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Finish draft of tensorflow install post

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Tyler Hallada 2017-06-20 18:11:12 -04:00
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@ -101,8 +101,97 @@ to see if you get any output. Hopefully you will see your GPU listed.
## Install cuDNN v5.1
[This AskUbuntu answer](https://askubuntu.com/a/767270) has good instructions.
Here are the instructions specific to this set-up:
1. Visit the [NVIDIA cuDNN page](https://developer.nvidia.com/cudnn) and click
"Download".
2. Join the program and fill out the survey.
3. Agree to the terms of service.
4. Click the link for "Download cuDNN v5.1 (Jan 20, 2017), for CUDA 8.0"
5. Download the "cuDNN v5.1 Library for Linux" (3rd link from the top).
6. Untar the downloaded file. E.g.:
```bash
cd ~/Downloads
tar -xvf cudnn-8.0-linux-x64-v5.1.tgz
```
7. Install the cuDNN files to the CUDA folder:
```bash
cd cuda
sudo cp -P include/* /usr/local/cuda-8.0/include/
sudo cp -P lib64/* /usr/local/cuda-8.0/lib64/
sudo chmod a+r /usr/local/cuda-8.0/lib64/libcudnn*
```
## Install libcupti-dev
This one is simple. Just run:
```bash
sudo apt-get install libcupti-dev
```
## Create a Virtualenv
I recommend using
[virtualenvwrapper](https://virtualenvwrapper.readthedocs.io/en/latest/index.html)
to create the tensorflow virtualenv, but the TensorFlow docs still have
[instructions to create the virtualenv
manually](https://www.tensorflow.org/install/install_linux#InstallingVirtualenv).
1. [Install
virtualenvwrapper]https://virtualenvwrapper.readthedocs.io/en/latest/install.html).
Make sure to add [the required
lines](https://virtualenvwrapper.readthedocs.io/en/latest/install.html#shell-startup-file)
to your `~/.bashrc`.
2. Create the virtualenv:
```bash
mkvirtualenv --python=python3 tensorflow
```
## Install the TensorFlow with GPU support
If you just run `pip install tensorflow` you will not get GPU support. To
install the correct version you will have to install from a [particular
url](https://www.tensorflow.org/install/install_linux#python_35). Here is the
install command you will have to run to install TensorFlow 1.2 for Python 3.5
with GPU support:
```bash
pip install https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0-cp35-cp35m-linux_x86_64.whl
```
If you need a different version of TensorFlow, you can edit the version number
in the URL. Same with the Python version (change `cp35` to `cp36` to install for
Python 3.6 instead, for example).
## Test that the installation worked
Save this script from [the TensorFlow
tutorials](https://www.tensorflow.org/tutorials/using_gpu#logging_device_placement)
to a file called `test_gpu.py`:
```python
# Creates a graph.
with tf.device('/cpu:0'):
a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name='a')
b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name='b')
c = tf.matmul(a, b)
# Creates a session with log_device_placement set to True.
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
# Runs the op.
print(sess.run(c))
```
And then run it:
```bash
python test_gpu.py
```
You should see your GPU card listed under "Device mapping:" and that each task
in the compute graph is assigned to `gpu:0`.
If you see "Device mapping: no known devices" then something went wrong and
TensorFlow cannot access your GPU.