def test_pytorch_parallel() -> None: config = conf.load_config(conf.official_examples_path("mnist_pytorch/const.yaml")) config = conf.set_slots_per_trial(config, 8) config = conf.set_native_parallel(config, False) config = conf.set_max_steps(config, 2) config = conf.set_tensor_auto_tuning(config, True) exp.run_basic_test_with_temp_config(config, conf.official_examples_path("mnist_pytorch"), 1)
def test_tensorpack_native_parallel() -> None: config = conf.load_config( conf.official_examples_path("mnist_tp/const.yaml")) config = conf.set_slots_per_trial(config, 8) config = conf.set_native_parallel(config, True) config = conf.set_max_steps(config, 2) experiment_id = exp.run_basic_test_with_temp_config( config, conf.official_examples_path("mnist_tp"), 1) trials = exp.experiment_trials(experiment_id) assert len(trials) == 1
def test_tf_keras_mnist_parallel() -> None: config = conf.load_config( conf.official_examples_path("fashion_mnist_tf_keras/const.yaml")) config = conf.set_slots_per_trial(config, 8) config = conf.set_native_parallel(config, False) config = conf.set_max_steps(config, 2) experiment_id = exp.run_basic_test_with_temp_config( config, conf.official_examples_path("fashion_mnist_tf_keras"), 1) trials = exp.experiment_trials(experiment_id) assert len(trials) == 1
def test_tf_keras_native_parallel(tf2: bool) -> None: config = conf.load_config( conf.official_examples_path("cifar10_cnn_tf_keras/const.yaml")) config = conf.set_slots_per_trial(config, 8) config = conf.set_native_parallel(config, True) config = conf.set_max_steps(config, 2) config = conf.set_tf2_image(config) if tf2 else conf.set_tf1_image(config) experiment_id = exp.run_basic_test_with_temp_config( config, conf.official_examples_path("cifar10_cnn_tf_keras"), 1) trials = exp.experiment_trials(experiment_id) assert len(trials) == 1
def test_pytorch_const_parallel(aggregation_frequency: int, use_amp: bool) -> None: config = conf.load_config( conf.official_examples_path("mnist_pytorch/const.yaml")) config = conf.set_slots_per_trial(config, 8) config = conf.set_native_parallel(config, False) config = conf.set_max_steps(config, 2) config = conf.set_aggregation_frequency(config, aggregation_frequency) if use_amp: config = conf.set_amp_level(config, "O1") exp.run_basic_test_with_temp_config( config, conf.official_examples_path("mnist_pytorch"), 1)
def test_tf_keras_mnist_parallel() -> None: config = conf.load_config( conf.official_examples_path("fashion_mnist_tf_keras/const.yaml")) config["checkpoint_storage"] = exp.shared_fs_checkpoint_config() config.get("bind_mounts", []).append(exp.root_user_home_bind_mount()) config = conf.set_slots_per_trial(config, 8) config = conf.set_native_parallel(config, False) config = conf.set_max_steps(config, 2) experiment_id = exp.run_basic_test_with_temp_config( config, conf.official_examples_path("fashion_mnist_tf_keras"), 1) trials = exp.experiment_trials(experiment_id) assert len(trials) == 1
def test_tf_keras_native_parallel(tf2: bool) -> None: config = conf.load_config( conf.official_examples_path("cifar10_cnn_tf_keras/const.yaml")) config["checkpoint_storage"] = exp.shared_fs_checkpoint_config() config.get("bind_mounts", []).append(exp.root_user_home_bind_mount()) config = conf.set_slots_per_trial(config, 8) config = conf.set_native_parallel(config, True) config = conf.set_max_steps(config, 2) config = conf.set_tf2_image(config) if tf2 else conf.set_tf1_image(config) experiment_id = exp.run_basic_test_with_temp_config( config, conf.official_examples_path("cifar10_cnn_tf_keras"), 1) trials = exp.experiment_trials(experiment_id) assert len(trials) == 1
def test_tensorpack_parallel(aggregation_frequency: int) -> None: config = conf.load_config( conf.official_examples_path("mnist_tp/const.yaml")) config["checkpoint_storage"] = exp.shared_fs_checkpoint_config() config.get("bind_mounts", []).append(exp.root_user_home_bind_mount()) config = conf.set_slots_per_trial(config, 8) config = conf.set_native_parallel(config, False) config = conf.set_max_steps(config, 2) config = conf.set_aggregation_frequency(config, aggregation_frequency) experiment_id = exp.run_basic_test_with_temp_config( config, conf.official_examples_path("mnist_tp"), 1) trials = exp.experiment_trials(experiment_id) assert len(trials) == 1
def test_mnist_estimmator_const_parallel(native_parallel: bool, tf2: bool) -> None: if tf2 and native_parallel: pytest.skip("TF2 native parallel training is not currently supported.") config = conf.load_config( conf.fixtures_path("mnist_estimator/single-multi-slot.yaml")) config = conf.set_slots_per_trial(config, 8) config = conf.set_native_parallel(config, native_parallel) config = conf.set_max_steps(config, 2) config = conf.set_tf2_image(config) if tf2 else conf.set_tf1_image(config) exp.run_basic_test_with_temp_config( config, conf.official_examples_path("mnist_estimator"), 1)