### Short Notes: Get CUDA and gputools Running on Ubuntu 14.10

Here's a basic guide for getting CUDA 7.0 and the R package gputools running perfectly under Ubuntu 14.10. It's not difficult, but there are a few issues and this will be helpful to have in a single place.

If you're running Ubuntu 14.10, I'd recommend installing CUDA 7.0. NVIDIA has a 7.0 Debian package specifically for 14.10; this wasn't the case for CUDA 6.5, which only had a Debian package for 14.04.

To get access to CUDA 7.0, you'll first need to register as a CUDA developer.

Join The CUDA Registered Developer Program

Once you have access, navigate to the CUDA 7.0 download page and get the Debian package.

You'll either need to be running the NVIDIA 340 or 346 drivers. If you're having trouble upgrading, I'd suggest adding the xorg-edgers PPA.

Once your NVIDIA driver is set, install the CUDA 7.0 Debian package you've downloaded. Don't forget to remove any previously installed CUDA packages or repositories.

You'll need to add paths so everything knows where CUDA is installed. Append the following to the .bashrc in your home directory:

Execute "source ~/.bashrc" for these changes to be applied. If you want to test your new CUDA install, make the samples provided by NVIDIA.

I get the following output when running BlackScholes:

The next task is to install gputools for R. You can't unfortunately install the current package through R, as the source code contains references to CUDA architectures that are obsolete under CUDA 7.0. But that's easy to fix.

Now do some editing in gputools/src/Makefile:

Now build and install the patched gputools package while you're in the directory immediately above gputools:

If you want to make the gputools packages available for all R users

Keep in mind that they'll have to make the same environmental variable changes as above. Let's test it!

Running gives us:

A nice 26-fold speedup. We're all set!

1. thanks - got it working with the -arch change and a few other hacks

2. Hmm, I can't get it to work. GPUTools is up to version 0.5, and when I follow these steps I get this error:
** testing if installed package can be loaded
Error in library.dynam(lib, package, package.lib) :
Execution halted
* removing ‘/home/boris/R/x86_64-pc-linux-gnu-library/3.0/gputools’

1. I also couldn't get these steps to work with gputools version 0.5 and CUDA-7.5

Did you ever find a solution?

### A Bayes' Solution to Monty Hall

For any problem involving conditional probabilities one of your greatest allies is Bayes' Theorem. Bayes' Theorem says that for two events A and B, the probability of A given B is related to the probability of B given A in a specific way.

Standard notation:

probability of A given B is written $$\Pr(A \mid B)$$
probability of B is written $$\Pr(B)$$

Bayes' Theorem:

Using the notation above, Bayes' Theorem can be written: $\Pr(A \mid B) = \frac{\Pr(B \mid A)\times \Pr(A)}{\Pr(B)}$Let's apply Bayes' Theorem to the Monty Hall problem. If you recall, we're told that behind three doors there are two goats and one car, all randomly placed. We initially choose a door, and then Monty, who knows what's behind the doors, always shows us a goat behind one of the remaining doors. He can always do this as there are two goats; if we chose the car initially, Monty picks one of the two doors with a goat behind it at random.

Assume we pick Door 1 and then Monty sho…

### Mixed Models in R - Bigger, Faster, Stronger

When you start doing more advanced sports analytics you'll eventually starting working with what are known as hierarchical, nested or mixed effects models. These are models that contain both fixed and random effects. There are multiple ways of defining fixed vs random random effects, but one way I find particularly useful is that random effects are being "predicted" rather than "estimated", and this in turn involves some "shrinkage" towards the mean.

Here's some R code for NCAA ice hockey power rankings using a nested Poisson model (which can be found in my hockey GitHub repository):
model <- gs ~ year+field+d_div+o_div+game_length+(1|offense)+(1|defense)+(1|game_id) fit <- glmer(model, data=g, verbose=TRUE, family=poisson(link=log) ) The fixed effects are year, field (home/away/neutral), d_div (NCAA division of the defense), o_div (NCAA division of the offense) and game_length (number of overtime periods); off…

### Notes on Setting up a Titan V under Ubuntu 17.04

I recently purchased a Titan V GPU to use for machine and deep learning, and in the process of installing the latest Nvidia driver's hosed my Ubuntu 16.04 install. I was overdue for a fresh install of Linux, anyway, so I decided to upgrade some of my drives at the same time. Here are some of my notes for the process I went through to get the Titan V working perfectly with TensorFlow 1.5 under Ubuntu 17.04.

Old install:
Ubuntu 16.04
EVGA GeForce GTX Titan SuperClocked 6GB
2TB Seagate NAS HDD