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I'm following the Coursera TensorFlow Serving with Docker for Model Deployment course.

You have to train a simple Tensorflow model and export it in Protobuf.

tf.saved_model.save(model, path)

Then you have to deploy it with Docker and the tensorflow/serving image, as follows:

docker run -p 8500:8500 \ # Expose port 8500 for gRPC endpoints
           -p 8501:8501 \ # Expose port 8501 for REST endopoints
           --mount type=bind,\
           source=/path/amazon_review/,\ # Absolute path to the model
           target=/models/amazon_review \ # Where in the docker container load the model
           -e MODEL_NAME=amazon_review \ # Environment variable
           -t tensorflow/serving # Set the image to use

Everything goes well when the training and the deployment is on the same machine. However, when I train the model on the course machine and try to deploy it on my own Amazon EC2 instance, the deployment fails and I get the following error. The full stack trace can be found here.

2021-09-22 18:58:19.959241: E tensorflow_serving/util/retrier.cc:37] 
Loading servable: {name: amazon_review version: 1631554979} failed: Data loss: file is too short to be an sstable
         [[{{node RestoreV2}}]]

The error is not related to the deployment itself, but the loading of the saved model.

Related but not solving the issue:

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