Swift for TensorFlow: No boundaries.
Swift for TensorFlow is a next-generation platform for machine learning, incorporating the latest research across: machine learning, compilers, differentiable programming, systems design, and beyond. This project is approaching version 0.2; it is neither feature complete nor production-ready. But it is ready for pioneers to try it for your own projects, give us feedback, and help shape the future!
The Swift for TensorFlow project is currently focusing on 2 kinds of users:
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Advanced ML researchers who are limited by current ML frameworks. Swift for TensorFlow's advantages include a seamless integration with a modern general-purpose language, allowing for more dynamic and sophisticated models. Fast abstractions can be developed "in user-space" (as opposed to in C/C++ aka "framework-space"), resulting in modular APIs that can be easily customized.
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ML learners who are just getting started with machine learning. Thanks to Swift's support for quality tooling (e.g. context-aware autocomplete), Swift for TensorFlow can be one of the most productive ways to get started learning the fundamentals of machine learning.
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Google Colaboratory: The fastest way to get started is to try out Swift for TensorFlow right in your browser. Just open up our getting started notebook (or start from a blank notebook)! Read more in our usage guide.
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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.
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Compile from source: If you'd like to customize Swift for TensorFlow or even contribute back, follow our instructions on building the Swift for TensorFlow compiler from source.
Please do join the [email protected] mailing list to hear the latest announcements, get help, and share your thoughts!
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 custom functions using differential
operators like gradient(of:), or differentiate with respect to an entire
model by calling standard library method model.gradient { ... }.
// Custom differentiable type.
struct Model: Differentiable {
var w: Float
var b: Float
func applied(to input: Float) -> Float {
return w * input + b
}
}
// Differentiate using `Differentiable.gradient(at:in:)`.
let model = Model(w: 4.0, b: 3.0)
let (𝛁model, 𝛁input) = model.gradient(at: 2.0) { model, input in
model.applied(to: input)
}
print(𝛁model) // Model.AllDifferentiableVariables(w: 2.0, b: 1.0)
print(𝛁input) // 4.0Beyond 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([10, 10]) // Create a 10x10 numpy array.
let tensor = Tensor(numpy: array) // Seamless integration!- Swift for TensorFlow Design Overview
- Why Swift for TensorFlow?
- Sample Models
- Tutorials
- Frequently Asked Questions
- Swift Tensor API Reference
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.
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.
- Automatic Differentiation Whitepaper
- Automatic Differentiation Manifesto
- Python Interoperability
- Aggregate Parameters and Parameter Update
- Graph Program Extraction
Compiler and standard library development happens on the tensorflow branch of
the apple/swift repository.
Additional code repositories that make up the core of the project include:
- Swift fork of LLDB: debugger and REPL support.
- Deep learning library: high-level API familiar to Keras users.
Swift for TensorFlow is not intended to remain a long-term fork of the official Swift language. New language features will eventually go through the Swift evolution process as part of being considered for being pulled into master.
Jupyter Notebook support for Swift is under development at google/swift-jupyter.
Swift for TensorFlow discussions happen on the [email protected] mailing list.
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 source code contributions: please read Contributing Code. It is always a good idea to discuss your plans on the mailing list before making any major submissions.
Check out our starter bugs if you'd like to get involved but don't know where to start!
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.