TensorFlow Lite converter

The TensorFlow Lite converter takes a TensorFlow model and generates a TensorFlow Lite model, which is an optimized FlatBuffer (identified by the .tflite file extension).

Note: This page contains documentation on the converter API for TensorFlow 1.x. The API for TensorFlow 2.0 is available here.

Options

The TensorFlow Lite Converter can be used in two ways:

• Python API (recommended): 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

Workflow

Why use the 'FlatBuffer' format?

FlatBuffer is an efficient open-source cross-platform serialization library. It is similar to protocol buffers used in the TensorFlow model format, 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.

Convert the model

The converter supports the following input formats:

• SavedModels
• tf.keras H5 models.
• Frozen GraphDef models generated using freeze_graph.py.
• tf.Session models (Python API only).

Run inference

The TensorFlow Lite model 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: