Welcome to the Swift for TensorFlow development community!
Swift for TensorFlow is a result of first-principles thinking applied to machine learning frameworks, and works quite differently than existing TensorFlow language bindings. Whereas prior solutions are designed within the constraints of what can be achieved by a (typically Python or Lua) library, Swift for TensorFlow is based on the belief that machine learning is important enough to deserve first-class language and compiler support.
First-class language and compiler support allows us to innovate in areas that have traditionally been out of bounds for machine learning libraries. Our results provide the performance of TensorFlow graphs with the ease of use of define-by-run models, and provides a great user experience - for example, by catching more mistakes before you run your code.
As announced at the TensorFlow Developer Summit, we are planning to launch our open source project on GitHub in April. In addition to releasing the code, we will be using an open design model, where design discussions happen in public.
Between now and then, we are writing some technical white papers that explain in detail the design approach (e.g., the core compiler partitioning technique that underlies the whole thing, our approach to automatic differentiation, etc.), implementation tradeoffs, and the status of this work. We can’t wait to engage with the broader community, but prefer to start the conversation when these white papers are ready.
Sign up here to join the community Google group. We will initially use it for announcements, and then open it for general discussion when we are ready in April.