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Swift for TensorFlow (Archived) 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. Some significant achievements from this project include: Added language-integrated differentiable programming into the Swift language. This work continues in the official Swift compiler. Developed a mutable-value-semantics-oriented deep learning API. Fostered the development of a model garden with more than 30 models from a variety of deep learning disciplines. Enabled novel research that combines deep learning with probabilistic graphical models for 3D motion tracking and beyond. Powered a(n almost) pure-Swift prototype of a GPU+CPU runtime supporting parallel map. Spun off multiple open source side efforts which continue to be under active development: PythonKit: Python interoperability with Swift. swift-jupyter: Enables use of Swift within Jupyter notebooks. swift-benchmark: Provides a robust benchmarking suite for Swift code. Spun off several other open source efforts: penguin: Parallel programming, data structures, graph algorithms, and more. Tensors Fitting Perfectly: Static analysis of tensor shape mismatches. Swift Evolution proposals pitched, implemented, and accepted: SE-0195: User-defined "Dynamic Member Lookup" Types (@dynamicMemberLookup) SE-0216: Introduce user-defined dynamically "callable" types (@dynamicCallable) SE-0233: Make Numeric refine a new AdditiveArithmetic protocol SE-0253: Callable values of user-defined nominal types (func callAsFunction) This site will not receive further updates. The API documentation and binary downloads will continue to be accessible as well as the Open Design Review meeting recordings. Getting started Using Swift for TensorFlow Google Colaboratory: The fastest way to get started is to try out Swift for TensorFlow right in your browser. Just open up a tutorial, or start from a blank notebook! Read more in our usage guide. Install locally: You can download a pre-built Swift for TensorFlow package. After installation, you can follow these step-by-step instructions to build and execute a Swift script on your computer. Run on GCP: You can spin up a GCE instance using a Swift for TensorFlow Deep Learning VM image, with all drivers and the toolchain pre-installed. Instructions can be found in the Installation Guide. Compile from source: If you'd like to customize Swift for TensorFlow or contribute back, follow our instructions on building Swift for TensorFlow from source. Tutorials Tutorial Last Updated A Swift Tour March 2019 Protocol-Oriented Programming & Generics August 2019 Python Interoperability March 2019 Custom Differentiation March 2019 Sharp Edges in Differentiability November 2020 Model Training Walkthrough March 2019 Raw TensorFlow Operators December 2019 Introducing X10, an XLA-Based Backend May 2020 Resources Models and Examples TensorFlow Swift API Reference Release Notes Known Issues Frequently Asked Questions TensorFlow Blog Posts Forums The discussions happened on the swift@tensorflow.org mailing list. Why Swift for TensorFlow? Swift for TensorFlow is a new way to develop machine learning models. It gives you the power of TensorFlow directly integrated into the Swift programming language. We believe that machine learning paradigms are so important that they deserve first-class language and compiler support. A fundamental primitive in machine learning is gradient-based optimization: computing function derivatives to optimize parameters. With Swift for TensorFlow, you can easily differentiate functions using differential operators like gradient(of:), or differentiate with respect to an entire model by calling method gradient(in:). These differentiation APIs are not just available for Tensor-related concepts—they are generalized for all types that conform to the Differentiable protocol, including Float, Double, SIMD vectors, and your own data structures. // Custom differentiable type. struct Model: Differentiable { var w: Float var b: Float func applied(to input: Float) -> Float { return w * input + b } } // Differentiate using `gradient(at:_:in:)`. let model = Model(w: 4, b: 3) let input: Float = 2 let (𝛁model, 𝛁input) = gradient(at: model, input) { model, input in model.applied(to: input) } print(𝛁model) // Model.TangentVector(w: 2.0, b: 1.0) print(𝛁input) // 4.0 Beyond derivatives, the Swift for TensorFlow project comes with a sophisticated toolchain to make users more productive. You can run Swift interactively in a Jupyter notebook, and get helpful autocomplete suggestions to help you explore the massive API surface of a modern deep learning library. You can get started right in your browser in seconds! Migrating to Swift for TensorFlow is really easy thanks to Swift's powerful Python integration. You can incrementally migrate your Python code over (or continue to use your favorite Python libraries), because you can easily call your favorite Python library with a familiar syntax: import TensorFlow import Python let np = Python.import("numpy") let array = np.arange(100).reshape(10, 10) // Create a 10x10 numpy array. let tensor = Tensor<Float>(numpy: array) // Seamless integration! Documentation Beware: the project is moving very quickly, and thus some of these documents are slightly out of date as compared to the current state-of-the-art. Overview Document Last Updated Status Why Swift for TensorFlow? April 2018 Current Swift for TensorFlow Design Overview April 2018 Outdated Supported Backends May 2020 Current Technology deep dive The Swift for TensorFlow project builds on top of powerful theoretical foundations. For insight into some of the underlying technologies, check out the following documentation. Document Last Updated Status Swif