Metadata-Version: 2.1
Name: lpd
Version: 0.1.1
Summary: A Fast, Flexible Trainer and Extensions for Pytorch
Home-page: https://github.com/roysadaka/lpd
Author: Roy Sadaka
Maintainer: lpd developers
Maintainer-email: torch.lpd@gmail.com
License: MIT Licences
Description: ![Logo](https://raw.githubusercontent.com/RoySadaka/ReposMedia/main/lpd/images/logo.png)
        
        # lpd
        
        A Fast, Flexible Trainer and Extensions for Pytorch
        
        ``lpd`` derives from the Hebrew word *lapid* (לפיד) which means "torch".
        
        ## For latest PyPI stable release [![Downloads](https://pepy.tech/badge/lpd)](https://pepy.tech/project/lpd)
        
        ```sh
            pip install lpd
        ```
        
        ## Usage
        
        ``lpd`` intended to properly structure your pytorch model training. The main usages are given below.
        
        ### Training your model
        
        ```python
            from lpd.trainer import Trainer
            import lpd.utils.torch_utils as tu
            import lpd.utils.general_utils as gu
            from lpd.enums impoCallbackPhase, TrainerState, 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, ),
                        StatsPrint(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)
            
            trainer.train()
        ```
        
        ### Evaluating your model
        ```python
            trainer.evaluate(test_data_loader, test_steps)
        ```
        
        ### TrainerStats
        ``Trainer`` tracks stats for `train/val/test` and you can access them in your custom callbacks
        or any other place you see fit.
        
        Here are some examples
        ```python
            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 val losses
        
            train_metrics = trainer.train_stats.get_metrics()   # dictionary metric_name->mean of the last epoch's train metrics
            val_metrics = trainer.val_stats.get_metrics()       # dictionary metric_name->mean of the last epoch's val metrics
        ```
        
        
        ### Callbacks
        Some common callbacks are available under ``lpd.callbacks``. 
        
        Notice that ``cb_phase`` (``lpd.enums.CallbackPhase``) will determine the execution phase,
        
        and that ``apply_on_states`` (``lpd.enums.TrainerState``) will determine the execution state
        
        These are the current available phases and states, more might be added in future releases
        ```python
                TrainerState.EXTERNAL
                CallbackPhase.TRAIN_BEGIN
                # train loop:
                    CallbackPhase.EPOCH_BEGIN
        
                    TrainerState.TRAIN
                    # batches loop:
                        CallbackPhase.BATCH_BEGIN
                        # batch
                        CallbackPhase.BATCH_END
                    TrainerState.VAL
                    # batches loop:
                        CallbackPhase.BATCH_BEGIN
                        # batch
                        CallbackPhase.BATCH_END
                    TrainerState.EXTERNAL
        
                    CallbackPhase.EPOCH_END
                CallbackPhase.TRAIN_END
        ```
        
        Evaluation phases and states will be behave as follow
        ```python
                TrainerState.EXTERNAL
                CallbackPhase.TEST_BEGIN
                TrainerState.TEST
                # batches loop:
                    CallbackPhase.BATCH_BEGIN
                    # batch
                    CallbackPhase.BATCH_END
                TrainerState.EXTERNAL
                CallbackPhase.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 oppose to validation and test)
        then define your SchedulerStep callback like so:
        ```python
            from lpd.callbacks import SchedulerStep
            from lpd.enums import CallbackPhase, TrainerState
            SchedulerStep(cb_phase=CallbackPhase.BATCH_END, apply_on_states=TrainerState.TRAIN)
        ```
        In case you need it on validation state as well, pass a list for ``apply_on_states`` like so:
        ```python
            SchedulerStep(cb_phase=CallbackPhase.BATCH_END, apply_on_states=[TrainerState.TRAIN, TrainerState.VAL])
        ```
        Below is an output example for ``StatsPrint`` callback that will print an epoch summary at the end of every epoch
        
        ![EpochSummary](https://raw.githubusercontent.com/RoySadaka/ReposMedia/main/lpd/images/epoch_summary.png)
        
        You can also create your own custom callbacks
        
        ```python
            import lpd.enums as en
            from lpd.callbacks import CallbackBase
        
            class MyAwesomeCallback(CallbackBase):
                def __init__(self, cb_phase=CallbackPhase.BATCH_END, apply_on_states=[TrainerState.TRAIN, TrainerState.VAL]):
                    super(MyAwesomeCallback, self).__init__(cb_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
        
                    # you can also mark the trainer as STOP by calling the stop_training() method
                    if val_loss < 0.0001:
                        callback_context.trainer.stop_training()
        ```
        
        Lets expand ``MyAwesomeCallback`` with ``CallbackMonitor`` to track if our validation loss is getting better
        ```python
            from lpd.callbacks import CallbackBase, CallbackMonitor # <== CallbackMonitor added
            from lpd.enums import CallbackPhase, TrainerState, MonitorType, StatsType, MonitorMode # <== added few needed enums to configure CallbackMonitor
        
            class MyAwesomeCallback(CallbackBase):
                def __init__(self, cb_phase=CallbackPhase.BATCH_END, apply_on_states=[TrainerState.TRAIN, TrainerState.VAL]):
                    super(MyAwesomeCallback, self).__init__(cb_phase, apply_on_states)
                    
                    # adding CallbackMonitor to track VAL LOSS with regards to MIN (lower is better)
                    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 with 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 count: {monitor_result.patience_left}')
        
                    # Or, lets stop the trainer (by calling the trainer.stop_training() ) 
                    # 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_training()
        ```
        
        
        ### Utils
        ``lpd.utils`` provides few utils files (torch_utils, file_utils and general_utils)
        For example, a good practice is to use 
        ```python
            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, these are just some layers we like using when we create our models, to gain better flexibility.
        
        So you can use them at your own will, there youll also find custom metrics and schedulers.
        We will add more layers, metrics and schedulers 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 checkpoint to enable loading a model and continue training from 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 torch.lpd@gmail.com
        
Keywords: pytorch trainer extensions machine deep learning
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