Tensorlang addresses the usability and performance gaps in TensorFlow by introducing a dedicated programming language. It targets machine learning engineers and researchers seeking a more productive, powerful, and enjoyable development environment that leverages TensorFlow's runtime capabilities.

How It Works

Tensorlang compiles directly to TensorFlow MetaGraphDefs, offering a novel syntax tailored for machine learning concepts, such as function composition (f -> g -> h). It provides native support for symbolic differentiation, intuitive control flow (loops, conditionals), and features like attributes and macros for compile-time flexibility and higher-order programming. This approach aims to enhance expressiveness and development efficiency within the TensorFlow ecosystem.

Quick Start & Requirements

The README does not specify installation commands or detailed prerequisites beyond the TensorFlow runtime and Python 3 for the compiler. Information regarding setup time, specific dependencies (e.g., Python version, GPU requirements), or links to official quick-start guides is absent.

Highlighted Details

• ML-centric Syntax: Introduces syntax like f -> g -> h for function composition, mirroring ML diagrams.
• Integrated Symbolic Differentiation: Offers direct syntax (grad[func]) for automatic gradient computation.
• Native Control Flow: Implements more natural syntax for loops and conditionals than TensorFlow's Python API.
• Compile-time Metaprogramming: Supports attributes (func[attr: val]) and macros for flexible code generation.

Maintenance & Community

The provided README does not contain information regarding project maintenance, community channels (e.g., Discord, Slack), or notable contributors.

Licensing & Compatibility

The README does not specify the project's license or any compatibility notes for commercial use.

Limitations & Caveats

The project is explicitly in "early development," and some syntax elements are noted as "kludgy." While the compiler is Python 3-based, future versions aim for Python independence in graph generation. Users may need to adapt to its lazy/asynchronous execution model within functions.