Swift for TensorFlow aims for high-performance machine learning

Swift for TensorFlow aims for high-performance machine learning

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Swift for TensorFlow aims for high-performance machine learning | InfoWorld










for machine learning applications, shared project roadmap information in a recent talk. Future plans for Swift for TensorFlow include capabilities such as C++ interoperability, improved automatic differentiation, and support for distributed training.

Swift for TensorFlow is an early-stage, Google-led project that integrates Google’s with Swift, the modern general purpose language created by Apple. The use of Swift enables more powerful algorithms to be expressed in a new manner, and easy differentiation of functions via generalized differentiation APIs, according to the Swift for TensorFlow developers.

Open source Swift has been described on the Swift for TensorFlow project website as easy to use and elegant, with advantages such as a strong type system, which can help developers catch errors earlier and promotes good API design. Building on TensorFlow, Swift for TensorFlow APIs provide transparent access to low-level TensorFlow operators.

Swift for TensorFlow is focused on two sets of users: advanced researchers limited by current machine learning frameworks, and machine learning learners just getting started. Extensions to the Swift language provide interoperability between Swift and Python, a popular language in machine learning. Python can be imported within a Swift Jupyter Notebook and . Developers can write Swift to call into Python libraries, with no wrappers and no additional overhead. 

Where to download Swift for Tensorflow

You can from GitHub. Tutorials, documentation, and instructions for  in the project can be found at .