf.Module):
def __init__(self, model, other_variable):
self.model = model
self._other_variable = other_variable
#tf.function(input_signature=[tf.TensorSpec(shape=(None, None, 1), dtype=tf.float32)])
def score(self, waveform):
result = self.model(waveform)
return { "scores": results }
#tf.function(input_signature=[])
def metadata(self):
return { "other_variable": self._other_variable }
In this case, they're serving is a Module, but it could have been a Keras model as well.
Using the Serving:
I am not 100% sure how to access the serving (I haven't done it myself yet) but I think you'll be able to access the serving similarly to this:
from tensorflow_serving.apis import predict_pb2
from tensorflow_serving.apis import prediction_service_pb2_grpc
stub = prediction_service_pb2_grpc.PredictionServiceStub(channel)
request = predict_pb2.PredictRequest()
request.model_spec.name = model_name
request.model_spec.signature_name = 'serving_default'
request.model_spec.version_label = self.version
tensor_proto = tf.make_tensor_proto(my_input_data, dtype=tf.float32)
request.inputs['my_signatures_input'].CopyFrom(tensor_proto)
try:
response = self.stub.Predict(request, MAX_TIMEOUT)
except Exception as ex:
logging.error(str(ex))
return [None] * len(batch_of_texts)
Here I'm using gRPC to access the TensorFlow Server.
You'd probably need to substitute 'serving_default' with your serving name. Similarly, 'my_signature_input' should match the input to your tf.function (in your case, I think it's empty).
This is a normal standard Keras type prediction and is piggybacking of predict_pb2.PredictRequest. It might be necessary to create a custom Protobuf but that's a bit beyond my abilities at this point.
I hope it's enough to get you going.