change: add py2 deprecation message for the deep learning framework images (#768)

Author: chuyang-deng

src/sagemaker/chainer/estimator.py

@@ -15,7 +15,8 @@
 import logging
 
 from sagemaker.estimator import Framework
-from sagemaker.fw_utils import framework_name_from_image, framework_version_from_tag, empty_framework_version_warning
+from sagemaker.fw_utils import framework_name_from_image, framework_version_from_tag, empty_framework_version_warning, \
+    python_deprecation_warning
 from sagemaker.chainer.defaults import CHAINER_VERSION
 from sagemaker.chainer.model import ChainerModel
 from sagemaker.vpc_utils import VPC_CONFIG_DEFAULT
@@ -90,6 +91,10 @@ def __init__(self, entry_point, use_mpi=None, num_processes=None, process_slots_
 
         super(Chainer, self).__init__(entry_point, source_dir, hyperparameters,
                                       image_name=image_name, **kwargs)
+
+        if py_version == 'py2':
+            logger.warning(python_deprecation_warning(self.__framework_name__))
+
         self.py_version = py_version
         self.use_mpi = use_mpi
         self.num_processes = num_processes

src/sagemaker/chainer/model.py

@@ -12,12 +12,16 @@
 # language governing permissions and limitations under the License.
 from __future__ import absolute_import
 
+import logging
+
 import sagemaker
-from sagemaker.fw_utils import create_image_uri, model_code_key_prefix
+from sagemaker.fw_utils import create_image_uri, model_code_key_prefix, python_deprecation_warning
 from sagemaker.model import FrameworkModel, MODEL_SERVER_WORKERS_PARAM_NAME
 from sagemaker.chainer.defaults import CHAINER_VERSION
 from sagemaker.predictor import RealTimePredictor, npy_serializer, numpy_deserializer
 
+logger = logging.getLogger('sagemaker')
+
 
 class ChainerPredictor(RealTimePredictor):
     """A RealTimePredictor for inference against Chainer Endpoints.
@@ -66,6 +70,9 @@ def __init__(self, model_data, role, entry_point, image=None, py_version='py3',
         """
         super(ChainerModel, self).__init__(model_data, image, role, entry_point, predictor_cls=predictor_cls,
                                            **kwargs)
+        if py_version == 'py2':
+            logger.warning(python_deprecation_warning(self.__framework_name__))
+
         self.py_version = py_version
         self.framework_version = framework_version
         self.model_server_workers = model_server_workers

src/sagemaker/fw_utils.py

@@ -36,6 +36,10 @@
 LATER_FRAMEWORK_VERSION_WARNING = 'This is not the latest supported version. ' \
                                   'If you would like to use version {latest}, ' \
                                   'please add framework_version={latest} to your constructor.'
+PYTHON_2_DEPRECATION_WARNING = 'The Python 2 {framework} images will be soon deprecated and may not be ' \
+                               'supported for newer upcoming versions of the {framework} images.\n' \
+                               'Please set the argument \"py_version=\'py3\'\" to use the Python 3 {framework} image.'
+
 
 EMPTY_FRAMEWORK_VERSION_ERROR = 'framework_version is required for script mode estimator. ' \
                                 'Please add framework_version={} to your constructor to avoid this error.'
@@ -303,3 +307,7 @@ def empty_framework_version_warning(default_version, latest_version):
     if default_version != latest_version:
         msgs.append(LATER_FRAMEWORK_VERSION_WARNING.format(latest=latest_version))
     return ' '.join(msgs)
+
+
+def python_deprecation_warning(framework):
+    return PYTHON_2_DEPRECATION_WARNING.format(framework=framework)

src/sagemaker/mxnet/estimator.py

@@ -15,7 +15,8 @@
 import logging
 
 from sagemaker.estimator import Framework
-from sagemaker.fw_utils import framework_name_from_image, framework_version_from_tag, empty_framework_version_warning
+from sagemaker.fw_utils import framework_name_from_image, framework_version_from_tag, empty_framework_version_warning, \
+    python_deprecation_warning
 from sagemaker.mxnet.defaults import MXNET_VERSION
 from sagemaker.mxnet.model import MXNetModel
 from sagemaker.vpc_utils import VPC_CONFIG_DEFAULT
@@ -79,6 +80,10 @@ def __init__(self, entry_point, source_dir=None, hyperparameters=None, py_versio
 
         super(MXNet, self).__init__(entry_point, source_dir, hyperparameters,
                                     image_name=image_name, **kwargs)
+
+        if py_version == 'py2':
+            logger.warning(python_deprecation_warning(self.__framework_name__))
+
         self.py_version = py_version
         self._configure_distribution(distributions)
 

src/sagemaker/mxnet/model.py

@@ -12,12 +12,16 @@
 # language governing permissions and limitations under the License.
 from __future__ import absolute_import
 
+import logging
+
 import sagemaker
-from sagemaker.fw_utils import create_image_uri, model_code_key_prefix
+from sagemaker.fw_utils import create_image_uri, model_code_key_prefix, python_deprecation_warning
 from sagemaker.model import FrameworkModel, MODEL_SERVER_WORKERS_PARAM_NAME
 from sagemaker.mxnet.defaults import MXNET_VERSION
 from sagemaker.predictor import RealTimePredictor, json_serializer, json_deserializer
 
+logger = logging.getLogger('sagemaker')
+
 
 class MXNetPredictor(RealTimePredictor):
     """A RealTimePredictor for inference against MXNet Endpoints.
@@ -66,6 +70,10 @@ def __init__(self, model_data, role, entry_point, image=None, py_version='py2',
         """
         super(MXNetModel, self).__init__(model_data, image, role, entry_point, predictor_cls=predictor_cls,
                                          **kwargs)
+
+        if py_version == 'py2':
+            logger.warning(python_deprecation_warning(self.__framework_name__))
+
         self.py_version = py_version
         self.framework_version = framework_version
         self.model_server_workers = model_server_workers

src/sagemaker/pytorch/estimator.py

@@ -15,7 +15,8 @@
 import logging
 
 from sagemaker.estimator import Framework
-from sagemaker.fw_utils import framework_name_from_image, framework_version_from_tag, empty_framework_version_warning
+from sagemaker.fw_utils import framework_name_from_image, framework_version_from_tag, empty_framework_version_warning, \
+    python_deprecation_warning
 from sagemaker.pytorch.defaults import PYTORCH_VERSION, PYTHON_VERSION
 from sagemaker.pytorch.model import PyTorchModel
 from sagemaker.vpc_utils import VPC_CONFIG_DEFAULT
@@ -74,6 +75,10 @@ def __init__(self, entry_point, source_dir=None, hyperparameters=None, py_versio
         self.framework_version = framework_version or PYTORCH_VERSION
 
         super(PyTorch, self).__init__(entry_point, source_dir, hyperparameters, image_name=image_name, **kwargs)
+
+        if py_version == 'py2':
+            logger.warning(python_deprecation_warning(self.__framework_name__))
+
         self.py_version = py_version
 
     def create_model(self, model_server_workers=None, role=None, vpc_config_override=VPC_CONFIG_DEFAULT):

src/sagemaker/pytorch/model.py

@@ -11,12 +11,17 @@
 # ANY KIND, either express or implied. See the License for the specific
 # language governing permissions and limitations under the License.
 from __future__ import absolute_import
+
+import logging
+
 import sagemaker
-from sagemaker.fw_utils import create_image_uri, model_code_key_prefix
+from sagemaker.fw_utils import create_image_uri, model_code_key_prefix, python_deprecation_warning
 from sagemaker.model import FrameworkModel, MODEL_SERVER_WORKERS_PARAM_NAME
 from sagemaker.pytorch.defaults import PYTORCH_VERSION, PYTHON_VERSION
 from sagemaker.predictor import RealTimePredictor, npy_serializer, numpy_deserializer
 
+logger = logging.getLogger('sagemaker')
+
 
 class PyTorchPredictor(RealTimePredictor):
     """A RealTimePredictor for inference against PyTorch Endpoints.
@@ -65,6 +70,10 @@ def __init__(self, model_data, role, entry_point, image=None, py_version=PYTHON_
             **kwargs: Keyword arguments passed to the ``FrameworkModel`` initializer.
         """
         super(PyTorchModel, self).__init__(model_data, image, role, entry_point, predictor_cls=predictor_cls, **kwargs)
+
+        if py_version == 'py2':
+            logger.warning(python_deprecation_warning(self.__framework_name__))
+
         self.py_version = py_version
         self.framework_version = framework_version
         self.model_server_workers = model_server_workers

src/sagemaker/sklearn/estimator.py

@@ -16,7 +16,7 @@
 
 from sagemaker.estimator import Framework
 from sagemaker.fw_registry import default_framework_uri
-from sagemaker.fw_utils import framework_name_from_image, empty_framework_version_warning
+from sagemaker.fw_utils import framework_name_from_image, empty_framework_version_warning, python_deprecation_warning
 from sagemaker.sklearn.defaults import SKLEARN_VERSION, SKLEARN_NAME
 from sagemaker.sklearn.model import SKLearnModel
 from sagemaker.vpc_utils import VPC_CONFIG_DEFAULT
@@ -79,6 +79,9 @@ def __init__(self, entry_point, framework_version=SKLEARN_VERSION, source_dir=No
         super(SKLearn, self).__init__(entry_point, source_dir, hyperparameters, image_name=image_name,
                                       **dict(kwargs, train_instance_count=1))
 
+        if py_version == 'py2':
+            logger.warning(python_deprecation_warning(self.__framework_name__))
+
         self.py_version = py_version
 
         if framework_version is None:

src/sagemaker/sklearn/model.py

@@ -12,13 +12,17 @@
 # language governing permissions and limitations under the License.
 from __future__ import absolute_import
 
+import logging
+
 import sagemaker
-from sagemaker.fw_utils import model_code_key_prefix
+from sagemaker.fw_utils import model_code_key_prefix, python_deprecation_warning
 from sagemaker.fw_registry import default_framework_uri
 from sagemaker.model import FrameworkModel, MODEL_SERVER_WORKERS_PARAM_NAME
 from sagemaker.predictor import RealTimePredictor, npy_serializer, numpy_deserializer
 from sagemaker.sklearn.defaults import SKLEARN_VERSION, SKLEARN_NAME
 
+logger = logging.getLogger('sagemaker')
+
 
 class SKLearnPredictor(RealTimePredictor):
     """A RealTimePredictor for inference against Scikit-learn Endpoints.
@@ -68,6 +72,10 @@ def __init__(self, model_data, role, entry_point, image=None, py_version='py3',
         """
         super(SKLearnModel, self).__init__(model_data, image, role, entry_point, predictor_cls=predictor_cls,
                                            **kwargs)
+
+        if py_version == 'py2':
+            logger.warning(python_deprecation_warning(self.__framework_name__))
+
         self.py_version = py_version
         self.framework_version = framework_version
         self.model_server_workers = model_server_workers

src/sagemaker/tensorflow/estimator.py

@@ -29,7 +29,7 @@
 from sagemaker.utils import get_config_value
 from sagemaker.vpc_utils import VPC_CONFIG_DEFAULT
 
-LOGGER = logging.getLogger('sagemaker')
+logger = logging.getLogger('sagemaker')
 
 
 _FRAMEWORK_MODE_ARGS = ('training_steps', 'evaluation_steps', 'requirements_file', 'checkpoint_path')
@@ -154,7 +154,7 @@ def run(self):
         """Run TensorBoard process."""
         port, tensorboard_process = self.create_tensorboard_process()
 
-        LOGGER.info('TensorBoard 0.1.7 at http://localhost:{}'.format(port))
+        logger.info('TensorBoard 0.1.7 at http://localhost:{}'.format(port))
         while not self.estimator.checkpoint_path:
             self.event.wait(1)
         with self._temporary_directory() as aws_sync_dir:
@@ -231,11 +231,15 @@ def __init__(self, training_steps=None, evaluation_steps=None, checkpoint_path=N
             **kwargs: Additional kwargs passed to the Framework constructor.
         """
         if framework_version is None:
-            LOGGER.warning(fw.empty_framework_version_warning(TF_VERSION, self.LATEST_VERSION))
+            logger.warning(fw.empty_framework_version_warning(TF_VERSION, self.LATEST_VERSION))
         self.framework_version = framework_version or TF_VERSION
 
         super(TensorFlow, self).__init__(image_name=image_name, **kwargs)
         self.checkpoint_path = checkpoint_path
+
+        if py_version == 'py2':
+            logger.warning('tensorflow py2 container will be deprecated soon.')
+
         self.py_version = py_version
         self.training_steps = training_steps
         self.evaluation_steps = evaluation_steps
@@ -320,7 +324,7 @@ def fit_super():
             raise ValueError("Tensorboard is not supported with async fit")
 
         if self._script_mode_enabled() and run_tensorboard_locally:
-            LOGGER.warning(_SCRIPT_MODE_TENSORBOARD_WARNING.format(self.model_dir))
+            logger.warning(_SCRIPT_MODE_TENSORBOARD_WARNING.format(self.model_dir))
             fit_super()
         elif run_tensorboard_locally:
             tensorboard = Tensorboard(self)