feature: add TensorFlow 1.13 support

Author: icywang86rui

README.rst

@@ -172,7 +172,7 @@ TensorFlow SageMaker Estimators
 
 By using TensorFlow SageMaker ``Estimators``, you can train and host TensorFlow models on Amazon SageMaker.
 
-Supported versions of TensorFlow: ``1.4.1``, ``1.5.0``, ``1.6.0``, ``1.7.0``, ``1.8.0``, ``1.9.0``, ``1.10.0``, ``1.11.0``, ``1.12.0``.
+Supported versions of TensorFlow: ``1.4.1``, ``1.5.0``, ``1.6.0``, ``1.7.0``, ``1.8.0``, ``1.9.0``, ``1.10.0``, ``1.11.0``, ``1.12.0``, ``1.13.1``.
 
 Supported versions of TensorFlow for Elastic Inference: ``1.11.0``, ``1.12.0``.
 

src/sagemaker/tensorflow/estimator.py

@@ -26,7 +26,7 @@
 from sagemaker.tensorflow.defaults import TF_VERSION
 from sagemaker.tensorflow.model import TensorFlowModel
 from sagemaker.tensorflow.serving import Model
-from sagemaker.utils import get_config_value
+from sagemaker.utils import get_config_value, get_short_version
 from sagemaker.vpc_utils import VPC_CONFIG_DEFAULT
 
 logger = logging.getLogger('sagemaker')
@@ -171,9 +171,11 @@ class TensorFlow(Framework):
 
     __framework_name__ = 'tensorflow'
 
-    LATEST_VERSION = '1.12'
+    LATEST_VERSION = '1.13'
     """The latest version of TensorFlow included in the SageMaker pre-built Docker images."""
 
+    _LOWEST_SCRIPT_MODE_ONLY_VERSION = [1, 13]
+
     def __init__(self, training_steps=None, evaluation_steps=None, checkpoint_path=None, py_version='py2',
                  framework_version=None, model_dir=None, requirements_file='', image_name=None,
                  script_mode=False, distributions=None, **kwargs):
@@ -276,6 +278,13 @@ def _validate_args(self, py_version, script_mode, framework_version, training_st
                     .format(', '.join(_FRAMEWORK_MODE_ARGS), ', '.join(found_args))
                 )
 
+        if (not self._script_mode_enabled()) and \
+           [int(s) for s in self.framework_version.split('.')] >= self._LOWEST_SCRIPT_MODE_ONLY_VERSION:
+            raise AttributeError(
+                'Legacy mode is deprecated in versions 1.13 and higher.'
+                'Please set the script_mode argument to True to use Script Mode'
+            )
+
     def _validate_requirements_file(self, requirements_file):
         if not requirements_file:
             return
@@ -427,7 +436,7 @@ def _create_tfs_model(self, role=None, vpc_config_override=VPC_CONFIG_DEFAULT):
                      image=self.image_name,
                      name=self._current_job_name,
                      container_log_level=self.container_log_level,
-                     framework_version=self.framework_version,
+                     framework_version=get_short_version(self.framework_version),
                      sagemaker_session=self.sagemaker_session,
                      vpc_config=self.get_vpc_config(vpc_config_override))
 

src/sagemaker/utils.py

@@ -122,6 +122,10 @@ def get_config_value(key_path, config):
     return current_section
 
 
+def get_short_version(framework_version):
+    return '.'.join(framework_version.split('.')[:2])
+
+
 def to_str(value):
     """Convert the input to a string, unless it is a unicode string in Python 2.
 

tests/conftest.py

@@ -125,7 +125,8 @@ def sklearn_version(request):
 
 @pytest.fixture(scope='module', params=['1.4', '1.4.1', '1.5', '1.5.0', '1.6', '1.6.0',
                                         '1.7', '1.7.0', '1.8', '1.8.0', '1.9', '1.9.0',
-                                        '1.10', '1.10.0', '1.11', '1.11.0', '1.12', '1.12.0'])
+                                        '1.10', '1.10.0', '1.11', '1.11.0', '1.12', '1.12.0',
+                                        '1.13', '1.13.1'])
 def tf_version(request):
     return request.param
 

tests/data/tensorflow_mnist/mnist.py

@@ -14,17 +14,11 @@
 
 import argparse
 import json
-import logging as _logging
 import numpy as np
 import os
-import sys as _sys
 import tensorflow as tf
-from tensorflow.python.platform import tf_logging
 
 tf.logging.set_verbosity(tf.logging.DEBUG)
-_handler = _logging.StreamHandler(_sys.stdout)
-tf_logger = tf_logging._get_logger()
-tf_logger.handlers = [_handler]
 
 def cnn_model_fn(features, labels, mode):
   """Model function for CNN."""
@@ -188,5 +182,3 @@ def serving_input_fn():
 
     if args.current_host == args.hosts[0]:
         mnist_classifier.export_savedmodel('/opt/ml/model', serving_input_fn)
-
-    tf_logger.info('====== Training finished =========')

tests/unit/test_tf_estimator.py

@@ -738,6 +738,13 @@ def test_script_mode_deprecated_args(sagemaker_session):
     assert _deprecated_args_msg('training_steps, evaluation_steps, requirements_file, checkpoint_path') in str(e.value)
 
 
+def test_legacy_mode_deprecation_error(sagemaker_session):
+    with pytest.raises(AttributeError) as e:
+        _build_tf(sagemaker_session=sagemaker_session, framework_version='1.13.1',
+                  py_version='py2', script_mode=False)
+    assert 'Legacy mode is deprecated' in str(e.value)
+
+
 def test_script_mode_enabled(sagemaker_session):
     tf = _build_tf(sagemaker_session=sagemaker_session, py_version='py3')
     assert tf._script_mode_enabled() is True

tests/unit/test_utils.py

@@ -52,6 +52,11 @@ def test_get_config_value():
     assert sagemaker.utils.get_config_value('other.key', None) is None
 
 
+def test_get_short_version():
+    assert sagemaker.utils.get_short_version('1.13.1') == '1.13'
+    assert sagemaker.utils.get_short_version('1.13') == '1.13'
+
+
 def test_deferred_error():
     de = sagemaker.utils.DeferredError(ImportError("pretend the import failed"))
     with pytest.raises(ImportError) as _:  # noqa: F841