i am trying to deploy a custom trained tensorflow model using Amazon SageMaker. i have trained xlm roberta using tf 2.2.0 for multilingual sentiment analysis task.(please refer to this notebook : https://www.kaggle.com/mobassir/understanding-cross-lingual-models) now, using trained weight file of my model i am trying to deploy that in sagemaker, i was following this tutorial : https://aws.amazon.com/blogs/machine-learning/deploy-trained-keras-or-tensorflow-models-using-amazon-sagemaker/
converted some keras code from there to tensorflow.keras for 2.2.0 but when i do : !ls export/Servo/1/variables i can see that export as Savedmodel generating empty variables directory like this : https://github.com/tensorflow/models/issues/1988
i can't find any documentation help for tf 2.2.0 trained model deployment
need example like this : https://aws.amazon.com/blogs/machine-learning/deploy-trained-keras-or-tensorflow-models-using-amazon-sagemaker/ for tf 2.x models and not keras
even though !ls export/Servo/1/variables shows empty directory but An endpoint was created successfully and now i am not sure if my model was deployed successfully or not because when i try to test the model deployment inside aws notebook by using predictor = sagemaker.tensorflow.model.TensorFlowPredictor(endpoint_name, sagemaker_session) i.e. predictor.predict(data) i get the following error message:
ModelError: An error occurred (ModelError) when calling the InvokeEndpoint operation: Received client error (400) from model with message "{
"error": "Session was not created with a graph before Run()!"
}"
related problem : Inference error with TensorFlow C++ on iOS: "Invalid argument: Session was not created with a graph before Run()!"
the code i tried can be found here : https://pastebin.com/sGuTtnSD