TensorFlow Lite is our main mobile and embedded offering. We are working hard to close the feature gap between TensorFlow Mobile and TensorFlow Lite. We expect to deprecate TensorFlow Mobile in early 2019. We will give ample notice to our users when we get to that point and will provide help and support to ensure easy migrations.
In the meantime, please use TensorFlow Lite. If you have a feature request, such as a missing op, please post to our GitHub.
The simplest way to get started with TensorFlow on iOS is using the CocoaPods
package management system. You can add the
TensorFlow-experimental pod to your
Podfile, which installs a universal binary framework. This makes it easy to get
started but has the disadvantage of being hard to customize, which is important
in case you want to shrink your binary size. If you do need the ability to
customize your libraries, see later sections on how to do that.
Creating your own app
If you'd like to add TensorFlow capabilities to your own app, do the following:
Create your own app or load your already-created app in XCode.
Add a file named Podfile at the project root directory with the following content:
target 'YourProjectName' pod 'TensorFlow-experimental'
pod installto download and install the
YourProjectName.xcworkspaceand add your code.
In your app's Build Settings, make sure to add
$(inherited)to the Other Linker Flags, and Header Search Paths sections.
Running the Samples
You'll need Xcode 7.3 or later to run our iOS samples.
There are currently three examples: simple, benchmark, and camera. For now, you can download the sample code by cloning the main tensorflow repository (we are planning to make the samples available as a separate repository later).
From the root of the tensorflow folder, download Inception v1, and extract the label and graph files into the data folders inside both the simple and camera examples using these steps:
mkdir -p ~/graphs curl -o ~/graphs/inception5h.zip \ https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip \ && unzip ~/graphs/inception5h.zip -d ~/graphs/inception5h cp ~/graphs/inception5h/* tensorflow/examples/ios/benchmark/data/ cp ~/graphs/inception5h/* tensorflow/examples/ios/camera/data/ cp ~/graphs/inception5h/* tensorflow/examples/ios/simple/data/
Change into one of the sample directories, download the Tensorflow-experimental pod, and open the Xcode workspace. Note that installing the pod can take a long time since it is big (~450MB). If you want to run the simple example, then:
cd tensorflow/examples/ios/simple pod install open tf_simple_example.xcworkspace # note .xcworkspace, not .xcodeproj # this is created by pod install
Run the simple app in the XCode simulator. You should see a single-screen app with a Run Model button. Tap that, and you should see some debug output appear below indicating that the example Grace Hopper image in directory data has been analyzed, with a military uniform recognized.
Run the other samples using the same process. The camera example requires a real device connected. Once you build and run that, you should get a live camera view that you can point at objects to get real-time recognition results.
iOS Example details
There are three demo applications for iOS, all defined in Xcode projects inside tensorflow/examples/ios.
Simple: This is a minimal example showing how to load and run a TensorFlow model in as few lines as possible. It just consists of a single view with a button that executes the model loading and inference when its pressed.
Camera: This is very similar to the Android TF Classify demo. It loads Inception v3 and outputs its best label estimate for what’s in the live camera view. As with the Android version, you can train your own custom model using TensorFlow for Poets and drop it into this example with minimal code changes.
Benchmark: is quite close to Simple, but it runs the graph repeatedly and outputs similar statistics to the benchmark tool on Android.
Make sure you use the TensorFlow-experimental pod (and not TensorFlow).
The TensorFlow-experimental pod is current about ~450MB. The reason it is so big is because we are bundling multiple platforms, and the pod includes all TensorFlow functionality (e.g. operations). The final app size after build is substantially smaller though (~25MB). Working with the complete pod is convenient during development, but see below section on how you can build your own custom TensorFlow library to reduce the size.
Building the TensorFlow iOS libraries from source
While Cocoapods is the quickest and easiest way of getting started, you sometimes need more flexibility to determine which parts of TensorFlow your app should be shipped with. For such cases, you can build the iOS libraries from the sources. This guide contains detailed instructions on how to do that.