Python features_examples_path Examples

Example #1

File: test_tf_estimator.py Project: maridia/determined

def run_mnist_estimator_data_layer_test(tf2: bool, storage_type: str) -> None:
    config = conf.load_config(
        conf.features_examples_path("data_layer_mnist_estimator/const.yaml"))
    config = conf.set_max_length(config, {"batches": 200})
    config = conf.set_tf2_image(config) if tf2 else conf.set_tf1_image(config)
    if storage_type == "lfs":
        config = conf.set_shared_fs_data_layer(config)
    else:
        config = conf.set_s3_data_layer(config)

    exp.run_basic_test_with_temp_config(
        config, conf.features_examples_path("data_layer_mnist_estimator"), 1)

Example #2

def test_tf_keras_mnist_data_layer_parallel(tf2: bool, storage_type: str,
                                            secrets: Dict[str, str]) -> None:
    config = conf.load_config(
        conf.features_examples_path("data_layer_mnist_tf_keras/const.yaml"))
    config = conf.set_max_length(config, {"batches": 200})
    config = conf.set_slots_per_trial(config, 8)
    config = conf.set_tf2_image(config) if tf2 else conf.set_tf1_image(config)
    if storage_type == "lfs":
        config = conf.set_shared_fs_data_layer(config)
    else:
        config = conf.set_s3_data_layer(config)

    exp.run_basic_test_with_temp_config(
        config, conf.features_examples_path("data_layer_mnist_tf_keras"), 1)

Example #3

File: test_tf_estimator.py Project: maridia/determined

def test_mnist_estimator_adaptive_with_data_layer() -> None:
    config = conf.load_config(
        conf.fixtures_path("mnist_estimator/adaptive.yaml"))
    config = conf.set_tf2_image(config)
    config = conf.set_shared_fs_data_layer(config)

    exp.run_basic_test_with_temp_config(
        config, conf.features_examples_path("data_layer_mnist_estimator"),
        None)

Example #4

File: test_convergence.py Project: zkkxu/determined

def test_data_layer_mnist_estimator_accuracy() -> None:
    config = conf.load_config(
        conf.features_examples_path("data_layer_mnist_estimator/const.yaml"))
    experiment_id = exp.run_basic_test_with_temp_config(
        config, conf.features_examples_path("data_layer_mnist_estimator"), 1)

    trials = exp.experiment_trials(experiment_id)
    trial_metrics = exp.trial_metrics(trials[0]["id"])

    validation_accuracies = [
        step["validation"]["metrics"]["validation_metrics"]["accuracy"]
        for step in trial_metrics["steps"] if step.get("validation")
    ]

    target_accuracy = 0.92
    assert max(validation_accuracies) > target_accuracy, (
        "data_layer_mnist_estimator did not reach minimum target accuracy {} in {} steps."
        " full validation accuracy history: {}".format(
            target_accuracy, len(trial_metrics["steps"]),
            validation_accuracies))

Example #5

File: test_convergence.py Project: wbwatkinson/determined

def test_text_classification_tf_keras_accuracy() -> None:
    config = conf.load_config(
        conf.features_examples_path("text_classification_tf_keras/const.yaml")
    )
    experiment_id = exp.run_basic_test_with_temp_config(
        config, conf.features_examples_path("text_classification_tf_keras"), 1
    )

    trials = exp.experiment_trials(experiment_id)
    trial_metrics = exp.trial_metrics(trials[0]["id"])

    validation_accuracies = [
        step["validation"]["metrics"]["validation_metrics"]["val_sparse_categorical_accuracy"]
        for step in trial_metrics["steps"]
        if step.get("validation")
    ]

    target_accuracy = 0.95
    assert max(validation_accuracies) > target_accuracy, (
        "text_classification_tf_keras did not reach minimum target accuracy {} in {} steps."
        " full validation accuracy history: {}".format(
            target_accuracy, len(trial_metrics["steps"]), validation_accuracies
        )
    )