Manuel Raimann
ee59c9cdd0
- Split pruning_and_callbacks into pruning and early_stopping - Split advanced_features into async_parallel, journal_storage, ask_and_tell, multi_objective - Each example now requires only its own feature flag - Trim sampler_comparison winner logic and verbose header - Update CI workflow and README to match new example names
53 lines
1.5 KiB
Rust
53 lines
1.5 KiB
Rust
//! Async parallel optimization — evaluate multiple trials concurrently.
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//!
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//! Uses `optimize_parallel` with tokio to run several trials at once,
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//! reducing wall-clock time when the objective involves I/O or async work.
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//!
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//! Run with: `cargo run --example async_parallel --features async`
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use optimizer::prelude::*;
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#[tokio::main]
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async fn main() -> optimizer::Result<()> {
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let study: Study<f64> = Study::minimize(TpeSampler::new());
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let x = FloatParam::new(-5.0, 5.0).name("x");
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let y = FloatParam::new(-5.0, 5.0).name("y");
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let n_trials = 30;
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let concurrency = 4;
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println!("Running {n_trials} trials with {concurrency} concurrent workers...");
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study
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.optimize_parallel(n_trials, concurrency, {
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let x = x.clone();
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let y = y.clone();
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move |mut trial| {
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let x = x.clone();
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let y = y.clone();
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async move {
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let xv = x.suggest(&mut trial)?;
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let yv = y.suggest(&mut trial)?;
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// Simulate async I/O (e.g. calling an external service)
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tokio::time::sleep(std::time::Duration::from_millis(10)).await;
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let value = xv * xv + yv * yv;
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Ok::<_, optimizer::Error>((trial, value))
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}
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}
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})
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.await?;
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let best = study.best_trial()?;
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println!(
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"Best: f({:.3}, {:.3}) = {:.6}",
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best.get(&x).unwrap(),
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best.get(&y).unwrap(),
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best.value,
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);
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Ok(())
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}
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