Show HN: Jixp, a Lisp DSL for describing Jax neural nets (github.com)

Jixp, a new domain-specific language (DSL) based on Lisp, has been introduced to streamline the process of defining neural networks in JAX. By compiling Lisp-like s-expressions into pure JAX Python, jixp enables users to describe models with minimal boilerplate, enhancing readability and accessibility. The tool consists of three components: a parser that translates s-expressions into Python lists, a minimal evaluator executing basic Lisp functions, and a robust compiler that generates JAX-compatible modules. This allows users to build complex models like GPT-2 and multi-layer perceptrons while ensuring that compiled outputs are independent of runtime dependencies. This development is significant for the AI/ML community as it simplifies the construction and visualization of neural networks, streamlining model training and implementation. Jixp’s syntax facilitates progressive dimension binding and static shape checking, making it easier to maintain and debug models. Furthermore, the integrated HTML explorer for visualizing model structures will aid researchers and practitioners in understanding their models better. With jixp, deep learning practitioners can focus more on experimentation and less on coding intricacies, potentially accelerating advancements in the field.