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TensorFlow: How to freeze a model and serve it with a python API

We are going to explore two parts of using a ML model in production:

  • How to export a model and have a simple self-sufficient file for it
  • How to build a simple python server (using flask) to serve it with TF

Note: if you want to see the kind of graph I save/load/freeze, you can here

How to freeze (export) a saved model

If you wonder how to save a model with TensorFlow, please have a look at my previous article before going on.

let’s start from a folder containing a saved model, it probably looks something like this:

Screenshot of the result folder before freezing our model

The important files here are the “.chkp” ones. If you remember well, for each pair at different timesteps, one is holding the weights (“.data”) and the other one (“.meta”) is holding the graph and all its metadata (so you can retrain it etc…)

But when we want to serve a model in production, we don’t need any special metadata to clutter our files, we just want our model and its weights nicely packaged in one file. This facilitate storage, versioning and updates of your different models.

Luckily in TF, we can easily build our own function to do it. Let’s explore the different steps we have to perform:

  • Retrieve our saved graph: we need to load the previously saved meta graph in the default graph and retrieve its graph_def (the ProtoBuf definition of our graph)
  • Restore the weights: we start a Session and restore the weights of our graph inside that Session
  • Remove all metadata useless for inference: Here, TF helps us with a nice helper function which grab just what is needed in your graph to perform inference and returns what we will call our new “frozen graph_def”
  • Save it to the disk: Finally we will serialize our frozen graph_def ProtoBuf and dump it to the disk

Note that the two first steps are the same as when we load any graph in TF, the only tricky part is actually the “freezing” of the graph and TF has a built-in function to do it!

So let’s see:

Now we can see a new file in our folder: “frozen_model.pb”.

Screenshot of the result folder after freezing our model

As expected, its size is bigger than the weights file size and lower than the sum of the two checkpoints files sizes.

Note: In this very simple case, the weights size is very small, but it is usually multiple Mbs.

How to use the frozen model

Naturally, after knowing how to freeze a model, one might wonder how to use it.

The little trick to have in mind is to understand that what we dumped to the disk was a graph_def ProtoBuf. So to import it back in a python script we need to:

  • Import a graph_def ProtoBuf first
  • Load this graph_def into a actual Graph

We can build a convenient function to do so:

Now that we built our function to load our frozen model, let’s create a simple script to finally make use of it:

Note: when loading the frozen model, all operations got prefixed by “prefix”. This is due to the parameter “name” in the “import_graph_def” function, recheck it if you don’t understand. This can be useful to avoid name collisions if you want to import your graph_def alongside an other existing Graph.

How to build a (very) simple API

For this part, I will let the code speaks for itself, after all this is a TF series about TF and not so much about how to build a server in python. Yet it felt kind of unfinished without it, so here you go, the final workflow:

Note: We are using flask in this example


TensorFlow best practice series

This article is part of a more complete series of articles about TensorFlow. I’ve not yet defined all the different subjects of this series, so if you want to see any area of TensorFlow explored, add a comment! So far I wanted to explore those subjects (this list is subject to change and is in no particular order):

Note: TF is evolving fast right now, those articles are currently written for the 1.0.0 version.