Update default versions to TensorFlow 1.6 and MXNet 1.1

CHANGELOG.rst

@@ -2,10 +2,13 @@
 CHANGELOG
 =========
 
-1.1.dev4
+1.2.0
 ========
-* feature: Frameworks: Use more idiomatic ECR repository naming scheme
+
 * feature: Add Support for Local Mode
+* feature: Estimators: add support for TensorFlow 1.6.0
+* feature: Estimators: add support for MXNet 1.1.0
+* feature: Frameworks: Use more idiomatic ECR repository naming scheme
 
 1.1.3
 ========

setup.py

@@ -11,7 +11,7 @@ def read(fname):
 
 
 setup(name="sagemaker",
-      version="1.1.3",
+      version="1.2.0",
       description="Open source library for training and deploying models on Amazon SageMaker.",
       packages=find_packages('src'),
       package_dir={'': 'src'},

src/sagemaker/mxnet/defaults.py

@@ -10,4 +10,4 @@
 # distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
 # ANY KIND, either express or implied. See the License for the specific
 # language governing permissions and limitations under the License.
-MXNET_VERSION = '1.0'
+MXNET_VERSION = '1.1'

src/sagemaker/tensorflow/defaults.py

@@ -10,4 +10,4 @@
 # distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
 # ANY KIND, either express or implied. See the License for the specific
 # language governing permissions and limitations under the License.
-TF_VERSION = '1.5'
+TF_VERSION = '1.6'

tests/conftest.py

@@ -56,21 +56,21 @@ def sagemaker_session(sagemaker_client_config, sagemaker_runtime_config, boto_co
                    sagemaker_runtime_client=runtime_client)
 
 
-@pytest.fixture(scope='module', params=["1.4", "1.4.1", "1.5", "1.5.0"])
+@pytest.fixture(scope='module', params=['1.4', '1.4.1', '1.5', '1.5.0', '1.6', '1.6.0'])
 def tf_version(request):
     return request.param
 
 
-@pytest.fixture(scope='module', params=["0.12", "0.12.1", "1.0", "1.0.0"])
+@pytest.fixture(scope='module', params=['0.12', '0.12.1', '1.0', '1.0.0', '1.1', '1.1.0'])
 def mxnet_version(request):
     return request.param
 
 
-@pytest.fixture(scope='module', params=["1.4.1", "1.5.0"])
+@pytest.fixture(scope='module', params=['1.4.1', '1.5.0', '1.6.0'])
 def tf_full_version(request):
     return request.param
 
 
-@pytest.fixture(scope='module', params=["0.12.1", "1.0.0"])
+@pytest.fixture(scope='module', params=['0.12.1', '1.0.0', '1.1.0'])
 def mxnet_full_version(request):
     return request.param

tests/integ/test_mxnet_train.py

@@ -52,16 +52,28 @@ def test_attach_deploy(mxnet_training_job, sagemaker_session):
         predictor.predict(data)
 
 
-def test_async_fit(sagemaker_session, mxnet_full_version):
+def test_deploy_model(mxnet_training_job, sagemaker_session):
+    endpoint_name = 'test-mxnet-deploy-model-{}'.format(sagemaker_timestamp())
+
+    with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session, minutes=20):
+        desc = sagemaker_session.sagemaker_client.describe_training_job(TrainingJobName=mxnet_training_job)
+        model_data = desc['ModelArtifacts']['S3ModelArtifacts']
+        script_path = os.path.join(DATA_DIR, 'mxnet_mnist', 'mnist.py')
+        model = MXNetModel(model_data, 'SageMakerRole', entry_point=script_path, sagemaker_session=sagemaker_session)
+        predictor = model.deploy(1, 'ml.m4.xlarge', endpoint_name=endpoint_name)
 
-    training_job_name = ""
+        data = numpy.zeros(shape=(1, 1, 28, 28))
+        predictor.predict(data)
+
+
+def test_async_fit(sagemaker_session):
     endpoint_name = 'test-mxnet-attach-deploy-{}'.format(sagemaker_timestamp())
 
     with timeout(minutes=5):
         script_path = os.path.join(DATA_DIR, 'mxnet_mnist', 'mnist.py')
         data_path = os.path.join(DATA_DIR, 'mxnet_mnist')
 
-        mx = MXNet(entry_point=script_path, role='SageMakerRole', framework_version=mxnet_full_version,
+        mx = MXNet(entry_point=script_path, role='SageMakerRole',
                    train_instance_count=1, train_instance_type='ml.c4.xlarge',
                    sagemaker_session=sagemaker_session)
 
@@ -84,20 +96,6 @@ def test_async_fit(sagemaker_session, mxnet_full_version):
         predictor.predict(data)
 
 
-def test_deploy_model(mxnet_training_job, sagemaker_session):
-    endpoint_name = 'test-mxnet-deploy-model-{}'.format(sagemaker_timestamp())
-
-    with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session, minutes=20):
-        desc = sagemaker_session.sagemaker_client.describe_training_job(TrainingJobName=mxnet_training_job)
-        model_data = desc['ModelArtifacts']['S3ModelArtifacts']
-        script_path = os.path.join(DATA_DIR, 'mxnet_mnist', 'mnist.py')
-        model = MXNetModel(model_data, 'SageMakerRole', entry_point=script_path, sagemaker_session=sagemaker_session)
-        predictor = model.deploy(1, 'ml.m4.xlarge', endpoint_name=endpoint_name)
-
-        data = numpy.zeros(shape=(1, 1, 28, 28))
-        predictor.predict(data)
-
-
 def test_failed_training_job(sagemaker_session, mxnet_full_version):
     with timeout(minutes=15):
         script_path = os.path.join(DATA_DIR, 'mxnet_mnist', 'failure_script.py')

tests/integ/test_tf.py

@@ -55,14 +55,12 @@ def test_tf(sagemaker_session, tf_full_version):
         assert dict_result == list_result
 
 
-def test_tf_async(sagemaker_session, tf_full_version):
-    training_job_name = ""
+def test_tf_async(sagemaker_session):
     with timeout(minutes=5):
         script_path = os.path.join(DATA_DIR, 'iris', 'iris-dnn-classifier.py')
 
         estimator = TensorFlow(entry_point=script_path,
                                role='SageMakerRole',
-                               framework_version=tf_full_version,
                                training_steps=1,
                                evaluation_steps=1,
                                hyperparameters={'input_tensor_name': 'inputs'},