lpd 0.1.4

A Fast, Flexible Trainer with Callbacks and Extensions for PyTorch

lpd

A Fast, Flexible Trainer with Callbacks and Extensions for PyTorch

lpd derives from the Hebrew word lapid (לפיד) which means "torch".

For latest PyPI stable release

    pip install lpd

Usage

lpd intended to properly structure your PyTorch model training. The main usages are given below.

Training your model

    from lpd.trainer import Trainer
    import lpd.utils.torch_utils as tu
    import lpd.utils.general_utils as gu
    from lpd.enums import Phase, State, MonitorType, MonitorMode, StatsType
    from lpd.callbacks import StatsPrint, ModelCheckPoint, Tensorboard, EarlyStopping, SchedulerStep
    from lpd.extensions.custom_metrics import binary_accuracy_with_logits

    gu.seed_all(seed=42)

    device = tu.get_gpu_device_if_available() # with fallback to CPU if GPU not avilable
    model = TestModel(config, num_embeddings).to(device) #this is your model class, and its being sent to the relevant device
    optimizer = optim.SGD(params=model.parameters())
    scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', patience=5, verbose=True)
    loss_func = nn.BCEWithLogitsLoss().to(device) #this is your loss class, already sent to the relevant device
    metric_name_to_func = {'acc':binary_accuracy_with_logits} # add as much metrics as you like

    # you can use some of the defined callbacks, or you can create your own
    callbacks = [
                SchedulerStep(scheduler_parameters_func=lambda trainer: trainer.val_stats.get_loss()), # notice lambda for scheduler that takes loss in step()
                ModelCheckPoint(checkpoint_dir, checkpoint_file_name, MonitorType.LOSS, StatsType.VAL, MonitorMode.MIN, save_best_only=True), 
                Tensorboard(summary_writer_dir=summary_writer_dir),
                EarlyStopping(patience=10, MonitorType.METRIC, StatsType.VAL, MonitorMode.MAX, metric_name='acc'),
                StatsPrint(apply_on_phase=Phase.EPOCH_END, metric_names=metric_name_to_func.keys())
            ]

    trainer = Trainer(model, 
                      device, 
                      loss_func, 
                      optimizer,
                      scheduler,
                      metric_name_to_func, 
                      train_data_loader,  # DataLoader, Iterable or Generator
                      val_data_loader,    # DataLoader, Iterable or Generator
                      train_steps,
                      val_steps,
                      num_epochs,
                      callbacks,
                      name='Readme-Example')
    
    trainer.train()

Evaluating your model

    trainer.evaluate(test_data_loader, test_steps)

TrainerStats

Trainer tracks stats for train/validate/test and you can access them in your custom callbacks or any other place that has access to your trainer.

Here are some examples

    train_loss = trainer.train_stats.get_loss()         # the mean of the last epoch's train losses
    val_loss = trainer.val_stats.get_loss()             # the mean of the last epoch's validation losses
    test_loss = trainer.test_stats.get_loss()           # the mean of the test losses (available only after calling evaluate)

    train_metrics = trainer.train_stats.get_metrics()   # dict(metric_name, mean(values)) of the current epoch in train state
    val_metrics = trainer.val_stats.get_metrics()       # dict(metric_name, mean(values)) of the current epoch in validation state
    test_metrics = trainer.test_stats.get_metrics()     # dict(metric_name, mean(values)) of the test (available only after calling evaluate)

Callbacks

Some common callbacks are available under lpd.callbacks.

Notice that apply_on_phase (lpd.enums.Phase) will determine the execution phase,

and that apply_on_states (lpd.enums.State or list(lpd.enums.State)) will determine the execution states

These are the current available phases and states, more might be added in future releases

        State.EXTERNAL
        Phase.TRAIN_BEGIN
        # train loop:
            Phase.EPOCH_BEGIN

            State.TRAIN
            # batches loop:
                Phase.BATCH_BEGIN
                # batch
                Phase.BATCH_END
            State.VAL
            # batches loop:
                Phase.BATCH_BEGIN
                # batch
                Phase.BATCH_END
            State.EXTERNAL

            Phase.EPOCH_END
        Phase.TRAIN_END

Evaluation phases and states will behave as follow

        State.EXTERNAL
        Phase.TEST_BEGIN
        State.TEST
        # batches loop:
            Phase.BATCH_BEGIN
            # batch
            Phase.BATCH_END
        State.EXTERNAL
        Phase.TEST_END

With phases and states, you'll have full control over the timing of your callbacks,

so for example, say you need SchedulerStep callback to control your scheduler,

but only at the end of every batch, and only when in train state (as opposed to validation and test) then define your SchedulerStep callback like so:

    from lpd.callbacks import SchedulerStep
    from lpd.enums import Phase, State
    SchedulerStep(apply_on_phase=Phase.BATCH_END, apply_on_states=State.TRAIN)

In case you need it on validation state as well, pass a list for apply_on_states like so:

    SchedulerStep(apply_on_phase=Phase.BATCH_END, apply_on_states=[State.TRAIN, State.VAL])

Below is an output example for StatsPrint callback that will print an epoch summary at the end of every epoch

You can also create custom callbacks

    from lpd.enums import Phase, State
    from lpd.callbacks import CallbackBase

    class MyAwesomeCallback(CallbackBase):
        def __init__(self, apply_on_phase=Phase.BATCH_END, apply_on_states=[State.TRAIN, State.VAL]):
            # make sure to call init parent class
            super(MyAwesomeCallback, self).__init__(apply_on_phase, apply_on_states)

        def __call__(self, callback_context): # <=== implement this method!
            # your implementation here
            # using callback_context, you can access anything in your trainer
            # below are some examples to get the hang of it
            val_loss = callback_context.val_stats.get_loss()
            train_loss = callback_context.train_stats.get_loss()
            train_metrics = callback_context.train_stats.get_metrics()
            val_metrics = callback_context.val_stats.get_metrics()
            opt = callback_context.trainer.optimizer
            scheduler = callback_context.trainer.scheduler

            if val_loss < 0.0001:
                # you can also mark the trainer to STOP training by calling stop()
                callback_context.trainer.stop()

Lets expand MyAwesomeCallback with CallbackMonitor to track if our validation loss is getting better

    from lpd.callbacks import CallbackBase, CallbackMonitor # <== CallbackMonitor added
    from lpd.enums import Phase, State, MonitorType, StatsType, MonitorMode # <== added few needed enums to configure CallbackMonitor

    class MyAwesomeCallback(CallbackBase):
        def __init__(self, apply_on_phase=Phase.BATCH_END, apply_on_states=[State.TRAIN, State.VAL]):
            super(MyAwesomeCallback, self).__init__(apply_on_phase, apply_on_states)
            
            # adding CallbackMonitor to track VAL LOSS with regards to MIN (lower is better) and patience or 20 epochs
            self.val_loss_monitor = CallbackMonitor(patience=20, MonitorType.LOSS, StatsType.VAL, MonitorMode.MIN)

        def __call__(self, callback_context: CallbackContext): # <=== implement this method!
            # same as before, using callback_context, you can access anything in your trainer
            train_metrics = callback_context.train_stats.get_metrics()
            val_metrics = callback_context.val_stats.get_metrics()

            # invoke track() method on your monitor and pass callback_context as parameter
            # since you configured your val_loss_monitor, it will get the relevant parameters from callback_context
            monitor_result = self.val_loss_monitor.track(callback_context)

            # monitor_result (lpd.callbacks.CallbackMonitorResult) contains lots of informative properties
            # for example lets check the status of the patience countdown

            if monitor_result.has_patience():
                print(f'[MyAwesomeCallback] - patience left: {monitor_result.patience_left}')

            # Or, let's stop the trainer, by calling the trainer.stop()
            # if our monitored value did not improve

            if not monitor_result.has_improved():
                print(f'[MyAwesomeCallback] - {monitor_result.description} has stopped improving')
                callback_context.trainer.stop()

Utils

lpd.utils provides few files (torch_utils, file_utils and general_utils) For example, a good practice is to use

    import lpd.utils.general_utils as gu
    gu.seed_all(seed=42)  # because its the answer to life and the universe

As early as possible in your code, to make sure that results are reproducible

Extensions

lpd.extensions provides some custom PyTorch layers, metrics, and schedulers, these are just some stuff we like using when we create our models, to gain better flexibility.

So you can use them at your own will. We will add more extensions from time to time.

TODOS (more added frequently)

• Add callback descriptions to summary
• Add support for multiple schedulers
• Add support for multiple losses
• Save trainer in save_checkpoint() to enable loading a model and continue training from the last checkpoint
• Add colab examples

Something is missing?! please share with us

You can open an issue, but also feel free to email us at [e-mail]