def test_ddp_sampler_error(): """ Make sure DDP + AMP work :return: """ if not can_run_gpu_test(): return os.environ['MASTER_PORT'] = str(np.random.randint(12000, 19000, 1)[0]) hparams = get_hparams() model = LightningTestModel(hparams, force_remove_distributed_sampler=True) exp = get_exp(True) exp.save() trainer = Trainer(experiment=exp, show_progress_bar=False, max_nb_epochs=1, gpus=[0, 1], distributed_backend='ddp', use_amp=True) with pytest.warns(UserWarning): trainer.get_dataloaders(model) clear_save_dir()
def test_ddp_sampler_error(): """ Make sure DDP + AMP work :return: """ if not can_run_gpu_test(): return reset_seed() set_random_master_port() hparams = get_hparams() model = LightningTestModel(hparams, force_remove_distributed_sampler=True) logger = get_test_tube_logger(True) trainer = Trainer(logger=logger, show_progress_bar=False, max_nb_epochs=1, gpus=[0, 1], distributed_backend='ddp', use_amp=True) with pytest.warns(UserWarning): trainer.get_dataloaders(model) clear_save_dir()
def test_ddp_sampler_error(): """ Make sure DDP + AMP work :return: """ if not torch.cuda.is_available(): warnings.warn('test_amp_gpu_ddp cannot run. Rerun on a GPU node to run this test') return if not torch.cuda.device_count() > 1: warnings.warn('test_amp_gpu_ddp cannot run. Rerun on a node with 2+ GPUs to run this test') return os.environ['MASTER_PORT'] = str(np.random.randint(12000, 19000, 1)[0]) hparams = get_hparams() model = LightningTestModel(hparams, force_remove_distributed_sampler=True) exp = get_exp(True) exp.save() trainer = Trainer( experiment=exp, progress_bar=False, max_nb_epochs=1, gpus=[0, 1], distributed_backend='ddp', use_amp=True ) with pytest.warns(UserWarning): trainer.get_dataloaders(model) clear_save_dir()
def test_ddp_sampler_error(tmpdir): """Make sure DDP + AMP work.""" if not tutils.can_run_gpu_test(): return tutils.reset_seed() tutils.set_random_master_port() hparams = tutils.get_hparams() model = LightningTestModel(hparams, force_remove_distributed_sampler=True) logger = tutils.get_test_tube_logger(tmpdir, True) trainer = Trainer(logger=logger, show_progress_bar=False, max_epochs=1, gpus=[0, 1], distributed_backend='ddp', precision=16) with pytest.warns(UserWarning): trainer.get_dataloaders(model)