add warning when creating a constructor

Author: laurenyu

src/sagemaker/chainer/estimator.py

@@ -12,12 +12,17 @@
 # language governing permissions and limitations under the License.
 from __future__ import absolute_import
 
+import logging
+
 from sagemaker.estimator import Framework
-from sagemaker.fw_utils import framework_name_from_image, framework_version_from_tag
+from sagemaker.fw_utils import framework_name_from_image, framework_version_from_tag, empty_framework_version_warning
 from sagemaker.chainer.defaults import CHAINER_VERSION
 from sagemaker.chainer.model import ChainerModel
 from sagemaker.vpc_utils import VPC_CONFIG_DEFAULT
 
+logging.basicConfig()
+logger = logging.getLogger('sagemaker')
+
 
 class Chainer(Framework):
     """Handle end-to-end training and deployment of custom Chainer code."""
@@ -32,7 +37,7 @@ class Chainer(Framework):
 
     def __init__(self, entry_point, use_mpi=None, num_processes=None, process_slots_per_host=None,
                  additional_mpi_options=None, source_dir=None, hyperparameters=None, py_version='py3',
-                 framework_version=CHAINER_VERSION, image_name=None, **kwargs):
+                 framework_version=None, image_name=None, **kwargs):
         """
         This ``Estimator`` executes an Chainer script in a managed Chainer execution environment, within a SageMaker
         Training Job. The managed Chainer environment is an Amazon-built Docker container that executes functions
@@ -79,12 +84,15 @@ 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)
         self.py_version = py_version
-        self.framework_version = framework_version
         self.use_mpi = use_mpi
         self.num_processes = num_processes
         self.process_slots_per_host = process_slots_per_host
         self.additional_mpi_options = additional_mpi_options
 
+        if framework_version is None:
+            logger.warning(empty_framework_version_warning(CHAINER_VERSION))
+        self.framework_version = framework_version or CHAINER_VERSION
+
     def hyperparameters(self):
         """Return hyperparameters used by your custom Chainer code during training."""
         hyperparameters = super(Chainer, self).hyperparameters()

src/sagemaker/fw_utils.py

@@ -23,14 +23,18 @@
 
 """This module contains utility functions shared across ``Framework`` components."""
 
-
 UploadedCode = namedtuple('UserCode', ['s3_prefix', 'script_name'])
 """sagemaker.fw_utils.UserCode: An object containing the S3 prefix and script name.
 
 This is for the source code used for the entry point with an ``Estimator``. It can be
 instantiated with positional or keyword arguments.
 """
 
+EMPTY_FRAMEWORK_VERSION_WARNING = 'In an upcoming version of the SageMaker Python SDK, ' \
+                                  'framework_version will be required to create an estimator. ' \
+                                  'Please add framework_version={} to your constructor to avoid ' \
+                                  'an error in the future.'
+
 
 def create_image_uri(region, framework, instance_type, framework_version, py_version, account='520713654638',
                      optimized_families=[]):
@@ -223,3 +227,7 @@ def model_code_key_prefix(code_location_key_prefix, model_name, image):
         str: the key prefix to be used in uploading code
     """
     return '/'.join(filter(None, [code_location_key_prefix, model_name or name_from_image(image)]))
+
+
+def empty_framework_version_warning(default_version):
+    return EMPTY_FRAMEWORK_VERSION_WARNING.format(default_version)

src/sagemaker/mxnet/estimator.py

@@ -12,20 +12,25 @@
 # language governing permissions and limitations under the License.
 from __future__ import absolute_import
 
+import logging
+
 from sagemaker.estimator import Framework
-from sagemaker.fw_utils import framework_name_from_image, framework_version_from_tag
+from sagemaker.fw_utils import framework_name_from_image, framework_version_from_tag, empty_framework_version_warning
 from sagemaker.mxnet.defaults import MXNET_VERSION
 from sagemaker.mxnet.model import MXNetModel
 from sagemaker.vpc_utils import VPC_CONFIG_DEFAULT
 
+logging.basicConfig()
+logger = logging.getLogger('sagemaker')
+
 
 class MXNet(Framework):
     """Handle end-to-end training and deployment of custom MXNet code."""
 
     __framework_name__ = "mxnet"
 
     def __init__(self, entry_point, source_dir=None, hyperparameters=None, py_version='py2',
-                 framework_version=MXNET_VERSION, image_name=None, **kwargs):
+                 framework_version=None, image_name=None, **kwargs):
         """
         This ``Estimator`` executes an MXNet script in a managed MXNet execution environment, within a SageMaker
         Training Job. The managed MXNet environment is an Amazon-built Docker container that executes functions
@@ -64,7 +69,10 @@ 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)
         self.py_version = py_version
-        self.framework_version = framework_version
+
+        if framework_version is None:
+            logger.warning(empty_framework_version_warning(MXNET_VERSION))
+        self.framework_version = framework_version or MXNET_VERSION
 
     def create_model(self, model_server_workers=None, role=None, vpc_config_override=VPC_CONFIG_DEFAULT):
         """Create a SageMaker ``MXNetModel`` object that can be deployed to an ``Endpoint``.

src/sagemaker/pytorch/estimator.py

@@ -11,20 +11,26 @@
 # 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
+
 from sagemaker.estimator import Framework
-from sagemaker.fw_utils import framework_name_from_image, framework_version_from_tag
+from sagemaker.fw_utils import framework_name_from_image, framework_version_from_tag, empty_framework_version_warning
 from sagemaker.pytorch.defaults import PYTORCH_VERSION, PYTHON_VERSION
 from sagemaker.pytorch.model import PyTorchModel
 from sagemaker.vpc_utils import VPC_CONFIG_DEFAULT
 
+logging.basicConfig()
+logger = logging.getLogger('sagemaker')
+
 
 class PyTorch(Framework):
     """Handle end-to-end training and deployment of custom PyTorch code."""
 
     __framework_name__ = "pytorch"
 
     def __init__(self, entry_point, source_dir=None, hyperparameters=None, py_version=PYTHON_VERSION,
-                 framework_version=PYTORCH_VERSION, image_name=None, **kwargs):
+                 framework_version=None, image_name=None, **kwargs):
         """
         This ``Estimator`` executes an PyTorch script in a managed PyTorch execution environment, within a SageMaker
         Training Job. The managed PyTorch environment is an Amazon-built Docker container that executes functions
@@ -62,7 +68,10 @@ def __init__(self, entry_point, source_dir=None, hyperparameters=None, py_versio
         """
         super(PyTorch, self).__init__(entry_point, source_dir, hyperparameters, image_name=image_name, **kwargs)
         self.py_version = py_version
-        self.framework_version = framework_version
+
+        if framework_version is None:
+            logger.warning(empty_framework_version_warning(PYTORCH_VERSION))
+        self.framework_version = framework_version or PYTORCH_VERSION
 
     def create_model(self, model_server_workers=None, role=None, vpc_config_override=VPC_CONFIG_DEFAULT):
         """Create a SageMaker ``PyTorchModel`` object that can be deployed to an ``Endpoint``.

src/sagemaker/tensorflow/estimator.py

@@ -22,7 +22,7 @@
 import time
 
 from sagemaker.estimator import Framework
-from sagemaker.fw_utils import framework_name_from_image, framework_version_from_tag
+from sagemaker.fw_utils import framework_name_from_image, framework_version_from_tag, empty_framework_version_warning
 from sagemaker.utils import get_config_value
 from sagemaker.vpc_utils import VPC_CONFIG_DEFAULT
 
@@ -159,7 +159,7 @@ class TensorFlow(Framework):
     __framework_name__ = 'tensorflow'
 
     def __init__(self, training_steps=None, evaluation_steps=None, checkpoint_path=None, py_version='py2',
-                 framework_version=TF_VERSION, requirements_file='', image_name=None, **kwargs):
+                 framework_version=None, requirements_file='', image_name=None, **kwargs):
         """Initialize an ``TensorFlow`` estimator.
         Args:
             training_steps (int): Perform this many steps of training. `None`, the default means train forever.
@@ -184,13 +184,16 @@ def __init__(self, training_steps=None, evaluation_steps=None, checkpoint_path=N
         super(TensorFlow, self).__init__(image_name=image_name, **kwargs)
         self.checkpoint_path = checkpoint_path
         self.py_version = py_version
-        self.framework_version = framework_version
         self.training_steps = training_steps
         self.evaluation_steps = evaluation_steps
 
         self._validate_requirements_file(requirements_file)
         self.requirements_file = requirements_file
 
+        if framework_version is None:
+            LOGGER.warning(empty_framework_version_warning(TF_VERSION))
+        self.framework_version = framework_version or TF_VERSION
+
     def _validate_requirements_file(self, requirements_file):
         if not requirements_file:
             return