Increase endpoint creation timeout to 20 minutes (#11)

Author: mvsusp

tests/integ/test_kmeans.py

@@ -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])

tests/integ/test_linear_learner.py

@@ -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)

tests/integ/test_mxnet_train.py

@@ -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))
@@ -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')

tests/integ/test_pca.py

@@ -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",

tests/integ/test_tf.py

@@ -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])

tests/integ/test_tf_cifar.py

@@ -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')