Python stopit.ThreadingTimeout方法代码示例

# 需要导入模块: import stopit [as 别名]
# 或者: from stopit import ThreadingTimeout [as 别名]
def timeout(seconds=0, minutes=0, hours=0):
    """
    Add a signal-based timeout to any block of code.
    If multiple time units are specified, they will be added together to determine time limit.
    Usage:
    with timeout(seconds=5):
        my_slow_function(...)
    Args:
        - seconds: The time limit, in seconds.
        - minutes: The time limit, in minutes.
        - hours: The time limit, in hours.
    """

    limit = seconds + 60 * minutes + 3600 * hours

    with stopit.ThreadingTimeout(limit, swallow_exc=False) as t:
        yield [t] 

# 需要导入模块: import stopit [as 别名]
# 或者: from stopit import ThreadingTimeout [as 别名]
def timeout_and_delete_endpoint_by_name(
    endpoint_name,
    sagemaker_session,
    seconds=0,
    minutes=45,
    hours=0,
    sleep_between_cleanup_attempts=10,
):
    limit = seconds + 60 * minutes + 3600 * hours

    with stopit.ThreadingTimeout(limit, swallow_exc=False) as t:
        no_errors = False
        try:
            yield [t]
            no_errors = True
        finally:
            attempts = 3

            while attempts > 0:
                attempts -= 1
                try:
                    _delete_schedules_associated_with_endpoint(
                        sagemaker_session=sagemaker_session, endpoint_name=endpoint_name
                    )
                    sagemaker_session.delete_endpoint(endpoint_name)
                    LOGGER.info("deleted endpoint {}".format(endpoint_name))

                    _show_logs(endpoint_name, "Endpoints", sagemaker_session)
                    if no_errors:
                        _cleanup_logs(endpoint_name, "Endpoints", sagemaker_session)
                    break
                except ClientError as ce:
                    if ce.response["Error"]["Code"] == "ValidationException":
                        # avoids the inner exception to be overwritten
                        pass
                # trying to delete the resource again in 10 seconds
                sleep(sleep_between_cleanup_attempts) 

# 需要导入模块: import stopit [as 别名]
# 或者: from stopit import ThreadingTimeout [as 别名]
def timeout_and_delete_model_with_transformer(
    transformer, sagemaker_session, seconds=0, minutes=0, hours=0, sleep_between_cleanup_attempts=10
):
    limit = seconds + 60 * minutes + 3600 * hours

    with stopit.ThreadingTimeout(limit, swallow_exc=False) as t:
        no_errors = False
        try:
            yield [t]
            no_errors = True
        finally:
            attempts = 3

            while attempts > 0:
                attempts -= 1
                try:
                    transformer.delete_model()
                    LOGGER.info("deleted SageMaker model {}".format(transformer.model_name))

                    _show_logs(transformer.model_name, "Models", sagemaker_session)
                    if no_errors:
                        _cleanup_logs(transformer.model_name, "Models", sagemaker_session)
                    break
                except ClientError as ce:
                    if ce.response["Error"]["Code"] == "ValidationException":
                        pass
                sleep(sleep_between_cleanup_attempts) 

# 需要导入模块: import stopit [as 别名]
# 或者: from stopit import ThreadingTimeout [as 别名]
def test_tf_local_mode(sagemaker_local_session):
    with stopit.ThreadingTimeout(5 * 60, swallow_exc=False):
        script_path = os.path.join(DATA_DIR, "iris", "iris-dnn-classifier.py")

        estimator = TensorFlow(
            entry_point=script_path,
            role="SageMakerRole",
            framework_version="1.12",
            training_steps=1,
            evaluation_steps=1,
            hyperparameters={"input_tensor_name": "inputs"},
            train_instance_count=1,
            train_instance_type="local",
            base_job_name="test-tf",
            sagemaker_session=sagemaker_local_session,
        )

        inputs = estimator.sagemaker_session.upload_data(
            path=DATA_PATH, key_prefix="integ-test-data/tf_iris"
        )
        estimator.fit(inputs)
        print("job succeeded: {}".format(estimator.latest_training_job.name))

    endpoint_name = estimator.latest_training_job.name
    with lock.lock(LOCK_PATH):
        try:
            json_predictor = estimator.deploy(
                initial_instance_count=1, instance_type="local", endpoint_name=endpoint_name
            )

            features = [6.4, 3.2, 4.5, 1.5]
            dict_result = json_predictor.predict({"inputs": features})
            print("predict result: {}".format(dict_result))
            list_result = json_predictor.predict(features)
            print("predict result: {}".format(list_result))

            assert dict_result == list_result
        finally:
            estimator.delete_endpoint() 

# 需要导入模块: import stopit [as 别名]
# 或者: from stopit import ThreadingTimeout [as 别名]
def test_tf_distributed_local_mode(sagemaker_local_session):
    with stopit.ThreadingTimeout(5 * 60, swallow_exc=False):
        script_path = os.path.join(DATA_DIR, "iris", "iris-dnn-classifier.py")

        estimator = TensorFlow(
            entry_point=script_path,
            role="SageMakerRole",
            framework_version="1.12",
            training_steps=1,
            evaluation_steps=1,
            hyperparameters={"input_tensor_name": "inputs"},
            train_instance_count=3,
            train_instance_type="local",
            base_job_name="test-tf",
            sagemaker_session=sagemaker_local_session,
        )

        inputs = "file://" + DATA_PATH
        estimator.fit(inputs)
        print("job succeeded: {}".format(estimator.latest_training_job.name))

    endpoint_name = estimator.latest_training_job.name

    with lock.lock(LOCK_PATH):
        try:
            json_predictor = estimator.deploy(
                initial_instance_count=1, instance_type="local", endpoint_name=endpoint_name
            )

            features = [6.4, 3.2, 4.5, 1.5]
            dict_result = json_predictor.predict({"inputs": features})
            print("predict result: {}".format(dict_result))
            list_result = json_predictor.predict(features)
            print("predict result: {}".format(list_result))

            assert dict_result == list_result
        finally:
            estimator.delete_endpoint() 

# 需要导入模块: import stopit [as 别名]
# 或者: from stopit import ThreadingTimeout [as 别名]
def test_tf_local_data_local_script():
    with stopit.ThreadingTimeout(5 * 60, swallow_exc=False):
        script_path = os.path.join(DATA_DIR, "iris", "iris-dnn-classifier.py")

        estimator = TensorFlow(
            entry_point=script_path,
            role="SageMakerRole",
            framework_version="1.12",
            training_steps=1,
            evaluation_steps=1,
            hyperparameters={"input_tensor_name": "inputs"},
            train_instance_count=1,
            train_instance_type="local",
            base_job_name="test-tf",
            sagemaker_session=LocalNoS3Session(),
        )

        inputs = "file://" + DATA_PATH

        estimator.fit(inputs)
        print("job succeeded: {}".format(estimator.latest_training_job.name))

    endpoint_name = estimator.latest_training_job.name
    with lock.lock(LOCK_PATH):
        try:
            json_predictor = estimator.deploy(
                initial_instance_count=1, instance_type="local", endpoint_name=endpoint_name
            )

            features = [6.4, 3.2, 4.5, 1.5]
            dict_result = json_predictor.predict({"inputs": features})
            print("predict result: {}".format(dict_result))
            list_result = json_predictor.predict(features)
            print("predict result: {}".format(list_result))

            assert dict_result == list_result
        finally:
            estimator.delete_endpoint() 

# 需要导入模块: import stopit [as 别名]
# 或者: from stopit import ThreadingTimeout [as 别名]
def timed(timeout):
    def outer_wrapper(f):
        @wraps(f)
        def wrapper(*args, **kwargs):
            with stopit.ThreadingTimeout(timeout) as to_ctx_mgr:
                f(*args, **kwargs)
            if to_ctx_mgr.state != to_ctx_mgr.EXECUTED:
                raise Exception("Test function timed out.")

        return wrapper

    return outer_wrapper 

# 需要导入模块: import stopit [as 别名]
# 或者: from stopit import ThreadingTimeout [as 别名]
def __call__(self, worker: Stage, **kwargs):
        for x in worker.iter_dependencies():
            with (
                stopit.ThreadingTimeout(worker.timeout)
                if worker.timeout
                else utils.NoOpContext()
            ):
                yield from self.apply(worker, x, **kwargs) 

# 需要导入模块: import stopit [as 别名]
# 或者: from stopit import ThreadingTimeout [as 别名]
def test_local_transform_mxnet(
    sagemaker_local_session, tmpdir, mxnet_full_version, cpu_instance_type
):
    data_path = os.path.join(DATA_DIR, "mxnet_mnist")
    script_path = os.path.join(data_path, "mnist.py")

    mx = MXNet(
        entry_point=script_path,
        role="SageMakerRole",
        train_instance_count=1,
        train_instance_type="local",
        framework_version=mxnet_full_version,
        sagemaker_session=sagemaker_local_session,
    )

    train_input = mx.sagemaker_session.upload_data(
        path=os.path.join(data_path, "train"), key_prefix="integ-test-data/mxnet_mnist/train"
    )
    test_input = mx.sagemaker_session.upload_data(
        path=os.path.join(data_path, "test"), key_prefix="integ-test-data/mxnet_mnist/test"
    )

    with stopit.ThreadingTimeout(5 * 60, swallow_exc=False):
        mx.fit({"train": train_input, "test": test_input})

    transform_input_path = os.path.join(data_path, "transform")
    transform_input_key_prefix = "integ-test-data/mxnet_mnist/transform"
    transform_input = mx.sagemaker_session.upload_data(
        path=transform_input_path, key_prefix=transform_input_key_prefix
    )

    output_path = "file://%s" % (str(tmpdir))
    transformer = mx.transformer(
        1,
        "local",
        assemble_with="Line",
        max_payload=1,
        strategy="SingleRecord",
        output_path=output_path,
    )

    with lock.lock(LOCK_PATH):
        transformer.transform(transform_input, content_type="text/csv", split_type="Line")
        transformer.wait()

    assert os.path.exists(os.path.join(str(tmpdir), "data.csv.out"))