breaking: require framework_version, py_version for tensorflow

doc/frameworks/tensorflow/upgrade_from_legacy.rst

@@ -104,6 +104,7 @@ the difference in code would be as follows:
         ...
         source_dir="code",
         framework_version="1.10.0",
+        py_version="py2",
         train_instance_type="ml.m4.xlarge",
         image_name="520713654638.dkr.ecr.us-west-2.amazonaws.com/sagemaker-tensorflow:1.10.0-cpu-py2",
         hyperparameters={

doc/frameworks/tensorflow/using_tf.rst

@@ -319,7 +319,7 @@ To run training job with Pipe input mode, pass in ``input_mode='Pipe'`` to your
     tf_estimator = TensorFlow(entry_point='tf-train-with-pipemodedataset.py', role='SageMakerRole',
                               training_steps=10000, evaluation_steps=100,
                               train_instance_count=1, train_instance_type='ml.p2.xlarge',
-                              framework_version='1.10.0', input_mode='Pipe')
+                              framework_version='1.10.0', py_version='py3', input_mode='Pipe')
 
     tf_estimator.fit('s3://bucket/path/to/training/data')
 
@@ -383,7 +383,8 @@ estimator object to create a SageMaker Endpoint:
   from sagemaker.tensorflow import TensorFlow
 
   estimator = TensorFlow(entry_point='tf-train.py', ..., train_instance_count=1,
-                         train_instance_type='ml.c4.xlarge', framework_version='1.11')
+                         train_instance_type='ml.c4.xlarge', framework_version='1.11',
+                         py_version='py3')
 
   estimator.fit(inputs)
 

src/sagemaker/tensorflow/estimator.py

@@ -56,11 +56,11 @@ def __init__(
 
         Args:
             py_version (str): Python version you want to use for executing your model training
-                code (default: 'py2').
+                code. Defaults to ``None``. Required unless ``image_name`` is provided.
             framework_version (str): TensorFlow version you want to use for executing your model
-                training code. List of supported versions
+                training code. Defaults to ``None``. Required unless ``image_name`` is provided.
+                List of supported versions:
                 https://github.com/aws/sagemaker-python-sdk#tensorflow-sagemaker-estimators.
-                If not specified, this will default to 1.11.
             model_dir (str): S3 location where the checkpoint data and models can be exported to
                 during training (default: None). It will be passed in the training script as one of
                 the command line arguments. If not specified, one is provided based on
@@ -81,6 +81,10 @@ def __init__(
                 Examples:
                     123.dkr.ecr.us-west-2.amazonaws.com/my-custom-image:1.0
                     custom-image:latest.
+
+                If ``framework_version`` or ``py_version`` are ``None``, then
+                ``image_name`` is required. If also ``None``, then a ``ValueError``
+                will be raised.
             distributions (dict): A dictionary with information on how to run distributed training
                 (default: None). Currently we support distributed training with parameter servers
                 and MPI.
@@ -114,18 +118,13 @@ def __init__(
             :class:`~sagemaker.estimator.Framework` and
             :class:`~sagemaker.estimator.EstimatorBase`.
         """
-        if framework_version is None:
-            logger.warning(
-                fw.empty_framework_version_warning(defaults.TF_VERSION, self.LATEST_VERSION)
-            )
-        self.framework_version = framework_version or defaults.TF_VERSION
-
-        if not py_version:
-            py_version = "py3" if self._only_python_3_supported() else "py2"
+        fw.validate_version_or_image_args(framework_version, py_version, image_name)
         if py_version == "py2":
             logger.warning(
                 fw.python_deprecation_warning(self.__framework_name__, defaults.LATEST_PY2_VERSION)
             )
+        self.framework_version = framework_version
+        self.py_version = py_version
 
         if distributions is not None:
             logger.warning(fw.parameter_v2_rename_warning("distribution", distributions))
@@ -136,32 +135,27 @@ def __init__(
 
         if "enable_sagemaker_metrics" not in kwargs:
             # enable sagemaker metrics for TF v1.15 or greater:
-            if fw.is_version_equal_or_higher([1, 15], self.framework_version):
+            if framework_version and fw.is_version_equal_or_higher([1, 15], framework_version):
                 kwargs["enable_sagemaker_metrics"] = True
 
         super(TensorFlow, self).__init__(image_name=image_name, **kwargs)
 
-        self.py_version = py_version
         self.model_dir = model_dir
         self.distributions = distributions or {}
 
-        self._validate_args(py_version=py_version, framework_version=self.framework_version)
+        self._validate_args(py_version=py_version)
 
-    def _validate_args(self, py_version, framework_version):
+    def _validate_args(self, py_version):
         """Placeholder docstring"""
 
-        if py_version == "py3":
-            if framework_version is None:
-                raise AttributeError(fw.EMPTY_FRAMEWORK_VERSION_ERROR)
-
         if py_version == "py2" and self._only_python_3_supported():
             msg = (
                 "Python 2 containers are only available with {} and lower versions. "
                 "Please use a Python 3 container.".format(defaults.LATEST_PY2_VERSION)
             )
             raise AttributeError(msg)
 
-        if self._only_legacy_mode_supported() and self.image_name is None:
+        if self.image_name is None and self._only_legacy_mode_supported():
             legacy_image_uri = fw.create_image_uri(
                 self.sagemaker_session.boto_region_name,
                 "tensorflow",

src/sagemaker/tensorflow/model.py

@@ -19,7 +19,6 @@
 from sagemaker.content_types import CONTENT_TYPE_JSON
 from sagemaker.fw_utils import create_image_uri
 from sagemaker.predictor import json_serializer, json_deserializer
-from sagemaker.tensorflow.defaults import TF_VERSION
 
 
 class TensorFlowPredictor(sagemaker.RealTimePredictor):
@@ -138,7 +137,7 @@ def __init__(
         role,
         entry_point=None,
         image=None,
-        framework_version=TF_VERSION,
+        framework_version=None,
         container_log_level=None,
         predictor_cls=TensorFlowPredictor,
         **kwargs
@@ -158,9 +157,12 @@ def __init__(
                 hosting. If ``source_dir`` is specified, then ``entry_point``
                 must point to a file located at the root of ``source_dir``.
             image (str): A Docker image URI (default: None). If not specified, a
-                default image for TensorFlow Serving will be used.
+                default image for TensorFlow Serving will be used. If
+                ``framework_version`` is ``None``, then ``image`` is required.
+                If also ``None``, then a ``ValueError`` will be raised.
             framework_version (str): Optional. TensorFlow Serving version you
-                want to use.
+                want to use. Defaults to ``None``. Required unless ``image`` is
+                provided.
             container_log_level (int): Log level to use within the container
                 (default: logging.ERROR). Valid values are defined in the Python
                 logging module.
@@ -176,6 +178,13 @@ def __init__(
             :class:`~sagemaker.model.FrameworkModel` and
             :class:`~sagemaker.model.Model`.
         """
+        if framework_version is None and image is None:
+            raise ValueError(
+                "Both framework_version and image were None. "
+                "Either specify framework_version or specify image_name."
+            )
+        self.framework_version = framework_version
+
         super(TensorFlowModel, self).__init__(
             model_data=model_data,
             role=role,
@@ -184,7 +193,6 @@ def __init__(
             entry_point=entry_point,
             **kwargs
         )
-        self.framework_version = framework_version
         self._container_log_level = container_log_level
 
     def deploy(

tests/conftest.py

@@ -226,6 +226,16 @@ def tf_version(request):
     return request.param
 
 
+@pytest.fixture(scope="module", params=["py2", "py3"])
+def tf_py_version(tf_version, request):
+    version = [int(val) for val in tf_version.split(".")]
+    if version < [1, 11]:
+        return "py2"
+    if version < [2, 2]:
+        return request.param
+    return "py37"
+
+
 @pytest.fixture(scope="module", params=["0.10.1", "0.10.1", "0.11", "0.11.0", "0.11.1"])
 def rl_coach_tf_version(request):
     return request.param
@@ -290,6 +300,23 @@ def tf_full_version(request):
         return tf_version
 
 
+@pytest.fixture(scope="module")
+def tf_full_py_version(tf_full_version, request):
+    """fixture to match tf_full_version
+
+    Fixture exists as such, since tf_full_version may be overridden --tf-full-version.
+    Otherwise, this would simply be py37 to match the latest version support.
+
+    TODO: Evaluate use of --tf-full-version with possible eye to remove and simplify code.
+    """
+    version = [int(val) for val in tf_full_version.split(".")]
+    if version < [1, 11]:
+        return "py2"
+    if tf_full_version in [TensorFlow._LATEST_1X_VERSION, LATEST_VERSION]:
+        return "py37"
+    return "py3"
+
+
 @pytest.fixture(scope="module", params=["1.15.0", "2.0.0"])
 def ei_tf_full_version(request):
     tf_ei_version = request.config.getoption("--ei-tf-full-version")

tests/integ/test_airflow_config.py

@@ -562,7 +562,7 @@ def test_tf_airflow_config_uploads_data_source_to_s3(sagemaker_session, cpu_inst
             train_instance_type=cpu_instance_type,
             sagemaker_session=sagemaker_session,
             framework_version=TensorFlow.LATEST_VERSION,
-            py_version=PYTHON_VERSION,
+            py_version="py37",  # only version available with 2.2.0
             metric_definitions=[
                 {"Name": "train:global_steps", "Regex": r"global_step\/sec:\s(.*)"}
             ],

tests/integ/test_tf.py

@@ -23,8 +23,7 @@
 from sagemaker.utils import unique_name_from_base, sagemaker_timestamp
 
 import tests.integ
-from tests.integ import timeout
-from tests.integ import kms_utils
+from tests.integ import kms_utils, timeout, PYTHON_VERSION
 from tests.integ.retry import retries
 from tests.integ.s3_utils import assert_s3_files_exist
 
@@ -40,13 +39,8 @@
 TAGS = [{"Key": "some-key", "Value": "some-value"}]
 
 
-@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 test_mnist_with_checkpoint_config(
-    sagemaker_session, instance_type, tf_full_version, py_version
+    sagemaker_session, instance_type, tf_full_version, tf_full_py_version
 ):
     checkpoint_s3_uri = "s3://{}/checkpoints/tf-{}".format(
         sagemaker_session.default_bucket(), sagemaker_timestamp()
@@ -59,7 +53,7 @@ def test_mnist_with_checkpoint_config(
         train_instance_type=instance_type,
         sagemaker_session=sagemaker_session,
         framework_version=tf_full_version,
-        py_version="py37",
+        py_version=tf_full_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,
@@ -89,7 +83,7 @@ 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_serving_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")
@@ -103,7 +97,7 @@ def test_server_side_encryption(sagemaker_session, tf_serving_version, py_versio
             train_instance_type="ml.c5.xlarge",
             sagemaker_session=sagemaker_session,
             framework_version=tf_serving_version,
-            py_version=py_version,
+            py_version=PYTHON_VERSION,
             code_location=output_path,
             output_path=output_path,
             model_dir="/opt/ml/model",
@@ -130,15 +124,15 @@ def test_server_side_encryption(sagemaker_session, tf_serving_version, py_versio
 
 
 @pytest.mark.canary_quick
-def test_mnist_distributed(sagemaker_session, instance_type, tf_full_version, py_version):
+def test_mnist_distributed(sagemaker_session, instance_type, tf_full_version, tf_full_py_version):
     estimator = TensorFlow(
         entry_point=SCRIPT,
         role=ROLE,
         train_instance_count=2,
         train_instance_type=instance_type,
         sagemaker_session=sagemaker_session,
-        py_version="py37",
         framework_version=tf_full_version,
+        py_version=tf_full_py_version,
         distributions=PARAMETER_SERVER_DISTRIBUTION,
     )
     inputs = estimator.sagemaker_session.upload_data(
@@ -154,13 +148,13 @@ def test_mnist_distributed(sagemaker_session, instance_type, tf_full_version, py
     )
 
 
-def test_mnist_async(sagemaker_session, cpu_instance_type, tf_full_version, py_version):
+def test_mnist_async(sagemaker_session, cpu_instance_type):
     estimator = TensorFlow(
         entry_point=SCRIPT,
         role=ROLE,
         train_instance_count=1,
         train_instance_type="ml.c5.4xlarge",
-        py_version=tests.integ.PYTHON_VERSION,
+        py_version=PYTHON_VERSION,
         sagemaker_session=sagemaker_session,
         # testing py-sdk functionality, no need to run against all TF versions
         framework_version=LATEST_SERVING_VERSION,
@@ -195,18 +189,16 @@ 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_serving_version):
     estimator = TensorFlow(
         entry_point="training.py",
         source_dir=TFS_RESOURCE_PATH,
         role=ROLE,
         train_instance_count=1,
         train_instance_type=instance_type,
-        py_version=py_version,
-        sagemaker_session=sagemaker_session,
         framework_version=tf_serving_version,
+        py_version=PYTHON_VERSION,
+        sagemaker_session=sagemaker_session,
         tags=TAGS,
     )
 

tests/integ/test_tf_efs_fsx.py

@@ -36,7 +36,7 @@
 FSX_DIR_PATH = "/fsx/tensorflow"
 MAX_JOBS = 2
 MAX_PARALLEL_JOBS = 2
-PY_VERSION = "py3"
+PY_VERSION = "py37"
 
 
 @pytest.fixture(scope="module")
@@ -139,8 +139,8 @@ def test_tuning_tf_efs(efs_fsx_setup, sagemaker_session, cpu_instance_type):
         train_instance_count=1,
         train_instance_type=cpu_instance_type,
         sagemaker_session=sagemaker_session,
-        py_version=PY_VERSION,
         framework_version=TensorFlow.LATEST_VERSION,
+        py_version=PY_VERSION,
         subnets=subnets,
         security_group_ids=security_group_ids,
     )
@@ -186,8 +186,8 @@ def test_tuning_tf_lustre(efs_fsx_setup, sagemaker_session, cpu_instance_type):
         train_instance_count=1,
         train_instance_type=cpu_instance_type,
         sagemaker_session=sagemaker_session,
-        py_version=PY_VERSION,
         framework_version=TensorFlow.LATEST_VERSION,
+        py_version=PY_VERSION,
         subnets=subnets,
         security_group_ids=security_group_ids,
     )