Build TensorFlow Lite for iOS

This document describes how to build TensorFlow Lite iOS library on your own. Normally, you do not need to locally build TensorFlow Lite iOS library. If you just want to use it, the easiest way is using the prebuilt stable or nightly releases of the TensorFlow Lite CocoaPods. See iOS quickstart for more details on how to use them in your iOS projects.

Building locally

In some cases, you might wish to use a local build of TensorFlow Lite, for example when you want to make local changes to TensorFlow Lite and test those changes in your iOS app or you prefer using static framework to our provided dynamic one. To create a universal iOS framework for TensorFlow Lite locally, you need to build it using Bazel on a macOS machine.

Install Xcode

If you have not already, you will need to install Xcode 8 or later and the tools using xcode-select:

xcode-select --install

If this is a new install, you will need to accept the license agreement for all users with the following command:

sudo xcodebuild -license accept

Install Bazel

Bazel is the primary build system for TensorFlow. Install Bazel as per the instructions on the Bazel website. Make sure to choose a version between _TF_MIN_BAZEL_VERSION and _TF_MAX_BAZEL_VERSION in configure.py file at the root of tensorflow repository.

Configure WORKSPACE and .bazelrc

Run the ./configure script in the root TensorFlow checkout directory, and answer "Yes" when the script asks if you wish to build TensorFlow with iOS support.

Once Bazel is properly configured with iOS support, you can build the TensorFlowLiteC framework with the following command.

bazel build --config=ios_fat -c opt \
  //tensorflow/lite/ios:TensorFlowLiteC_framework

This command will generate the TensorFlowLiteC_framework.zip file under bazel-bin/tensorflow/lite/ios/ directory under your TensorFlow root directory. By default, the generated framework contains a "fat" binary, containing armv7, arm64, and x86_64 (but no i386). To see the full list of build flags used when you specify --config=ios_fat, please refer to the iOS configs section in the .bazelrc file.

Build TensorFlowLiteC static framework

By default, we only distribute the dynamic framework via Cocoapods. If you want to use the static framework instead, you can build the TensorFlowLiteC static framework with the following command:

bazel build --config=ios_fat -c opt \
  //tensorflow/lite/ios:TensorFlowLiteC_static_framework

The command will generate a file named TensorFlowLiteC_static_framework.zip under bazel-bin/tensorflow/lite/ios/ directory under your TensorFlow root directory. This static framework can be used in the exact same way as the dynamic one.

Selectively build TFLite frameworks

You can build smaller frameworks targeting only a set of models using selective build, which will skip unused operations in your model set and only include the op kernels required to run the given set of models. The command is as following:

bash tensorflow/lite/ios/build_frameworks.sh \
  --input_models=model1.tflite,model2.tflite \
  --target_archs=x86_64,armv7,arm64

The above command will generate the static framework bazel-bin/tensorflow/lite/ios/tmp/TensorFlowLiteC_framework.zip for TensorFlow Lite built-in and custom ops; and optionally, generates the static framework bazel-bin/tensorflow/lite/ios/tmp/TensorFlowLiteSelectTfOps_framework.zip if your models contain Select TensorFlow ops. Note that the --target_archs flag can be used to specify your deployment architectures.

Use in your own application

CocoaPods developers

There are three CocoaPods for TensorFlow Lite:

  • TensorFlowLiteSwift: Provides the Swift APIs for TensorFlow Lite.
  • TensorFlowLiteObjC: Provides the Objective-C APIs for TensorFlow Lite.
  • TensorFlowLiteC: Common base pod, which embeds the TensorFlow Lite core runtime and exposes the base C APIs used by the above two pods. Not meant to be directly used by users.

As a developer, you should choose either TensorFlowLiteSwift or TensorFlowLiteObjC pod based on the language in which your app is written, but not both. The exact steps for using local builds of TensorFlow Lite differ, depending on which exact part you would like to build.

Using local Swift or Objective-C APIs

If you are using CocoaPods, and only wish to test some local changes to the TensorFlow Lite's Swift APIs or Objective-C APIs, follow the steps here.

  1. Make changes to the Swift or Objective-C APIs in your tensorflow checkout.

  2. Open the TensorFlowLite(Swift|ObjC).podspec file, and update this line:
    s.dependency 'TensorFlowLiteC', "#{s.version}"
    to be:
    s.dependency 'TensorFlowLiteC', "~> 0.0.1-nightly"
    This is to ensure that you are building your Swift or Objective-C APIs against the latest available nightly version of TensorFlowLiteC APIs (built every night between 1-4AM Pacific Time) rather than the stable version, which may be outdated compared to your local tensorflow checkout. Alternatively, you could choose to publish your own version of TensorFlowLiteC and use that version (see Using local TensorFlow Lite core section below).

  3. In the Podfile of your iOS project, change the dependency as follows to point to the local path to your tensorflow root directory.
    For Swift:
    pod 'TensorFlowLiteSwift', :path => '<your_tensorflow_root_dir>'
    For Objective-C:
    pod 'TensorFlowLiteObjC', :path => '<your_tensorflow_root_dir>'

  4. Update your pod installation from your iOS project root directory.
    $ pod update

  5. Reopen the generated workspace (<project>.xcworkspace) and rebuild your app within Xcode.

Using local TensorFlow Lite core

You can set up a private CocoaPods specs repository, and publish your custom TensorFlowLiteC framework to your private repo. You can copy this podspec file and modify a few values:

  ...
  s.version      = <your_desired_version_tag>
  ...
  # Note the `///`, two from the `file://` and one from the `/path`.
  s.source       = { :http => "file:///path/to/TensorFlowLiteC_framework.zip" }
  ...
  s.vendored_frameworks = 'TensorFlowLiteC.framework'
  ...

After creating your own TensorFlowLiteC.podspec file, you can follow the instructions on using private CocoaPods to use it in your own project. You can also modify the TensorFlowLite(Swift|ObjC).podspec to point to your custom TensorFlowLiteC pod and use either Swift or Objective-C pod in your app project.

Bazel developers

If you are using Bazel as the main build tool, you can simply add TensorFlowLite dependency to your target in your BUILD file.

For Swift:

swift_library(
  deps = [
      "//tensorflow/lite/swift:TensorFlowLite",
  ],
)

For Objective-C:

objc_library(
  deps = [
      "//tensorflow/lite/objc:TensorFlowLite",
  ],
)

When you build your app project, any changes to the TensorFlow Lite library will be picked up and built into your app.

Modify Xcode project settings directly

It is highly recommended to use CocoaPods or Bazel for adding TensorFlow Lite dependency into your project. If you still wish to add TensorFlowLiteC framework manually, you'll need to add the TensorFlowLiteC framework as an embedded framework to your application project. Unzip the TensorFlowLiteC_framework.zip generated from the above build to get the TensorFlowLiteC.framework directory. This directory is the actual framework which Xcode can understand.

Once you've prepared the TensorFlowLiteC.framework, first you need to add it as an embedded binary to your app target. The exact project settings section for this may differ depending on your Xcode version.

  • Xcode 11: Go to the 'General' tab of the project editor for your app target, and add the TensorFlowLiteC.framework under 'Frameworks, Libraries, and Embedded Content' section.
  • Xcode 10 and below: Go to the 'General' tab of the project editor for your app target, and add the TensorFlowLiteC.framework under 'Embedded Binaries'. The framework should also be added automatically under 'Linked Frameworks and Libraries' section.

When you add the framework as an embedded binary, Xcode would also update the 'Framework Search Paths' entry under 'Build Settings' tab to include the parent directory of your framework. In case this does not happen automatically, you should manually add the parent directory of the TensorFlowLiteC.framework directory.

Once these two settings are done, you should be able to import and call the TensorFlow Lite's C API, defined by the header files under TensorFlowLiteC.framework/Headers directory.