TensorFlow 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.
To build the Swift 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
.
Add the TensorFlow Lite pod to your Podfile
:
pod 'TensorFlowLiteSwift'
Then, run pod install
.
In your Swift files, import the module:
import TensorFlowLite
In your BUILD
file, add the TensorFlowLite
dependency to your target:
swift_library(
deps = [
"//tensorflow/lite/swift:TensorFlowLite",
],
)
In your Swift files, import the module:
import TensorFlowLite
Build the TensorFlowLite
Swift library target:
bazel build tensorflow/lite/swift:TensorFlowLite
Build the Tests
target:
bazel test tensorflow/lite/swift:Tests --swiftcopt=-enable-testing
Note: --swiftcopt=-enable-testing
is required for optimized builds (-c opt
).
Open the //tensorflow/lite/swift/TensorFlowLite.tulsiproj
using
the TulsiApp
or by running the
generate_xcodeproj.sh
script from the root tensorflow
directory:
generate_xcodeproj.sh --genconfig tensorflow/lite/swift/TensorFlowLite.tulsiproj:TensorFlowLite --outputfolder ~/path/to/generated/TensorFlowLite.xcodeproj