# TensorFlow Lite for Swift [TensorFlow Lite](https://www.tensorflow.org/lite/) is TensorFlow's lightweight solution for Swift developers. It enables low-latency inference of on-device machine learning models with a small binary size and fast performance supporting hardware acceleration. ## Build TensorFlow with iOS support To build the Swift TensorFlow Lite library on Apple platforms, [install from source](https://www.tensorflow.org/install/source#setup_for_linux_and_macos) or [clone the GitHub repo](https://github.com/tensorflow/tensorflow). Then, configure TensorFlow by navigating to the root directory and executing the `configure.py` script: ```shell python configure.py ``` Follow the prompts and when asked to build TensorFlow with iOS support, enter `y`. ### CocoaPods developers Add the TensorFlow Lite pod to your `Podfile`: ```ruby pod 'TensorFlowLiteSwift' ``` Then, run `pod install`. In your Swift files, import the module: ```swift import TensorFlowLite ``` ### Bazel developers In your `BUILD` file, add the `TensorFlowLite` dependency to your target: ```python swift_library( deps = [ "//tensorflow/lite/swift:TensorFlowLite", ], ) ``` In your Swift files, import the module: ```swift import TensorFlowLite ``` Build the `TensorFlowLite` Swift library target: ```shell bazel build tensorflow/lite/swift:TensorFlowLite ``` Build the `Tests` target: ```shell bazel test tensorflow/lite/swift:Tests --swiftcopt=-enable-testing ``` Note: `--swiftcopt=-enable-testing` is required for optimized builds (`-c opt`). #### Generate the Xcode project using Tulsi Open the `//tensorflow/lite/swift/TensorFlowLite.tulsiproj` using the [TulsiApp](https://github.com/bazelbuild/tulsi) or by running the [`generate_xcodeproj.sh`](https://github.com/bazelbuild/tulsi/blob/master/src/tools/generate_xcodeproj.sh) script from the root `tensorflow` directory: ```shell generate_xcodeproj.sh --genconfig tensorflow/lite/swift/TensorFlowLite.tulsiproj:TensorFlowLite --outputfolder ~/path/to/generated/TensorFlowLite.xcodeproj ```