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+# Download and Setup
+
+## Binary Installation
+
+### Ubuntu/Linux
+
+Make sure you have `pip` and `numpy` installed :
+
+```sh
+$ sudo apt-get install python-pip python-numpy
+```
+
+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
+
+# For GPU-enabled version
+$ 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` 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
+```
+
+### Try your first TensorFlow program
+
+```sh
+$ python
+
+>>> import tensorflow as tf
+>>> hello = tf.constant('Hello, TensorFlow!')
+>>> sess = tf.Session()
+>>> print sess.run(hello)
+Hello, TensorFlow!
+>>> a = tf.constant(10)
+>>> b = tf.constant(32)
+>>> print sess.run(a+b)
+42
+>>>
+
+```
+
+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.
+
+### Train the MNIST neural net model
+
+```sh
+$ 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.
+Succesfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.
+Succesfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.
+Extracting data/train-images-idx3-ubyte.gz
+Extracting data/train-labels-idx1-ubyte.gz
+Extracting data/t10k-images-idx3-ubyte.gz
+Extracting data/t10k-labels-idx1-ubyte.gz
+can't determine number of CPU cores: assuming 4
+I tensorflow/core/common_runtime/local_device.cc:25] Local device intra op
+parallelism threads: 3
+can't determine number of CPU cores: assuming 4
+I tensorflow/core/common_runtime/local_session.cc:45] Local session inter op
+parallelism threads: 4
+Initialized!
+Epoch 0.00
+Minibatch loss: 12.054, learning rate: 0.010000
+Minibatch error: 90.6%
+Validation error: 84.6%
+...
+...
+
+```
+
+## Source Installation {#source}
+
+### Clone the TensorFlow repository
+
+TODO(keveman): Supply clone command for external users.
+
+```sh
+$ git clone --recurse-submodules https://YOUR_WHITELISTED_EMAIL_WITH_AT_REPLACED_BY_DOT@tensorflow.googlesource.com/tf3
+```
+
+
+### Installation for Linux
+
+#### Install Bazel
+
+
+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
+$ git clone https://github.com/bazelbuild/bazel.git
+$ cd bazel
+$ git checkout tags/0.1.0
+$ ./compile.sh
+```
+
+These commands use the commit tag `0.1.0`, which is known to work with
+TensorFlow. `HEAD` may be unstable.
+
+Add the executable `output/bazel` to your `$PATH` environment variable.
+
+#### Install other dependencies
+
+```sh
+$ sudo apt-get install python-numpy swig python-dev
+```
+
+#### Optional: Install CUDA (GPUs on Linux)
+
+In order to build TensorFlow with GPU support, both Cuda Toolkit 7.0 and CUDNN
+6.5 V2 from NVIDIA need to be installed.
+
+##### Download and install Cuda Toolkit 7.0
+
+https://developer.nvidia.com/cuda-toolkit-70
+
+Install the toolkit into e.g. `/usr/local/cuda`
+
+##### Download and install CUDNN Toolkit 6.5
+
+https://developer.nvidia.com/rdp/cudnn-archive
+
+Uncompress and copy the cudnn files into the toolkit directory.  Assuming the
+toolkit is installed in `/usr/local/cuda`:
+
+``` bash
+tar xvzf cudnn-6.5-linux-x64-v2.tgz
+sudo cp cudnn-6.5-linux-x64-v2/cudnn.h /usr/local/cuda/include
+sudo cp cudnn-6.5-linux-x64-v2/libcudnn* /usr/local/cuda/lib64
+```
+
+##### Configure TensorFlow's canonical view of Cuda libraries
+From the root of your source tree, run:
+
+``` bash
+$ ./configure
+Do you wish to bulid TensorFlow with GPU support? [y/n] y
+GPU support will be enabled for TensorFlow
+
+Please specify the location where CUDA 7.0 toolkit is installed. Refer to
+README.md for more details. [default is: /usr/local/cuda]: /usr/local/cuda
+CUDA 7.0 toolkit found
+
+Please specify the location where CUDNN 6.5 V2 library is installed. Refer to
+README.md for more details. [default is: /usr/local/cuda]: /usr/local/cuda
+CUDNN 6.5 V2 library found
+
+Setting up Cuda include
+Setting up Cuda lib64
+Setting up Cuda bin
+Setting up Cuda nvvm
+Configuration finished
+```
+
+This creates a canonical set of symbolic links to the Cuda libraries on your system.
+Every time you change the Cuda library paths you need to run this step again before
+you invoke the bazel build command.
+
+##### Build your target with GPU support.
+From the root of your source tree, run:
+
+```sh
+$ bazel build -c opt --config=cuda //tensorflow/cc:tutorials_example_trainer
+
+$ bazel-bin/tensorflow/cc/tutorials_example_trainer --use_gpu
+# Lots of output. This tutorial iteratively calculates the major eigenvalue of
+# a 2x2 matrix, on GPU. The last few lines look like this.
+000009/000005 lambda = 2.000000 x = [0.894427 -0.447214] y = [1.788854 -0.894427]
+000006/000001 lambda = 2.000000 x = [0.894427 -0.447214] y = [1.788854 -0.894427]
+000009/000009 lambda = 2.000000 x = [0.894427 -0.447214] y = [1.788854 -0.894427]
+000006/000008 lambda = 2.000000 x = [0.894427 -0.447214] y = [1.788854 -0.894427]
+000009/000003 lambda = 2.000000 x = [0.894427 -0.447214] y = [1.788854 -0.894427]
+000006/000006 lambda = 2.000000 x = [0.894427 -0.447214] y = [1.788854 -0.894427]
+```
+
+Note that "--config=cuda" is needed to enable the GPU support.
+
+##### Known issues
+
+* Although it is possible to build both Cuda and non-Cuda configs under the same
+source tree, we recommend to run "bazel clean" when switching between these two
+configs in the same source tree.
+
+* You have to run configure before running bazel build. Otherwise, the build
+will fail with a clear error message. In the future, we might consider making
+this more conveninent by including the configure step in our build process,
+given necessary bazel new feature support.
+
+### Installation for Mac OS X
+
+Mac needs the same set of dependencies as Linux, however their installing those
+dependencies is different. Here is a set of useful links to help with installing
+the dependencies on Mac OS X :
+
+#### Bazel
+
+Look for installation instructions for Mac OS X on
+[this](http://bazel.io/docs/install.html) page.
+
+#### SWIG
+
+[Mac OS X installation](http://www.swig.org/Doc3.0/Preface.html#Preface_osx_installation).
+
+Notes : You need to install
+[PCRE](ftp://ftp.csx.cam.ac.uk/pub/software/programming/pcre/) and *NOT* PCRE2.
+
+#### Numpy
+
+Follow installation instructions [here](http://docs.scipy.org/doc/numpy/user/install.html).
+
+### Build and train your first TensorFlow neural net model
+
+```sh
+$ cd tf3
+
+$ bazel build tensorflow/models/image/mnist:convolutional
+
+$ bazel-bin/tensorflow/models/image/mnist/convolutional
+Succesfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.
+Succesfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.
+Succesfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.
+Succesfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.
+Extracting data/train-images-idx3-ubyte.gz
+Extracting data/train-labels-idx1-ubyte.gz
+Extracting data/t10k-images-idx3-ubyte.gz
+Extracting data/t10k-labels-idx1-ubyte.gz
+Initialized!
+Epoch 0.00
+Minibatch loss: 12.054, learning rate: 0.010000
+Minibatch error: 90.6%
+Validation error: 84.6%
+Epoch 0.12
+Minibatch loss: 3.285, learning rate: 0.010000
+Minibatch error: 6.2%
+Validation error: 7.0%
+...
+...
+```