Solving Incompatibility Between the Saved TensorFlow Model and the TensorFlow Serving Server

If you have trained a TensorFlow model and are trying to serve it using TensorFlow Serving, you may encounter an error that indicates an incompatibility between the saved model and the TensorFlow Serving server. This can be frustrating, as it can be difficult to understand what the problem is and how to fix it.

The root cause of this error is typically that the model was not saved in the correct format. TensorFlow Serving requires that the model be in a specific format in order to load it and serve it. If the model is not saved in this format, TensorFlow Serving will not be able to load it and will return an error.

To solve this problem, you need to ensure that the model is saved in the correct format. The easiest way to do this is to use the tf.saved_model.save function, which will save the model in a format that is compatible with TensorFlow Serving.

Here is an example of how to use the tf.saved_model.save function to save a TensorFlow model:

import tensorflow as tf

# Define the model
def model(x):
  return x * W + b
# Save the model
tf.saved_model.save(model, './model')

This will save the model to the ./model directory in a format that is compatible with TensorFlow Serving. You can then point the TensorFlow Serving server to this directory when starting the server.

tensorflow_model_server --model_name=my_model --model_base_path=./model

If you have already saved the model in a different format, you may need to convert it to the correct format before you can use it with TensorFlow Serving. You can use the tf.saved_model.convert_v1_to_v2_saved_model function to convert a TensorFlow 1.x saved model to the TensorFlow 2.x format.

import tensorflow as tf

# Convert the model to the TensorFlow 2.x format
tf.saved_model.convert_v1_to_v2_saved_model('./model', './model_v2')

This will convert the model in the ./model directory to the TensorFlow 2.x format and save it to the ./model_v2 directory. You can then use the ./model_v2 directory with the TensorFlow Serving server.

By following these steps, you should be able to resolve the issue of incompatibility between the saved model and the TensorFlow Serving server. With the model saved in the correct format, you can now use TensorFlow Serving to serve the model and make predictions.