TensorFlow: Upstream changes to git.

Changes: - Updates to installation instructions. Base CL: 107352130
AshBT · Nov 9, 2015 · dd0e509 · dd0e509
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diff --git a/README.md b/README.md
@@ -24,45 +24,34 @@ and discussion.**
 
 # Download and Setup
 
-To install TensorFlow using a binary package, see the instructions below.  For
-more detailed installation instructions, including installing from source, see
+To install the CPU version of TensorFlow using a binary package, see the
+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
 
-### Ubuntu/Linux
+The TensorFlow Python API requires Python 2.7.
 
-Make sure you have [pip](https://pypi.python.org/pypi/pip) installed:
+The simplest way to install TensorFlow is using
+[pip](https://pypi.python.org/pypi/pip) for both Linux and Mac.
 
-```sh
-$ sudo apt-get install python-pip
-```
+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).
 
-Install TensorFlow:
+### Ubuntu/Linux
 
-```sh
+```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
-
-# 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
+$ pip install https://storage.googleapis.com/tensorflow/linux/cpu/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:
-
-If using `easy_install`:
-
-```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
+```bash
+# Only CPU-version is available at the moment.
+$ 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)
diff --git a/tensorflow/g3doc/extras/tensorflow-whitepaper2015.pdf b/tensorflow/g3doc/extras/tensorflow-whitepaper2015.pdf
diff --git a/tensorflow/g3doc/get_started/os_setup.md b/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.
 
-### 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:
+The simplest way to install TensorFlow is using
+[pip](https://pypi.python.org/pypi/pip) for both Linux and Mac.
 
-```bash
-$ source bin/activate  # If using bash
-$ source bin/activate.csh  # If using csh
-(tensorflow)$  # Your prompt should change
-```
+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).
 
-Inside the virtualenv, install TensorFlow:
+### Ubuntu/Linux <a class="md-anchor" id="AUTOGENERATED-ubuntu-linux"></a>
 
 ```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
-
-# When you are done using TensorFlow:
-(tensorflow)$ deactivate  # Deactivate the virtualenv
-$  # Your prompt should change back
+$ pip install https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.5.0-cp27-none-linux_x86_64.whl
 ```
 
 ### 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
-$ 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).
-
-```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
+# Only CPU-version is available at the moment.
+$ pip install https://storage.googleapis.com/tensorflow/mac/tensorflow-0.5.0-py2-none-any.whl
 ```
 
 ## 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
-(tensorflow)$ python
+$ 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.
+
+
+
+
diff --git a/tensorflow/g3doc/how_tos/index.md b/tensorflow/g3doc/how_tos/index.md
@@ -1,15 +1,15 @@
-# 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.
 
 [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
@@ -28,54 +28,54 @@ 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.
 
 [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.
 
 [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.
 
 [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.
 
 [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.
 
 [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

diff --git a/tensorflow/g3doc/navbar.md b/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