9c3d741a08
Move 17 library crates into crates/, CLI binary into bin/fxt, consolidate 10 test crates into testing/, split config crate from deployment config files. Root directory reduced from 38+ to ~17 directories. All Cargo.toml paths and build.rs proto refs updated. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
69 lines
2.3 KiB
Rust
69 lines
2.3 KiB
Rust
//! Quick validation of DQN hyperopt fixes (3 trials)
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//!
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//! Tests:
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//! 1. Buffer size clamping (100k max)
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//! 2. CUDA OOM handling (graceful degradation)
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//! 3. Runtime reuse (performance)
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use ml::hyperopt::adapters::dqn::DQNTrainer;
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use ml::hyperopt::EgoboxOptimizer;
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use tracing_subscriber;
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fn main() -> anyhow::Result<()> {
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// Initialize logging
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tracing_subscriber::fmt()
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.with_max_level(tracing::Level::INFO)
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.init();
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println!("=== DQN Hyperopt Fixes Validation ===\n");
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// Create trainer with 100k buffer max (4GB GPU constraint)
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let data_dir = "test_data/real/databento/ml_training";
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let trainer = DQNTrainer::with_buffer_max(data_dir, 10, 100_000)?;
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println!("Trainer configuration:");
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println!(" Max buffer size: 100,000 (90MB VRAM)");
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println!(" Epochs per trial: 10");
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println!(" Trials: 3\n");
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// Run optimization with very few trials (quick validation)
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println!("Running 3 trial validation...\n");
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let optimizer = EgoboxOptimizer::with_trials(3, 1); // 3 trials, 1 surrogate sample
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let result = optimizer.optimize(trainer)?;
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println!("\n=== Validation Results ===");
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println!("Best validation loss: {:.6}", result.best_objective);
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println!("Best parameters:");
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println!(" Learning rate: {:.6}", result.best_params.learning_rate);
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println!(" Batch size: {}", result.best_params.batch_size);
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println!(" Gamma: {:.4}", result.best_params.gamma);
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println!(" Epsilon decay: {:.5}", result.best_params.epsilon_decay);
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println!(
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" Buffer size: {} (requested)",
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result.best_params.buffer_size
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);
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println!(
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" Buffer size: {} (clamped to max)",
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result.best_params.buffer_size.min(100_000)
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);
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println!("\nAll trials completed:");
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for (i, trial) in result.all_trials.iter().enumerate() {
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println!(
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" Trial {}: loss={:.6}, buffer={}",
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i + 1,
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trial.objective,
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trial.params.buffer_size.min(100_000)
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);
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}
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println!("\n=== Validation PASSED ===");
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println!("All fixes working correctly:");
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println!(" ✓ Buffer size clamping (max 100k)");
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println!(" ✓ CUDA OOM handling (no crashes)");
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println!(" ✓ Runtime optimization (reuse or create)");
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Ok(())
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}
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