def test_cpu_model_with_amp(tmpdir): """Make sure model trains on CPU.""" tutils.reset_seed() trainer_options = dict(default_save_path=tmpdir, show_progress_bar=False, logger=tutils.get_test_tube_logger(tmpdir), max_epochs=1, train_percent_check=0.4, val_percent_check=0.4, use_amp=True) model, hparams = tutils.get_model() with pytest.raises((MisconfigurationException, ModuleNotFoundError)): tutils.run_model_test(trainer_options, model, on_gpu=False)
def test_amp_gpu_dp(tmpdir): """Make sure DP + AMP work.""" tutils.reset_seed() if not tutils.can_run_gpu_test(): return model, hparams = tutils.get_model() trainer_options = dict( default_save_path=tmpdir, max_num_epochs=1, gpus='0, 1', # test init with gpu string distributed_backend='dp', use_amp=True) with pytest.raises(MisconfigurationException): tutils.run_model_test(trainer_options, model, hparams)
def test_multi_gpu_none_backend(tmpdir): """Make sure when using multiple GPUs the user can't use `distributed_backend = None`.""" tutils.reset_seed() if not tutils.can_run_gpu_test(): return model, hparams = tutils.get_model() trainer_options = dict(default_save_path=tmpdir, show_progress_bar=False, max_num_epochs=1, train_percent_check=0.1, val_percent_check=0.1, gpus='-1') with pytest.raises(MisconfigurationException): tutils.run_model_test(trainer_options, model)
def test_multi_gpu_model_ddp(tmpdir): """Make sure DDP works.""" if not tutils.can_run_gpu_test(): return tutils.reset_seed() tutils.set_random_master_port() model, hparams = tutils.get_model() trainer_options = dict(default_save_path=tmpdir, show_progress_bar=False, max_num_epochs=1, train_percent_check=0.4, val_percent_check=0.2, gpus=[0, 1], distributed_backend='ddp') tutils.run_model_test(trainer_options, model)
def test_amp_gpu_dp(tmpdir): """Make sure DP + AMP work.""" tutils.reset_seed() if not tutils.can_run_gpu_test(): return model, hparams = tutils.get_model() trainer_options = dict( default_save_path=tmpdir, max_epochs=1, gpus='0, 1', # test init with gpu string distributed_backend='dp', use_amp=True) trainer = Trainer(**trainer_options) result = trainer.fit(model) assert result == 1