Introduction to Parallel Computing and Machine Learning with pbdR

The lessons below were designed for those interested in using R for big data analysis and machine learning.

This introduction assumes some prior programming experience for example from one of the Carpentries R or Python lessons. Additionally, some experience with the command line from one of the Carpentries Unix shell lessons would be beneficial, but is not strictly required.

This lesson assumes no prior knowledge of R or RStudio.

Episodes

Preparations

Carpentry’s teaching is hands-on, and to follow this lesson learners must have a linux shell environment, R and RStudio installed on their computers. They also need to be able to install a number of R packages, create directories, and download files.

To avoid troubleshooting during the lesson, learners should follow the instructions below to download and install everything beforehand. If they are using their own computers this should be no problem, but if the computer is managed by their organization’s IT department they might need help from an IT administrator.

Acknowledgments

This lesson draws material from

Software Setup

Install a Linux Shell Environment

Windows

Download the Git for Windows installer
Run the installer and follow the steps below:
1. Git Setup
  - Click on “Next” four times (two times if you’ve previously installed Git). You don’t need to change anything in the Information, location, components, and start menu screens.
  - From the dropdown menu, “Choosing the default editor used by Git”, select “Use the Nano editor by default” (NOTE: you will need to scroll up to find it) and click on “Next”.
2. Adjusting the name of the initial branch in new repositories
  - On the page that says “Adjusting the name of the initial branch in new repositories”, ensure that “Let Git decide” is selected. This will ensure the highest level of compatibility for our lessons.
3. Adjusting your PATH environment
  - Ensure that “Git from the command line and also from 3rd-party software” is selected and click on “Next”. (If you don’t do this Git Bash will not work properly, requiring you to remove the Git Bash installation, re-run the installer and to select the “Git from the command line and also from 3rd-party software” option.)
4. Choosing the SSH executable
  - Select “Use bundled OpenSSH”.
5. Choosing HTTPS transport backend
  - Ensure that “Use the native Windows Secure Channel Library” is selected and click on “Next”.
  - This should mean that people behind firewalls with their own root certificate authorities are still able to access remote git repos.
6. Configuring the line ending conversions
  - Ensure that “Checkout Windows-style, commit Unix-style line endings” is selected and click on “Next”.
7. Configuring the terminal emulator to use with Git Bash
  - Ensure that “Use Windows’ default console window” is selected and click on “Next”
8. Configuring extra options
  - Ensure that “Default (fast-forward or merge) is selected and click”Next”
  - Ensure that “Git Credential Manager” is selected and click on “Next”.
  - Ensure that “Enable file system caching” is selected and click on “Next”.
9. Configuring experimental options
  - Click on “Install”
    OUTPUT
```
Installing
Completing the Git Setup Wizard
```
10. As of 2020-06-02, the Window will say “click Finish”, but the button is labelled as “Next”
  - Click on “Finish” or “Next”.
11. If your “HOME” environment variable is not set (or you don’t know what this is):
  - Open command prompt (Open Start Menu then type cmd and press Enter)
  - Type the following line into the command prompt window exactly as shown:
    BASH
```
setx HOME "%USERPROFILE%"
```
  - Press Enter, you should see
    OUTPUT
```
SUCCESS: Specified value was saved.
```
  - Quit command prompt by typing exit then pressing Enter

This will provide you with both Git and Bash in the Git Bash program.

Video Tutorial

MacOS

The default shell in some versions of macOS is Bash, and Bash is available in all versions, so no need to install anything. You access Bash from the Terminal (found in /Applications/Utilities). See the Git installation video tutorial for an example on how to open the Terminal. You may want to keep Terminal in your dock for this workshop.

To see if your default shell is Bash type echo $SHELL in Terminal and press the Return key. If the message printed does not end with ‘/bash’ then your default is something else and you can run Bash by typing bash.

If you want to change your default shell, see this Apple Support article and follow the instructions on “How to change your default shell”.

Video Tutorial

Linux

The default shell is usually Bash and there is usually no need to install anything.

To see if your default shell is Bash type echo $SHELL in a terminal and press the Enter key. If the message printed does not end with ‘/bash’ then your default is something else and you can run Bash by typing bash.

Install R and RStudio

R and RStudio are two separate pieces of software:

R is a programming language that is especially powerful for data exploration, visualization, and statistical analysis
RStudio is an integrated development environment (IDE) that makes using R easier. In this course we use RStudio to interact with R.

If you don’t already have R and RStudio installed, follow the instructions for your operating system below. You have to install R before you install RStudio.

Windows

Download R from the CRAN website.
Run the .exe file that was just downloaded
Go to the RStudio download page
Under All Installers, download the RStudio Installer for Windows.
Double click the file to install it
Once it’s installed, open RStudio to make sure it works and you don’t get any error messages.

MacOS

Download R from the CRAN website.
Select the .pkg file for the latest R version
Double click on the downloaded file to install R
It is also a good idea to install XQuartz (needed by some packages)
Go to the RStudio download page
Under All Installers, download the RStudio Installer for MacOS.
Double click the file to install RStudio
Once it’s installed, open RStudio to make sure it works and you don’t get any error messages.

Linux

Follow the instructions for your distribution from CRAN, they provide information to get the most recent version of R for common distributions.
Go to the RStudio download page
Under All Installers, select the version that matches your distribution and install it with your preferred method (e.g., with Debian/Ubuntu sudo dpkg -i rstudio-YYYY.MM.X-ZZZ-amd64.deb at the terminal).
Once it’s installed, open RStudio to make sure it works and you don’t get any error messages.

Update R and RStudio

If you already have R and RStudio installed, first check if your R version is up to date:

When you open RStudio your R version will be printed in the console on the bottom left. Alternatively, you can type sessionInfo() into the console. If your R version is 4.0.0 or later, you don’t need to update R for this lesson. If your version of R is older than that, download and install the latest version of R from the R project website for Windows, for MacOS, or for Linux
It is not necessary to remove old versions of R from your system, but if you wish to do so you can check How do I uninstall R?
Note: The changes introduced by new R versions are usually backwards-compatible. That is, your old code should still work after updating your R version. However, if breaking changes happen, it is useful to know that you can have multiple versions of R installed in parallel and that you can switch between them in RStudio by going to Tools > Global Options > General > Basic.
After installing a new version of R, you will have to reinstall all your packages with the new version. For Windows, there is a package called installr that can help you with upgrading your R version and migrate your package library.

To update RStudio to the latest version, open RStudio and click on Help > Check for Updates. If a new version is available follow the instruction on screen. By default, RStudio will also automatically notify you of new versions every once in a while.

Install required R packages

During the course we will need a number of R packages. Packages contain useful R code written by other people. We will use the packages pbdR, pbdML, pbdMPI, pbdMAT, flexiblas, memuse, mlbench, parallel, remotes and randomForest.

To try to install these packages, open RStudio and copy and paste the following command into the console window (look for a blinking cursor on the bottom left), then press the Enter (Windows and Linux) or Return (MacOS) to execute the command.

R

install.packages(c("pbdR", "pbdML", "pbdMPI", "pbdMAT",
       "flexiblas", "memuse", "mlbench", 
       "parallel", "remotes", "randomForest"))

Alternatively, you can install the packages using RStudio’s graphical user interface by going to Tools > Install Packages and typing the names of the packages separated by a comma.

R tries to download and install the packages on your machine. When the installation has finished, you can try to load the packages by pasting the following code into the console:

R

library(pbdR)
library(pbdML)
library(pbdMPI)
library(pbdMAT)
library(flexiblas)
library(memuse)
library(mlbench)
library(parallel)
library(randomForest)
library(remotes)

If you do not see an error like there is no package called ‘...' you are good to go!

Updating R packages

Generally, it is recommended to keep your R version and all packages up to date, because new versions bring improvements and important bugfixes. To update the packages that you have installed, click Update in the Packages tab in the bottom right panel of RStudio, or go to Tools > Check for Package Updates....

Sometimes, package updates introduce changes that break your old code, which can be very frustrating. To avoid this problem, you can use a package called renv. It locks the package versions you have used for a given project and makes it straightforward to reinstall those exact package version in a new environment, for example after updating your R version or on another computer. However, the details are outside of the scope of this lesson.

Introduction to Parallel Computing and Machine Learning with pbdR

Episodes

Preparations

Acknowledgments

Software Setup

Install a Linux Shell Environment

Windows

OUTPUT

BASH

OUTPUT

Video Tutorial

MacOS

Video Tutorial

Linux

Install R and RStudio

Windows

MacOS

Linux

Update R and RStudio

Install required R packages

R

R

Updating R packages