# Installing TensorFlow on Instructional Machines with GPU support

These instructions were adapted from [here](http://ramhiser.com/2016/01/05/installing-tensorflow-on-an-aws-ec2-instance-with-gpu-support/).

The following was tested on the hive machines (e.g. `hive10.cs.berkeley.edu`).

## Install cuDNN and setup CUDA environment variables

Download and install cuDNN.

https://developer.nvidia.com/cudnn

Uncompress and copy the cuDNN files into the `~/local` directory.
```
mkdir local
mv cuda local
```

Define CUDA and cuDNN environment variables and add them to the path so that TensorFlow can find them. You may add these at the end of your `~/.bashrc` so that they are automatically defined next time you log in (assuming you are using bash as your shell).
```
export CUDA_HOME=/usr/local/cuda-7.5
export CUDA_ROOT=/usr/local/cuda-7.5
export PATH=$PATH:$CUDA_ROOT/bin
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CUDA_ROOT/lib64
export CUDNN_ROOT=~/local/cuda
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CUDNN_ROOT/lib64
```


## Install Bazel (needed to compile TensorFlow)

Download and install Bazel.
```
wget https://github.com/bazelbuild/bazel/releases/download/0.1.4/bazel-0.1.4-installer-linux-x86_64.sh
chmod +x bazel-0.1.4-installer-linux-x86_64.sh
./bazel-0.1.4-installer-linux-x86_64.sh --user
```
If the installation was successful, the compiled Basel binaries should be in `~/bin`. Add this directory to your `PATH`.
```bash
export PATH=~/bin:$PATH
```


## Install TensorFlow
Clone the TensorFlow repo and initialize all submodules using their default settings.
```
git clone --recurse-submodules https://github.com/tensorflow/tensorflow
cd tensorflow
```

Configure the TensorFlow options to build it with GPU support using CUDA compute capability 3.0 (this is what the instructional machines support) via the unofficial settings.
```
TF_UNOFFICIAL_SETTING=1 ./configure
```
You will be asked for settings and you should specify the following answers (or press ENTER to specify the default one).
```
Please specify the location of python. [Default is /usr/bin/python]: 
Do you wish to build TensorFlow with GPU support? [y/N] y
GPU support will be enabled for TensorFlow
Please specify which gcc nvcc should use as the host compiler. [Default is /usr/bin/gcc]: 
Please specify the Cuda SDK version you want to use, e.g. 7.0. [Leave empty to use system default]: 7.5
Please specify the location where CUDA 7.5 toolkit is installed. Refer to README.md for more details. [Default is /usr/local/cuda]: /usr/local/cuda-7.5
Please specify the Cudnn version you want to use. [Leave empty to use system default]: 
Please specify the location where cuDNN  library is installed. Refer to README.md for more details. [Default is /usr/local/cuda-7.5]: ~/local/cuda
Please specify a list of comma-separated Cuda compute capabilities you want to build with.
You can find the compute capability of your device at: https://developer.nvidia.com/cuda-gpus.
Please note that each additional compute capability significantly increases your build time and binary size.
[Default is: "3.5,5.2"]: 3.0
Setting up Cuda include
Setting up Cuda lib64
Setting up Cuda bin
Setting up Cuda nvvm
Configuration finished
```

Build TensorFlow using Bazel. This should take a few minutes.
```
bazel build -c opt --config=cuda //tensorflow/cc:tutorials_example_trainer && \
bazel build -c opt --config=cuda //tensorflow/tools/pip_package:build_pip_package && \
bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg
```

Install the built binaries using `pip` so that your python program can find the TensorFlow libaries.
```
pip install --user --upgrade /tmp/tensorflow_pkg/tensorflow-0.7.1-cp27-none-linux_x86_64.whl
```

If the installation was succesful, you should be able to import and use TensorFlow in python. Make sure you start python *outside* the `tensorflow` directory.
```python
import tensorflow as tf
tf_session = tf.Session()
x = tf.constant(1)
y = tf.constant(1)
tf_session.run(x + y)
```