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npx skills add https://github.com/yumazak/kodo --skill domain-ml-yumazak
Please help me install this Agent Skill. Skill: domain-ml Source: https://github.com/yumazak/kodo/tree/main/.agents/skills/domain-ml Command: npx skills add https://github.com/yumazak/kodo --skill domain-ml-yumazak
Optimizes ML/AI development in Rust by enforcing domain-driven constraints for memory efficiency, GPU utilization, and model portability.
Start by outlining your ML inference workflow in Rust and plug in tract or tch-rs for ONNX or PyTorch models.
High-intent search queries and answers about installing and using this skill.
Run ONNX inference in Rust by using the tract crate for portable execution. This Skill enforces domain-driven constraints for memory efficiency and safe memory handling, ensuring deterministic and batched inference pipelines.
Handle tensors in Rust by applying zero-copy design patterns with the ndarray crate. This approach enforces memory efficiency and safe memory handling while maintaining deterministic execution for machine learning workflows.
Use GPU acceleration for PyTorch models in Rust via the tch-rs crate. This Skill enforces GPU utilization constraints and lazy initialization to optimize memory efficiency during cross-framework model deployment.
Cross-framework model deployment is supported by enforcing model portability through standard formats like ONNX. The Skill guides using portable ML crates such as candle, burn, and tract for seamless inference server setup.
Build a memory-safe inference server in Rust by leveraging domain-driven constraints and crates like tract or tch-rs. This enforces safe memory handling, lazy initialization, and deterministic execution for batched inference.
Avoid Rust for machine learning inference if your workflow requires dynamic tensor shapes that break deterministic execution constraints. This Skill enforces strict memory efficiency and standard format usage, which may limit highly dynamic model architectures.
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JylhisGuide Rust ML design with domain constraints and crate recommendations.
