def test_multi_gpu_model_dp(tmpdir): """ Make sure DP works :return: """ 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, distributed_backend='dp', max_nb_epochs=1, train_percent_check=0.1, val_percent_check=0.1, gpus='-1' ) tutils.run_model_test(trainer_options, model, hparams) # test memory helper functions memory.get_memory_profile('min_max')
def test_early_stopping_cpu_model(tmpdir): """Test each of the trainer options.""" tutils.reset_seed() stopping = EarlyStopping(monitor='val_loss', min_delta=0.1) trainer_options = dict( default_save_path=tmpdir, min_epochs=2, early_stop_callback=stopping, gradient_clip_val=1.0, overfit_pct=0.20, track_grad_norm=2, print_nan_grads=True, show_progress_bar=True, logger=tutils.get_test_tube_logger(tmpdir), train_percent_check=0.1, val_percent_check=0.1, ) model, hparams = tutils.get_model() tutils.run_model_test(trainer_options, model, on_gpu=False, early_stop=True) # test freeze on cpu model.freeze() model.unfreeze()
def test_cpu_model(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) model, hparams = tutils.get_model() tutils.run_model_test(trainer_options, model, on_gpu=False)
def test_cpu_model(): """ Make sure model trains on CPU :return: """ tutils.reset_seed() trainer_options = dict(show_progress_bar=False, logger=tutils.get_test_tube_logger(), max_nb_epochs=1, train_percent_check=0.4, val_percent_check=0.4) model, hparams = tutils.get_model() tutils.run_model_test(trainer_options, model, hparams, on_gpu=False)
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_single_gpu_model(tmpdir): """Make sure single GPU works (DP mode).""" tutils.reset_seed() if not torch.cuda.is_available(): warnings.warn('test_single_gpu_model cannot run.' ' Rerun on a GPU node to run this test') return model, hparams = tutils.get_model() trainer_options = dict(default_save_path=tmpdir, show_progress_bar=False, max_epochs=1, train_percent_check=0.1, val_percent_check=0.1, gpus=1) tutils.run_model_test(trainer_options, model)
def test_all_features_cpu_model(tmpdir): """Test each of the trainer options.""" tutils.reset_seed() trainer_options = dict(default_save_path=tmpdir, gradient_clip_val=1.0, overfit_pct=0.20, track_grad_norm=2, print_nan_grads=True, show_progress_bar=False, logger=tutils.get_test_tube_logger(tmpdir), accumulate_grad_batches=2, max_epochs=1, train_percent_check=0.4, val_percent_check=0.4) model, hparams = tutils.get_model() tutils.run_model_test(trainer_options, model, on_gpu=False)
def test_amp_single_gpu(tmpdir): """Make sure DDP + AMP work.""" tutils.reset_seed() if not tutils.can_run_gpu_test(): return hparams = tutils.get_hparams() model = LightningTestModel(hparams) trainer_options = dict(default_save_path=tmpdir, show_progress_bar=True, max_epochs=1, gpus=1, distributed_backend='ddp', use_amp=True) 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_no_amp_single_gpu(tmpdir): """Make sure DDP + AMP work.""" tutils.reset_seed() if not tutils.can_run_gpu_test(): return hparams = tutils.get_hparams() model = LightningTestModel(hparams) trainer_options = dict(default_save_path=tmpdir, show_progress_bar=True, max_num_epochs=1, gpus=1, distributed_backend='dp', use_amp=True) with pytest.raises((MisconfigurationException, ModuleNotFoundError)): tutils.run_model_test(trainer_options, model)
def test_amp_gpu_ddp(): """ Make sure DDP + AMP work :return: """ if not tutils.can_run_gpu_test(): return tutils.reset_seed() tutils.set_random_master_port() hparams = tutils.get_hparams() model = LightningTestModel(hparams) trainer_options = dict(show_progress_bar=True, max_nb_epochs=1, gpus=2, distributed_backend='ddp', use_amp=True) tutils.run_model_test(trainer_options, model, hparams)
def test_multi_gpu_model_ddp2(): """ Make sure DDP2 works :return: """ if not tutils.can_run_gpu_test(): return tutils.reset_seed() tutils.set_random_master_port() model, hparams = tutils.get_model() trainer_options = dict(show_progress_bar=True, max_nb_epochs=1, train_percent_check=0.4, val_percent_check=0.2, gpus=2, weights_summary=None, distributed_backend='ddp2') tutils.run_model_test(trainer_options, model, hparams)