Upgrade to TensorFlow 1.12 (#588)

* Upgrade to TensorFlow 1.12

* Update CHANGELOG.rst

* Update README

Author: icywang86rui

CHANGELOG.rst

@@ -14,6 +14,7 @@ CHANGELOG
 * doc-fix: Add missing classes to API docs
 * doc-fix: Add information on necessary AWS permissions
 * bug-fix: Remove PyYAML to let docker-compose install the right version
+* feature: Update TensorFlow latest version to 1.12
 * enhancement: Add Model.transformer()
 * bug-fix: HyperparameterTuner: make ``include_cls_metadata`` default to ``False`` for everything except Frameworks
 

README.rst

@@ -388,7 +388,7 @@ 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``.
+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 for Elastic Inference: ``1.11.0``.
 

src/sagemaker/tensorflow/README.rst

@@ -148,7 +148,7 @@ Please note that the following args are not permitted when using Script Mode:
 
   tf_estimator = TensorFlow(entry_point='tf-train.py', role='SageMakerRole',
                             train_instance_count=1, train_instance_type='ml.p2.xlarge',
-                            framework_version='1.11', py_version='py3')
+                            framework_version='1.12', py_version='py3')
   tf_estimator.fit('s3://bucket/path/to/training/data')
 
 Where the S3 url is a path to your training data, within Amazon S3. The
@@ -417,9 +417,9 @@ The containers include the following Python packages:
 +--------------------------------+---------------+-------------------+
 | curl                           | Latest        | Latest            |
 +--------------------------------+---------------+-------------------+
-| tensorflow                     | 1.11.0        | 1.11.0            |
+| tensorflow                     | 1.12.0        | 1.12.0            |
 +--------------------------------+---------------+-------------------+
-| tensorflow-serving-api         | 1.11.0        | None              |
+| tensorflow-serving-api         | 1.12.0        | None              |
 +--------------------------------+---------------+-------------------+
 | sagemaker-containers           | >=2.3.5       | >=2.3.5           |
 +--------------------------------+---------------+-------------------+

src/sagemaker/tensorflow/estimator.py

@@ -171,6 +171,7 @@ class TensorFlow(Framework):
     """Handle end-to-end training and deployment of user-provided TensorFlow code."""
 
     __framework_name__ = 'tensorflow'
+    LATEST_VERSION = '1.12'
 
     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,

tests/conftest.py

@@ -26,7 +26,7 @@
 from sagemaker.pytorch import PyTorch
 from sagemaker.rl import RLEstimator
 from sagemaker.sklearn.defaults import SKLEARN_VERSION
-from sagemaker.tensorflow.defaults import TF_VERSION
+from sagemaker.tensorflow.estimator import TensorFlow
 
 DEFAULT_REGION = 'us-west-2'
 
@@ -43,7 +43,7 @@ def pytest_addoption(parser):
     parser.addoption('--rl-ray-full-version', action='store',
                      default=RLEstimator.RAY_LATEST_VERSION)
     parser.addoption('--sklearn-full-version', action='store', default=SKLEARN_VERSION)
-    parser.addoption('--tf-full-version', action='store', default=TF_VERSION)
+    parser.addoption('--tf-full-version', action='store', default=TensorFlow.LATEST_VERSION)
 
 
 def pytest_configure(config):
@@ -126,7 +126,7 @@ 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'])
+                                        '1.10', '1.10.0', '1.11', '1.11.0', '1.12', '1.12.0'])
 def tf_version(request):
     return request.param