increase endpoint creation timeouts to 35 minutes across the board for tests (#158)

Author: winstonaws

tests/integ/test_byo_estimator.py

@@ -91,7 +91,7 @@ def test_byo_estimator(sagemaker_session, region):
 
     endpoint_name = name_from_base('byo')
 
-    with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session, minutes=20):
+    with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
         model = estimator.create_model()
         predictor = model.deploy(1, 'ml.m4.xlarge', endpoint_name=endpoint_name)
         predictor.serializer = fm_serializer
@@ -145,7 +145,7 @@ def test_async_byo_estimator(sagemaker_session, region):
         estimator.fit({'train': s3_train_data}, wait=False)
         training_job_name = estimator.latest_training_job.name
 
-    with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session, minutes=30):
+    with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
         estimator = Estimator.attach(training_job_name=training_job_name, sagemaker_session=sagemaker_session)
         model = estimator.create_model()
         predictor = model.deploy(1, 'ml.m4.xlarge', endpoint_name=endpoint_name)

tests/integ/test_factorization_machines.py

@@ -41,7 +41,7 @@ def test_factorization_machines(sagemaker_session):
         fm.fit(fm.record_set(train_set[0][:200], train_set[1][:200].astype('float32')))
 
     endpoint_name = name_from_base('fm')
-    with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session, minutes=20):
+    with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
         model = FactorizationMachinesModel(fm.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])
@@ -77,7 +77,7 @@ def test_async_factorization_machines(sagemaker_session):
         time.sleep(20)
         print("attaching now...")
 
-    with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session, minutes=35):
+    with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
         estimator = FactorizationMachines.attach(training_job_name=training_job_name,
                                                  sagemaker_session=sagemaker_session)
         model = FactorizationMachinesModel(estimator.model_data, role='SageMakerRole',

tests/integ/test_kmeans.py

@@ -47,7 +47,7 @@ def test_kmeans(sagemaker_session):
         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=20):
+    with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
         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])
@@ -90,7 +90,7 @@ def test_async_kmeans(sagemaker_session):
         time.sleep(20)
         print("attaching now...")
 
-    with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session, minutes=35):
+    with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
         estimator = KMeans.attach(training_job_name=training_job_name, sagemaker_session=sagemaker_session)
         model = KMeansModel(estimator.model_data, role='SageMakerRole', sagemaker_session=sagemaker_session)
         predictor = model.deploy(1, 'ml.c4.xlarge', endpoint_name=endpoint_name)

tests/integ/test_lda.py

@@ -41,7 +41,7 @@ def test_lda(sagemaker_session):
         lda.fit(record_set, 100)
 
     endpoint_name = name_from_base('lda')
-    with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session, minutes=20):
+    with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
         model = LDAModel(lda.model_data, role='SageMakerRole', sagemaker_session=sagemaker_session)
         predictor = model.deploy(1, 'ml.c4.xlarge', endpoint_name=endpoint_name)
 

tests/integ/test_linear_learner.py

@@ -77,7 +77,7 @@ def test_linear_learner(sagemaker_session):
         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=20):
+    with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
 
         predictor = ll.deploy(1, 'ml.c4.xlarge', endpoint_name=endpoint_name)
 
@@ -147,7 +147,7 @@ def test_async_linear_learner(sagemaker_session):
         print("Waiting to re-attach to the training job: %s" % training_job_name)
         time.sleep(20)
 
-    with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session, minutes=35):
+    with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
         estimator = LinearLearner.attach(training_job_name=training_job_name, sagemaker_session=sagemaker_session)
         model = LinearLearnerModel(estimator.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

@@ -45,7 +45,7 @@ def mxnet_training_job(sagemaker_session, mxnet_full_version):
 def test_attach_deploy(mxnet_training_job, sagemaker_session):
     endpoint_name = 'test-mxnet-attach-deploy-{}'.format(sagemaker_timestamp())
 
-    with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session, minutes=20):
+    with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
         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))
@@ -55,7 +55,7 @@ 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(sagemaker_timestamp())
 
-    with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session, minutes=20):
+    with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
         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')
@@ -88,7 +88,7 @@ def test_async_fit(sagemaker_session):
         print("Waiting to re-attach to the training job: %s" % training_job_name)
         time.sleep(20)
 
-    with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session, minutes=35):
+    with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
         print("Re-attaching now to: %s" % training_job_name)
         estimator = MXNet.attach(training_job_name=training_job_name, sagemaker_session=sagemaker_session)
         predictor = estimator.deploy(1, 'ml.m4.xlarge', endpoint_name=endpoint_name)

tests/integ/test_ntm.py

@@ -41,7 +41,7 @@ def test_ntm(sagemaker_session):
         ntm.fit(record_set, None)
 
     endpoint_name = name_from_base('ntm')
-    with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session, minutes=20):
+    with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
         model = NTMModel(ntm.model_data, role='SageMakerRole', sagemaker_session=sagemaker_session)
         predictor = model.deploy(1, 'ml.c4.xlarge', endpoint_name=endpoint_name)
 

tests/integ/test_pca.py

@@ -41,7 +41,7 @@ def test_pca(sagemaker_session):
         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=20):
+    with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
         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",
@@ -79,7 +79,7 @@ def test_async_pca(sagemaker_session):
         print("Detached from training job. Will re-attach in 20 seconds")
         time.sleep(20)
 
-    with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session, minutes=35):
+    with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
         estimator = sagemaker.amazon.pca.PCA.attach(training_job_name=training_job_name,
                                                     sagemaker_session=sagemaker_session)
 

tests/integ/test_tf.py

@@ -42,7 +42,7 @@ def test_tf(sagemaker_session, tf_full_version):
         print('job succeeded: {}'.format(estimator.latest_training_job.name))
 
     endpoint_name = estimator.latest_training_job.name
-    with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session, minutes=20):
+    with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
         json_predictor = estimator.deploy(initial_instance_count=1, instance_type='ml.c4.xlarge',
                                           endpoint_name=endpoint_name)
 
@@ -75,7 +75,7 @@ def test_tf_async(sagemaker_session):
         time.sleep(20)
 
     endpoint_name = training_job_name
-    with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session, minutes=35):
+    with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
         estimator = TensorFlow.attach(training_job_name=training_job_name, sagemaker_session=sagemaker_session)
         json_predictor = estimator.deploy(initial_instance_count=1, instance_type='ml.c4.xlarge',
                                           endpoint_name=endpoint_name)

tests/integ/test_tf_cifar.py

@@ -47,7 +47,7 @@ def test_cifar(sagemaker_session, tf_full_version):
         print('job succeeded: {}'.format(estimator.latest_training_job.name))
 
     endpoint_name = estimator.latest_training_job.name
-    with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session, minutes=20):
+    with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
         predictor = estimator.deploy(initial_instance_count=1, instance_type='ml.p2.xlarge')
         predictor.serializer = PickleSerializer()
         predictor.content_type = PICKLE_CONTENT_TYPE