domain-ml
Use when building ML/AI apps in Rust. Keywords: machine learning, ML, AI, tensor, model, inference, neural network, deep learning, training, prediction, ndarray, tch-rs, burn, candle, 机器学习, 人工智能, 模型推理
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/actionbook/rust-skills --skill domain-ml- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads domain-ml automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
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):
"Need efficient data pipelines"
↓ m10-performance: Streaming, batching
↓ polars: Lazy evaluation
"Need GPU inference"
↓ m07-concurrency: Async data loading
↓ candle/tch-rs: CUDA backend
"Need model loading"
↓ m12-lifecycle: Lazy init, caching
↓ tract: ONNX runtime
Use Case → Framework
| Use Case | Recommended | Why |
|---|---|---|
| Inference only | tract (ONNX) | Lightweight, portable |
| Training + inference | candle, burn | Pure Rust, GPU |
| PyTorch models | tch-rs | Direct bindings |
| Data pipelines | polars | Fast, lazy eval |
Key Crates
| Purpose | Crate |
|---|---|
| Tensors | ndarray |
| ONNX inference | tract |
| ML framework | candle, burn |
| PyTorch bindings | tch-rs |
| Data processing | polars |
| Embeddings | fastembed |
Design Patterns
| Pattern | Purpose | Implementation |
|---|---|---|
| Model loading | Once, reuse | OnceLock<Model> |
| Batching | Throughput | Collect then process |
| Streaming | Large data | Iterator-based |
| GPU async | Parallelism | Data loading parallel to compute |
Code Pattern: Inference Server
use std::sync::OnceLock;
use tract_onnx::prelude::*;
static MODEL: OnceLock<SimplePlan<TypedFact, Box<dyn TypedOp>, Graph<TypedFact, Box<dyn TypedOp>>>> = OnceLock::new();
fn get_model() -> &'static SimplePlan<...> {
MODEL.get_or_init(|| {
tract_onnx::onnx()
.model_for_path("model.onnx")
.unwrap()
.into_optimized()
.unwrap()
.into_runnable()
.unwrap()
})
}
async fn predict(input: Vec<f32>) -> anyhow::Result<Vec<f32>> {
let model = get_model();
let input = tract_ndarray::arr1(&input).into_shape((1, input.len()))?;
let result = model.run(tvec!(input.into()))?;
Ok(result[0].to_array_view::<f32>()?.iter().copied().collect())
}
Code Pattern: Batched Inference
async fn batch_predict(inputs: Vec<Vec<f32>>, batch_size: usize) -> Vec<Vec<f32>> {
let mut results = Vec::with_capacity(inputs.len());
for batch in inputs.chunks(batch_size) {
// Stack inputs into batch tensor
let batch_tensor = stack_inputs(batch);
// Run inference on batch
let batch_output = model.run(batch_tensor).await;
// Unstack results
results.extend(unstack_outputs(batch_output));
}
results
}
Common Mistakes
| Mistake | Domain Violation | Fix |
|---|---|---|
| Clone tensors | Memory waste | Use views |
| Single inference | GPU underutilized | Batch processing |
| Load model per request | Slow | Singleton pattern |
| Sync data loading | GPU idle | Async pipeline |
Trace to Layer 1
| Constraint | Layer 2 Pattern | Layer 1 Implementation |
|---|---|---|
| Memory efficiency | Zero-copy | ndarray views |
| Model singleton | Lazy init | OnceLock<Model> |
| Batch processing | Chunked iteration | chunks() + parallel |
| GPU async | Concurrent loading | tokio::spawn + GPU |
Related Skills
| When | See |
|---|---|
| Performance | m10-performance |
| Lazy initialization | m12-lifecycle |
| Async patterns | m07-concurrency |
| Memory efficiency | m01-ownership |
FAQ
What does the domain-ml skill do?
Use when building ML/AI apps in Rust. Keywords: machine learning, ML, AI, tensor, model, inference, neural network, deep learning, training, prediction, ndarray, tch-rs, burn, candle, 机器学习, 人工智能, 模型推理
How do I install domain-ml?
Run npx skills add https://github.com/actionbook/rust-skills --skill domain-ml in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/domain-ml/ (personal) or .claude/skills/domain-ml/ (project).
Which AI agents can use domain-ml?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is domain-ml safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains domain-ml?
It is published by actionbook in actionbook/rust-skills. It has 2,819 recorded installs.
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