Source code for pytorch_lightning.trainer.trainer

# Copyright The PyTorch Lightning team.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.

import os
import warnings
from typing import Dict, Iterable, List, Optional, Union

import torch
from import DataLoader

from pytorch_lightning.callbacks import Callback, ModelCheckpoint
from pytorch_lightning.core.datamodule import LightningDataModule
from pytorch_lightning.core.lightning import LightningModule
from pytorch_lightning.core.step_result import EvalResult
from pytorch_lightning.loggers import LightningLoggerBase
from pytorch_lightning.profiler import BaseProfiler
from pytorch_lightning.trainer.callback_hook import TrainerCallbackHookMixin
from pytorch_lightning.trainer.configuration_validator import ConfigValidator
from pytorch_lightning.trainer.connectors.env_vars_connector import overwrite_by_env_vars
from pytorch_lightning.trainer.data_loading import TrainerDataLoadingMixin
from pytorch_lightning.trainer.logging import TrainerLoggingMixin
from pytorch_lightning.trainer.model_hooks import TrainerModelHooksMixin
from pytorch_lightning.trainer.optimizers import TrainerOptimizersMixin
from pytorch_lightning.trainer.states import TrainerState, trainer_state
from pytorch_lightning.trainer.training_tricks import TrainerTrainingTricksMixin
from pytorch_lightning.utilities import rank_zero_warn
from pytorch_lightning.utilities.debugging import InternalDebugger
from pytorch_lightning.utilities.exceptions import MisconfigurationException
from pytorch_lightning.trainer.evaluation_loop import EvaluationLoop
from pytorch_lightning.trainer.training_loop import TrainLoop
from pytorch_lightning.accelerators.accelerator_connector import AcceleratorConnector
from pytorch_lightning.trainer.connectors.logger_connector import LoggerConnector
from pytorch_lightning.trainer.connectors.optimizer_connector import OptimizerConnector
from pytorch_lightning.trainer.connectors.training_trick_connector import TrainingTricksConnector
from pytorch_lightning.trainer.connectors.callback_connector import CallbackConnector
from pytorch_lightning.trainer.connectors.model_connector import ModelConnector
from pytorch_lightning.trainer.connectors.debugging_connector import DebuggingConnector
from pytorch_lightning.trainer.connectors.checkpoint_connector import CheckpointConnector
from pytorch_lightning.trainer.connectors.slurm_connector import SLURMConnector
from pytorch_lightning import _logger as log
from pytorch_lightning.tuner.tuning import Tuner
from pytorch_lightning.trainer.connectors.precision_connector import PrecisionConnector
from pytorch_lightning.trainer.connectors.profiler_connector import ProfilerConnector
from pytorch_lightning.trainer.connectors.data_connector import DataConnector
from pytorch_lightning.utilities.cloud_io import load as pl_load
from pytorch_lightning.utilities.model_utils import is_overridden
from import TrainerProperties
from pytorch_lightning.plugins.plugin_connector import PluginConnector
from pytorch_lightning.accelerators.accelerator import Accelerator
from pytorch_lightning.accelerators.cpu_accelerator import CPUAccelerator

# warnings to ignore in trainer
    'ignore', message='torch.distributed.reduce_op is deprecated, ' 'please use torch.distributed.ReduceOp instead'
os.environ['PYTHONWARNINGS'] = 'ignore:semaphore_tracker:UserWarning'

    from apex import amp
except ImportError:
    amp = None

[docs]class Trainer( TrainerProperties, TrainerCallbackHookMixin, TrainerModelHooksMixin, TrainerOptimizersMixin, TrainerLoggingMixin, TrainerTrainingTricksMixin, TrainerDataLoadingMixin, ): @overwrite_by_env_vars def __init__( self, logger: Union[LightningLoggerBase, Iterable[LightningLoggerBase], bool] = True, checkpoint_callback: Union[ModelCheckpoint, bool] = True, callbacks: Optional[List[Callback]] = None, default_root_dir: Optional[str] = None, gradient_clip_val: float = 0, process_position: int = 0, num_nodes: int = 1, num_processes: int = 1, gpus: Optional[Union[List[int], str, int]] = None, auto_select_gpus: bool = False, tpu_cores: Optional[Union[List[int], str, int]] = None, log_gpu_memory: Optional[str] = None, progress_bar_refresh_rate: int = 1, overfit_batches: Union[int, float] = 0.0, track_grad_norm: Union[int, float, str] = -1, check_val_every_n_epoch: int = 1, fast_dev_run: bool = False, accumulate_grad_batches: Union[int, Dict[int, int], List[list]] = 1, max_epochs: int = 1000, min_epochs: int = 1, max_steps: Optional[int] = None, min_steps: Optional[int] = None, limit_train_batches: Union[int, float] = 1.0, limit_val_batches: Union[int, float] = 1.0, limit_test_batches: Union[int, float] = 1.0, val_check_interval: Union[int, float] = 1.0, flush_logs_every_n_steps: int = 100, log_every_n_steps: int = 50, accelerator: Optional[Union[str, Accelerator]] = None, sync_batchnorm: bool = False, precision: int = 32, weights_summary: Optional[str] = 'top', weights_save_path: Optional[str] = None, num_sanity_val_steps: int = 2, truncated_bptt_steps: Optional[int] = None, resume_from_checkpoint: Optional[str] = None, profiler: Optional[Union[BaseProfiler, bool, str]] = None, benchmark: bool = False, deterministic: bool = False, reload_dataloaders_every_epoch: bool = False, auto_lr_find: Union[bool, str] = False, replace_sampler_ddp: bool = True, terminate_on_nan: bool = False, auto_scale_batch_size: Union[str, bool] = False, prepare_data_per_node: bool = True, plugins: Optional[list] = None, amp_backend: str = 'native', amp_level: str = 'O2', distributed_backend: Optional[str] = None, automatic_optimization: bool = True, ): r""" Customize every aspect of training via flags Args: accelerator: Previously known as distributed_backend (dp, ddp, ddp2, etc...). Can also take in an accelerator object for custom hardware. accumulate_grad_batches: Accumulates grads every k batches or as set up in the dict. amp_backend: The mixed precision backend to use ("native" or "apex") amp_level: The optimization level to use (O1, O2, etc...). auto_lr_find: If set to True, will make trainer.tune() run a learning rate finder, trying to optimize initial learning for faster convergence. trainer.tune() method will set the suggested learning rate in or self.learning_rate in the LightningModule. To use a different key set a string instead of True with the key name. auto_scale_batch_size: If set to True, will `initially` run a batch size finder trying to find the largest batch size that fits into memory. The result will be stored in self.batch_size in the LightningModule. Additionally, can be set to either `power` that estimates the batch size through a power search or `binsearch` that estimates the batch size through a binary search. auto_select_gpus: If enabled and `gpus` is an integer, pick available gpus automatically. This is especially useful when GPUs are configured to be in "exclusive mode", such that only one process at a time can access them. benchmark: If true enables cudnn.benchmark. callbacks: Add a list of callbacks. checkpoint_callback: Callback for checkpointing. check_val_every_n_epoch: Check val every n train epochs. default_root_dir: Default path for logs and weights when no logger/ckpt_callback passed. Default: ``os.getcwd()``. Can be remote file paths such as `s3://mybucket/path` or 'hdfs://path/' deterministic: If true enables cudnn.deterministic. distributed_backend: deprecated. Please use 'accelerator' fast_dev_run: runs 1 batch of train, test and val to find any bugs (ie: a sort of unit test). flush_logs_every_n_steps: How often to flush logs to disk (defaults to every 100 steps). gpus: number of gpus to train on (int) or which GPUs to train on (list or str) applied per node gradient_clip_val: 0 means don't clip. limit_train_batches: How much of training dataset to check (floats = percent, int = num_batches) limit_val_batches: How much of validation dataset to check (floats = percent, int = num_batches) limit_test_batches: How much of test dataset to check (floats = percent, int = num_batches) logger: Logger (or iterable collection of loggers) for experiment tracking. log_gpu_memory: None, 'min_max', 'all'. Might slow performance log_every_n_steps: How often to log within steps (defaults to every 50 steps). automatic_optimization: If False you are responsible for calling .backward, .step, zero_grad. Meant to be used with multiple optimizers by advanced users. prepare_data_per_node: If True, each LOCAL_RANK=0 will call prepare data. Otherwise only NODE_RANK=0, LOCAL_RANK=0 will prepare data process_position: orders the progress bar when running multiple models on same machine. progress_bar_refresh_rate: How often to refresh progress bar (in steps). Value ``0`` disables progress bar. Ignored when a custom callback is passed to :paramref:`~Trainer.callbacks`. profiler: To profile individual steps during training and assist in identifying bottlenecks. Passing bool value is deprecated in v1.1 and will be removed in v1.3. overfit_batches: Overfit a percent of training data (float) or a set number of batches (int). Default: 0.0 plugins: Plugins allow modification of core behavior like ddp and amp. precision: Full precision (32), half precision (16). Can be used on CPU, GPU or TPUs. max_epochs: Stop training once this number of epochs is reached. min_epochs: Force training for at least these many epochs max_steps: Stop training after this number of steps. Disabled by default (None). min_steps: Force training for at least these number of steps. Disabled by default (None). num_nodes: number of GPU nodes for distributed training. num_sanity_val_steps: Sanity check runs n validation batches before starting the training routine. Set it to `-1` to run all batches in all validation dataloaders. Default: 2 reload_dataloaders_every_epoch: Set to True to reload dataloaders every epoch. replace_sampler_ddp: Explicitly enables or disables sampler replacement. If not specified this will toggled automatically when DDP is used. By default it will add ``shuffle=True`` for train sampler and ``shuffle=False`` for val/test sampler. If you want to customize it, you can set ``replace_sampler_ddp=False`` and add your own distributed sampler. resume_from_checkpoint: To resume training from a specific checkpoint pass in the path here. This can be a URL. sync_batchnorm: Synchronize batch norm layers between process groups/whole world. terminate_on_nan: If set to True, will terminate training (by raising a `ValueError`) at the end of each training batch, if any of the parameters or the loss are NaN or +/-inf. tpu_cores: How many TPU cores to train on (1 or 8) / Single TPU to train on [1] track_grad_norm: -1 no tracking. Otherwise tracks that p-norm. May be set to 'inf' infinity-norm. truncated_bptt_steps: Truncated back prop breaks performs backprop every k steps of much longer sequence. val_check_interval: How often to check the validation set. Use float to check within a training epoch, use int to check every n steps (batches). weights_summary: Prints a summary of the weights when training begins. weights_save_path: Where to save weights if specified. Will override default_root_dir for checkpoints only. Use this if for whatever reason you need the checkpoints stored in a different place than the logs written in `default_root_dir`. Can be remote file paths such as `s3://mybucket/path` or 'hdfs://path/' Defaults to `default_root_dir`. """ super().__init__() # init connectors self.dev_debugger = InternalDebugger(self) self.config_validator = ConfigValidator(self) self.data_connector = DataConnector(self) self.optimizer_connector = OptimizerConnector(self) self.accelerator_connector = AcceleratorConnector(self) self.logger_connector = LoggerConnector(self) self.model_connector = ModelConnector(self) self.precision_connector = PrecisionConnector(self) self.callback_connector = CallbackConnector(self) self.debugging_connector = DebuggingConnector(self) self.training_tricks_connector = TrainingTricksConnector(self) self.profile_connector = ProfilerConnector(self) self.checkpoint_connector = CheckpointConnector(self) self.slurm_connector = SLURMConnector(self) self.tuner = Tuner(self) self.accelerator_backend = None self.evaluation_loop = EvaluationLoop(self) self.train_loop = TrainLoop(self) self.plugin_connector = PluginConnector(self) # training state self.weights_summary = weights_summary self.model = None self.shown_warnings = set() # init callbacks # Declare attributes to be set in callback_connector on_trainer_init self.checkpoint_callback: Union[ModelCheckpoint, bool] = checkpoint_callback self.callback_connector.on_trainer_init( callbacks, checkpoint_callback, progress_bar_refresh_rate, process_position, default_root_dir, weights_save_path, resume_from_checkpoint, ) # hook self.on_init_start() # init optimizer + lr scheduler related flags self.optimizer_connector.on_trainer_init() # init data flags self.data_connector.on_trainer_init( check_val_every_n_epoch, reload_dataloaders_every_epoch, prepare_data_per_node ) # init training tricks self.training_tricks_connector.on_trainer_init( gradient_clip_val, track_grad_norm, accumulate_grad_batches, truncated_bptt_steps, terminate_on_nan ) # init accelerator related flags self.accelerator_connector.on_trainer_init( num_processes, tpu_cores, accelerator, distributed_backend, auto_select_gpus, gpus, num_nodes, log_gpu_memory, sync_batchnorm, benchmark, replace_sampler_ddp, deterministic, ) # init train loop related flags self.train_loop.on_trainer_init( max_epochs, min_epochs, max_steps, min_steps, num_sanity_val_steps, automatic_optimization ) self.evaluation_loop.on_trainer_init() # configure tuner self.tuner.on_trainer_init(auto_lr_find, auto_scale_batch_size) # configure profiler self.profile_connector.on_trainer_init(profiler) # init logger flags self.logger_connector.on_trainer_init(logger, flush_logs_every_n_steps, log_every_n_steps) # init debugging flags self.debugging_connector.on_init_start( limit_train_batches, limit_val_batches, limit_test_batches, val_check_interval, overfit_batches, fast_dev_run, ) # set precision self.precision_connector.on_trainer_init(precision, amp_level, amp_backend) # last thing are the plugins which override whatever the trainer used by default self.plugin_connector.on_trainer_init(plugins) # Callback system self.on_init_end()
[docs] def fit( self, model: LightningModule, train_dataloader: Optional[DataLoader] = None, val_dataloaders: Optional[Union[DataLoader, List[DataLoader]]] = None, datamodule: Optional[LightningDataModule] = None, ): r""" Runs the full optimization routine. Args: datamodule: A instance of :class:`LightningDataModule`. model: Model to fit. train_dataloader: A Pytorch DataLoader with training samples. If the model has a predefined train_dataloader method this will be skipped. val_dataloaders: Either a single Pytorch Dataloader or a list of them, specifying validation samples. If the model has a predefined val_dataloaders method this will be skipped """ # bookkeeping self._state = TrainerState.RUNNING # ---------------------------- # LINK DATA # ---------------------------- # setup data, etc... self.train_loop.setup_fit(model, train_dataloader, val_dataloaders, datamodule) # hook self.data_connector.prepare_data(model) # bookkeeping # we reuse fit in .test() but change its behavior using this flag self.testing = os.environ.get('PL_TESTING_MODE', self.testing) # ---------------------------- # SET UP TRAINING # ---------------------------- self.accelerator_backend = self.accelerator_connector.select_accelerator() self.accelerator_backend.setup(model) # ---------------------------- # INSPECT THESE FOR MAIN LOOPS # ---------------------------- # assign training and eval functions... inspect these to see the train and eval loops :) self.accelerator_backend.train_loop = self.train self.accelerator_backend.validation_loop = self.run_evaluation self.accelerator_backend.test_loop = self.run_evaluation # ---------------------------- # TRAIN # ---------------------------- # hook self.call_hook('on_fit_start') results = self.accelerator_backend.train() self.accelerator_backend.teardown() # ---------------------------- # POST-Training CLEAN UP # ---------------------------- # hook self.call_hook('on_fit_end') # hook self.teardown('fit') if self.is_function_implemented('teardown'): model.teardown('fit') # return 1 when finished # used for testing or when we need to know that training succeeded if self._state != TrainerState.INTERRUPTED: self._state = TrainerState.FINISHED return results or 1
def train(self): self.run_sanity_check(self.get_model()) self.checkpoint_connector.has_trained = False # enable train mode model = self.get_model() model.train() torch.set_grad_enabled(True) # reload data when needed self.train_loop.reset_train_val_dataloaders(model) # hook self.train_loop.on_train_start() try: # run all epochs for epoch in range(self.current_epoch, self.max_epochs): # hook self.train_loop.on_train_epoch_start(epoch) # run train epoch self.train_loop.run_training_epoch() if self.max_steps and self.max_steps <= self.global_step: # hook self.train_loop.on_train_end() return # update LR schedulers self.optimizer_connector.update_learning_rates(interval='epoch') # early stopping met_min_epochs = epoch >= self.min_epochs - 1 met_min_steps = self.global_step >= self.min_steps if self.min_steps else True if self.should_stop: if met_min_epochs and met_min_steps: self.train_loop.on_train_end() return else: 'Trainer was signaled to stop but required minimum epochs' f' ({self.min_epochs}) or minimum steps ({self.min_steps}) has' ' not been met. Training will continue...' ) # hook self.train_loop.on_train_end() except KeyboardInterrupt: rank_zero_warn('Detected KeyboardInterrupt, attempting graceful shutdown...') # user could press ctrl+c many times... only shutdown once if not self.interrupted: self.interrupted = True self._state = TrainerState.INTERRUPTED self.on_keyboard_interrupt() # hook self.train_loop.on_train_end() def run_evaluation(self, test_mode: bool = False, max_batches=None): # bookkeeping self.evaluation_loop.testing = test_mode dataloaders, max_batches = self.evaluation_loop.get_evaluation_dataloaders(max_batches) if self.evaluation_loop.should_skip_evaluation(dataloaders, max_batches): return [], [] # enable eval mode + no grads model = self.get_model() self.evaluation_loop.on_evaluation_model_eval() model.zero_grad() torch.set_grad_enabled(False) # hook self.evaluation_loop.on_evaluation_start() # set up the eval loop self.evaluation_loop.setup(model, max_batches, dataloaders) # hook # TODO: should this be insider the dataloader loop? self.evaluation_loop.on_evaluation_epoch_start() # run validation/testing for dataloader_idx, dataloader in enumerate(dataloaders): # bookkeeping dl_outputs = [] dl_step_metrics = [] dataloader = self.accelerator_backend.process_dataloader(dataloader) dl_max_batches = self.evaluation_loop.max_batches[dataloader_idx] for batch_idx, batch in enumerate(dataloader): if batch is None: continue # stop short when running on limited batches if batch_idx >= dl_max_batches: break # hook self.evaluation_loop.on_evaluation_batch_start(batch, batch_idx, dataloader_idx) # lightning module methods output = self.evaluation_loop.evaluation_step(test_mode, batch, batch_idx, dataloader_idx) output = self.evaluation_loop.evaluation_step_end(output) # hook self.evaluation_loop.on_evaluation_batch_end(output, batch, batch_idx, dataloader_idx) # clean up self.evaluation_loop.evaluation_batch_end_cleanup(output, batch_idx, dataloader_idx) # TODO: deprecate 1.0 self.evaluation_loop.log_evaluation_step_metrics_legacy(output, batch_idx) # log step metrics step_metrics = self.evaluation_loop.log_evaluation_step_metrics(batch, batch_idx) if step_metrics is not None: dl_step_metrics.append(step_metrics) # track epoch level outputs if output is not None: dl_outputs.append(output) self.evaluation_loop.outputs.append(dl_outputs) self.evaluation_loop.step_metrics.append(dl_step_metrics) # lightning module method deprecated_eval_results, epoch_logs = self.evaluation_loop.evaluation_epoch_end( num_dataloaders=len(dataloaders) ) # bookkeeping eval_loop_results = self.evaluation_loop.log_epoch_metrics(deprecated_eval_results, epoch_logs, test_mode) self.evaluation_loop.predictions.to_disk() # hook self.evaluation_loop.on_evaluation_epoch_end() # enable train mode again self.evaluation_loop.on_evaluation_model_train() torch.set_grad_enabled(True) # hook self.evaluation_loop.on_evaluation_end() return eval_loop_results, deprecated_eval_results def run_test(self): # only load test dataloader for testing # self.reset_test_dataloader(ref_model) eval_loop_results, _ = self.run_evaluation(test_mode=True) if len(eval_loop_results) == 0: return 1 # remove the tensors from the eval results for i, result in enumerate(eval_loop_results): if isinstance(result, dict): for k, v in result.items(): if isinstance(v, torch.Tensor): result[k] = v.cpu().item() return eval_loop_results def run_sanity_check(self, ref_model): using_val_step = ref_model.val_dataloader is not None and is_overridden('validation_step', ref_model) should_sanity_check = using_val_step and self.num_sanity_val_steps > 0 and self.limit_val_batches > 0 # run tiny validation (if validation defined) # to make sure program won't crash during val if should_sanity_check: self.reset_val_dataloader(ref_model) self.num_sanity_val_batches = [ min(self.num_sanity_val_steps, val_batches) for val_batches in self.num_val_batches ] # hook and callback self.running_sanity_check = True self.on_sanity_check_start() # run eval step _, eval_results = self.run_evaluation(test_mode=False, max_batches=self.num_sanity_val_batches) # allow no returns from eval if eval_results is not None and len(eval_results) > 0: # when we get a list back, used only the last item if isinstance(eval_results, list): eval_results = eval_results[-1] if isinstance(eval_results, EvalResult): callback_metrics = eval_results.callback_metrics else: _, _, _, callback_metrics, _ = self.process_dict_result(eval_results) self.logger_connector.callback_metrics = callback_metrics self.on_sanity_check_end() self.running_sanity_check = False
[docs] def test( self, model: Optional[LightningModule] = None, test_dataloaders: Optional[Union[DataLoader, List[DataLoader]]] = None, ckpt_path: Optional[str] = 'best', verbose: bool = True, datamodule: Optional[LightningDataModule] = None, ): r""" Separates from fit to make sure you never run on your test set until you want to. Args: ckpt_path: Either ``best`` or path to the checkpoint you wish to test. If ``None``, use the weights from the last epoch to test. Default to ``best``. datamodule: A instance of :class:`LightningDataModule`. model: The model to test. test_dataloaders: Either a single Pytorch Dataloader or a list of them, specifying validation samples. verbose: If True, prints the test results Returns: The final test result dictionary. If no test_epoch_end is defined returns a list of dictionaries """ # -------------------- # SETUP HOOK # -------------------- self.verbose_test = verbose # If you supply a datamodule you can't supply train_dataloader or val_dataloaders if test_dataloaders and datamodule: raise MisconfigurationException( 'You cannot pass test_dataloaders to trainer.test if you supply a datamodule' ) # Attach datamodule to get setup/prepare_data added to model before the call to it below self.data_connector.attach_datamodule(model or self.get_model(), datamodule, 'test') if model is not None: results = self.__test_given_model(model, test_dataloaders) else: results = self.__test_using_best_weights(ckpt_path, test_dataloaders) self.teardown('test') return results
def __test_using_best_weights(self, ckpt_path, test_dataloaders): model = self.get_model() # if user requests the best checkpoint but we don't have it, error if ckpt_path == 'best' and not self.checkpoint_callback.best_model_path: raise MisconfigurationException( 'ckpt_path is "best", but ModelCheckpoint is not configured to save the best model.' ) # load best weights if ckpt_path is not None: # ckpt_path is 'best' so load the best model if ckpt_path == 'best': ckpt_path = self.checkpoint_callback.best_model_path if len(ckpt_path) == 0: rank_zero_warn( f'.test() found no path for the best weights, {ckpt_path}. Please ' f'specify a path for a checkpoint .test(ckpt_path=PATH)' ) return {} if self.accelerator_backend is not None: self.accelerator_backend.barrier() ckpt = pl_load(ckpt_path, map_location=lambda storage, loc: storage) model.load_state_dict(ckpt['state_dict']) # attach dataloaders if test_dataloaders is not None: self.data_connector.attach_dataloaders(model, test_dataloaders=test_dataloaders) # run tests self.tested_ckpt_path = ckpt_path self.testing = True os.environ['PL_TESTING_MODE'] = '1' self.model = model results = self.testing = False del os.environ['PL_TESTING_MODE'] # teardown if self.is_function_implemented('teardown'): model_ref = self.get_model() model_ref.teardown('test') return results def __test_given_model(self, model, test_dataloaders): # attach data if test_dataloaders is not None: self.data_connector.attach_dataloaders(model, test_dataloaders=test_dataloaders) # run test # sets up testing so we short circuit to eval self.testing = True self.model = model results = self.testing = False # teardown if self.is_function_implemented('teardown'): model.teardown('test') return results
[docs] def tune( self, model: LightningModule, train_dataloader: Optional[DataLoader] = None, val_dataloaders: Optional[Union[DataLoader, List[DataLoader]]] = None, datamodule: Optional[LightningDataModule] = None, ): r""" Runs routines to tune hyperparameters before training. Args: datamodule: A instance of :class:`LightningDataModule`. model: Model to tune. train_dataloader: A Pytorch DataLoader with training samples. If the model has a predefined train_dataloader method this will be skipped. val_dataloaders: Either a single Pytorch Dataloader or a list of them, specifying validation samples. If the model has a predefined val_dataloaders method this will be skipped """ self.tuner.tune(model, train_dataloader, val_dataloaders, datamodule)
def call_setup_hook(self, model): # call setup after the ddp process has connected stage_name = 'test' if self.testing else 'fit' if self.datamodule is not None: called = self.datamodule.has_setup_test if self.testing else self.datamodule.has_setup_fit if not called: self.datamodule.setup(stage_name) self.setup(stage_name) model.setup(stage_name) def call_hook(self, hook_name, *args, **kwargs): # always profile hooks with self.profiler.profile(hook_name): # first call trainer hook if hasattr(self, hook_name): trainer_hook = getattr(self, hook_name) trainer_hook(*args, **kwargs) # next call hook in lightningModule output = None model_ref = self.get_model() if is_overridden(hook_name, model_ref): hook_fx = getattr(model_ref, hook_name) output = hook_fx(*args, **kwargs) # if the PL module doesn't have the hook then call the accelator # used to auto-reduce things for the user with Results obj elif hasattr(self.accelerator_backend, hook_name): accelerator_hook = getattr(self.accelerator_backend, hook_name) output = accelerator_hook(*args, **kwargs) return output

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