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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:

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


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



The discussions happened on the [email protected] 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 conceptsthey 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!


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.


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
Swift Differentiable Programming Manifesto January 2020 Current
Swift Differentiable Programming Implementation Overview August 2019 Current
Swift Differentiable Programming Design Overview June 2019 Outdated
Differentiable Types March 2019 Outdated
Differentiable Functions and Differentiation APIs March 2019 Outdated
Dynamic Property Iteration using Key Paths March 2019 Current
Hierarchical Parameter Iteration and Optimization March 2019 Current
First-Class Automatic Differentiation in Swift: A Manifesto October 2018 Outdated
Automatic Differentiation Whitepaper April 2018 Outdated
Python Interoperability April 2018 Current
Graph Program Extraction April 2018 Outdated

Source code

Compiler and standard library development happens on the main branch of the apple/swift repository.

Additional code repositories that make up the core of the project include:

Swift for TensorFlow is no longer a fork of the official Swift language; development was previously done on the tensorflow branch of the apple/swift repository. Language additions were designed to fit with the direction of Swift and are going through the Swift Evolution process.

Jupyter Notebook support

Jupyter Notebook support for Swift is under development at google/swift-jupyter.

Model garden

tensorflow/swift-models is a repository of machine learning models built with Swift for TensorFlow. It intended to provide examples of how to use Swift for TensorFlow, to allow for end-to-end tests of machine learning APIs, and to host model benchmarking infrastructure.


fastai/swiftai is a high-level API for Swift for TensorFlow, modeled after the fastai Python library.


Swift for TensorFlow discussions happen on the [email protected] mailing list.

Bugs reports and feature requests

Before reporting an issue, please check the Frequently Asked Questions to see if your question has already been addressed.

For questions about general use or feature requests, please send an email to the mailing list or search for relevant issues in the JIRA issue tracker.

For the most part, the core team's development is also tracked in JIRA.


We welcome contributions from everyone. Read the contributing guide for information on how to get started.

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, which we encourage everybody to read before participating.

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