0xharryriddle 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 |
| 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 |