New Workshop in Washington DC (August)

I'll be conducting a workshop called "Applied Machine Learning" in Washington DC on August 15 and 16. The last one, at the RStudio conference, sold out quickly.

The 2 day course is a blend of caret and the newer tidy modeling pacakges (recipes, rsample, etc):

Machine learning is the study and application of algorithms that learn from and make predictions on data. From search results to self-driving cars, it has manifested itself in all areas of our lives and is one of the most exciting and fast-growing fields of research in the world of data science.

This two-day course will provide an overview of using R for supervised learning. The session will step through the process of building, visualizing, testing, and comparing models that are focused on prediction. The goal of the course is to provide a thorough workflow in R that can be used with many different regression or classification techniques. Case studies on real data will be used to illustrate the functionality and several different predictive models are illustrated.

The course focuses on both high-level approaches to modeling (e.g., the caret package) and newer modeling packages in the tidyverse: recipes, rsample, yardstick, and tidyposterior. Basic familiarity with R and the tidyverse is required.

RStudio 2018 Conference Presentation and Materials

We've released our videos of the talks at the 2018 RStudio conference. My talk was Modeling in the Tidyverse (video) and I was also in the Tidyverse fireside chat (video). There are a lot of great talks on the conference website.

I also conducted a two day workshop on caret and the new modeling packages (recipes, rsample, tidyposterior and others) at the conference. The was no video of that, but all of the materials can be found in the course GitHub repository.

There will be at least one other workshop for this year on these topics. When that is finalized, I'll post the details here and on on twitter.

(the image above is from the workshop and is from tidyposterior)

While you wait for that to finish, can I interest you in parallel processing?

caret has been able to utilize parallel processing for some time (before it was on CRAN in October 2007) using slightly different versions of the package. Around September of 2011, caret started using the foreach package was used to "harmonize" the parallel processing technologies thanks to a super smart guy named Steve Weston.

I've done a few benchmarks to quantify the benefits to parallel processing over the years. I just did another set since I just got a new iMacPro. This time, I put the code and results in a GitHub repo. The main questions that I wanted to answer were:

• Is the benefit the same across computers and operating systems?
• Is there any performance difference between parallel processing technologies (or packages)?
• For my test cases, is it better to parallelize resampling loops or the individual model fit?

The task I used was to tune a boosted classification tree model (via the xgboost) package over 25 random values of the seven tuning parameters. The data were simulated and contained 2,000 training set points and 115 numeric predictors. Five repeats of 10-fold cross-validation was used so that 1,250 separate models could be run in parallel. An example of the code is here.

I tested this using three machines:

• A 2015 MacBookPro (version 11,4) with a 2.5 GHz Intel Core i7 processor. It has 4 physical cores.
• A DIY machine built in January of 2016 using a 3.4 GHz Intel Core i7 processor (6 cores).
• A 2017 iMacPro (1,1) with a 3 GHz Intel Xeon W processor. There are 10 cores.

All the computers have >=8GB of memory. The DIY machine was running Ubuntu 16.04 as well as Windows 10. All of the builds used R 3.4.2 or higher.

The methods for parallelization were:

• forking uses the fork system call to create new instances of the session (i.e. workers) that run parts of the computations. This is available on MacOS, unix, and linux. It is available to foreach via the doMC and doParallel packages. Historically, I have used doMC for my work but both were tested here.

• sockets via the doParallel package. I don't know a whole lot about socket connections but they have been around a long time via the snow package and can run on all major operating systems.

• threading: xgboost has the option to use openMP to parallelize the individual model fits.

This JSS paper from 2009 might be helpful.

Once nice thing about parallelism in caret is that you do not have to modify the package code. With foreach, you simply "register" a parallel backend and the caret functions automatically utilize the workers.

To be clear, the forking/socket approaches split the 1,250 model fits across the workers while the threading affects each individual model fit (one-at-a-time).

In each case, different number of workers were applied to the problems and the execution time was measured. Since these are all recent Intel processors, hyper-threading can be used. This means that, even though you have X physical cores, there are 2X "logical cores" that can perform independent operations. For that reason, we can add more workers than physical cores. The order of all runs were randomized and run in clear R sessions.

First, let's look at the non-threading results. The raw execution times were:

When converted to speed-ups, we get:

The speed-up is the sequential time divided by the parallel time; a speed-up of 3 means that the parallel execution took a third less time than the sequential code.

My observations:

• As expected, the desktops were substantially faster than the laptop. The speeds also rank-ordered with the age of the computer.
• On the same machine, linux ran faster than windows by slightly less than a fold.
• It appears that the speed-ups are linear up until we go beyond the number of physical cores. There is incremental benefit to using logical cores but it is small.
• I've never seen a case where performance get's substantially worse as you increase the number of workers. If the model is already fast (e.g. PLS) or your workers exceed system memory, this isn't an issue.
• There was no real difference in performance between forking and sockets (at least using doParallel).
• The new iMacPro does really well. The speed-ups are linear up until about 8 or 9 cores. This means that an execution time of 38 minutes can be cut down to 4 or 5 minutes.

The doMC results are strange. When the number of workers is a multiple of 5, there is a performance hit. At first I thought this was an aberration until it was apparent that it occurred over two operating systems and three computers. Also, it is specific to doMC; forking using doParallel does not have the same issue. I've notified the doMC package maintainer about it.

What about threading? These runs had foreach run sequentially and allowed xgb.train to run in parallel by adjusting the nthread argument. The results:

This isn't so great and really surprising (or fair). We are starting and stopping threads 1,250 times across the model tuning process. In general, we want to parallelize the longest running part of the algorithm. I have no doubt of the utility of threading the computations for a single model but it can be counterproductive when model is called repeatedly.

The code and results can be found here if you are interested in more details.