Welcome to the Swift for TensorFlow development community! For discussions, join the [email protected] mailing list.
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. With Swift, you can write the following imperative code, and Swift automatically turns it into a single TensorFlow Graph and runs it with the full performance of TensorFlow Sessions on CPU, GPU and TPU.
import TensorFlow
var x = Tensor<Float>([[1, 2], [3, 4]])
for _ in 1...5 {
x += x • x // alternatively: x += matmul(x, x)
print(x)
}Swift combines the flexibility of Eager Execution with the high performance of Graphs and Sessions. Behind the scenes, Swift analyzes your Tensor code and automatically builds graphs for you. Swift also catches type errors and shape mismatches before running your code, has the ability to import any Python library, and has Automatic Differentiation built right in. We believe that machine learning tools are so important that they deserve a first-class language and a compiler.
Note: Swift for TensorFlow is an early stage project. It has been released to enable open source development and is not yet ready for general use by machine learning developers.
You can download a pre-built package for Swift for TensorFlow. After installation, you can follow these instructions to try it out.
For instructions on building from source, visit apple/swift.
Below are some documents explaining the Swift for TensorFlow project.
- Graph Program Extraction
- Automatic Differentiation Whitepaper
- Automatic Differentiation Manifesto
- Python Interoperability
- Aggregate Parameters and Parameter Update
The active development of Swift for TensorFlow will happen under the "tensorflow" branch of apple/swift.
These projects include:
- The compiler and standard libraries: apple/swift
- Debugger and REPL support: apple/swift-lldb
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.
You can find example machine learning models at tensorflow/swift-models, or try out a tutorial at tensorflow/swift-tutorials.
Jupyter Notebook support for Swift for TensorFlow is under development at google/swift-jupyter.
Discussion about Swift for TensorFlow happens 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 report them in this repository or send an email to the mailing list.
For Swift compiler bugs introduced by Swift for TensorFlow, please file them to bugs.swift.org within the “Swift for TensorFlow” component.
For bugs in the example models, please report them to tensorflow/swift-models.
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.
The compiler and the standard library have the most open issues. To get started, you can try to tackle issues labeled "good first issue".
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.