I have trained a TensorFlow model and saved a checkpoint, and would like to deploy it to Google Cloud Platform. In the model deployment documentation it says that you need to create a SavedModel. It seems that others also use checkpoints instead of SavedModel.
Given that I have already spent time training this model and only have checkpoints instead of a SavedModel, is there a method I can use to deploy the model still or will I need to retrain?
A checkpoint maps variable names to tensor values. This, as is, is not enough for higher-level systems to use your model. On the other hand, a SavedModel is complete and airtight. As is made clear in the answer you link to in your post, a SavedModel provides all the info needed for serving TensorFlow models: a set of MetaGraphs, a checkpoint compatible with these Graphs and all necessary asset files. If you look at it this way, it makes sense that you need to export your model to a SavedModel in order to deploy it to ML Engine. Now, this does not imply that you need to retrain. What you need to do instead is to wrap one of your checkpoints into a SavedModel.
