def test_amp_gpu_ddp_slurm_managed(tmpdir): """Make sure DDP + AMP work.""" # simulate setting slurm flags tutils.set_random_main_port() model = AMPTestModel() # exp file to get meta logger = tutils.get_default_logger(tmpdir) # exp file to get weights checkpoint = tutils.init_checkpoint_callback(logger) # fit model trainer = Trainer( default_root_dir=tmpdir, max_epochs=1, gpus=[0], strategy="ddp_spawn", precision=16, callbacks=[checkpoint], logger=logger, ) trainer.fit(model) # correct result and ok accuracy assert trainer.state.finished, "amp + ddp model failed to complete" # test root model address assert isinstance(trainer.strategy.cluster_environment, SLURMEnvironment) assert trainer.strategy.cluster_environment.resolve_root_node_address("abc") == "abc" assert trainer.strategy.cluster_environment.resolve_root_node_address("abc[23]") == "abc23" assert trainer.strategy.cluster_environment.resolve_root_node_address("abc[23-24]") == "abc23" generated = trainer.strategy.cluster_environment.resolve_root_node_address("abc[23-24, 45-40, 40]") assert generated == "abc23"
def test_amp_gpu_ddp_slurm_managed(tmpdir): """Make sure DDP + AMP work.""" # simulate setting slurm flags tutils.set_random_master_port() model = BoringModel() # exp file to get meta logger = tutils.get_default_logger(tmpdir) # exp file to get weights checkpoint = tutils.init_checkpoint_callback(logger) # fit model trainer = Trainer( default_root_dir=tmpdir, max_epochs=1, gpus=[0], accelerator='ddp_spawn', precision=16, callbacks=[checkpoint], logger=logger, ) _ = trainer.fit(model) # correct result and ok accuracy assert trainer.state == TrainerState.FINISHED, 'amp + ddp model failed to complete' # test root model address assert isinstance(trainer.training_type_plugin.cluster_environment, SLURMEnvironment) assert trainer.training_type_plugin.cluster_environment.resolve_root_node_address('abc') == 'abc' assert trainer.training_type_plugin.cluster_environment.resolve_root_node_address('abc[23]') == 'abc23' assert trainer.training_type_plugin.cluster_environment.resolve_root_node_address('abc[23-24]') == 'abc23' generated = trainer.training_type_plugin.cluster_environment.resolve_root_node_address('abc[23-24, 45-40, 40]') assert generated == 'abc23'
def run_model_test( trainer_options, model: LightningModule, data: LightningDataModule = None, on_gpu: bool = True, version=None, with_hpc: bool = True, min_acc: float = 0.25, ): reset_seed() save_dir = trainer_options["default_root_dir"] # logger file to get meta logger = get_default_logger(save_dir, version=version) trainer_options.update(logger=logger) trainer = Trainer(**trainer_options) initial_values = torch.tensor( [torch.sum(torch.abs(x)) for x in model.parameters()]) trainer.fit(model, datamodule=data) post_train_values = torch.tensor( [torch.sum(torch.abs(x)) for x in model.parameters()]) assert trainer.state.finished, f"Training failed with {trainer.state}" # Check that the model is actually changed post-training change_ratio = torch.norm(initial_values - post_train_values) assert change_ratio > 0.1, f"the model is changed of {change_ratio}" # test model loading pretrained_model = load_model_from_checkpoint( logger, trainer.checkpoint_callback.best_model_path, type(model)) # test new model accuracy test_loaders = model.test_dataloader( ) if not data else data.test_dataloader() if not isinstance(test_loaders, list): test_loaders = [test_loaders] if not isinstance(model, BoringModel): for dataloader in test_loaders: run_prediction_eval_model_template(model, dataloader, min_acc=min_acc) if with_hpc: if trainer._distrib_type in (DistributedType.DDP, DistributedType.DDP_SPAWN, DistributedType.DDP2): # on hpc this would work fine... but need to hack it for the purpose of the test trainer.optimizers, trainer.lr_schedulers, trainer.optimizer_frequencies = trainer.init_optimizers( pretrained_model) # test HPC saving trainer.checkpoint_connector.hpc_save(save_dir, logger) # test HPC loading checkpoint_path = trainer.checkpoint_connector.get_max_ckpt_path_from_folder( save_dir) trainer.checkpoint_connector.restore(checkpoint_path)
def test_strict_model_load_less_params(monkeypatch, tmpdir, tmpdir_server, url_ckpt): """Tests use case where trainer saves the model, and user loads it from tags independently.""" # set $TORCH_HOME, which determines torch hub's cache path, to tmpdir monkeypatch.setenv('TORCH_HOME', tmpdir) model = BoringModel() # logger file to get meta logger = tutils.get_default_logger(tmpdir) # fit model trainer = Trainer( default_root_dir=tmpdir, max_epochs=1, limit_train_batches=2, limit_val_batches=2, logger=logger, callbacks=[ModelCheckpoint(dirpath=tmpdir)], ) trainer.fit(model) # traning complete assert trainer.state == TrainerState.FINISHED, f"Training failed with {trainer.state}" # save model new_weights_path = os.path.join(tmpdir, 'save_test.ckpt') trainer.save_checkpoint(new_weights_path) # load new model hparams_path = os.path.join(tutils.get_data_path(logger, path_dir=tmpdir), 'hparams.yaml') hparams_url = f'http://{tmpdir_server[0]}:{tmpdir_server[1]}/{os.path.basename(new_weights_path)}' ckpt_path = hparams_url if url_ckpt else new_weights_path class CurrentModel(BoringModel): def __init__(self): super().__init__() self.c_d3 = torch.nn.Linear(7, 7) CurrentModel.load_from_checkpoint( checkpoint_path=ckpt_path, hparams_file=hparams_path, strict=False, ) with pytest.raises( RuntimeError, match=r'Missing key\(s\) in state_dict: "c_d3.weight", "c_d3.bias"' ): CurrentModel.load_from_checkpoint( checkpoint_path=ckpt_path, hparams_file=hparams_path, strict=True, )
def test_model_saving_loading(tmpdir): """Tests use case where trainer saves the model, and user loads it from tags independently.""" model = BoringModel() # logger file to get meta logger = tutils.get_default_logger(tmpdir) # fit model trainer = Trainer( max_epochs=1, limit_train_batches=2, limit_val_batches=2, logger=logger, callbacks=[ModelCheckpoint(dirpath=tmpdir)], default_root_dir=tmpdir, ) trainer.fit(model) # traning complete assert trainer.state == TrainerState.FINISHED, f"Training failed with {trainer.state}" # make a prediction dataloaders = model.test_dataloader() if not isinstance(dataloaders, list): dataloaders = [dataloaders] batch = next(iter(dataloaders[0])) # generate preds before saving model model.eval() pred_before_saving = model(batch) # save model new_weights_path = os.path.join(tmpdir, 'save_test.ckpt') trainer.save_checkpoint(new_weights_path) # load new model hparams_path = tutils.get_data_path(logger, path_dir=tmpdir) hparams_path = os.path.join(hparams_path, 'hparams.yaml') model_2 = BoringModel.load_from_checkpoint( checkpoint_path=new_weights_path, hparams_file=hparams_path, ) model_2.eval() # make prediction # assert that both predictions are the same new_pred = model_2(batch) assert torch.all(torch.eq(pred_before_saving, new_pred)).item() == 1
def test_running_test_pretrained_model_distrib_ddp_spawn(tmpdir): """Verify `test()` on pretrained model.""" tutils.set_random_master_port() dm = ClassifDataModule() model = ClassificationModel() # exp file to get meta logger = tutils.get_default_logger(tmpdir) # exp file to get weights checkpoint = tutils.init_checkpoint_callback(logger) trainer_options = dict( progress_bar_refresh_rate=0, max_epochs=2, limit_train_batches=2, limit_val_batches=2, callbacks=[checkpoint], logger=logger, gpus=[0, 1], accelerator='ddp_spawn', default_root_dir=tmpdir, ) # fit model trainer = Trainer(**trainer_options) trainer.fit(model, datamodule=dm) log.info(os.listdir(tutils.get_data_path(logger, path_dir=tmpdir))) # correct result and ok accuracy assert trainer.state == TrainerState.FINISHED, f"Training failed with {trainer.state}" pretrained_model = ClassificationModel.load_from_checkpoint( trainer.checkpoint_callback.best_model_path) # run test set new_trainer = Trainer(**trainer_options) new_trainer.test(pretrained_model) pretrained_model.cpu() dataloaders = dm.test_dataloader() if not isinstance(dataloaders, list): dataloaders = [dataloaders] for dataloader in dataloaders: tpipes.run_prediction_eval_model_template(pretrained_model, dataloader, min_acc=0.1)
def test_strict_model_load_more_params(monkeypatch, tmpdir, tmpdir_server, url_ckpt): """Tests use case where trainer saves the model, and user loads it from tags independently.""" # set $TORCH_HOME, which determines torch hub's cache path, to tmpdir monkeypatch.setenv("TORCH_HOME", tmpdir) model = BoringModel() # Extra layer model.c_d3 = torch.nn.Linear(32, 32) # logger file to get meta logger = tutils.get_default_logger(tmpdir) # fit model trainer = Trainer( default_root_dir=tmpdir, max_epochs=1, limit_train_batches=2, limit_val_batches=2, logger=logger, callbacks=[ModelCheckpoint(dirpath=tmpdir)], ) trainer.fit(model) # traning complete assert trainer.state.finished, f"Training failed with {trainer.state}" # save model new_weights_path = os.path.join(tmpdir, "save_test.ckpt") trainer.save_checkpoint(new_weights_path) # load new model hparams_path = os.path.join(tutils.get_data_path(logger, path_dir=tmpdir), "hparams.yaml") hparams_url = f"http://{tmpdir_server[0]}:{tmpdir_server[1]}/{os.path.basename(new_weights_path)}" ckpt_path = hparams_url if url_ckpt else new_weights_path BoringModel.load_from_checkpoint(checkpoint_path=ckpt_path, hparams_file=hparams_path, strict=False) with pytest.raises( RuntimeError, match= r'Unexpected key\(s\) in state_dict: "c_d3.weight", "c_d3.bias"'): BoringModel.load_from_checkpoint(checkpoint_path=ckpt_path, hparams_file=hparams_path, strict=True)
def test_default_args(mock_argparse, tmpdir): """Tests default argument parser for Trainer.""" mock_argparse.return_value = Namespace(**Trainer.default_attributes()) # logger file to get meta logger = tutils.get_default_logger(tmpdir) parser = ArgumentParser(add_help=False) args = parser.parse_args() args.logger = logger args.max_epochs = 5 trainer = Trainer.from_argparse_args(args) assert isinstance(trainer, Trainer) assert trainer.max_epochs == 5
def test_running_test_pretrained_model_distrib_dp(tmpdir): """Verify `test()` on pretrained model.""" tutils.set_random_main_port() dm = ClassifDataModule() model = CustomClassificationModelDP(lr=0.1) # exp file to get meta logger = tutils.get_default_logger(tmpdir) # exp file to get weights checkpoint = tutils.init_checkpoint_callback(logger) trainer_options = dict( enable_progress_bar=False, max_epochs=2, limit_train_batches=5, limit_val_batches=5, callbacks=[checkpoint], logger=logger, accelerator="gpu", devices=[0, 1], strategy="dp", default_root_dir=tmpdir, ) # fit model trainer = Trainer(**trainer_options) trainer.fit(model, datamodule=dm) # correct result and ok accuracy assert trainer.state.finished, f"Training failed with {trainer.state}" pretrained_model = CustomClassificationModelDP.load_from_checkpoint( trainer.checkpoint_callback.best_model_path) # run test set new_trainer = Trainer(**trainer_options) new_trainer.test(pretrained_model, datamodule=dm) pretrained_model.cpu() dataloaders = dm.test_dataloader() if not isinstance(dataloaders, list): dataloaders = [dataloaders] for dataloader in dataloaders: tpipes.run_model_prediction(pretrained_model, dataloader)
def run_model_test( trainer_options, model: LightningModule, data: LightningDataModule = None, on_gpu: bool = True, version=None, with_hpc: bool = True, min_acc: float = 0.25, ): reset_seed() save_dir = trainer_options["default_root_dir"] # logger file to get meta logger = get_default_logger(save_dir, version=version) trainer_options.update(logger=logger) trainer = Trainer(**trainer_options) initial_values = torch.tensor([torch.sum(torch.abs(x)) for x in model.parameters()]) trainer.fit(model, datamodule=data) post_train_values = torch.tensor([torch.sum(torch.abs(x)) for x in model.parameters()]) assert trainer.state.finished, f"Training failed with {trainer.state}" # Check that the model is actually changed post-training change_ratio = torch.norm(initial_values - post_train_values) assert change_ratio > 0.03, f"the model is changed of {change_ratio}" # test model loading _ = load_model_from_checkpoint(logger, trainer.checkpoint_callback.best_model_path, type(model)) # test new model accuracy test_loaders = model.test_dataloader() if not data else data.test_dataloader() if not isinstance(test_loaders, list): test_loaders = [test_loaders] if not isinstance(model, BoringModel): for dataloader in test_loaders: run_model_prediction(model, dataloader, min_acc=min_acc) if with_hpc: # test HPC saving # save logger to make sure we get all the metrics if logger: logger.finalize("finished") hpc_save_path = trainer.checkpoint_connector.hpc_save_path(save_dir) trainer.save_checkpoint(hpc_save_path) # test HPC loading checkpoint_path = trainer.checkpoint_connector._CheckpointConnector__get_max_ckpt_path_from_folder(save_dir) trainer.checkpoint_connector.restore(checkpoint_path)
def test_running_test_no_val(tmpdir): """Verify `test()` works on a model with no `val_dataloader`. It performs train and test only """ class ModelTrainTest(BoringModel): def val_dataloader(self): pass def test_step(self, *args, **kwargs): output = super().test_step(*args, **kwargs) self.log("test_loss", output["y"]) return output model = ModelTrainTest() # logger file to get meta logger = tutils.get_default_logger(tmpdir) # logger file to get weights checkpoint = tutils.init_checkpoint_callback(logger) # fit model trainer = Trainer( default_root_dir=tmpdir, enable_progress_bar=False, max_epochs=1, limit_train_batches=0.4, limit_val_batches=0.2, limit_test_batches=0.2, callbacks=[checkpoint], logger=logger, ) trainer.fit(model) assert trainer.state.finished, f"Training failed with {trainer.state}" trainer.test() # test we have good test accuracy tutils.assert_ok_model_acc(trainer, key="test_loss")
def test_running_test_after_fitting(tmpdir): """Verify test() on fitted model.""" class ModelTrainValTest(BoringModel): def validation_step(self, *args, **kwargs): output = super().validation_step(*args, **kwargs) self.log('val_loss', output['x']) return output def test_step(self, *args, **kwargs): output = super().test_step(*args, **kwargs) self.log('test_loss', output['y']) return output model = ModelTrainValTest() # logger file to get meta logger = tutils.get_default_logger(tmpdir) # logger file to get weights checkpoint = tutils.init_checkpoint_callback(logger) # fit model trainer = Trainer( default_root_dir=tmpdir, progress_bar_refresh_rate=0, max_epochs=2, limit_train_batches=0.4, limit_val_batches=0.2, limit_test_batches=0.2, callbacks=[checkpoint], logger=logger, ) trainer.fit(model) assert trainer.state.finished, f"Training failed with {trainer.state}" trainer.test() # test we have good test accuracy tutils.assert_ok_model_acc(trainer, key='test_loss', thr=0.5)
def test_running_test_pretrained_model_cpu(tmpdir): """Verify test() on pretrained model.""" tutils.reset_seed() dm = ClassifDataModule() model = ClassificationModel() # logger file to get meta logger = tutils.get_default_logger(tmpdir) # logger file to get weights checkpoint = tutils.init_checkpoint_callback(logger) trainer_options = dict( progress_bar_refresh_rate=0, max_epochs=2, limit_train_batches=2, limit_val_batches=2, limit_test_batches=2, callbacks=[checkpoint], logger=logger, default_root_dir=tmpdir, ) # fit model trainer = Trainer(**trainer_options) trainer.fit(model, datamodule=dm) # correct result and ok accuracy assert trainer.state == TrainerState.FINISHED, f"Training failed with {trainer.state}" pretrained_model = ClassificationModel.load_from_checkpoint( trainer.checkpoint_callback.best_model_path) new_trainer = Trainer(**trainer_options) new_trainer.test(pretrained_model, datamodule=dm) # test we have good test accuracy tutils.assert_ok_model_acc(new_trainer, key='test_acc', thr=0.45)
def test_dp_resume(tmpdir): """Make sure DP continues training correctly.""" model = BoringModel() trainer_options = dict(max_epochs=1, gpus=2, accelerator='dp', default_root_dir=tmpdir) # get logger logger = tutils.get_default_logger(tmpdir) # exp file to get weights # logger file to get weights checkpoint = tutils.init_checkpoint_callback(logger) # add these to the trainer options trainer_options['logger'] = logger trainer_options['callbacks'] = [checkpoint] # fit model trainer = Trainer(**trainer_options) trainer.is_slurm_managing_tasks = True trainer.fit(model) # track epoch before saving. Increment since we finished the current epoch, don't want to rerun real_global_epoch = trainer.current_epoch + 1 # correct result and ok accuracy assert trainer.state == TrainerState.FINISHED, f"Training failed with {trainer.state}" # --------------------------- # HPC LOAD/SAVE # --------------------------- # save trainer.checkpoint_connector.hpc_save(tmpdir, logger) # init new trainer new_logger = tutils.get_default_logger(tmpdir, version=logger.version) trainer_options['logger'] = new_logger trainer_options['callbacks'] = [ModelCheckpoint(dirpath=tmpdir)] trainer_options['limit_train_batches'] = 0.5 trainer_options['limit_val_batches'] = 0.2 trainer_options['max_epochs'] = 1 new_trainer = Trainer(**trainer_options) class CustomModel(BoringModel): def __init__(self): super().__init__() self.on_train_start_called = False # set the epoch start hook so we can predict before the model does the full training def on_train_start(self): assert self.trainer.current_epoch == real_global_epoch and self.trainer.current_epoch > 0 # if model and state loaded correctly, predictions will be good even though we # haven't trained with the new loaded model dp_model = new_trainer.model dp_model.eval() dp_model.module.module.running_stage = RunningStage.EVALUATING dataloader = self.train_dataloader() tpipes.run_prediction(self.trainer.lightning_module, dataloader) self.on_train_start_called = True # new model model = CustomModel() # fit new model which should load hpc weights new_trainer.fit(model) assert model.on_train_start_called # test freeze on gpu model.freeze() model.unfreeze()
def test_running_test_pretrained_model_distrib_dp(tmpdir): """Verify `test()` on pretrained model.""" tutils.set_random_master_port() class CustomClassificationModelDP(ClassificationModel): def _step(self, batch, batch_idx): x, y = batch logits = self(x) return {'logits': logits, 'y': y} def training_step(self, batch, batch_idx): _, y = batch out = self._step(batch, batch_idx) loss = F.cross_entropy(out['logits'], y) return loss def validation_step(self, batch, batch_idx): return self._step(batch, batch_idx) def test_step(self, batch, batch_idx): return self._step(batch, batch_idx) def validation_step_end(self, outputs): self.log('val_acc', self.valid_acc(outputs['logits'], outputs['y'])) dm = ClassifDataModule() model = CustomClassificationModelDP(lr=0.1) # exp file to get meta logger = tutils.get_default_logger(tmpdir) # exp file to get weights checkpoint = tutils.init_checkpoint_callback(logger) trainer_options = dict( progress_bar_refresh_rate=0, max_epochs=2, limit_train_batches=5, limit_val_batches=5, callbacks=[checkpoint], logger=logger, gpus=[0, 1], accelerator='dp', default_root_dir=tmpdir, ) # fit model trainer = Trainer(**trainer_options) trainer.fit(model, datamodule=dm) # correct result and ok accuracy assert trainer.state == TrainerState.FINISHED, f"Training failed with {trainer.state}" pretrained_model = ClassificationModel.load_from_checkpoint( trainer.checkpoint_callback.best_model_path) # run test set new_trainer = Trainer(**trainer_options) new_trainer.test(pretrained_model) pretrained_model.cpu() dataloaders = model.test_dataloader() if not isinstance(dataloaders, list): dataloaders = [dataloaders] for dataloader in dataloaders: tpipes.run_prediction(pretrained_model, dataloader)
def test_dp_resume(tmpdir): """Make sure DP continues training correctly.""" model = CustomClassificationModelDP(lr=0.1) dm = ClassifDataModule() trainer_options = dict(max_epochs=1, accelerator="gpu", devices=2, strategy="dp", default_root_dir=tmpdir) # get logger logger = tutils.get_default_logger(tmpdir) # exp file to get weights # logger file to get weights checkpoint = tutils.init_checkpoint_callback(logger) # add these to the trainer options trainer_options["logger"] = logger trainer_options["callbacks"] = [checkpoint] # fit model trainer = Trainer(**trainer_options) trainer.fit(model, datamodule=dm) # track epoch before saving real_global_epoch = trainer.current_epoch # correct result and ok accuracy assert trainer.state.finished, f"Training failed with {trainer.state}" # --------------------------- # HPC LOAD/SAVE # --------------------------- # save # save logger to make sure we get all the metrics if logger: logger.finalize("finished") hpc_save_path = trainer._checkpoint_connector.hpc_save_path(tmpdir) trainer.save_checkpoint(hpc_save_path) # init new trainer new_logger = tutils.get_default_logger(tmpdir, version=logger.version) trainer_options["logger"] = new_logger trainer_options["callbacks"] = [ModelCheckpoint(dirpath=tmpdir)] trainer_options["limit_train_batches"] = 0.5 trainer_options["limit_val_batches"] = 0.2 trainer_options["max_epochs"] = 1 new_trainer = Trainer(**trainer_options) class CustomModel(CustomClassificationModelDP): def __init__(self): super().__init__() self.on_train_start_called = False def on_validation_start(self): assert self.trainer.current_epoch == real_global_epoch and self.trainer.current_epoch > 0 dataloader = dm.val_dataloader() tpipes.run_model_prediction(self.trainer.lightning_module, dataloader=dataloader) # new model model = CustomModel() # validate new model which should load hpc weights new_trainer.validate(model, datamodule=dm, ckpt_path=hpc_save_path) # test freeze on gpu model.freeze() model.unfreeze()
def test_dp_resume(tmpdir): """Make sure DP continues training correctly.""" model = CustomClassificationModelDP(lr=0.1) dm = ClassifDataModule() trainer_options = dict(max_epochs=1, gpus=2, strategy="dp", default_root_dir=tmpdir) # get logger logger = tutils.get_default_logger(tmpdir) # exp file to get weights # logger file to get weights checkpoint = tutils.init_checkpoint_callback(logger) # add these to the trainer options trainer_options["logger"] = logger trainer_options["callbacks"] = [checkpoint] # fit model trainer = Trainer(**trainer_options) trainer._is_slurm_managing_tasks = True trainer.fit(model, datamodule=dm) # track epoch before saving. Increment since we finished the current epoch, don't want to rerun real_global_epoch = trainer.current_epoch + 1 # correct result and ok accuracy assert trainer.state.finished, f"Training failed with {trainer.state}" # --------------------------- # HPC LOAD/SAVE # --------------------------- # save trainer.checkpoint_connector.hpc_save(tmpdir, logger) # init new trainer new_logger = tutils.get_default_logger(tmpdir, version=logger.version) trainer_options["logger"] = new_logger trainer_options["callbacks"] = [ModelCheckpoint(dirpath=tmpdir)] trainer_options["limit_train_batches"] = 0.5 trainer_options["limit_val_batches"] = 0.2 trainer_options["max_epochs"] = 1 new_trainer = Trainer(**trainer_options) class CustomModel(CustomClassificationModelDP): def __init__(self): super().__init__() self.on_pretrain_routine_end_called = False # set the epoch start hook so we can predict before the model does the full training def on_pretrain_routine_end(self): assert self.trainer.current_epoch == real_global_epoch and self.trainer.current_epoch > 0 # if model and state loaded correctly, predictions will be good even though we # haven't trained with the new loaded model new_trainer.state.stage = RunningStage.VALIDATING dataloader = dm.train_dataloader() tpipes.run_prediction_eval_model_template( self.trainer.lightning_module, dataloader=dataloader) self.on_pretrain_routine_end_called = True # new model model = CustomModel() # fit new model which should load hpc weights new_trainer.fit(model, datamodule=dm) assert model.on_pretrain_routine_end_called # test freeze on gpu model.freeze() model.unfreeze()
def test_cpu_slurm_save_load(tmpdir): """Verify model save/load/checkpoint on CPU.""" model = BoringModel() # logger file to get meta logger = tutils.get_default_logger(tmpdir) version = logger.version # fit model trainer = Trainer( default_root_dir=tmpdir, max_epochs=1, logger=logger, limit_train_batches=0.2, limit_val_batches=0.2, callbacks=[ModelCheckpoint(dirpath=tmpdir)], ) trainer.fit(model) real_global_step = trainer.global_step # traning complete assert trainer.state.finished, "cpu model failed to complete" # predict with trained model before saving # make a prediction dataloaders = model.test_dataloader() if not isinstance(dataloaders, list): dataloaders = [dataloaders] for dataloader in dataloaders: for batch in dataloader: break model.eval() pred_before_saving = model(batch) # test HPC saving # simulate snapshot on slurm # save logger to make sure we get all the metrics if logger: logger.finalize("finished") hpc_save_path = trainer.checkpoint_connector.hpc_save_path( trainer.weights_save_path) trainer.save_checkpoint(hpc_save_path) assert os.path.exists(hpc_save_path) # new logger file to get meta logger = tutils.get_default_logger(tmpdir, version=version) model = BoringModel() class _StartCallback(Callback): # set the epoch start hook so we can predict before the model does the full training def on_train_epoch_start(self, trainer, model): assert trainer.global_step == real_global_step and trainer.global_step > 0 # predict with loaded model to make sure answers are the same mode = model.training model.eval() new_pred = model(batch) assert torch.eq(pred_before_saving, new_pred).all() model.train(mode) trainer = Trainer( default_root_dir=tmpdir, max_epochs=1, logger=logger, callbacks=[_StartCallback(), ModelCheckpoint(dirpath=tmpdir)], ) # by calling fit again, we trigger training, loading weights from the cluster # and our hook to predict using current model before any more weight updates trainer.fit(model)