TensorFlow: Upstream changes to git.

Changes:
- Updates to installation instructions.

Base CL: 107352130

README.md

@@ -24,45 +24,34 @@ and discussion.**
 
 # Download and Setup
 
-To install TensorFlow using a binary package, see the instructions below.  For
+To install the CPU version of TensorFlow using a binary package, see the
-more detailed installation instructions, including installing from source, see
+instructions below.  For more detailed installation instructions, including
+installing from source, GPU-enabled support, etc., see
 [here](tensorflow/g3doc/get_started/os_setup.md).
 
 ## Binary Installation
 
+The TensorFlow Python API requires Python 2.7.
+
+The simplest way to install TensorFlow is using
+[pip](https://pypi.python.org/pypi/pip) for both Linux and Mac.
+
+For the GPU-enabled version, or if you encounter installation errors, or for
+more detailed installation instructions, see
+[here](tensorflow/g3doc/get_started/os_setup.md#detailed_install).
+
 ### Ubuntu/Linux
 
-Make sure you have [pip](https://pypi.python.org/pypi/pip) installed:
+```bash
-
-```sh
-$ sudo apt-get install python-pip
-```
-
-Install TensorFlow:
-
-```sh
 # For CPU-only version
-$ sudo pip install https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.5.0-cp27-none-linux_x86_64.whl
+$ pip install https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.5.0-cp27-none-linux_x86_64.whl
-
-# For GPU-enabled version.  See detailed install instructions
-# for GPU configuration information.
-$ sudo pip install https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.5.0-cp27-none-linux_x86_64.whl
 ```
 
 ### Mac OS X
 
-Make sure you have [pip](https://pypi.python.org/pypi/pip) installed:
+```bash
-
+# Only CPU-version is available at the moment.
-If using `easy_install`:
+$ pip install https://storage.googleapis.com/tensorflow/mac/tensorflow-0.5.0-py2-none-any.whl
-
-```sh
-$ sudo easy_install pip
-```
-
-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
 ```
 
 ### Try your first TensorFlow program
@@ -83,7 +72,6 @@ Hello, TensorFlow!
 
 ```
 
-
 ##For more information
 
 * [TensorFlow website](http://tensorflow.org)

tensorflow/g3doc/get_started/os_setup.md

@@ -1,90 +1,33 @@
 # Download and Setup <a class="md-anchor" id="AUTOGENERATED-download-and-setup"></a>
 
+You can install TensorFlow using our provided binary packages or from source.
+
 ## Binary Installation <a class="md-anchor" id="AUTOGENERATED-binary-installation"></a>
 
 The TensorFlow Python API requires Python 2.7.
 
+The simplest way to install TensorFlow is using
+[pip](https://pypi.python.org/pypi/pip) for both Linux and Mac.
+
+If you encounter installation errors, see [common problems](#common_problems)
+for some solutions. To simplify installation, please consider using our
+virtualenv-based instructions [here](#virtualenv_install).
+
 ### Ubuntu/Linux <a class="md-anchor" id="AUTOGENERATED-ubuntu-linux"></a>
 
-**Note**: All the virtualenv-related instructions are optional, but we recommend
-using the virtualenv on any multi-user system.
-
-Make sure you have [pip](https://pypi.python.org/pypi/pip), the python headers,
-and (optionally) [virtualenv](https://pypi.python.org/pypi/virtualenv) installed:
-
-```bash
-$ sudo apt-get install python-pip python-dev python-virtualenv
-```
-
-Set up a new virtualenv environment.  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
-(tensorflow)$ pip install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.5.0-cp27-none-linux_x86_64.whl
+$ pip install https://storage.googleapis.com/tensorflow/linux/cpu/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
+$ pip install https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.5.0-cp27-none-linux_x86_64.whl
-
-# When you are done using TensorFlow:
-(tensorflow)$ deactivate  # Deactivate the virtualenv
-$  # Your prompt should change back
 ```
 
 ### Mac OS X <a class="md-anchor" id="AUTOGENERATED-mac-os-x"></a>
 
-**Note**: All the virtualenv-related instructions are optional, but we recommend
-using the virtualenv on any multi-user system.
-
-Make sure you have [pip](https://pypi.python.org/pypi/pip) and
-(optionally) [virtualenv](https://pypi.python.org/pypi/virtualenv) installed:
-
-If using `easy_install`:
-
 ```bash
-$ sudo easy_install pip  # If pip is not already installed
+# Only CPU-version is available at the moment.
-$ sudo pip install --upgrade virtualenv
+$ pip install https://storage.googleapis.com/tensorflow/mac/tensorflow-0.5.0-py2-none-any.whl
-```
-
-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).
-
-```bash
-(tensorflow)$ pip install --upgrade https://storage.googleapis.com/tensorflow/mac/tensorflow-0.5.0-py2-none-any.whl
-
-# When you are done using TensorFlow:
-(tensorflow)$ deactivate  # Deactivate the virtualenv
-$  # Your prompt should change back
 ```
 
 ## Docker-based installation <a class="md-anchor" id="AUTOGENERATED-docker-based-installation"></a>
@@ -132,11 +75,10 @@ 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:
+Open a python terminal:
 
 ```bash
-$ source ~/tensorflow/bin/activate  # Assuming the tensorflow virtualenv is ~/tensorflow
+$ python
-(tensorflow)$ python
 
 >>> import tensorflow as tf
 >>> hello = tf.constant('Hello, TensorFlow!')
@@ -332,3 +274,100 @@ Validation error: 7.0%
 ...
 ...
 ```
+
+## VirtualEnv-based installation <a class="md-anchor" id="virtualenv_install"></a>
+
+We recommend using [virtualenv](https://pypi.python.org/pypi/virtualenv) to
+create an isolated container and install TensorFlow in that container -- it is
+optional but makes verifying installation issues easier.
+
+First, install all required tools:
+
+```bash
+# On Linux:
+$ sudo apt-get install python-pip python-dev python-virtualenv
+
+# On Mac:
+$ sudo easy_install pip  # If pip is not already installed
+$ sudo pip install --upgrade virtualenv
+```
+
+Next, set up a new virtualenv environment.  To set it up in the
+directory `~/tensorflow`, run:
+
+```bash
+$ virtualenv --system-site-packages ~/tensorflow
+$ cd ~/tensorflow
+```
+
+Then 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
+(tensorflow)$ pip install --upgrade <$url_to_binary.whl>
+```
+
+You can then run your TensorFlow program like:
+
+```bash
+(tensorflow)$ python tensorflow/models/image/mnist/convolutional.py
+
+# When you are done using TensorFlow:
+(tensorflow)$ deactivate  # Deactivate the virtualenv
+
+$  # Your prompt should change back
+```
+
+
+## Common Problems <a class="md-anchor" id="AUTOGENERATED-common-problems"></a>
+
+### GPU-related issues <a class="md-anchor" id="AUTOGENERATED-gpu-related-issues"></a>
+
+If you encounter the following when trying to run a TensorFlow program:
+
+```python
+ImportError: libcudart.so.7.0: cannot open shared object file: No such file or directory
+```
+
+Make sure you followed the the GPU installation [instructions](#install_cuda).
+
+### On Linux <a class="md-anchor" id="AUTOGENERATED-on-linux"></a>
+
+If you encounter:
+
+```python
+...
+ "__add__", "__radd__",
+             ^
+SyntaxError: invalid syntax
+```
+
+Solution: make sure you are using Python 2.7.
+
+### On MacOSX <a class="md-anchor" id="AUTOGENERATED-on-macosx"></a>
+
+
+If you encounter:
+
+```python
+    import six.moves.copyreg as copyreg
+
+ImportError: No module named copyreg
+```
+
+Solution: TensorFlow depends on protobuf which require six-1.10.0. The
+installation on some machines may only have an earlier version of six that was
+installed using distutils.  Unfortunately, upgrading a distutils installed
+project via `pip` is deprecated and may fail.  If you having difficulty
+upgrading six, we recommend playing around with tensorflow using virtualenv.
+
+
+
+

tensorflow/g3doc/how_tos/index.md

@@ -1,7 +1,7 @@
-# Overview <a class="md-anchor" id="AUTOGENERATED-overview"></a>
+# Overview
 
 
-## Variables: Creation, Initializing, Saving, and Restoring <a class="md-anchor" id="AUTOGENERATED-variables--creation--initializing--saving--and-restoring"></a>
+## Variables: Creation, Initializing, Saving, and Restoring
 
 TensorFlow Variables are in-memory buffers containing tensors.  Learn how to
 use them to hold and update model parameters during training.
@@ -9,7 +9,7 @@ use them to hold and update model parameters during training.
 [View Tutorial](../how_tos/variables/index.md)
 
 
-## TensorFlow Mechanics 101 <a class="md-anchor" id="AUTOGENERATED-tensorflow-mechanics-101"></a>
+## TensorFlow Mechanics 101
 
 A step-by-step walk through of the details of using TensorFlow infrastructure
 to train models at scale, using MNIST handwritten digit recognition as a toy
@@ -18,7 +18,7 @@ example.
 [View Tutorial](../tutorials/mnist/tf/index.md)
 
 
-## TensorBoard: Visualizing Learning <a class="md-anchor" id="AUTOGENERATED-tensorboard--visualizing-learning"></a>
+## TensorBoard: Visualizing Learning
 
 TensorBoard is a useful tool for visualizing the training and evaluation of
 your model(s).  This tutorial describes how to build and run TensorBoard as well
@@ -28,7 +28,7 @@ TensorBoard uses for display.
 [View Tutorial](../how_tos/summaries_and_tensorboard/index.md)
 
 
-## TensorBoard: Graph Visualization <a class="md-anchor" id="AUTOGENERATED-tensorboard--graph-visualization"></a>
+## TensorBoard: Graph Visualization
 
 This tutorial describes how to use the graph visualizer in TensorBoard to help
 you understand the dataflow graph and debug it.
@@ -36,7 +36,7 @@ you understand the dataflow graph and debug it.
 [View Tutorial](../how_tos/graph_viz/index.md)
 
 
-## Reading Data <a class="md-anchor" id="AUTOGENERATED-reading-data"></a>
+## Reading Data
 
 This tutorial describes the three main methods of getting data into your
 TensorFlow program: Feeding, Reading and Preloading.
@@ -44,7 +44,7 @@ TensorFlow program: Feeding, Reading and Preloading.
 [View Tutorial](../how_tos/reading_data/index.md)
 
 
-## Threading and Queues <a class="md-anchor" id="AUTOGENERATED-threading-and-queues"></a>
+## Threading and Queues
 
 This tutorial describes the various constructs implemented by TensorFlow
 to facilitate asynchronous and concurrent training.
@@ -52,7 +52,7 @@ to facilitate asynchronous and concurrent training.
 [View Tutorial](../how_tos/threading_and_queues/index.md)
 
 
-## Adding a New Op <a class="md-anchor" id="AUTOGENERATED-adding-a-new-op"></a>
+## Adding a New Op
 
 TensorFlow already has a large suite of node operations from which you can
 compose in your graph, but here are the details of how to add you own custom Op.
@@ -60,7 +60,7 @@ compose in your graph, but here are the details of how to add you own custom Op.
 [View Tutorial](../how_tos/adding_an_op/index.md)
 
 
-## Custom Data Readers <a class="md-anchor" id="AUTOGENERATED-custom-data-readers"></a>
+## Custom Data Readers
 
 If you have a sizable custom data set, you may want to consider extending
 TensorFlow to read your data directly in it's native format.  Here's how.
@@ -68,14 +68,14 @@ TensorFlow to read your data directly in it's native format.  Here's how.
 [View Tutorial](../how_tos/new_data_formats/index.md)
 
 
-## Using GPUs <a class="md-anchor" id="AUTOGENERATED-using-gpus"></a>
+## Using GPUs
 
 This tutorial describes how to construct and execute models on GPU(s).
 
 [View Tutorial](../how_tos/using_gpu/index.md)
 
 
-## Sharing Variables <a class="md-anchor" id="AUTOGENERATED-sharing-variables"></a>
+## Sharing Variables
 
 When deploying large models on multiple GPUs, or when unrolling complex LSTMs
 or RNNs, it is often necessary to access the same Variable objects from

tensorflow/g3doc/navbar.md

@@ -0,0 +1,11 @@
+# TensorFlow
+
+* [Home][home]
+* [Getting Started](/get_started/index.md)
+* [Mechanics](/how_tos/index.md)
+* [Tutorials](/tutorials/index.md)
+* [Python API](/api_docs/python/index.md)
+* [C++ API](/api_docs/cc/index.md)
+* [Other Resources](/resources/index.md)
+
+[home]: /index.md

tensorflow/g3doc/resources/dims_types.md

@@ -15,7 +15,7 @@ Python list) has a rank of 2:
 
     t = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
 
-A rank two tensor is what we typically think of as a matrix, a rank on tensor
+A rank two tensor is what we typically think of as a matrix, a rank one tensor
 is a vector. For a rank two tensor you can acccess any element with the syntax
 `t[i, j]`.  For a rank three tensor you would need to address an element with
 't[i, j, k]'.
@@ -65,4 +65,3 @@ Data type | Python type | Description
 `DT_QINT32` | `tf.qint32` | 32 bits signed integer used in quantized Ops.
 `DT_QINT8` | `tf.qint8` | 8 bits signed integer used in quantized Ops.
 `DT_QUINT8` | `tf.quint8` | 8 bits unsigned integer used in quantized Ops.
-

tensorflow/g3doc/resources/index.md

@@ -6,7 +6,7 @@
 Additional details about the TensorFlow programming model and the underlying
 implementation can be found in out white paper:
 
-* [TensorFlow: Large-scale machine learning on heterogeneous systems](http://tensorflow.org/tensorflow-whitepaper2015.pdf)
+* [TensorFlow: Large-scale machine learning on heterogeneous systems](http://www.tensorflow.org/whitepaper2015.pdf)
 
 ### Citation <a class="md-anchor" id="AUTOGENERATED-citation"></a>
 

tensorflow/g3doc/tutorials/index.md

@@ -1,7 +1,7 @@
-# Overview <a class="md-anchor" id="AUTOGENERATED-overview"></a>
+# Overview
 
 
-## MNIST For ML Beginners <a class="md-anchor" id="AUTOGENERATED-mnist-for-ml-beginners"></a>
+## MNIST For ML Beginners
 
 If you're new to machine learning, we recommend starting here.  You'll learn
 about a classic problem, handwritten digit classification (MNIST), and get a
@@ -10,7 +10,7 @@ gentle introduction to multiclass classification.
 [View Tutorial](../tutorials/mnist/beginners/index.md)
 
 
-## Deep MNIST for Experts <a class="md-anchor" id="AUTOGENERATED-deep-mnist-for-experts"></a>
+## Deep MNIST for Experts
 
 If you're already familiar with other deep learning software packages, and are
 already familiar with MNIST, this tutorial with give you a very brief primer on
@@ -19,7 +19,7 @@ TensorFlow.
 [View Tutorial](../tutorials/mnist/pros/index.md)
 
 
-## TensorFlow Mechanics 101 <a class="md-anchor" id="AUTOGENERATED-tensorflow-mechanics-101"></a>
+## TensorFlow Mechanics 101
 
 This is a technical tutorial, where we walk you through the details of using
 TensorFlow infrastructure to train models at scale.  We use again MNIST as the
@@ -28,7 +28,7 @@ example.
 [View Tutorial](../tutorials/mnist/tf/index.md)
 
 
-## Convolutional Neural Networks <a class="md-anchor" id="AUTOGENERATED-convolutional-neural-networks"></a>
+## Convolutional Neural Networks
 
 An introduction to convolutional neural networks using the CIFAR-10 data set.
 Convolutional neural nets are particularly tailored to images, since they
@@ -38,7 +38,7 @@ representations of visual content.
 [View Tutorial](../tutorials/deep_cnn/index.md)
 
 
-## Vector Representations of Words <a class="md-anchor" id="AUTOGENERATED-vector-representations-of-words"></a>
+## Vector Representations of Words
 
 This tutorial motivates why it is useful to learn to represent words as vectors
 (called *word embeddings*). It introduces the word2vec model as an efficient
@@ -49,7 +49,7 @@ embeddings).
 [View Tutorial](../tutorials/word2vec/index.md)
 
 
-## Recurrent Neural Networks <a class="md-anchor" id="AUTOGENERATED-recurrent-neural-networks"></a>
+## Recurrent Neural Networks
 
 An introduction to RNNs, wherein we train an LSTM network to predict the next
 word in an English sentence.  (A task sometimes called language modeling.)
@@ -57,7 +57,7 @@ word in an English sentence.  (A task sometimes called language modeling.)
 [View Tutorial](../tutorials/recurrent/index.md)
 
 
-## Sequence-to-Sequence Models <a class="md-anchor" id="AUTOGENERATED-sequence-to-sequence-models"></a>
+## Sequence-to-Sequence Models
 
 A follow on to the RNN tutorial, where we assemble a sequence-to-sequence model
 for machine translation.  You will learn to build your own English-to-French
@@ -66,7 +66,7 @@ translator, entirely machine learned, end-to-end.
 [View Tutorial](../tutorials/seq2seq/index.md)
 
 
-## Mandelbrot Set <a class="md-anchor" id="AUTOGENERATED-mandelbrot-set"></a>
+## Mandelbrot Set
 
 TensorFlow can be used for computation that has nothing to do with machine
 learning.  Here's a naive implementation of Mandelbrot set visualization.
@@ -74,7 +74,7 @@ learning.  Here's a naive implementation of Mandelbrot set visualization.
 [View Tutorial](../tutorials/mandelbrot/index.md)
 
 
-## Partial Differential Equations <a class="md-anchor" id="AUTOGENERATED-partial-differential-equations"></a>
+## Partial Differential Equations
 
 As another example of non-machine learning computation, we offer an example of
 a naive PDE simulation of raindrops landing on a pond.
@@ -82,7 +82,7 @@ a naive PDE simulation of raindrops landing on a pond.
 [View Tutorial](../tutorials/pdes/index.md)
 
 
-## MNIST Data Download <a class="md-anchor" id="AUTOGENERATED-mnist-data-download"></a>
+## MNIST Data Download
 
 Details about downloading the MNIST handwritten digits data set.  Exciting
 stuff.
@@ -90,7 +90,7 @@ stuff.
 [View Tutorial](../tutorials/mnist/download/index.md)
 
 
-## Visual Object Recognition <a class="md-anchor" id="AUTOGENERATED-visual-object-recognition"></a>
+## Visual Object Recognition
 
 We will be releasing our state-of-the-art Inception object recognition model,
 complete and already trained.
@@ -98,7 +98,7 @@ complete and already trained.
 COMING SOON
 
 
-## Deep Dream Visual Hallucinations <a class="md-anchor" id="AUTOGENERATED-deep-dream-visual-hallucinations"></a>
+## Deep Dream Visual Hallucinations
 
 Building on the Inception recognition model, we will release a TensorFlow
 version of the [Deep Dream](https://github.com/google/deepdream) neural network

tensorflow/g3doc/tutorials/word2vec/index.md

@@ -106,7 +106,7 @@ P(w_t | h) &= \text{softmax}(\exp \{ \text{score}(w_t, h) \}) \\
 \end{align}
 $$
 
-where \\(\text{score}(w_t, h)\\) computes the compatibility of word \\(w_t\\) with
+where \\( \text{score}(w_t, h) \\) computes the compatibility of word \\(w_t\\) with
 the context \\(h\\) (a dot product is commonly used). We train this model by
 maximizing its log-likelihood on the training set, i.e. by maximizing