History

YoungSeok Yoon 772433a2a2 Add flag for using optimized TFLite CPU kernels on iOS This adds new experimental flags to the interpreter options of TFLite Obj-C and Swift APIs, which can be used for opting in to a set of highly optimized floating point kernels provided via the XNNPACK delegate. The flags can be used as follows. Obj-C: TFLInterpreterOptions options = [[TFLInterpreterOptions alloc] init]; options.useXNNPACK = YES; NSError error; TFLInterpreter *interpreter = [[TFLInterpreter alloc] initWithModelPath:@"model/path" options:options error:&error]; Swift: var options = InterpreterOptions() options.isXNNPackEnabled = true var interpreter = try Interpreter(modelPath: "model/path", options: options) PiperOrigin-RevId: 317270012 Change-Id: I82aae43c3de13ab08af3c70513e2a458e807b0f1		2020-06-19 02:03:48 -07:00
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apis	Add flag for using optimized TFLite CPU kernels on iOS	2020-06-19 02:03:48 -07:00
apps/TestApp	Add a test app for TFLite ObjC API.	2019-05-21 23:03:01 -07:00
sources	Add flag for using optimized TFLite CPU kernels on iOS	2020-06-19 02:03:48 -07:00
TensorFlowLite.tulsiproj	Migrate the TFLite C API out of lite/experimental	2019-11-27 13:46:35 -08:00
tests	Add flag for using optimized TFLite CPU kernels on iOS	2020-06-19 02:03:48 -07:00
BUILD.apple	Add flag for using optimized TFLite CPU kernels on iOS	2020-06-19 02:03:48 -07:00
README.md	Add a test app for TFLite ObjC API.	2019-05-21 23:03:01 -07:00
TensorFlowLiteObjC-nightly.podspec	Add flag for using optimized TFLite CPU kernels on iOS	2020-06-19 02:03:48 -07:00
TensorFlowLiteObjC.podspec	Add flag for using optimized TFLite CPU kernels on iOS	2020-06-19 02:03:48 -07:00
TensorFlowLiteObjC.podspec.template	Add flag for using optimized TFLite CPU kernels on iOS	2020-06-19 02:03:48 -07:00

README.md

TensorFlow Lite for Objective-C

TensorFlow Lite is TensorFlow's lightweight solution for Objective-C 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 Objective-C TensorFlow Lite library on Apple platforms, install from source or clone the GitHub repo. Then, configure TensorFlow by navigating to the root directory and executing the configure.py script:

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:

pod 'TensorFlowLiteObjC'

Then, run pod install.

In your Objective-C files, import the umbrella header:

#import "TFLTensorFlowLite.h"

Or, the module if you set CLANG_ENABLE_MODULES = YES in your Xcode project:

@import TFLTensorFlowLite;

Note: To import the TensorFlow Lite module in your Objective-C files, you must also include use_frameworks! in your Podfile.

Bazel developers

In your BUILD file, add the TensorFlowLite dependency to your target:

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

In your Objective-C files, import the umbrella header:

#import "TFLTensorFlowLite.h"

Or, the module if you set CLANG_ENABLE_MODULES = YES in your Xcode project:

@import TFLTensorFlowLite;

Build the TensorFlowLite Objective-C library target:

bazel build tensorflow/lite/experimental/objc:TensorFlowLite

Build the Tests target:

bazel test tensorflow/lite/experimental/objc:Tests

Generate the Xcode project using Tulsi

Open the //tensorflow/lite/experimental/objc/TensorFlowLite.tulsiproj using the TulsiApp or by running the generate_xcodeproj.sh script from the root tensorflow directory:

generate_xcodeproj.sh --genconfig tensorflow/lite/experimental/objc/TensorFlowLite.tulsiproj:TensorFlowLite --outputfolder ~/path/to/generated/TensorFlowLite.xcodeproj