train - Module

`struct TrainConfig`

Source: train.joule:28

`fn default() -> Self`

Source: train.joule:76

`struct MixedPrecisionConfig`

Source: train.joule:99

`fn default() -> Self`

Source: train.joule:107

`enum EnergyMode`

Source: train.joule:119

`struct LoggingConfig`

Source: train.joule:132

`fn default() -> Self`

Source: train.joule:142

`struct CheckpointConfig`

Source: train.joule:156

`fn default() -> Self`

Source: train.joule:167

`struct EarlyStoppingConfig`

Source: train.joule:182

`fn default() -> Self`

Source: train.joule:190

`struct TrainState`

Source: train.joule:206

`fn new() -> Self`

Source: train.joule:216

`struct TrainMetrics`

Source: train.joule:234

`fn new() -> Self`

Source: train.joule:264

`fn compute_derived(&mut self, batch_size: usize)`

Compute derived metrics

Source: train.joule:279

`fn summary(&self) -> String`

Summary string

Source: train.joule:288

`struct Trainer`

Energy-aware trainer

Source: train.joule:309

`fn new(model: M, optimizer: O, loss_fn: L, config: TrainConfig) -> Self`

Create new trainer

Source: train.joule:351

`fn add_callback(&mut self, callback: Box<dyn TrainCallback>)`

Add callback

Source: train.joule:385

`fn model(&self) -> &M`

Get model reference

Source: train.joule:390

`fn model_mut(&mut self) -> &mut M`

Get mutable model reference

Source: train.joule:395

`fn state(&self) -> &TrainState`

Get training state

Source: train.joule:400

`fn train<D>(&mut self, train_data: DataLoader<D, DefaultCollate>, val_data: Option<DataLoader<D, DefaultCollate>>) -> TrainResult`

Source: train.joule:406

`fn train_epoch<D>(&mut self, data_loader: &DataLoader<D, DefaultCollate>) -> Result<TrainMetrics, TrainError>`

Train one epoch

Source: train.joule:527

`fn train_step(&mut self, inputs: &Tensor, targets: &Tensor) -> Result<TrainMetrics, TrainError>`

Execute one training step

Source: train.joule:606

`fn validate<D>(&mut self, data_loader: &DataLoader<D, DefaultCollate>) -> Result<TrainMetrics, TrainError>`

Validate on dataset

Source: train.joule:677

`fn clip_gradients(&mut self, max_norm: f64)`

Clip gradients by norm

Source: train.joule:739

`fn unscale_gradients(&mut self)`

Unscale gradients for mixed precision

Source: train.joule:764

`fn throttle_training(&mut self)`

Throttle training for energy efficiency

Source: train.joule:775

`fn save_checkpoint(&mut self, metric: Option<f64>)`

Save checkpoint

Source: train.joule:785

`fn energy_report(&self) -> EnergyReport`

Get energy report

Source: train.joule:801

`struct TrainResult`

Source: train.joule:812

`fn new() -> Self`

Source: train.joule:822

`fn best_val_loss(&self) -> Option<f64>`

Best validation loss

Source: train.joule:834

`fn epochs_trained(&self) -> usize`

Total epochs trained

Source: train.joule:844

`fn avg_energy_per_epoch(&self) -> Joules`

Average energy per epoch

Source: train.joule:849

`fn print_summary(&self)`

Print summary

Source: train.joule:858

`enum StopReason`

Source: train.joule:882

`enum TrainError`

Source: train.joule:892

`trait TrainCallback`

Training callback trait

Source: train.joule:906

`fn on_train_begin(&mut self, _config: &TrainConfig, _state: &TrainState)`

Source: train.joule:907

`fn on_train_end(&mut self, _result: &TrainResult)`

Source: train.joule:908

`fn on_epoch_begin(&mut self, _epoch: u32)`

Source: train.joule:909

`fn on_epoch_end(&mut self, _epoch: u32, _metrics: &TrainMetrics)`

Source: train.joule:910

`fn on_step_begin(&mut self, _step: u64)`

Source: train.joule:911

`fn on_step_end(&mut self, _step: u64, _metrics: &TrainMetrics)`

Source: train.joule:912

`fn on_validation_begin(&mut self)`

Source: train.joule:913

`fn on_validation_end(&mut self, _metrics: &TrainMetrics)`

Source: train.joule:914

`struct EnergyLoggerCallback`

Energy logging callback

Source: train.joule:918

`struct EnergyLogEntry`

Source: train.joule:924

`fn new(log_path: &str) -> Self`

Source: train.joule:933

`fn on_step_end(&mut self, step: u64, metrics: &TrainMetrics)`

Source: train.joule:942

`fn on_train_end(&mut self, _result: &TrainResult)`

Source: train.joule:952

`struct PowerLimitCallback`

Power limit callback (throttles when exceeding power budget)

Source: train.joule:971

`fn new(power_limit: Watts, window_size: usize) -> Self`

Source: train.joule:978

`fn on_step_end(&mut self, _step: u64, metrics: &TrainMetrics)`

Source: train.joule:988

`fn train_simple<M, D, L>(`

Source: train.joule:1013

`fn gradient_norm<M: Module>(model: &M) -> f64`

Compute gradient norm

Source: train.joule:1069

`fn count_parameters<M: Module>(model: &M) -> usize`

Count trainable parameters

Source: train.joule:1080

`fn estimate_training_memory(`

Estimate memory usage for training

Source: train.joule:1085

`fn estimate_training_energy(`

Estimate energy for training run

Source: train.joule:1105