doc: add clarification around framework version constants

src/sagemaker/chainer/defaults.py

@@ -13,3 +13,6 @@
 from __future__ import absolute_import
 
 CHAINER_VERSION = '4.1.0'
+"""Default Chainer version for when the framework version is not specified.
+This is no longer updated so as to not break existing workflows.
+"""

src/sagemaker/chainer/estimator.py

@@ -35,6 +35,7 @@ class Chainer(Framework):
     _additional_mpi_options = "sagemaker_additional_mpi_options"
 
     LATEST_VERSION = '5.0.0'
+    """The latest version of Chainer included in the SageMaker pre-built Docker images."""
 
     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',
@@ -73,7 +74,8 @@ def __init__(self, entry_point, use_mpi=None, num_processes=None, process_slots_
             py_version (str): Python version you want to use for executing your model training code (default: 'py2').
                               One of 'py2' or 'py3'.
             framework_version (str): Chainer version you want to use for executing your model training code.
-                List of supported versions https://github.com/aws/sagemaker-python-sdk#chainer-sagemaker-estimators
+                List of supported versions https://github.com/aws/sagemaker-python-sdk#chainer-sagemaker-estimators.
+                If not specified, this will default to 4.1.
             image_name (str): If specified, the estimator will use this image for training and hosting, instead of
                 selecting the appropriate SageMaker official image based on framework_version and py_version. It can
                 be an ECR url or dockerhub image and tag.

src/sagemaker/mxnet/defaults.py

@@ -13,3 +13,6 @@
 from __future__ import absolute_import
 
 MXNET_VERSION = '1.2'
+"""Default MXNet version for when the framework version is not specified.
+This is no longer updated so as to not break existing workflows.
+"""

src/sagemaker/mxnet/estimator.py

@@ -27,9 +27,10 @@ class MXNet(Framework):
     """Handle end-to-end training and deployment of custom MXNet code."""
 
     __framework_name__ = 'mxnet'
-
     _LOWEST_SCRIPT_MODE_VERSION = ['1', '3']
+
     LATEST_VERSION = '1.3'
+    """The latest version of MXNet included in the SageMaker pre-built Docker images."""
 
     def __init__(self, entry_point, source_dir=None, hyperparameters=None, py_version='py2',
                  framework_version=None, image_name=None, distributions=None, **kwargs):
@@ -59,7 +60,8 @@ def __init__(self, entry_point, source_dir=None, hyperparameters=None, py_versio
             py_version (str): Python version you want to use for executing your model training code (default: 'py2').
                 One of 'py2' or 'py3'.
             framework_version (str): MXNet version you want to use for executing your model training code.
-                List of supported versions https://github.com/aws/sagemaker-python-sdk#mxnet-sagemaker-estimators
+                List of supported versions https://github.com/aws/sagemaker-python-sdk#mxnet-sagemaker-estimators.
+                If not specified, this will default to 1.2.1.
             image_name (str): If specified, the estimator will use this image for training and hosting, instead of
                 selecting the appropriate SageMaker official image based on framework_version and py_version. It can
                 be an ECR url or dockerhub image and tag.

src/sagemaker/pytorch/defaults.py

@@ -13,4 +13,8 @@
 from __future__ import absolute_import
 
 PYTORCH_VERSION = '0.4'
+"""Default PyTorch version for when the framework version is not specified.
+This is no longer updated so as to not break existing workflows.
+"""
+
 PYTHON_VERSION = 'py3'

src/sagemaker/pytorch/estimator.py

@@ -29,6 +29,7 @@ class PyTorch(Framework):
     __framework_name__ = "pytorch"
 
     LATEST_VERSION = '1.0'
+    """The latest version of PyTorch included in the SageMaker pre-built Docker images."""
 
     def __init__(self, entry_point, source_dir=None, hyperparameters=None, py_version=PYTHON_VERSION,
                  framework_version=None, image_name=None, **kwargs):
@@ -58,7 +59,8 @@ def __init__(self, entry_point, source_dir=None, hyperparameters=None, py_versio
             py_version (str): Python version you want to use for executing your model training code (default: 'py3').
                               One of 'py2' or 'py3'.
             framework_version (str): PyTorch version you want to use for executing your model training code.
-                List of supported versions https://github.com/aws/sagemaker-python-sdk#pytorch-sagemaker-estimators
+                List of supported versions https://github.com/aws/sagemaker-python-sdk#pytorch-sagemaker-estimators.
+                If not specified, this will default to 0.4.
             image_name (str): If specified, the estimator will use this image for training and hosting, instead of
                 selecting the appropriate SageMaker official image based on framework_version and py_version. It can
                 be an ECR url or dockerhub image and tag.

src/sagemaker/tensorflow/defaults.py

@@ -13,3 +13,6 @@
 from __future__ import absolute_import
 
 TF_VERSION = '1.11'
+"""Default TF version for when the framework version is not specified.
+This is no longer updated so as to not break existing workflows.
+"""

src/sagemaker/tensorflow/estimator.py

@@ -170,7 +170,9 @@ class TensorFlow(Framework):
     """Handle end-to-end training and deployment of user-provided TensorFlow code."""
 
     __framework_name__ = 'tensorflow'
+
     LATEST_VERSION = '1.12'
+    """The latest version of TensorFlow included in the SageMaker pre-built Docker images."""
 
     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,
@@ -185,7 +187,8 @@ def __init__(self, training_steps=None, evaluation_steps=None, checkpoint_path=N
                 saved (default: None). For distributed model training, this parameter is required.
             py_version (str): Python version you want to use for executing your model training code (default: 'py2').
             framework_version (str): TensorFlow version you want to use for executing your model training code.
-                List of supported versions https://github.com/aws/sagemaker-python-sdk#tensorflow-sagemaker-estimators
+                List of supported versions https://github.com/aws/sagemaker-python-sdk#tensorflow-sagemaker-estimators.
+                If not specified, this will default to 1.11.
             model_dir (str): S3 location where the checkpoint data and models can be exported to during training
                 (default: None). If not specified a default S3 URI will be generated. It will be passed in the
                 training script as one of the command line arguments.