infra: use generated TensorFlow version fixtures

tests/conftest.py

@@ -164,17 +164,18 @@ def xgboost_version(request):
     return request.param
 
 
-@pytest.fixture(scope="module")
-def tf_version(tensorflow_training_version):
-    # TODO: remove this fixture and update tests
-    if tensorflow_training_version in ("1.13.1", "2.2", "2.2.0"):
-        pytest.skip("version isn't compatible with both training and inference.")
-    return tensorflow_training_version
+@pytest.fixture(scope="module", params=["py2", "py3"])
+def tensorflow_training_py_version(tensorflow_training_version, request):
+    return _tf_py_version(tensorflow_training_version, request)
 
 
 @pytest.fixture(scope="module", params=["py2", "py3"])
-def tf_py_version(tensorflow_training_version, request):
-    version = Version(tensorflow_training_version)
+def tensorflow_inference_py_version(tensorflow_inference_version, request):
+    return _tf_py_version(tensorflow_inference_version, request)
+
+
+def _tf_py_version(tf_version, request):
+    version = Version(tf_version)
     if version < Version("1.11"):
         return "py2"
     if version < Version("2.2"):
@@ -253,28 +254,18 @@ def sklearn_full_py_version():
 
 
 @pytest.fixture(scope="module")
-def tf_training_latest_version():
-    return "2.2.0"
-
-
-@pytest.fixture(scope="module")
-def tf_training_latest_py_version():
-    return "py37"
-
-
-@pytest.fixture(scope="module")
-def tf_serving_latest_version():
-    return "2.1.0"
-
-
-@pytest.fixture(scope="module")
-def tf_full_version(tf_training_latest_version, tf_serving_latest_version):
+def tf_full_version(tensorflow_training_latest_version, tensorflow_inference_latest_version):
     """Fixture for TF tests that test both training and inference.
 
     Fixture exists as such, since TF training and TFS have different latest versions.
     Otherwise, this would simply be a single latest version.
     """
-    return str(min(Version(tf_training_latest_version), Version(tf_serving_latest_version)))
+    return str(
+        min(
+            Version(tensorflow_training_latest_version),
+            Version(tensorflow_inference_latest_version),
+        )
+    )
 
 
 @pytest.fixture(scope="module")
@@ -292,11 +283,6 @@ def tf_full_py_version(tf_full_version):
     return "py37"
 
 
-@pytest.fixture(scope="module")
-def ei_tf_full_version():
-    return "2.0.0"
-
-
 @pytest.fixture(scope="module")
 def xgboost_full_version():
     return "1.0-1"

tests/integ/test_airflow_config.py

@@ -512,7 +512,10 @@ def test_sklearn_airflow_config_uploads_data_source_to_s3(
 
 @pytest.mark.canary_quick
 def test_tf_airflow_config_uploads_data_source_to_s3(
-    sagemaker_session, cpu_instance_type, tf_training_latest_version, tf_training_latest_py_version
+    sagemaker_session,
+    cpu_instance_type,
+    tensorflow_training_latest_version,
+    tensorflow_training_latest_py_version,
 ):
     with timeout(seconds=AIRFLOW_CONFIG_TIMEOUT_IN_SECONDS):
         tf = TensorFlow(
@@ -524,8 +527,8 @@ def test_tf_airflow_config_uploads_data_source_to_s3(
             instance_count=SINGLE_INSTANCE_COUNT,
             instance_type=cpu_instance_type,
             sagemaker_session=sagemaker_session,
-            framework_version=tf_training_latest_version,
-            py_version=tf_training_latest_py_version,
+            framework_version=tensorflow_training_latest_version,
+            py_version=tensorflow_training_latest_py_version,
             metric_definitions=[
                 {"Name": "train:global_steps", "Regex": r"global_step\/sec:\s(.*)"}
             ],

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_latest_version
+    sagemaker_session, tensorflow_inference_latest_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 = TensorFlowModel(
             model_data=model_data,
             role=ROLE,
-            framework_version=tf_serving_latest_version,
+            framework_version=tensorflow_inference_latest_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_latest_version
+    sagemaker_session, tensorflow_inference_latest_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 = TensorFlowModel(
             model_data=model_data,
             role=ROLE,
-            framework_version=tf_serving_latest_version,
+            framework_version=tensorflow_inference_latest_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_latest_version
+    sagemaker_session, tensorflow_inference_latest_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 = TensorFlowModel(
             model_data=model_data,
             role=ROLE,
-            framework_version=tf_serving_latest_version,
+            framework_version=tensorflow_inference_latest_version,
             sagemaker_session=sagemaker_session,
         )
         destination_s3_uri = os.path.join(

tests/integ/test_horovod.py

@@ -31,15 +31,15 @@
 @pytest.mark.canary_quick
 def test_hvd_cpu(
     sagemaker_session,
-    tf_training_latest_version,
-    tf_training_latest_py_version,
+    tensorflow_training_latest_version,
+    tensorflow_training_latest_py_version,
     cpu_instance_type,
     tmpdir,
 ):
     _create_and_fit_estimator(
         sagemaker_session,
-        tf_training_latest_version,
-        tf_training_latest_py_version,
+        tensorflow_training_latest_version,
+        tensorflow_training_latest_py_version,
         cpu_instance_type,
         tmpdir,
     )
@@ -50,12 +50,15 @@ def test_hvd_cpu(
     integ.test_region() in integ.TRAINING_NO_P2_REGIONS, reason="no ml.p2 instances in this region"
 )
 def test_hvd_gpu(
-    sagemaker_session, tf_training_latest_version, tf_training_latest_py_version, tmpdir
+    sagemaker_session,
+    tensorflow_training_latest_version,
+    tensorflow_training_latest_py_version,
+    tmpdir,
 ):
     _create_and_fit_estimator(
         sagemaker_session,
-        tf_training_latest_version,
-        tf_training_latest_py_version,
+        tensorflow_training_latest_version,
+        tensorflow_training_latest_py_version,
         "ml.p2.xlarge",
         tmpdir,
     )
@@ -65,8 +68,8 @@ def test_hvd_gpu(
 @pytest.mark.parametrize("instances, processes", [[1, 2], (2, 1), (2, 2)])
 def test_horovod_local_mode(
     sagemaker_local_session,
-    tf_training_latest_version,
-    tf_training_latest_py_version,
+    tensorflow_training_latest_version,
+    tensorflow_training_latest_py_version,
     instances,
     processes,
     tmpdir,
@@ -80,8 +83,8 @@ def test_horovod_local_mode(
         instance_type="local",
         sagemaker_session=sagemaker_local_session,
         output_path=output_path,
-        framework_version=tf_training_latest_version,
-        py_version=tf_training_latest_py_version,
+        framework_version=tensorflow_training_latest_version,
+        py_version=tensorflow_training_latest_py_version,
         distribution={"mpi": {"enabled": True, "processes_per_host": processes}},
     )
 

tests/integ/test_model_monitor.py

@@ -88,7 +88,7 @@
 
 
 @pytest.fixture(scope="module")
-def predictor(sagemaker_session, tf_serving_latest_version):
+def predictor(sagemaker_session, tensorflow_inference_latest_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_latest_version):
         model = TensorFlowModel(
             model_data=model_data,
             role=ROLE,
-            framework_version=tf_serving_latest_version,
+            framework_version=tensorflow_inference_latest_version,
             sagemaker_session=sagemaker_session,
         )
         predictor = model.deploy(

tests/integ/test_tf.py

@@ -39,7 +39,10 @@
 
 
 def test_mnist_with_checkpoint_config(
-    sagemaker_session, instance_type, tf_training_latest_version, tf_training_latest_py_version
+    sagemaker_session,
+    instance_type,
+    tensorflow_training_latest_version,
+    tensorflow_training_latest_py_version,
 ):
     checkpoint_s3_uri = "s3://{}/checkpoints/tf-{}".format(
         sagemaker_session.default_bucket(), sagemaker_timestamp()
@@ -51,8 +54,8 @@ def test_mnist_with_checkpoint_config(
         instance_count=1,
         instance_type=instance_type,
         sagemaker_session=sagemaker_session,
-        framework_version=tf_training_latest_version,
-        py_version=tf_training_latest_py_version,
+        framework_version=tensorflow_training_latest_version,
+        py_version=tensorflow_training_latest_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,
@@ -124,16 +127,19 @@ def test_server_side_encryption(sagemaker_session, tf_full_version, tf_full_py_v
 
 @pytest.mark.canary_quick
 def test_mnist_distributed(
-    sagemaker_session, instance_type, tf_training_latest_version, tf_training_latest_py_version
+    sagemaker_session,
+    instance_type,
+    tensorflow_training_latest_version,
+    tensorflow_training_latest_py_version,
 ):
     estimator = TensorFlow(
         entry_point=SCRIPT,
         role=ROLE,
         instance_count=2,
         instance_type=instance_type,
         sagemaker_session=sagemaker_session,
-        framework_version=tf_training_latest_version,
-        py_version=tf_training_latest_py_version,
+        framework_version=tensorflow_training_latest_version,
+        py_version=tensorflow_training_latest_py_version,
         distribution=PARAMETER_SERVER_DISTRIBUTION,
     )
     inputs = estimator.sagemaker_session.upload_data(

tests/integ/test_tf_efs_fsx.py

@@ -58,8 +58,8 @@ def test_mnist_efs(
     efs_fsx_setup,
     sagemaker_session,
     cpu_instance_type,
-    tf_training_latest_version,
-    tf_training_latest_py_version,
+    tensorflow_training_latest_version,
+    tensorflow_training_latest_py_version,
 ):
     role = efs_fsx_setup["role_name"]
     subnets = [efs_fsx_setup["subnet_id"]]
@@ -71,8 +71,8 @@ def test_mnist_efs(
         instance_count=1,
         instance_type=cpu_instance_type,
         sagemaker_session=sagemaker_session,
-        framework_version=tf_training_latest_version,
-        py_version=tf_training_latest_py_version,
+        framework_version=tensorflow_training_latest_version,
+        py_version=tensorflow_training_latest_py_version,
         subnets=subnets,
         security_group_ids=security_group_ids,
     )
@@ -103,8 +103,8 @@ def test_mnist_lustre(
     efs_fsx_setup,
     sagemaker_session,
     cpu_instance_type,
-    tf_training_latest_version,
-    tf_training_latest_py_version,
+    tensorflow_training_latest_version,
+    tensorflow_training_latest_py_version,
 ):
     role = efs_fsx_setup["role_name"]
     subnets = [efs_fsx_setup["subnet_id"]]
@@ -116,8 +116,8 @@ def test_mnist_lustre(
         instance_count=1,
         instance_type=cpu_instance_type,
         sagemaker_session=sagemaker_session,
-        framework_version=tf_training_latest_version,
-        py_version=tf_training_latest_py_version,
+        framework_version=tensorflow_training_latest_version,
+        py_version=tensorflow_training_latest_py_version,
         subnets=subnets,
         security_group_ids=security_group_ids,
     )
@@ -144,8 +144,8 @@ def test_tuning_tf_efs(
     efs_fsx_setup,
     sagemaker_session,
     cpu_instance_type,
-    tf_training_latest_version,
-    tf_training_latest_py_version,
+    tensorflow_training_latest_version,
+    tensorflow_training_latest_py_version,
 ):
     role = efs_fsx_setup["role_name"]
     subnets = [efs_fsx_setup["subnet_id"]]
@@ -157,8 +157,8 @@ def test_tuning_tf_efs(
         instance_count=1,
         instance_type=cpu_instance_type,
         sagemaker_session=sagemaker_session,
-        framework_version=tf_training_latest_version,
-        py_version=tf_training_latest_py_version,
+        framework_version=tensorflow_training_latest_version,
+        py_version=tensorflow_training_latest_py_version,
         subnets=subnets,
         security_group_ids=security_group_ids,
     )
@@ -197,8 +197,8 @@ def test_tuning_tf_lustre(
     efs_fsx_setup,
     sagemaker_session,
     cpu_instance_type,
-    tf_training_latest_version,
-    tf_training_latest_py_version,
+    tensorflow_training_latest_version,
+    tensorflow_training_latest_py_version,
 ):
     role = efs_fsx_setup["role_name"]
     subnets = [efs_fsx_setup["subnet_id"]]
@@ -210,8 +210,8 @@ def test_tuning_tf_lustre(
         instance_count=1,
         instance_type=cpu_instance_type,
         sagemaker_session=sagemaker_session,
-        framework_version=tf_training_latest_version,
-        py_version=tf_training_latest_py_version,
+        framework_version=tensorflow_training_latest_version,
+        py_version=tensorflow_training_latest_py_version,
         subnets=subnets,
         security_group_ids=security_group_ids,
     )