# swift-models **Repository Path**: mirrors_tensorflow/swift-models ## Basic Information - **Project Name**: swift-models - **Description**: Models and examples built with Swift for TensorFlow - **Primary Language**: Unknown - **License**: Apache-2.0 - **Default Branch**: main - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2020-08-22 - **Last Updated**: 2026-02-21 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # Swift for TensorFlow Models Swift for TensorFlow was an experiment in the next-generation platform for machine learning, incorporating the latest research across machine learning, compilers, differentiable programming, systems design, and beyond. It was archived in February 2021. This repository contains many examples of how Swift for TensorFlow can be used to build machine learning applications, as well as the models, datasets, and other components required to build them. These examples are intended to demonstrate best practices for the use of [Swift for TensorFlow APIs](https://github.com/tensorflow/swift-apis) and act as end-to-end tests to validate the function and performance of those APIs. Active development occurs on the `main` branch, and that is kept current against the `main` branch of [the Swift compiler](https://github.com/apple/swift) and the `main` branch of [the Swift for TensorFlow APIs](https://github.com/tensorflow/swift-apis). For stable snapshots, use the ```tensorflow-xx``` branch that corresponds to the toolchain you are using from the [Swift for TensorFlow releases](https://github.com/tensorflow/swift/blob/master/Installation.md#releases). For example, for the 0.12 release, use the ```tensorflow-0.12``` branch. To learn more about Swift for TensorFlow development, please visit [tensorflow/swift](https://github.com/tensorflow/swift). ## Examples The examples within this repository are all designed to be run as standalone applications. The easiest way to do this is to use Swift Package Manager to build and run individual examples. This can be accomplished by changing to the root directory of the project and typing something like ```bash swift run -c release [Example] [Options] ``` For Windows, an additional flag may be required: ```cmd swift run -Xswiftc -use-ld=lld -c release [Example] [Options] ``` This will build and run a specific example in the release configuration. Due to significant performance differences between debug and release builds in Swift, we highly recommend running the examples from a release build. Some examples have additional command-line options, and those will be described in the example's README. The following is a catalog of the current examples, grouped by subject area, with links to their location within the project. Each example should have documentation for what it is demonstrating and how to use it. ### Image classification - [A custom model training against CIFAR-10](Examples/Custom-CIFAR10) - [LeNet-5 training against MNIST](Examples/LeNet-MNIST) - [MobileNet V1 training against Imagenette](Examples/MobileNetV1-Imagenette) - [MobileNet V2 training against Imagenette](Examples/MobileNetV2-Imagenette) - [ResNet-56 training against CIFAR-10](Examples/ResNet-CIFAR10) - [ResNet-50 training against ImageNet](Examples/ResNet50-ImageNet) - [VGG-16 training against Imagewoof](Examples/VGG-Imagewoof) ### Text - [BERT training against CoLA](Examples/BERT-CoLA) - [Pretrained GPT-2 performing text generation](Examples/GPT2-Inference) - [GPT-2 training against WikiText-2](Examples/GPT2-WikiText2) - [WordSeg](Examples/WordSeg) ### Generative models - [1-D Autoencoder](Autoencoder/Autoencoder1D) - [2-D Autoencoder](Autoencoder/Autoencoder2D) - [1-D Variational Autoencoder](Autoencoder/VAE1D) - [CycleGAN](CycleGAN) - [GAN](GAN) - [DCGAN](DCGAN) - [pix2pix](pix2pix) ### Reinforcement learning - [Blackjack](Gym) - [CartPole](Gym) - [Catch](Catch) - [FrozenLake](Gym) - [MiniGo](MiniGo) ### Standalone - [Differentiable Shallow Water PDE Solver](Examples/Shallow-Water-PDE) - [Fast Style Transfer](FastStyleTransfer) - [Fractals](Examples/Fractals) - [Growing Neural Cellular Automata](Examples/GrowingNeuralCellularAutomata) - [Neural Collaborative Filtering using MovieLens](Examples/NeuMF-MovieLens) - [PersonLab Human Pose Estimator](PersonLab) - [Regression using BostonHousing](Examples/Regression-BostonHousing) ## Components Beyond examples that use Swift for TensorFlow, this repository also contains reusable components for constructing machine learning applications. These components reside in modules that can be imported into separate Swift projects and used by themselves. These components provide standalone machine learning models, datasets, image loading and saving, TensorBoard integration, and a training loop abstraction, among other capabilities. The Swift for TensorFlow models repository has acted as a staging ground for experimental capabilities, letting us evaluate new components and interfaces before elevating them into the core Swift for TensorFlow APIs. As a result, the design and interfaces of these components may change regularly. ### Models Several modules are provided that contain reusable Swift models for image classification, text processing, and more. These modules are used within the example applications to demonstrate the capabilities of these models, but they can also be imported into many other projects. #### Image classification Many common image classification models are present within [the ImageClassificationModels module](Models/ImageClassification). To use them within a Swift project, add ImageClassificationModels as a dependency and import the module: ```swift import ImageClassificationModels ``` These models include: - DenseNet121 - EfficientNet - LeNet-5 - MobileNetV1 - MobileNetV2 - MobileNetV3 - ResNet - ResNetV2 - ShuffleNetV2 - SqueezeNet - VGG - WideResNet - Xception #### Recommendation Several recommendation models are present within [the RecommendationModels module](Models/Recommendation). To use them within a Swift project, add RecommendationModels as a dependency and import the module: ```swift import RecommendationModels ``` These models include: - DLRM - MLP - NeuMF #### Text Several text models are present within [the TextModels module](Models/Text). To use them within a Swift project, add TextModels as a dependency and import the module: ```swift import TextModels ``` These models include: - [BERT](Models/Text/BERT) - [GPT-2](Models/Text/GPT2) - [WordSeg](Models/Text/WordSeg) ### Datasets In addition to the machine learning model itself, a dataset is usually required when training. Swift wrappers have been built for many common datasets to ease their use within machine learning applications. Most of these use the [Epochs](https://github.com/tensorflow/swift-apis/tree/main/Sources/TensorFlow/Epochs) API that provides a generalized abstraction of common dataset operations. The [Datasets](Datasets) module provides these wrappers. To use them within a Swift project, add Datasets as a dependency and import the module: ```swift import Datasets ``` These are the currently provided dataset wrappers: - [BostonHousing](Datasets/BostonHousing) - [CIFAR-10](Datasets/CIFAR10) - [MS COCO](Datasets/COCO) - [CoLA](Datasets/CoLA) - [ImageNet](Datasets/Imagenette) - [Imagenette](Datasets/Imagenette) - [Imagewoof](Datasets/Imagenette) - [FashionMNIST](Datasets/MNIST) - [KuzushijiMNIST](Datasets/MNIST) - [MNIST](Datasets/MNIST) - [MovieLens](Datasets/MovieLens) - [Oxford-IIIT Pet](Datasets/OxfordIIITPets) - [WordSeg](Datasets/WordSeg) ### Model checkpoints Model saving and loading is provided by the [Checkpoints](Checkpoints) module. To use the model checkpointing functionality, add Checkpoints as a dependency and import the module: ```swift import Checkpoints ``` ### Image loading and saving The [ModelSupport](Support) module contains many shared utilites that are needed within the Swift machine learning examples. This includes the loading, saving, and processing of [still images](Support/Image.swift) via the [stb_image](https://github.com/nothings/stb) library. [Animated images](Support/AnimatedImage.swift) can also be written out as GIF files from multiple tensors. Experimental support for libjpeg-turbo as an accelerated image loader [is present](ImageLoader), but has not yet been incorporated into the main image loading capabilities. ### Generalized training loop A generalized training loop that can be customized via callbacks is provided within the [TrainingLoop](TrainingLoop) module. All of the image classification examples use this training loop, with the exception of the Custom-CIFAR10 example that demonstrates how to define your own training loop from scratch. Other examples are being gradually converted to use this training loop. ### TensorBoard integration [TensorBoard](https://www.tensorflow.org/tensorboard) integration is provided in the [TensorBoard module](TensorBoard) as a callback for the generalized training loop. TensorBoard lets you visualize the progress of your model as it trains by plotting model statistics as they update, or to review the training process afterward. The [GPT2-WikiText2](Examples/GPT2-WikiText2) example demonstrates how this can be used when training your own models. ## Benchmarks and tests A core goal of this repository is to validate the proper function of the Swift for TensorFlow APIs. In addition to the models and end-to-end applications present within this project, a suite of benchmarks and unit tests reside here. The benchmarks are split into a core of functionality, the [SwiftModelsBenchmarksCore](SwiftModelsBenchmarksCore) module, and a [Benchmarks](SwiftModelsBenchmarks) command-line application for running these benchmarks. Refer to the [documentation](SwiftModelsBenchmarks) for how to run the benchmarks on your system. The [unit tests](Tests) verify functionality within models, datasets and other components. To run them using Swift Package Manager on macOS or Linux: ```bash swift test ``` and to run them on Windows: ```cmd swift test -Xswiftc -use-ld=lld -c debug ``` ## Using CMake for Development In addition to Swift Package Manager, CMake can be used to build and run Swift for TensorFlow models. ### *Experimental* CMake Support There is experimental support for building with CMake. This can be used to cross-compile the models and the demo programs. It is highly recommended that you use CMake 3.16 or newer to ensure that `-B` and parallel builds function properly in the example commands below. To install this version on Ubuntu, we recommend following the instructions at [Kitware's apt repo](https://apt.kitware.com/). **Prerequisite:** [Ninja build tool](https://ninja-build.org/). Find installation commands for your favorite package manager [here](https://github.com/ninja-build/ninja/wiki/Pre-built-Ninja-packages). macOS: ``` # Configure cmake \ -B /BinaryCache/tensorflow-swift-models \ -D BUILD_TESTING=YES \ -D CMAKE_BUILD_TYPE=Release \ -D CMAKE_Swift_COMPILER=$(TOOLCHAINS=tensorflow xcrun -f swiftc) \ -G Ninja \ -S /SourceCache/tensorflow-swift-models # Build cmake --build /BinaryCache/tensorflow-swift-models # Test cmake --build /BinaryCache/tensorflow-swift-models --target test ``` Linux: ``` # Configure cmake \ -B /BinaryCache/tensorflow-swift-models \ -D BUILD_TESTING=NO \ -D CMAKE_BUILD_TYPE=Release \ -D CMAKE_Swift_COMPILER=$(which swiftc) \ -G Ninja \ -S /SourceCache/tensorflow-swift-models # Build cmake --build /BinaryCache/tensorflow-swift-models ``` Windows: ``` set DEVELOPER_LIBRARY_DIR=%SystemDrive%/Library/Developer/Platforms/Windows.platform/Developer/Library :: Configure "%ProgramFiles%\CMake\bin\cmake.exe" ^ -B %SystemDrive%/BinaryCache/tensorflow-swift-models ^ -D BUILD_SHARED_LIBS=YES ^ -D BUILD_TESTING=YES ^ -D CMAKE_BUILD_TYPE=Release ^ -D CMAKE_Swift_COMPILER=%SystemDrive%/Library/Developer/Toolchains/unknown-Asserts-development.xctoolchain/usr/bin/swiftc.exe ^ -D CMAKE_Swift_FLAGS="-sdk %SDKROOT% -I %DEVELOPER_LIBRARY_DIR%/XCTest-development/usr/lib/swift/windows/x86_64 -L %DEVELOPER_LIBRARY_DIR%/XCTest-development/usr/lib/swift/windows" ^ -G Ninja ^ -S %SystemDrive%/SourceCache/tensorflow-swift-models :: Build "%ProgramFiles%\CMake\bin\cmake.exe" --build %SystemDrive%/BinaryCache/tensorflow-swift-models :: Test "%ProgramFiles%\CMake\bin\cmake.exe" --build %SystemDrive%/BinaryCache/tensorflow-swift-models --target test ``` ## Bugs Please report model-related bugs and feature requests using GitHub issues in this repository. ## Community Discussion about Swift for TensorFlow happens on the [swift@tensorflow.org](https://groups.google.com/a/tensorflow.org/d/forum/swift) mailing list. ## Contributing We welcome contributions: please read the [Contributor Guide](CONTRIBUTING.md) to get started. It's always a good idea to discuss your plans on [the mailing list](https://groups.google.com/a/tensorflow.org/d/forum/swift) before making any major submissions. We have labeled some issues as ["good first issue"](https://github.com/tensorflow/swift-models/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22) or ["help wanted"](https://github.com/tensorflow/swift-models/issues?q=is%3Aissue+is%3Aopen+label%3A%22help+wanted%22) to provide some suggestions for where new contributors might be able to start. ## Code of Conduct In the interest of fostering an open and welcoming environment, we as contributors and maintainers pledge to making participation in our project and our community a harassment-free experience for everyone, regardless of age, body size, disability, ethnicity, gender identity and expression, level of experience, education, socio-economic status, nationality, personal appearance, race, religion, or sexual identity and orientation. The Swift for TensorFlow community is guided by our [Code of Conduct](CODE_OF_CONDUCT.md), which we encourage everybody to read before participating.