TFX for Mobile

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This guide demonstrates how Tensorflow Extended (TFX) can create and evaluate machine learning models that will be deployed on-device. TFX now provides native support for TFLite, which makes it possible to perform highly efficient inference on mobile devices.

This guide walks you through the changes that can be made to any pipeline to generate and evaluate TFLite models. We provide a complete example here, demonstrating how TFX can train and evaluate TFLite models that are trained off of the MNIST dataset. Further, we show how the same pipeline can be used to simulataneously export both the standard Keras-based SavedModel as well as the TFLite one, allowing users to compare the quality of the two.

We assume you are familiar with TFX, our components, and our pipelines. If not, then please see this tutorial.


Only two steps are required to create and evaluate a TFLite model in TFX. The first step is invoking the TFLite rewriter within the context of the TFX Trainer to convert the trained TensorFlow model into a TFLite one. The second step is configuring the Evaluator to evaluate TFLite models. We now discuss each in turn.

Invoking the TFLite rewriter within the Trainer.

The TFX Trainer expects a user-defined run_fn to be specified in a module file. This run_fn defines the model to be trained, trains it for the specified number of iterations, and exports the trained model.

In the rest of this section, we provide code snippets which show the changes required to invoke the TFLite rewriter and export a TFLite model. All of this code is located in the run_fn of the MNIST TFLite module.

As shown in the code below, we must first create a signature that takes a Tensor for every feature as input. Note that this is a departure from most existing models in TFX, which take serialized tf.Example protos as input.

 signatures = {
              model, tf_transform_output).get_concrete_function(
                      shape=[None, 784],

Then the Keras model is saved as a SavedModel in the same way that it normally is.

  temp_saving_model_dir = os.path.join(fn_args.serving_model_dir, 'temp'), save_format='tf', signatures=signatures)

Finally, we create an instance of the TFLite rewriter (tfrw), and invoke it on the SavedModel to obtain the TFLite model. We store this TFLite model in the serving_model_dir provided by the caller of the run_fn. This way, the TFLite model is stored in the location where all downstream TFX components will be expecting to find the model.

  tfrw = rewriter_factory.create_rewriter(
      rewriter_factory.TFLITE_REWRITER, name='tflite_rewriter')

Evaluating the TFLite model.

The TFX Evaluator provides the ability to analyze trained models to understand their quality across a wide range of metrics. In addition to analyzing SavedModels, the TFX Evaluator is now able to analyze TFLite models as well.

The following code snippet (reproduced from the MNIST pipeline), shows how to configure an Evaluator that analyzes a TFLite model.

  # Informs the evaluator that the model is a TFLite model.
  eval_config_lite.model_specs[0].model_type = 'tf_lite'


  # Uses TFMA to compute the evaluation statistics over features of a TFLite
  # model.
  model_analyzer_lite = Evaluator(

As shown above, the only change that we need to make is to set the model_type field to tf_lite. No other configuration changes are required to analyze the TFLite model. Regardless of whether a TFLite model or the a SavedModel is analyzed, the output of the Evaluator will have exactly the same structure.

However, please note that the Evaluator assumes that the TFLite model is saved in a file named tflite within trainer_lite.outputs['model'].