Struct ProfilingCallback 

pub struct ProfilingCallback {
    pub stats: ProfilingStats,
    /* private fields */
}

Callback for profiling training performance.

Tracks timing information and throughput metrics during training. Useful for:

• Identifying performance bottlenecks
• Comparing different configurations
• Monitoring training speed
• Resource utilization tracking

Example

use tensorlogic_train::{CallbackList, ProfilingCallback};

let mut callbacks = CallbackList::new();
callbacks.add(Box::new(ProfilingCallback::new(
    true,  // verbose: Print detailed stats
    5,     // log_frequency: Every 5 epochs
)));

Fields

stats: ProfilingStats

Accumulated statistics.

Implementations

impl ProfilingCallback

pub fn new(verbose: bool, log_frequency: usize) -> Self

Create a new profiling callback.

Arguments

• verbose - Print detailed profiling information
• log_frequency - Log stats every N epochs

pub fn get_stats(&self) -> &ProfilingStats

Get profiling statistics.

Trait Implementations

impl Callback for ProfilingCallback

fn on_train_begin(&mut self, _state: &TrainingState) -> TrainResult<()>

Called at the beginning of training.

fn on_train_end(&mut self, _state: &TrainingState) -> TrainResult<()>

Called at the end of training.

fn on_epoch_begin( &mut self, epoch: usize, _state: &TrainingState, ) -> TrainResult<()>

Called at the beginning of an epoch.

fn on_epoch_end( &mut self, epoch: usize, _state: &TrainingState, ) -> TrainResult<()>

Called at the end of an epoch.

fn on_batch_begin( &mut self, _batch: usize, _state: &TrainingState, ) -> TrainResult<()>

Called at the beginning of a batch.

fn on_batch_end( &mut self, _batch: usize, _state: &TrainingState, ) -> TrainResult<()>

Called at the end of a batch.

fn on_validation_end(&mut self, _state: &TrainingState) -> TrainResult<()>

Called after validation.

fn should_stop(&self) -> bool

Check if training should stop early.