Use a custom TensorFlow Lite build

If you're an experienced ML developer and the pre-built TensorFlow Lite library doesn't meet your needs, you can use a custom TensorFlow Lite build with ML Kit. For example, you may want to add custom ops.

Prerequisites

• A working TensorFlow Lite build environment
• A checkout of TensorFlow Lite 1.10.1

You can check out the correct version using Git:

git checkout -b work
git reset --hard tflite-v1.10.1
git cherry-pick 4dcfddc5d12018a5a0fdca652b9221ed95e9eb23

Building the Tensorflow Lite library

1. Build Tensorflow Lite (with your modifications) following the standard instructions
2. Build the framework:

tensorflow/lite/lib_package/create_ios_frameworks.sh

The generated framework can be found at tensorflow/lite/gen/ios_frameworks/tensorflow_lite.framework.zip

Creating a local pod

1. Create a directory for your local pod
2. Run pod lib create TensorFlowLite in the directory you created
3. Create a Frameworks directory inside the TensorFlowLite directory
4. Unzip the tensorflow_lite.framework.zip file generated above
5. Copy the unzipped tensorflow_lite.framework to TensorFlowLite/Frameworks
6. Modify the generated TensorFlowLite/TensorFlowLite.podspec to reference the library:

    Pod::Spec.new do |s|
      s.name             = 'TensorFlowLite'
      s.version          = '0.1.7' # Version must match.
      s.ios.deployment_target = '9.0'
      
      # ... make other changes as desired
      
      internal_pod_root = Pathname.pwd
      s.frameworks = 'Accelerate'
      s.libraries = 'c++'
      s.vendored_frameworks = 'Frameworks/tensorflow_lite.framework'

      s.pod_target_xcconfig = {
        'SWIFT_VERSION' => '4.0',
        'INTERNAL_POD_ROOT' => "#{internal_pod_root}",
        'HEADER_SEARCH_PATHS' => "$(inherited) '${INTERNAL_POD_ROOT}/Frameworks/tensorflow_lite.framework/Headers'",
        'OTHER_LDFLAGS' => "-force_load '${INTERNAL_POD_ROOT}/Frameworks/tensorflow_lite.framework/tensorflow_lite'"
      }
    end

Referencing the custom pod in your project

You can include the custom pod by referencing it directly from your app's Podfile:

pod 'Firebase/MLModelInterpreter'
pod 'TensorFlowLite', :path => 'path/to/your/TensorflowLite'

For other options for managing private pods, see Private Pods in the Cocoapods documentation. Note that the version must exactly match, and you should reference this version when including the pod from your private repository, e.g. pod 'TensorFlowLite', "1.10.1".