TensorFlow Lite converter

The TensorFlow Lite converter is used to convert TensorFlow models into an optimized FlatBuffer format, so that they can be used by the TensorFlow Lite interpreter.


FlatBuffer is an efficient open-source cross-platform serialization library. It is similar to protocol buffers, with the distinction that FlatBuffers do not need a parsing/unpacking step to a secondary representation before data can be accessed, avoiding per-object memory allocation. The code footprint of FlatBuffers is an order of magnitude smaller than protocol buffers.

From model training to device deployment

The TensorFlow Lite converter generates a TensorFlow Lite FlatBuffer file (.tflite) from a TensorFlow model.

The converter supports the following input formats:

  • SavedModels
  • Frozen GraphDef: Models generated by freeze_graph.py.
  • tf.keras HDF5 models.
  • Any model taken from a tf.Session (Python API only).

The TensorFlow Lite FlatBuffer file is then deployed to a client device, and the TensorFlow Lite interpreter uses the compressed model for on-device inference. This conversion process is shown in the diagram below:

TFLite converter workflow


The TensorFlow Lite Converter can be used from either of these two options:

  • Python (Preferred): Using the Python API makes it easier to convert models as part of a model development pipeline, and helps mitigate compatibility issues early on.
  • Command line