Create configurable sagemaker_session fixture for all integ tests (#104)

* Create configurable sagemaker_session fixture for all integ tests

* Update changelog

CHANGELOG.rst

@@ -2,13 +2,18 @@
 CHANGELOG
 =========
 
+1.1.dev3
+========
+
+* feature: Tests: create configurable ``sagemaker_session`` pytest fixture for all integration tests
+
 1.1.2
-=======
+=====
 
 * bug-fix: AmazonEstimators: do not call create bucket if data location is provided
 
 1.1.1
-========
+=====
 
 * feature: Estimators: add ``requirements.txt`` support for TensorFlow
 

tests/conftest.py

@@ -10,8 +10,50 @@
 # distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
 # ANY KIND, either express or implied. See the License for the specific
 # language governing permissions and limitations under the License.
+import json
+
+import boto3
 import pytest
 
+from sagemaker import Session
+
+DEFAULT_REGION = 'us-west-2'
+
+
+def pytest_addoption(parser):
+    parser.addoption('--sagemaker-client-config', action='store', default=None)
+    parser.addoption('--sagemaker-runtime-config', action='store', default=None)
+    parser.addoption('--boto-config', action='store', default=None)
+
+
+@pytest.fixture(scope='session')
+def sagemaker_client_config(request):
+    config = request.config.getoption('--sagemaker-client-config')
+    return json.loads(config) if config else None
+
+
+@pytest.fixture(scope='session')
+def sagemaker_runtime_config(request):
+    config = request.config.getoption('--sagemaker-runtime-config')
+    return json.loads(config) if config else None
+
+
+@pytest.fixture(scope='session')
+def boto_config(request):
+    config = request.config.getoption('--boto-config')
+    return json.loads(config) if config else None
+
+
+@pytest.fixture(scope='session')
+def sagemaker_session(sagemaker_client_config, sagemaker_runtime_config, boto_config):
+    sagemaker_client = boto3.client('sagemaker', **sagemaker_client_config) if sagemaker_client_config else None
+    runtime_client = boto3.client('sagemaker-runtime', **sagemaker_runtime_config) if sagemaker_runtime_config else None
+    boto_session = boto3.Session(**boto_config) if boto_config else boto3.Session(region_name=DEFAULT_REGION)
+
+    return Session(boto_session=boto_session,
+                   sagemaker_client=sagemaker_client,
+                   sagemaker_runtime_client=runtime_client)
+
 
 @pytest.fixture(scope='module', params=["1.4", "1.4.1", "1.5", "1.5.0"])
 def tf_version(request):

tests/integ/__init__.py

@@ -12,8 +12,8 @@
 # language governing permissions and limitations under the License.
 import logging
 import os
+
 DATA_DIR = os.path.join(os.path.dirname(__file__), '..', 'data')
-REGION = 'us-west-2'
 
 logging.getLogger('boto3').setLevel(logging.INFO)
 logging.getLogger('botocore').setLevel(logging.INFO)

tests/integ/test_byo_estimator.py

@@ -13,30 +13,36 @@
 import gzip
 import io
 import json
-import numpy as np
 import os
 import pickle
 import sys
 
 import boto3
+import numpy as np
+import pytest
 
 import sagemaker
-from sagemaker.estimator import Estimator
 from sagemaker.amazon.amazon_estimator import registry
 from sagemaker.amazon.common import write_numpy_to_dense_tensor
+from sagemaker.estimator import Estimator
 from sagemaker.utils import name_from_base
-from tests.integ import DATA_DIR, REGION
+from tests.integ import DATA_DIR
 from tests.integ.timeout import timeout, timeout_and_delete_endpoint_by_name
 
 
+@pytest.fixture(scope='module')
+def region(sagemaker_session):
+    return sagemaker_session.boto_session.region_name
+
+
 def fm_serializer(data):
     js = {'instances': []}
     for row in data:
         js['instances'].append({'features': row.tolist()})
     return json.dumps(js)
 
 
-def test_byo_estimator():
+def test_byo_estimator(sagemaker_session, region):
     """Use Factorization Machines algorithm as an example here.
 
     First we need to prepare data for training. We take standard data set, convert it to the
@@ -47,10 +53,9 @@ def test_byo_estimator():
     Default predictor is updated with json serializer and deserializer.
 
     """
-    image_name = registry(REGION) + "/factorization-machines:1"
+    image_name = registry(region) + "/factorization-machines:1"
 
     with timeout(minutes=15):
-        sagemaker_session = sagemaker.Session(boto_session=boto3.Session(region_name=REGION))
         data_path = os.path.join(DATA_DIR, 'one_p_mnist', 'mnist.pkl.gz')
         pickle_args = {} if sys.version_info.major == 2 else {'encoding': 'latin1'}
 
@@ -100,13 +105,12 @@ def test_byo_estimator():
             assert prediction['score'] is not None
 
 
-def test_async_byo_estimator():
-    image_name = registry(REGION) + "/factorization-machines:1"
+def test_async_byo_estimator(sagemaker_session, region):
+    image_name = registry(region) + "/factorization-machines:1"
     endpoint_name = name_from_base('byo')
     training_job_name = ""
 
     with timeout(minutes=5):
-        sagemaker_session = sagemaker.Session(boto_session=boto3.Session(region_name=REGION))
         data_path = os.path.join(DATA_DIR, 'one_p_mnist', 'mnist.pkl.gz')
         pickle_args = {} if sys.version_info.major == 2 else {'encoding': 'latin1'}
 

tests/integ/test_factorization_machines.py

@@ -11,24 +11,19 @@
 # ANY KIND, either express or implied. See the License for the specific
 # language governing permissions and limitations under the License.
 import gzip
+import os
 import pickle
 import sys
 import time
 
-import boto3
-import os
-
-import sagemaker
 from sagemaker import FactorizationMachines, FactorizationMachinesModel
 from sagemaker.utils import name_from_base
-from tests.integ import DATA_DIR, REGION
+from tests.integ import DATA_DIR
 from tests.integ.timeout import timeout, timeout_and_delete_endpoint_by_name
 
 
-def test_factorization_machines():
-
+def test_factorization_machines(sagemaker_session):
     with timeout(minutes=15):
-        sagemaker_session = sagemaker.Session(boto_session=boto3.Session(region_name=REGION))
         data_path = os.path.join(DATA_DIR, 'one_p_mnist', 'mnist.pkl.gz')
         pickle_args = {} if sys.version_info.major == 2 else {'encoding': 'latin1'}
 
@@ -56,14 +51,11 @@ def test_factorization_machines():
             assert record.label["score"] is not None
 
 
-def test_async_factorization_machines():
-
+def test_async_factorization_machines(sagemaker_session):
     training_job_name = ""
     endpoint_name = name_from_base('factorizationMachines')
-    sagemaker_session = sagemaker.Session(boto_session=boto3.Session(region_name=REGION))
 
     with timeout(minutes=5):
-
         data_path = os.path.join(DATA_DIR, 'one_p_mnist', 'mnist.pkl.gz')
         pickle_args = {} if sys.version_info.major == 2 else {'encoding': 'latin1'}
 

tests/integ/test_kmeans.py

@@ -11,24 +11,19 @@
 # ANY KIND, either express or implied. See the License for the specific
 # language governing permissions and limitations under the License.
 import gzip
+import os
 import pickle
 import sys
-
-import boto3
-import os
 import time
 
-import sagemaker
 from sagemaker import KMeans, KMeansModel
 from sagemaker.utils import name_from_base
-from tests.integ import DATA_DIR, REGION
+from tests.integ import DATA_DIR
 from tests.integ.timeout import timeout, timeout_and_delete_endpoint_by_name
 
 
-def test_kmeans():
-
+def test_kmeans(sagemaker_session):
     with timeout(minutes=15):
-        sagemaker_session = sagemaker.Session(boto_session=boto3.Session(region_name=REGION))
         data_path = os.path.join(DATA_DIR, 'one_p_mnist', 'mnist.pkl.gz')
         pickle_args = {} if sys.version_info.major == 2 else {'encoding': 'latin1'}
 
@@ -63,13 +58,11 @@ def test_kmeans():
             assert record.label["distance_to_cluster"] is not None
 
 
-def test_async_kmeans():
-
+def test_async_kmeans(sagemaker_session):
     training_job_name = ""
     endpoint_name = name_from_base('kmeans')
 
     with timeout(minutes=5):
-        sagemaker_session = sagemaker.Session(boto_session=boto3.Session(region_name=REGION))
         data_path = os.path.join(DATA_DIR, 'one_p_mnist', 'mnist.pkl.gz')
         pickle_args = {} if sys.version_info.major == 2 else {'encoding': 'latin1'}
 

tests/integ/test_lda.py

@@ -10,24 +10,20 @@
 # distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
 # ANY KIND, either express or implied. See the License for the specific
 # language governing permissions and limitations under the License.
-import boto3
-import numpy as np
 import os
 
-import sagemaker
+import numpy as np
+
 from sagemaker import LDA, LDAModel
 from sagemaker.amazon.common import read_records
 from sagemaker.utils import name_from_base
-
-from tests.integ import DATA_DIR, REGION
+from tests.integ import DATA_DIR
 from tests.integ.timeout import timeout, timeout_and_delete_endpoint_by_name
 from tests.integ.record_set import prepare_record_set_from_local_files
 
 
-def test_lda():
-
+def test_lda(sagemaker_session):
     with timeout(minutes=15):
-        sagemaker_session = sagemaker.Session(boto_session=boto3.Session(region_name=REGION))
         data_path = os.path.join(DATA_DIR, 'lda')
         data_filename = 'nips-train_1.pbr'
 

tests/integ/test_linear_learner.py

@@ -15,21 +15,17 @@
 import pickle
 import sys
 import time
-import pytest  # noqa
-import boto3
+
 import numpy as np
 
-import sagemaker
 from sagemaker.amazon.linear_learner import LinearLearner, LinearLearnerModel
 from sagemaker.utils import name_from_base, sagemaker_timestamp
-
-from tests.integ import DATA_DIR, REGION
+from tests.integ import DATA_DIR
 from tests.integ.timeout import timeout, timeout_and_delete_endpoint_by_name
 
 
-def test_linear_learner():
+def test_linear_learner(sagemaker_session):
     with timeout(minutes=15):
-        sagemaker_session = sagemaker.Session(boto_session=boto3.Session(region_name=REGION))
         data_path = os.path.join(DATA_DIR, 'one_p_mnist', 'mnist.pkl.gz')
         pickle_args = {} if sys.version_info.major == 2 else {'encoding': 'latin1'}
 
@@ -87,14 +83,11 @@ def test_linear_learner():
             assert record.label["score"] is not None
 
 
-def test_async_linear_learner():
-
+def test_async_linear_learner(sagemaker_session):
     training_job_name = ""
     endpoint_name = 'test-linear-learner-async-{}'.format(sagemaker_timestamp())
-    sagemaker_session = sagemaker.Session(boto_session=boto3.Session(region_name=REGION))
 
     with timeout(minutes=5):
-
         data_path = os.path.join(DATA_DIR, 'one_p_mnist', 'mnist.pkl.gz')
         pickle_args = {} if sys.version_info.major == 2 else {'encoding': 'latin1'}
 

tests/integ/test_mxnet_train.py

@@ -13,23 +13,16 @@
 import os
 import time
 
-import boto3
 import numpy
 import pytest
-from sagemaker import Session
+
 from sagemaker.mxnet.estimator import MXNet
 from sagemaker.mxnet.model import MXNetModel
 from sagemaker.utils import sagemaker_timestamp
-
-from tests.integ import DATA_DIR, REGION
+from tests.integ import DATA_DIR
 from tests.integ.timeout import timeout, timeout_and_delete_endpoint_by_name
 
 
-@pytest.fixture(scope='module')
-def sagemaker_session():
-    return Session(boto_session=boto3.Session(region_name=REGION))
-
-
 @pytest.fixture(scope='module')
 def mxnet_training_job(sagemaker_session, mxnet_full_version):
     with timeout(minutes=15):

tests/integ/test_ntm.py

@@ -10,24 +10,20 @@
 # distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
 # ANY KIND, either express or implied. See the License for the specific
 # language governing permissions and limitations under the License.
-import boto3
-import numpy as np
 import os
 
-import sagemaker
+import numpy as np
+
 from sagemaker import NTM, NTMModel
 from sagemaker.amazon.common import read_records
 from sagemaker.utils import name_from_base
-
-from tests.integ import DATA_DIR, REGION
+from tests.integ import DATA_DIR
 from tests.integ.timeout import timeout, timeout_and_delete_endpoint_by_name
 from tests.integ.record_set import prepare_record_set_from_local_files
 
 
-def test_ntm():
-
+def test_ntm(sagemaker_session):
     with timeout(minutes=15):
-        sagemaker_session = sagemaker.Session(boto_session=boto3.Session(region_name=REGION))
         data_path = os.path.join(DATA_DIR, 'ntm')
         data_filename = 'nips-train_1.pbr'