Merge branch 'master' into master

Author: nadiaya

src/sagemaker/tensorflow/defaults.py

@@ -21,7 +21,7 @@
 LATEST_VERSION = "2.2.0"
 """The latest version of TensorFlow included in the SageMaker pre-built Docker images."""
 
-LATEST_SERVING_VERSION = "2.1.0"
+LATEST_SERVING_VERSION = "2.2.0"
 """The latest version of TensorFlow Serving included in the SageMaker pre-built Docker images."""
 
 LATEST_PY2_VERSION = "2.1.0"

tests/conftest.py

@@ -28,7 +28,7 @@
 from sagemaker.rl import RLEstimator
 from sagemaker.sklearn.defaults import SKLEARN_VERSION
 from sagemaker.tensorflow import TensorFlow
-from sagemaker.tensorflow.defaults import LATEST_VERSION, LATEST_SERVING_VERSION
+from sagemaker.tensorflow.defaults import LATEST_VERSION
 
 DEFAULT_REGION = "us-west-2"
 CUSTOM_BUCKET_NAME_PREFIX = "sagemaker-custom-bucket"
@@ -336,10 +336,3 @@ def pytest_generate_tests(metafunc):
 @pytest.fixture(scope="module")
 def xgboost_full_version(request):
     return request.config.getoption("--xgboost-full-version")
-
-
-@pytest.fixture(scope="module")
-def tf_serving_version(tf_full_version):
-    if tf_full_version == LATEST_VERSION:
-        return LATEST_SERVING_VERSION
-    return tf_full_version

tests/integ/test_data_capture_config.py

@@ -41,7 +41,7 @@
 
 
 def test_enabling_data_capture_on_endpoint_shows_correct_data_capture_status(
-    sagemaker_session, tf_serving_version
+    sagemaker_session, tf_full_version
 ):
     endpoint_name = unique_name_from_base("sagemaker-tensorflow-serving")
     model_data = sagemaker_session.upload_data(
@@ -52,7 +52,7 @@ def test_enabling_data_capture_on_endpoint_shows_correct_data_capture_status(
         model = Model(
             model_data=model_data,
             role=ROLE,
-            framework_version=tf_serving_version,
+            framework_version=tf_full_version,
             sagemaker_session=sagemaker_session,
         )
         predictor = model.deploy(
@@ -98,7 +98,7 @@ def test_enabling_data_capture_on_endpoint_shows_correct_data_capture_status(
 
 
 def test_disabling_data_capture_on_endpoint_shows_correct_data_capture_status(
-    sagemaker_session, tf_serving_version
+    sagemaker_session, tf_full_version
 ):
     endpoint_name = unique_name_from_base("sagemaker-tensorflow-serving")
     model_data = sagemaker_session.upload_data(
@@ -109,7 +109,7 @@ def test_disabling_data_capture_on_endpoint_shows_correct_data_capture_status(
         model = Model(
             model_data=model_data,
             role=ROLE,
-            framework_version=tf_serving_version,
+            framework_version=tf_full_version,
             sagemaker_session=sagemaker_session,
         )
         destination_s3_uri = os.path.join(
@@ -184,7 +184,7 @@ def test_disabling_data_capture_on_endpoint_shows_correct_data_capture_status(
 
 
 def test_updating_data_capture_on_endpoint_shows_correct_data_capture_status(
-    sagemaker_session, tf_serving_version
+    sagemaker_session, tf_full_version
 ):
     endpoint_name = sagemaker.utils.unique_name_from_base("sagemaker-tensorflow-serving")
     model_data = sagemaker_session.upload_data(
@@ -195,7 +195,7 @@ def test_updating_data_capture_on_endpoint_shows_correct_data_capture_status(
         model = Model(
             model_data=model_data,
             role=ROLE,
-            framework_version=tf_serving_version,
+            framework_version=tf_full_version,
             sagemaker_session=sagemaker_session,
         )
         destination_s3_uri = os.path.join(

tests/integ/test_model_monitor.py

@@ -88,7 +88,7 @@
 
 
 @pytest.fixture(scope="module")
-def predictor(sagemaker_session, tf_serving_version):
+def predictor(sagemaker_session, tf_full_version):
     endpoint_name = unique_name_from_base("sagemaker-tensorflow-serving")
     model_data = sagemaker_session.upload_data(
         path=os.path.join(tests.integ.DATA_DIR, "tensorflow-serving-test-model.tar.gz"),
@@ -100,7 +100,7 @@ def predictor(sagemaker_session, tf_serving_version):
         model = Model(
             model_data=model_data,
             role=ROLE,
-            framework_version=tf_serving_version,
+            framework_version=tf_full_version,
             sagemaker_session=sagemaker_session,
         )
         predictor = model.deploy(

tests/integ/test_tf_script_mode.py

@@ -19,7 +19,7 @@
 import pytest
 
 from sagemaker.tensorflow import TensorFlow
-from sagemaker.tensorflow.defaults import LATEST_SERVING_VERSION
+from sagemaker.tensorflow.defaults import LATEST_VERSION
 from sagemaker.utils import unique_name_from_base, sagemaker_timestamp
 
 import tests.integ
@@ -41,8 +41,8 @@
 
 
 @pytest.fixture(scope="module")
-def py_version(tf_full_version, tf_serving_version):
-    return "py37" if tf_full_version == tf_serving_version else tests.integ.PYTHON_VERSION
+def py_version(tf_full_version):
+    return "py37" if tf_full_version == LATEST_VERSION else tests.integ.PYTHON_VERSION
 
 
 def test_mnist_with_checkpoint_config(
@@ -60,7 +60,7 @@ def test_mnist_with_checkpoint_config(
         sagemaker_session=sagemaker_session,
         script_mode=True,
         framework_version=tf_full_version,
-        py_version="py37",
+        py_version=py_version,
         metric_definitions=[{"Name": "train:global_steps", "Regex": r"global_step\/sec:\s(.*)"}],
         checkpoint_s3_uri=checkpoint_s3_uri,
         checkpoint_local_path=checkpoint_local_path,
@@ -90,7 +90,7 @@ def test_mnist_with_checkpoint_config(
     assert actual_training_checkpoint_config == expected_training_checkpoint_config
 
 
-def test_server_side_encryption(sagemaker_session, tf_serving_version, py_version):
+def test_server_side_encryption(sagemaker_session, tf_full_version, py_version):
     with kms_utils.bucket_with_encryption(sagemaker_session, ROLE) as (bucket_with_kms, kms_key):
         output_path = os.path.join(
             bucket_with_kms, "test-server-side-encryption", time.strftime("%y%m%d-%H%M")
@@ -104,7 +104,7 @@ def test_server_side_encryption(sagemaker_session, tf_serving_version, py_versio
             train_instance_type="ml.c5.xlarge",
             sagemaker_session=sagemaker_session,
             script_mode=True,
-            framework_version=tf_serving_version,
+            framework_version=tf_full_version,
             py_version=py_version,
             code_location=output_path,
             output_path=output_path,
@@ -139,7 +139,7 @@ def test_mnist_distributed(sagemaker_session, instance_type, tf_full_version, py
         train_instance_count=2,
         train_instance_type=instance_type,
         sagemaker_session=sagemaker_session,
-        py_version="py37",
+        py_version=py_version,
         script_mode=True,
         framework_version=tf_full_version,
         distributions=PARAMETER_SERVER_DISTRIBUTION,
@@ -163,11 +163,11 @@ def test_mnist_async(sagemaker_session, cpu_instance_type, tf_full_version, py_v
         role=ROLE,
         train_instance_count=1,
         train_instance_type="ml.c5.4xlarge",
-        py_version=tests.integ.PYTHON_VERSION,
+        py_version=py_version,
         sagemaker_session=sagemaker_session,
         script_mode=True,
         # testing py-sdk functionality, no need to run against all TF versions
-        framework_version=LATEST_SERVING_VERSION,
+        framework_version=tf_full_version,
         tags=TAGS,
     )
     inputs = estimator.sagemaker_session.upload_data(
@@ -199,9 +199,7 @@ def test_mnist_async(sagemaker_session, cpu_instance_type, tf_full_version, py_v
         _assert_model_name_match(sagemaker_session.sagemaker_client, endpoint_name, model_name)
 
 
-def test_deploy_with_input_handlers(
-    sagemaker_session, instance_type, tf_serving_version, py_version
-):
+def test_deploy_with_input_handlers(sagemaker_session, instance_type, tf_full_version, py_version):
     estimator = TensorFlow(
         entry_point="training.py",
         source_dir=TFS_RESOURCE_PATH,
@@ -211,7 +209,7 @@ def test_deploy_with_input_handlers(
         py_version=py_version,
         sagemaker_session=sagemaker_session,
         script_mode=True,
-        framework_version=tf_serving_version,
+        framework_version=tf_full_version,
         tags=TAGS,
     )
 

tests/integ/test_tfs.py

@@ -27,7 +27,7 @@
 
 
 @pytest.fixture(scope="module")
-def tfs_predictor(sagemaker_session, tf_serving_version):
+def tfs_predictor(sagemaker_session, tf_full_version):
     endpoint_name = sagemaker.utils.unique_name_from_base("sagemaker-tensorflow-serving")
     model_data = sagemaker_session.upload_data(
         path=os.path.join(tests.integ.DATA_DIR, "tensorflow-serving-test-model.tar.gz"),
@@ -37,7 +37,7 @@ def tfs_predictor(sagemaker_session, tf_serving_version):
         model = Model(
             model_data=model_data,
             role="SageMakerRole",
-            framework_version=tf_serving_version,
+            framework_version=tf_full_version,
             sagemaker_session=sagemaker_session,
         )
         predictor = model.deploy(1, "ml.c5.xlarge", endpoint_name=endpoint_name)
@@ -54,7 +54,7 @@ def tar_dir(directory, tmpdir):
 
 @pytest.fixture
 def tfs_predictor_with_model_and_entry_point_same_tar(
-    sagemaker_local_session, tf_serving_version, tmpdir
+    sagemaker_local_session, tf_full_version, tmpdir
 ):
     endpoint_name = sagemaker.utils.unique_name_from_base("sagemaker-tensorflow-serving")
 
@@ -65,7 +65,7 @@ def tfs_predictor_with_model_and_entry_point_same_tar(
     model = Model(
         model_data="file://" + model_tar,
         role="SageMakerRole",
-        framework_version=tf_serving_version,
+        framework_version=tf_full_version,
         sagemaker_session=sagemaker_local_session,
     )
     predictor = model.deploy(1, "local", endpoint_name=endpoint_name)
@@ -78,7 +78,7 @@ def tfs_predictor_with_model_and_entry_point_same_tar(
 
 @pytest.fixture(scope="module")
 def tfs_predictor_with_model_and_entry_point_and_dependencies(
-    sagemaker_local_session, tf_serving_version
+    sagemaker_local_session, tf_full_version
 ):
     endpoint_name = sagemaker.utils.unique_name_from_base("sagemaker-tensorflow-serving")
 
@@ -98,7 +98,7 @@ def tfs_predictor_with_model_and_entry_point_and_dependencies(
         model_data=model_data,
         role="SageMakerRole",
         dependencies=dependencies,
-        framework_version=tf_serving_version,
+        framework_version=tf_full_version,
         sagemaker_session=sagemaker_local_session,
     )
 

tests/integ/test_transformer.py

@@ -25,7 +25,7 @@
 from sagemaker.mxnet import MXNet
 from sagemaker.pytorch import PyTorchModel
 from sagemaker.tensorflow import TensorFlow
-from sagemaker.tensorflow.defaults import LATEST_SERVING_VERSION
+from sagemaker.tensorflow.defaults import LATEST_VERSION
 from sagemaker.transformer import Transformer
 from sagemaker.estimator import Estimator
 from sagemaker.utils import unique_name_from_base
@@ -42,6 +42,11 @@
 MXNET_MNIST_PATH = os.path.join(DATA_DIR, "mxnet_mnist")
 
 
+@pytest.fixture(scope="module")
+def py_version(tf_full_version):
+    return "py37" if tf_full_version == LATEST_VERSION else PYTHON_VERSION
+
+
 @pytest.fixture(scope="module")
 def mxnet_estimator(sagemaker_session, mxnet_full_version, cpu_instance_type):
     mx = MXNet(
@@ -364,17 +369,19 @@ def test_transform_mxnet_logs(
         transformer.wait()
 
 
-def test_transform_tf_kms_network_isolation(sagemaker_session, cpu_instance_type, tmpdir):
+def test_transform_tf_kms_network_isolation(
+    sagemaker_session, cpu_instance_type, tmpdir, tf_full_version, py_version
+):
     data_path = os.path.join(DATA_DIR, "tensorflow_mnist")
 
     tf = TensorFlow(
         entry_point=os.path.join(data_path, "mnist.py"),
         role="SageMakerRole",
         train_instance_count=1,
         train_instance_type=cpu_instance_type,
-        framework_version=LATEST_SERVING_VERSION,
+        framework_version=tf_full_version,
         script_mode=True,
-        py_version=PYTHON_VERSION,
+        py_version=py_version,
         sagemaker_session=sagemaker_session,
     )
 

tests/unit/test_fw_utils.py

@@ -109,7 +109,8 @@ def is_mxnet_1_4_py2(framework, framework_version, py_version):
 
 
 @pytest.fixture(
-    scope="module", params=["1.11", "1.11.0", "1.12", "1.12.0", "1.14", "1.14.0", "1.15", "1.15.0"]
+    scope="module",
+    params=["1.11", "1.11.0", "1.12", "1.12.0", "1.14", "1.14.0", "1.15", "1.15.0", "2.0", "2.2.0"],
 )
 def tf_version(request):
     return request.param