Fir for Pr 138 by Udit Bhatia

.coveragerc_py27

@@ -0,0 +1,20 @@
+[run]
+branch = True
+timid = True
+
+[report]
+exclude_lines =
+    pragma: no cover
+    pragma: py2 no cover
+    if six.PY3
+    elif six.PY3
+
+partial_branches =
+    pragma: no cover
+    pragma: py2 no cover
+    if six.PY3
+    elif six.PY3
+
+show_missing = True
+
+fail_under = 75

.coveragerc_py36

@@ -0,0 +1,20 @@
+[run]
+branch = True
+timid = True
+
+[report]
+exclude_lines =
+    pragma: no cover
+    pragma: py3 no cover
+    if six.PY2
+    elif six.PY2
+
+partial_branches =
+    pragma: no cover
+    pragma: py3 no cover
+    if six.PY3
+    elif six.PY3
+
+show_missing = True
+
+fail_under = 90

.flake8

@@ -0,0 +1,3 @@
+[flake8]
+application_import_names = sagemaker_tensorflow_container, test
+import-order-style = google

README.rst

@@ -57,7 +57,7 @@ The Docker files are grouped based on TensorFlow version and separated
 based on Python version and processor type.
 
 The Docker images, used to run training & inference jobs, are built from
-both corresponding “base” and “final” Dockerfiles.
+both corresponding "base" and "final" Dockerfiles.
 
 Base Images
 ~~~~~~~~~~~
@@ -66,10 +66,10 @@ The "base" Dockerfile encompass the installation of the framework and all of the
 needed. It is needed before building image for TensorFlow 1.8.0 and before.
 Building a base image is not required for images for TensorFlow 1.9.0 and onwards.
 
-Tagging scheme is based on <tensorflow_version>-<processor>-<python_version>. (e.g. 1.4
+Tagging scheme is based on <tensorflow_version>-<processor>-<python_version>. (e.g. 1.4
 .1-cpu-py2)
 
-All “final” Dockerfiles build images using base images that use the tagging scheme
+All "final" Dockerfiles build images using base images that use the tagging scheme
 above.
 
 If you want to build your "base" Docker image, then use:
@@ -99,15 +99,15 @@ Final Images
 
 The "final" Dockerfiles encompass the installation of the SageMaker specific support code.
 
-For images of TensorFlow 1.8.0 and before, all “final” Dockerfiles use `base images for building <https://github
+For images of TensorFlow 1.8.0 and before, all "final" Dockerfiles use `base images for building <https://github
 .com/aws/sagemaker-tensorflow-containers/blob/master/docker/1.4.1/final/py2/Dockerfile.cpu#L2>`__.
 
-These “base” images are specified with the naming convention of
+These "base" images are specified with the naming convention of
 tensorflow-base:<tensorflow_version>-<processor>-<python_version>.
 
-Before building “final” images:
+Before building "final" images:
 
-Build your “base” image. Make sure it is named and tagged in accordance with your “final”
+Build your "base" image. Make sure it is named and tagged in accordance with your "final"
 Dockerfile. Skip this step if you want to build image of Tensorflow Version 1.9.0 and above.
 
 Then prepare the SageMaker TensorFlow Container python package in the image folder like below:
@@ -118,7 +118,7 @@ Then prepare the SageMaker TensorFlow Container python package in the image fold
     cd sagemaker-tensorflow-containers
     python setup.py sdist
 
-    #. Copy your Python package to “final” Dockerfile directory that you are building.
+    #. Copy your Python package to "final" Dockerfile directory that you are building.
     cp dist/sagemaker_tensorflow_container-<package_version>.tar.gz docker/<tensorflow_version>/final/py2
 
 If you want to build "final" Docker images, for versions 1.6 and above, you will first need to download the appropriate tensorflow pip wheel, then pass in its location as a build argument. These can be obtained from pypi. For example, the files for 1.6.0 are here:

benchmarks/README.md

@@ -0,0 +1,66 @@
+# TensorFlow benchmarking scripts
+
+This folder contains the TF training scripts https://github.com/tensorflow/benchmarks/tree/master/scripts/tf_cnn_benchmarks.
+
+## Basic usage
+**execute_tensorflow_training.py train** uses SageMaker python sdk to start a training job. 
+
+```bash
+./execute_tensorflow_training.py train --help
+Usage: execute_tensorflow_training.py train [OPTIONS] [SCRIPT_ARGS]...
+
+Options:
+  --framework-version [1.11.0|1.12.0]
+                                  [required]
+  --device [cpu|gpu]              [required]
+  --py-versions TEXT
+  --training-input-mode [File|Pipe]
+  --networking-isolation / --no-networking-isolation
+  --wait / --no-wait
+  --security-groups TEXT
+  --subnets TEXT
+  --role TEXT
+  --instance-counts INTEGER
+  --batch-sizes INTEGER
+  --instance-types TEXT
+  --help                          Show this message and exit.
+
+```
+**execute_tensorflow_training.py generate_reports** generate benchmark reports. 
+
+## Examples:
+
+```bash
+#!/usr/bin/env bash
+
+./execute_tensorflow_training.py train \
+--framework-version  1.11.0 \
+--device gpu \
+\
+--instance-types ml.p3.2xlarge \
+--instance-types ml.p3.8xlarge \
+--instance-types ml.p3.16xlarge \
+--instance-types ml.p2.xlarge \
+--instance-types ml.p2.8xlarge \
+--instance-types ml.p2.16xlarge \
+\
+--instance-counts 1 \
+\
+--py-versions py3 \
+--py-versions py2 \
+\
+--subnets subnet-125fb674  \
+\
+--security-groups sg-ce5dd1b4  \
+\
+--batch-sizes 32 \
+--batch-sizes 64 \
+--batch-sizes 128 \
+--batch-sizes 256 \
+--batch-sizes 512 \
+\
+-- --model resnet32 --num_epochs 10 --data_format NHWC --summary_verbosity 1 --save_summaries_steps 10 --data_name cifar10
+```
+
+## Using other models, datasets and benchmarks configurations
+```python tf_cnn_benchmarks/tf_cnn_benchmarks.py --help``` shows all the options that the script has.