Domain Ml

This is your Layer 3 constraint reference for when you're building ML/AI apps in Rust and need to make the right tradeoffs. It maps domain rules like "large data means efficient memory" to concrete Rust implications like zero-copy views and streaming. The big win here is the decision matrix: it tells you tract for ONNX inference-only cases, candle or burn when you need training, tch-rs for PyTorch models. Includes patterns for model singletons with OnceLock and batched inference for GPU efficiency. The "trace down" section connects ML constraints to the right Layer 2 modules, so you're not guessing which skill to reach for when you need async data loading or lazy evaluation.

skills.sh installs: 573
GitHub stars: 1.3K
last updated: Jul 14, 2026

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Install

$ npx -y skills add zhanghandong/rust-skills --skill domain-ml --agent claude-code

Installs into .claude/skills of the current project.

Files

Machine Learning Domain

Layer 3: Domain Constraints

Domain Constraints → Design Implications

Domain Rule	Design Constraint	Rust Implication
Large data	Efficient memory	Zero-copy, streaming
GPU acceleration	CUDA/Metal support	candle, tch-rs
Model portability	Standard formats	ONNX
Batch processing	Throughput over latency	Batched inference
Numerical precision	Float handling	ndarray, careful f32/f64
Reproducibility	Deterministic	Seeded random, versioning

Critical Constraints

Memory Efficiency

RULE: Avoid copying large tensors
WHY: Memory bandwidth is bottleneck
RUST: References, views, in-place ops

GPU Utilization

RULE: Batch operations for GPU efficiency
WHY: GPU overhead per kernel launch
RUST: Batch sizes, async data loading

Model Portability

RULE: Use standard model formats
WHY: Train in Python, deploy in Rust
RUST: ONNX via tract or candle

Trace Down ↓

From constraints to design (Layer 2):

SKILL.md on GitHub

Details

Repository: github.com/zhanghandong/rust-skills
Categories: Data Science & ML, Rust
First seen: May 16, 2026