Add WIP draft about installing tensorflow
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_drafts/2017-06-20-how-to-install-tensorflow-on-ubuntu-16-04.md
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_drafts/2017-06-20-how-to-install-tensorflow-on-ubuntu-16-04.md
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---
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title: How to Install TensorFlow on Ubuntu 16.04 with GPU Support
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layout: post
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---
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I found the [tensorflow
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documentation](https://www.tensorflow.org/install/install_linux) rather lacking
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for installation instructions, especially in regards to getting GPU support.
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I'm going to write down my notes from wrangling with the installation here for
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future reference and hopefully this helps someone else too.
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This will invariably go out-of-date at some point, so be mindful of the publish
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date of this post. Make sure to cross-reference other documentation that has
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more up-to-date information.
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## Assumptions
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These instructions are very specific to my environment, so this is what I am
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assuming:
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1. You are running Ubuntu 16.04. (I have 16.04.1)
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- You can check this in the output of `uname -a`
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2. You have a 64 bit machine.
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- You can check this with `uname -m`. (should say `x86_64`)
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2. You have an NVIDIA GPU that has CUDA Compute Capability 3.0 or higher.
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[NVIDIA documentation] has a full table of cards and their Compute Capabilities.
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(I have a GeForce GTX 980 Ti)
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- You can check what card you have in Settings > Details under the label
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"Graphics"
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- You can also check by verifying there is any output when you run `lspci |
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grep -i nvidia`
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3. You have a linux kernel version 4.4.0 or higher. (I have 4.8.0)
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- You can check this by running `uname -r`
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4. You have gcc version 5.3.1 or higher installed. (I have 5.4.0)
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- You can check this by running `gcc --version`
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5. You have the latest [proprietary](https://i.imgur.com/8osspXj.jpg) NVIDIA
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drivers installed.
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- You can check this and install it if you haven't in the "Additional
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Drivers" tab in the "Software & Updates" application (`update-manager`).
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(I have version 375.66 installed)
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6. You have the kernel headers installed.
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- Just run `sudo apt-get install linux-headers-$(uname -r)` to install them
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if you don't have them installed already.
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7. You have Python installed. The exact version shouldn't matter, but for the
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rest of this post I'm going to assume you have `python3` installed.
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- You can install `python3` by running `sudo apt-get install python3`. This
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will install Python 3.5.
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- Bonus points: you can install Python 3.6 by following [this
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answer](https://askubuntu.com/a/865569), but Python 3.5 should be fine.
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## Install the CUDA Toolkit 8.0
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NVIDIA has [a big scary documentation
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page](http://docs.nvidia.com/cuda/cuda-installation-guide-linux/) on this, but I
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will summarize the only the parts you need to know here.
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Go to the [CUDA Toolkit Download](https://developer.nvidia.com/cuda-downloads)
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page. Click Linux > x86_64 > Ubuntu > 16.04 > deb (network).
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Click download and then follow the instructions, copied here:
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1. `sudo dpkg -i cuda-repo-ubuntu1604_8.0.61-1_amd64.deb`
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2. `sudo apt-get update`
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3. `sudo apt-get install cuda`
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This will install CUDA 8.0. It installed it to the directory
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`/usr/local/cuda-8.0/` on my machine.
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There are some [post-install
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actions](http://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#post-installation-actions)
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we must follow:
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1. Edit your `~/.bashrc`
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- Use your favorite editor `gedit ~/.bashrc`, `nano ~/.bashrc`, `vim
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~/.bashrc`, whatever.
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2. Add the following lines to the end of the file:
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```bash
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# CUDA 8.0 (nvidia) paths
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export CUDA_HOME=/usr/local/cuda-8.0
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export PATH=/usr/local/cuda-8.0/bin${PATH:+:${PATH}}
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export LD_LIBRARY_PATH=/usr/local/cuda-8.0/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
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```
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3. Save and exit.
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4. Run `source ~/.bashrc`.
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5. Install writable samples by running the script `cuda-install-samples-8.0.sh
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~/`.
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- If the script cannot be found, the above steps didn't work :(
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- I don't actually know if the samples are absolutely required for what I'm
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using CUDA for, but it's recommended according to NVIDIA, and compiling
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them will output a nifty `deviceQuery` binary which can be ran to test if
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everything is working properly.
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6. Make sure `nvcc -V` outputs something.
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- If an error, the above steps 1-4 didn't work :(
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7. `cd ~/NVIDIA_CUDA-8.0_Samples`, cross your fingers, and run `make`
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- The compile will take a while
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- My compile actually errored near the end with an error about `/usr/bin/ld:
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cannot find -lcudart`. I *think* that doesn't really matter because the
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binary files where still output.
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8. Try running `~/NVIDIA_CUDA-8.0_Samples/bin/x86_64/linux/release/deviceQuery`
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to see if you get any output. Hopefully you will see your GPU listed.
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## Install cuDNN v5.1
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## Install libcupti-dev
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## Create a Virtualenv
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## Install the TensorFlow with GPU support
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