本文整理匯總了Python中sagemaker.tensorflow.TensorFlow.delete_endpoint方法的典型用法代碼示例。如果您正苦於以下問題：Python TensorFlow.delete_endpoint方法的具體用法？Python TensorFlow.delete_endpoint怎麽用？Python TensorFlow.delete_endpoint使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類sagemaker.tensorflow.TensorFlow的用法示例。
在下文中一共展示了TensorFlow.delete_endpoint方法的1個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: test_tf_local_mode
# 需要導入模塊: from sagemaker.tensorflow import TensorFlow [as 別名]
# 或者: from sagemaker.tensorflow.TensorFlow import delete_endpoint [as 別名]
def test_tf_local_mode(tf_full_version, sagemaker_local_session):
local_mode_lock_fd = open(LOCK_PATH, 'w')
local_mode_lock = local_mode_lock_fd.fileno()
with timeout(minutes=5):
script_path = os.path.join(DATA_DIR, 'iris', 'iris-dnn-classifier.py')
estimator = TensorFlow(entry_point=script_path,
role='SageMakerRole',
framework_version=tf_full_version,
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
try:
# Since Local Mode uses the same port for serving, we need a lock in order
# to allow concurrent test execution. The serving test is really fast so it still
# makes sense to allow this behavior.
fcntl.lockf(local_mode_lock, fcntl.LOCK_EX)
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()
time.sleep(5)
fcntl.lockf(local_mode_lock, fcntl.LOCK_UN)