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increase endpoint creation timeout to 20 minutes #11

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Merged
merged 1 commit into from
Dec 7, 2017
Merged

increase endpoint creation timeout to 20 minutes #11

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2 changes: 1 addition & 1 deletion tests/integ/test_kmeans.py
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Expand Up @@ -51,7 +51,7 @@ def test_kmeans():
kmeans.fit(kmeans.record_set(train_set[0][:100]))

endpoint_name = name_from_base('kmeans')
with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session, minutes=15):
with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session, minutes=20):
model = KMeansModel(kmeans.model_data, role='SageMakerRole', sagemaker_session=sagemaker_session)
predictor = model.deploy(1, 'ml.c4.xlarge', endpoint_name=endpoint_name)
result = predictor.predict(train_set[0][:10])
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2 changes: 1 addition & 1 deletion tests/integ/test_linear_learner.py
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Expand Up @@ -74,7 +74,7 @@ def test_linear_learner():
ll.fit(ll.record_set(train_set[0][:200], train_set[1][:200]))

endpoint_name = name_from_base('linear-learner')
with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session, minutes=15):
with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session, minutes=20):

model = LinearLearnerModel(ll.model_data, role='SageMakerRole', sagemaker_session=sagemaker_session)
predictor = model.deploy(1, 'ml.c4.xlarge', endpoint_name=endpoint_name)
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4 changes: 2 additions & 2 deletions tests/integ/test_mxnet_train.py
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Expand Up @@ -51,7 +51,7 @@ def mxnet_training_job(sagemaker_session):
def test_attach_deploy(mxnet_training_job, sagemaker_session):
endpoint_name = 'test-mxnet-attach-deploy-{}'.format(int(time.time()))

with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session, minutes=15):
with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session, minutes=20):
estimator = MXNet.attach(mxnet_training_job, sagemaker_session=sagemaker_session)
predictor = estimator.deploy(1, 'ml.m4.xlarge', endpoint_name=endpoint_name)
data = numpy.zeros(shape=(1, 1, 28, 28))
Expand All @@ -61,7 +61,7 @@ def test_attach_deploy(mxnet_training_job, sagemaker_session):
def test_deploy_model(mxnet_training_job, sagemaker_session):
endpoint_name = 'test-mxnet-deploy-model-{}'.format(int(time.time()))

with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session, minutes=15):
with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session, minutes=20):
desc = sagemaker_session.sagemaker_client.describe_training_job(TrainingJobName=mxnet_training_job)
model_data = desc['ModelArtifacts']['S3ModelArtifacts']
script_path = os.path.join(DATA_DIR, 'mxnet_mnist', 'mnist.py')
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2 changes: 1 addition & 1 deletion tests/integ/test_pca.py
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Expand Up @@ -44,7 +44,7 @@ def test_pca():
pca.fit(pca.record_set(train_set[0][:100]))

endpoint_name = name_from_base('pca')
with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session, minutes=15):
with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session, minutes=20):
pca_model = sagemaker.amazon.pca.PCAModel(model_data=pca.model_data, role='SageMakerRole',
sagemaker_session=sagemaker_session)
predictor = pca_model.deploy(initial_instance_count=1, instance_type="ml.c4.xlarge",
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2 changes: 1 addition & 1 deletion tests/integ/test_tf.py
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Expand Up @@ -44,7 +44,7 @@ def test_tf(sagemaker_session):
estimator.fit(inputs)
print('job succeeded: {}'.format(estimator.latest_training_job.name))

with timeout_and_delete_endpoint(estimator=estimator, minutes=15):
with timeout_and_delete_endpoint(estimator=estimator, minutes=20):
json_predictor = estimator.deploy(initial_instance_count=1, instance_type='ml.c4.xlarge')

result = json_predictor.predict([6.4, 3.2, 4.5, 1.5])
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2 changes: 1 addition & 1 deletion tests/integ/test_tf_cifar.py
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Expand Up @@ -41,5 +41,5 @@ def test_cifar(sagemaker_session):
estimator.fit(inputs)
print('job succeeded: {}'.format(estimator.latest_training_job.name))

with timeout_and_delete_endpoint(estimator=estimator, minutes=15):
with timeout_and_delete_endpoint(estimator=estimator, minutes=20):
estimator.deploy(initial_instance_count=1, instance_type='ml.c4.xlarge')