Enable JAX-based ML inference from Rust via MEMORY_P v2.0 FFI bridge.
npx skills add https://github.com/Rigohl/MEMORY_P --skill jax-ml-inference
Please help me install this Agent Skill. Skill: jax-ml-inference Source: https://github.com/Rigohl/MEMORY_P/tree/main/.github/skills/jax-ml-inference Command: npx skills add https://github.com/Rigohl/MEMORY_P --skill jax-ml-inference
MEMORY_P users need to run JAX-based machine learning inference across language boundaries. This skill provides a ready-to-use bridge between Rust and JAX, enabling multi-language inference workflows within MEMORY_P v2.0.
Initialize and execute a sample JAX-based inference task through the MEMORY_P v2.0 FFI bridge.
High-intent search queries and answers about installing and using this skill.
You can run JAX ML inference from Rust within MEMORY_P by using a cross-language FFI bridge that enables typed data exchange and safe invocation of JAX components. This allows multi-language workflows to execute inference tasks directly.
A cross-language FFI bridge for JAX inference is used to enable Rust applications to call JAX-based machine learning models, facilitating safe data exchange and typed inputs/outputs across language boundaries within multi-language ML workflows.
Yes, you can use Rust to call JAX models in MEMORY_P to evaluate optimization tasks and predictive behaviors. The FFI bridge supports validating JAX models and executing inference workflows across language boundaries.
The JAX FFI bridge currently provides stub-backed execution with planned full functionality. It satisfies FFI compatibility and data exchange requirements, allowing you to validate multi-language ML workflows before full feature completion.
The limitation of using this FFI bridge for ML inference is that it currently operates with stub-backed execution rather than full functionality, meaning complex JAX inference workflows may not be fully supported until planned features are implemented.
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