Domain Ml

This is a Layer 3 domain constraint reference for building ML/AI applications in Rust. It maps ML requirements like memory efficiency and GPU utilization directly to Rust patterns, showing you why batching matters for GPUs, when to use tract versus candle versus tch-rs, and how to avoid copying giant tensors around. The code samples are practical: inference servers with OnceLock for model singletons, batched prediction pipelines, and async data loading to keep GPUs fed. It's narrow in scope but information-dense, assuming you already know the Layer 1 and Layer 2 fundamentals and just need the ML-specific constraints translated into concrete Rust decisions.

skills.sh installs: 3K
GitHub stars: 1.6K
last updated: Aug 21, 2026

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Install

$ npx -y skills add actionbook/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/actionbook/rust-skills
Categories: Data Science & ML, Rust
First seen: Jun 3, 2026