Reduce TensorFlow Lite binary size

Overview

When deploying models for on-device machine learning (ODML) applications, it is important to be aware of the limited memory that is available on mobile devices. Model binary sizes are closely correlated to the number of ops used in the model. TensorFlow Lite enables you to reduce model binary sizes by using selective builds. Selective builds skip unused operations in your model set and produce a compact library with just the runtime and the op kernels required for the model to run on your mobile device.

Selective build applies on the following three operations libraries.

1. TensorFlow Lite built-in ops library
2. TensorFlow Lite custom ops
3. Select TensorFlow ops library

The table below demonstrates the impact of selective builds for some common use cases:

Known issues/limitations

1. Selective Build for C API and iOS version is not supported currently.

Selectively build TensorFlow Lite with Bazel

This section assumes that you have downloaded TensorFlow source codes and set up the local development environment to Bazel.

Build AAR files for Android project

You can build the custom TensorFlow Lite AARs by providing your model file paths as follows.

sh tensorflow/lite/tools/build_aar.sh \
  --input_models=/a/b/model_one.tflite,/c/d/model_two.tflite \
  --target_archs=x86,x86_64,arm64-v8a,armeabi-v7a

The above command will generate the AAR file bazel-bin/tmp/tensorflow-lite.aar for TensorFlow Lite built-in and custom ops; and optionally, generates the aar file bazel-bin/tmp/tensorflow-lite-select-tf-ops.aar if your models contain Select TensorFlow ops. Note that this builds a "fat" AAR with several different architectures; if you don't need all of them, use the subset appropriate for your deployment environment.

Advanced Usage: Build with custom ops

If you have developed Tensorflow Lite models with custom ops, you can build them by adding the following flags to the build command:

sh tensorflow/lite/tools/build_aar.sh \
  --input_models=/a/b/model_one.tflite,/c/d/model_two.tflite \
  --target_archs=x86,x86_64,arm64-v8a,armeabi-v7a \
  --tflite_custom_ops_srcs=/e/f/file1.cc,/g/h/file2.h \
  --tflite_custom_ops_deps=dep1,dep2

The tflite_custom_ops_srcs flag contains source files of your custom ops and the tflite_custom_ops_deps flag contains dependencies to build those source files. Note that these dependencies must exist in the TensorFlow repo.

Selectively Build TensorFlow Lite with Docker

This section assumes that you have installed Docker on your local machine and downloaded the TensorFlow Lite Dockerfile here.

After downloading the above Dockerfile, you can build the docker image by running:

docker build . -t tflite-builder -f tflite-android.Dockerfile

Build AAR files for Android project

Download the script for building with Docker by running:

curl -o build_aar_with_docker.sh \
  https://raw.githubusercontent.com/tensorflow/tensorflow/master/tensorflow/lite/tools/build_aar_with_docker.sh &&
chmod +x build_aar_with_docker.sh

Then, you can build the custom TensorFlow Lite AAR by providing your model file paths as follows.

sh build_aar_with_docker.sh \
  --input_models=/a/b/model_one.tflite,/c/d/model_two.tflite \
  --target_archs=x86,x86_64,arm64-v8a,armeabi-v7a \
  --checkpoint=master \
  [--cache_dir=<path to cache directory>]

The checkpoint flag is a commit, a branch or a tag of the TensorFlow repo that you want to checkout before building the libraries; by default it is the latest release branch. The above command will generate the AAR file tensorflow-lite.aar for TensorFlow Lite built-in and custom ops and optionally the AAR file tensorflow-lite-select-tf-ops.aar for Select TensorFlow ops in your current directory.

The --cache_dir specify the cache directory. If not provided, the script will create a directory named bazel-build-cache under current working directory for caching.

Add AAR files to project

Add AAR files by directly importing the AAR into your project, or by publishing the custom AAR to your local Maven repository. Note that you have to add the AAR files for tensorflow-lite-select-tf-ops.aar as well if you generate it.