TensorFlow: Upstream latest changes to git.

Changes:
- Documentation changes: adding some examples
  for adding_an_op, fixes to some of the markdown,
  updates to docstrings, etc.

- Remove Dockerfile for now -- still undergoing
  changes.

Base CL: 107341050

3 files changed, 97 insertions, 39 deletions

diff --git a/tensorflow/g3doc/get_started/basic_usage.md b/tensorflow/g3doc/get_started/basic_usage.md
index a41ec36d56..7616c3f7ea 100644
--- a/tensorflow/g3doc/get_started/basic_usage.md
+++ b/tensorflow/g3doc/get_started/basic_usage.md
@@ -15,7 +15,7 @@ graphs.  Nodes in the graph are called *ops* (short for operations).  An op
 takes zero or more `Tensors`, performs some computation, and produces zero or
 more `Tensors`.  A `Tensor` is a typed multi-dimensional array. For example,
 you can represent a  mini-batch of images as a 4-D array of floating point
-numbers with dimensions `[batch, height, width, channels]`).
+numbers with dimensions `[batch, height, width, channels]`.
 
 A TensorFlow graph is a *description* of computations.  To compute anything,
 a graph must be launched in a `Session`.  A `Session` places the graph ops onto
diff --git a/tensorflow/g3doc/get_started/index.md b/tensorflow/g3doc/get_started/index.md
index f0222e818d..9476408a54 100644
--- a/tensorflow/g3doc/get_started/index.md
+++ b/tensorflow/g3doc/get_started/index.md
@@ -53,11 +53,11 @@ of MNIST, definitely take the blue pill.  If you're somewhere in between, we
 suggest skimming blue, then red.
 
 <div style="width:100%; margin:auto; margin-bottom:10px; margin-top:20px; display: flex; flex-direction: row">
- <a href="../tutorials/mnist/beginners/index.md">
-   <img style="flex-grow:1; flex-shrink:1; border: 1px solid black;" src="blue_pill.png">
+ <a href="../tutorials/mnist/beginners/index.md" title="MNIST for ML Beginners tutorial">
+   <img style="flex-grow:1; flex-shrink:1; border: 1px solid black;" src="blue_pill.png" alt="MNIST for machine learning beginners tutorial" />
  </a>
- <a href="../tutorials/mnist/pros/index.md">
-   <img style="flex-grow:1; flex-shrink:1; border: 1px solid black;" src="red_pill.png">
+ <a href="../tutorials/mnist/pros/index.md" title="Deep MNIST for ML Experts tutorial">
+   <img style="flex-grow:1; flex-shrink:1; border: 1px solid black;" src="red_pill.png" alt="Deep MNIST for machine learning experts tutorial" />
  </a>
 </div>
 <p style="font-size:10px;">Images licensed CC BY-SA 4.0; original by W. Carter</p>
diff --git a/tensorflow/g3doc/get_started/os_setup.md b/tensorflow/g3doc/get_started/os_setup.md
index f6b6bb4015..4db07c233b 100644
--- a/tensorflow/g3doc/get_started/os_setup.md
+++ b/tensorflow/g3doc/get_started/os_setup.md
@@ -4,36 +4,82 @@
 
 ### Ubuntu/Linux <a class="md-anchor" id="AUTOGENERATED-ubuntu-linux"></a>
 
-Make sure you have [pip](https://pypi.python.org/pypi/pip) installed:
+Make sure you have [pip](https://pypi.python.org/pypi/pip), the python headers,
+and (optionally) [virtualenv](https://pypi.python.org/pypi/virtualenv) installed:
 
-```sh
-$ sudo apt-get install python-pip
+```bash
+$ sudo apt-get install python-pip python-dev python-virtualenv
 ```
 
-Install TensorFlow:
+**Note**: All the virtualenv-related instructions are optional, but we recommend
+using the virtualenv on any multi-user system.
 
-```sh
+Set up a new virtualenv environment.  Assuming you want to set it up in the
+directory `~/tensorflow`, run:
+
+```bash
+$ virtualenv --system-site-packages ~/tensorflow
+$ cd ~/tensorflow
+```
+
+Activate the virtualenv:
+
+```bash
+$ source bin/activate  # If using bash
+$ source bin/activate.csh  # If using csh
+(tensorflow)$  # Your prompt should change
+```
+
+Inside the virtualenv, install TensorFlow:
+
+```bash
 # For CPU-only version
-$ sudo pip install https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.5.0-cp27-none-linux_x86_64.whl
+(tensorflow)$ pip install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.5.0-cp27-none-linux_x86_64.whl
 
-# For GPU-enabled version
-$ sudo pip install https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.5.0-cp27-none-linux_x86_64.whl
+# For GPU-enabled version (only install this version if you have the CUDA sdk installed)
+(tensorflow)$ pip install --upgrade https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.5.0-cp27-none-linux_x86_64.whl
+
+(tensorflow)$ deactivate  # Deactivate the virtualenv
+$  # Your prompt should change back
 ```
 
 ### Mac OS X <a class="md-anchor" id="AUTOGENERATED-mac-os-x"></a>
 
-Make sure you have [pip](https://pypi.python.org/pypi/pip) installed:
+Make sure you have [pip](https://pypi.python.org/pypi/pip) and
+(optionally) [virtualenv](https://pypi.python.org/pypi/virtualenv) installed:
+
+**Note**: All the virtualenv-related instructions are optional, but we recommend
+using the virtualenv on any multi-user system.
 
 If using `easy_install`:
 
-```sh
-$ sudo easy_install pip
+```bash
+$ sudo easy_install pip  # If pip is not already installed
+$ sudo pip install --upgrade virtualenv
+```
+
+Set up a new virtualenv environment.  Assuming you want to set it up in the
+directory `~/tensorflow`, run:
+
+```bash
+$ virtualenv --system-site-packages ~/tensorflow
+$ cd ~/tensorflow
+```
+
+Activate the virtualenv:
+
+```bash
+$ source bin/activate  # If using bash
+$ source bin/activate.csh  # If using csh
+(tensorflow)$  # Your prompt should change
 ```
 
 Install TensorFlow (only CPU binary version is currently available).
 
-```sh
-$ sudo pip install https://storage.googleapis.com/tensorflow/mac/tensorflow-0.5.0-py2-none-any.whl
+```bash
+(tensorflow)$ pip install --upgrade https://storage.googleapis.com/tensorflow/mac/tensorflow-0.5.0-py2-none-any.whl
+(tensorflow)$ deactivate  # Deactivate the virtualenv
+$  # Your prompt should change back
 ```
 
 ## Docker-based installation <a class="md-anchor" id="AUTOGENERATED-docker-based-installation"></a>
@@ -44,7 +90,7 @@ you avoid worrying about setting up dependencies.
 First, [install Docker](http://docs.docker.com/engine/installation/). Once
 Docker is up and running, you can start a container with one command:
 
-```sh
+```bash
 $ docker run -it b.gcr.io/tensorflow/tensorflow
 ```
 
@@ -64,8 +110,28 @@ which you can use in the `docker run` command above:
 
 ## Try your first TensorFlow program <a class="md-anchor" id="AUTOGENERATED-try-your-first-tensorflow-program"></a>
 
-```sh
-$ python
+### (Optional) Enable GPU Support <a class="md-anchor" id="AUTOGENERATED--optional--enable-gpu-support"></a>
+
+If you installed the GPU-enabled TensorFlow pip binary, you must have the
+correct versions of the CUDA SDK and CUDNN installed on your
+system.  Please see [the CUDA installation instructions](#install_cuda).
+
+You also need to set the `LD_LIBRARY_PATH` and `CUDA_HOME` environment
+variables.  Consider adding the commands below to your `~/.bash_profile`.  These
+assume your CUDA installation is in `/usr/local/cuda`:
+
+```bash
+export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/cuda/lib64"
+export CUDA_HOME=/usr/local/cuda
+```
+
+### Run TensorFlow <a class="md-anchor" id="AUTOGENERATED-run-tensorflow"></a>
+
+First, activate the TensorFlow virtualenv, then open a python terminal:
+
+```bash
+$ source ~/tensorflow/bin/activate  # Assuming the tensorflow virtualenv is ~/tensorflow
+(tensorflow)$ python
 
 >>> import tensorflow as tf
 >>> hello = tf.constant('Hello, TensorFlow!')
@@ -80,20 +146,12 @@ Hello, TensorFlow!
 
 ```
 
-If you are running the GPU version and you see
-```sh
-ImportError: libcudart.so.7.0: cannot open shared object file: No such file or directory
-```
-
-you most likely need to set your `LD_LIBRARY_PATH` to point to the location of
-your CUDA libraries.
-
 ## Installing from sources <a class="md-anchor" id="source"></a>
 
 ### Clone the TensorFlow repository <a class="md-anchor" id="AUTOGENERATED-clone-the-tensorflow-repository"></a>
 
-```sh
-$ git clone --recurse-submodules https://tensorflow.googlesource.com/tensorflow
+```bash
+$ git clone --recurse-submodules https://github.com/tensorflow/tensorflow
 ```
 
 `--recurse-submodules` is required to fetch the protobuf library that TensorFlow
@@ -108,7 +166,7 @@ Follow instructions [here](http://bazel.io/docs/install.html) to install the
 dependencies for Bazel. Then download and build the Bazel source with the
 following commands:
 
-```sh
+```bash
 $ git clone https://github.com/bazelbuild/bazel.git
 $ cd bazel
 $ git checkout tags/0.1.0
@@ -122,14 +180,14 @@ Add the executable `output/bazel` to your `$PATH` environment variable.
 
 #### Install other dependencies <a class="md-anchor" id="AUTOGENERATED-install-other-dependencies"></a>
 
-```sh
+```bash
 $ sudo apt-get install python-numpy swig python-dev
 ```
 
-#### Optional: Install CUDA (GPUs on Linux) <a class="md-anchor" id="AUTOGENERATED-optional--install-cuda--gpus-on-linux-"></a>
+#### <a name="install_cuda"></a>Optional: Install CUDA (GPUs on Linux) <a class="md-anchor" id="AUTOGENERATED--a-name--install_cuda----a-optional--install-cuda--gpus-on-linux-"></a>
 
-In order to build TensorFlow with GPU support, both Cuda Toolkit 7.0 and CUDNN
-6.5 V2 from NVIDIA need to be installed.
+In order to build or run TensorFlow with GPU support, both Cuda Toolkit 7.0 and
+CUDNN 6.5 V2 from NVIDIA need to be installed.
 
 TensorFlow GPU support requires having a GPU card with NVidia Compute Capability >= 3.5.  Supported cards include but are not limited to:
 
@@ -185,7 +243,7 @@ you invoke the bazel build command.
 ##### Build your target with GPU support. <a class="md-anchor" id="AUTOGENERATED-build-your-target-with-gpu-support."></a>
 From the root of your source tree, run:
 
-```sh
+```bash
 $ bazel build -c opt --config=cuda //tensorflow/cc:tutorials_example_trainer
 
 $ bazel-bin/tensorflow/cc/tutorials_example_trainer --use_gpu
@@ -234,7 +292,7 @@ Follow installation instructions [here](http://docs.scipy.org/doc/numpy/user/ins
 
 ### Create the pip package and install <a class="md-anchor" id="create-pip"></a>
 
-```sh
+```bash
 $ bazel build -c opt //tensorflow/tools/pip_package:build_pip_package
 
 $ bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg
@@ -247,7 +305,7 @@ $ pip install /tmp/tensorflow_pkg/tensorflow-0.5.0-cp27-none-linux_x86_64.whl
 
 From the root of your source tree, run:
 
-```sh
+```python
 $ python tensorflow/models/image/mnist/convolutional.py
 Succesfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.
 Succesfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.