Trans: Latin prefix implying "across" or "Beyond", often used in gender nonconforming situations – Scend: Archaic word describing a strong "surge" or "wave", originating with 15th century english sailors – Survival: 15th century english compound word describing an existence only worth transcending.

Author: Jess (Page 6 of 14)

Musings On Chapel Language and Parallel Processing

View below the readme mirror from my Github repo. Scroll down for my Python3 evaluation script.

....Or visit the page directly: https://github.com/Jesssullivan/ChapelTests 

ChapelTests

Investigating modern concurrent programming ideas with Chapel Language and Python 3

See here for dupe detection: /FileChecking-with-Chapel

Iterating through all files for custom tags / syntax: /GenericTagIterator

added 9/14/19:

The thinking here is one could write a global, shorthand / tag-based note manager making use of an efficient tag gathering tool like the example here. Gone are the days of actually needing a note manager- when the need presents itself, one could just add a calendar item, todo, etc with a global tag syntax.

The test uses $D for date: $D 09/14/19

//  Chapel-Language  //

// non-annotated file @ /GenericTagIterator/nScan.chpl //

use FileSystem;
use IO;
use Time;

config const V : bool=true;  // verbose logging, currently default!

module charMatches {
  var dates = {("")};  
}

// var sync1$ : sync bool=true;  not used in example- TODO: add sync$ var back in!!

proc charCheck(aFile, ref choice, sep, sepRange) {

    // note, reference argument (ref choice) is needed if using Chapel structure "module.domain"

    try {
        var line : string;
        var tmp = openreader(aFile);
        while(tmp.readline(line)) {
            if line.find(sep) > 0 {
                choice += line.split(sep)[sepRange];
                if V then writeln('adding '+ sep + ' ' + line.split(sep)[sepRange]);
            }
        }
    tmp.close();
    } catch {
      if V then writeln("caught err");
    }
}

coforall folder in walkdirs('check/') {
    for file in findfiles(folder) {
        charCheck(file, charMatches.dates, '$D ', 1..8);
    }
}

Get some Chapel:

In a (bash) shell, install Chapel:
Mac or Linux here, others refer to:

https://chapel-lang.org/docs/usingchapel/QUICKSTART.html

# For Linux bash:
git clone https://github.com/chapel-lang/chapel
tar xzf chapel-1.18.0.tar.gz
cd chapel-1.18.0
source util/setchplenv.bash
make
make check

#For Mac OSX bash:
# Just use homebrew
brew install chapel # :)

Get atom editor for Chapel Language support:

#Linux bash:
cd
sudo apt-get install atom
apm install language-chapel
# atom [yourfile.chpl]  # open/make a file with atom

# Mac OSX (download):
# https://github.com/atom/atom
# bash for Chapel language support
apm install language-chapel
# atom [yourfile.chpl]  # open/make a file with atom

Using the Chapel compiler

To compile with Chapel:

chpl MyFile.chpl # chpl command is self sufficient

# chpl one file class into another:

chpl -M classFile runFile.chpl

# to run a Chapel file:
./runFile

Now Some Python3 Evaluation:

# Ajacent to compiled FileCheck.chpl binary:

python3 Timer_FileCheck.py

Timer_FileCheck.py will loop FileCheck and find the average times it takes to complete, with a variety of additional arguments to toggle parallel and serial operation. The iterations are:

ListOptions = [Default, Serial_SE, Serial_SP, Serial_SE_SP]
  • Default - full parallel

  • Serial evaluation (--SE) but parallel domain creation

  • Serial domain creation (--SP) but parallel evaluation

  • Full serial (--SE --SP)

Output is saved as Time_FileCheck_Results.txt

  • Output is also logged after each of the (default 10) loops.

The idea is to evaluate a "--flag" -in this case, Serial or Parallel in FileCheck.chpl- to see of there are time benefits to parallel processing. In this case, there really are not any, because that program relies mostly on disk speed.

Evaluation Test:

# Time_FileCheck.py
#
# A WIP by Jess Sullivan
#
# evaluate average run speed of both serial and parallel versions
# of FileCheck.chpl  --  NOTE: coforall is used in both BY DEFAULT.
# This is to bypass the slow findfiles() method by dividing file searches
# by number of directories.

import subprocess
import time

File = "./FileCheck" # chapel to run

# default false, use for evaluation
SE = "--SE=true"

# default false, use for evaluation
SP = "--SP=true" # no coforall looping anywhere

# default true, make it false:
R = "--R=false"  #  do not let chapel compile a report per run

# default true, make it false:
T = "--T=false" # no internal chapel timers

# default true, make it false:
V = "--V=false"  #  use verbose logging?

# default is false
bug = "--debug=false"

Default = (File, R, T, V, bug) # default parallel operation
Serial_SE = (File, R, T, V, bug, SE)
Serial_SP = (File, R, T, V, bug, SP)
Serial_SE_SP = (File, R, T, V, bug, SP, SE)


ListOptions = [Default, Serial_SE, Serial_SP, Serial_SE_SP]

loopNum = 10 # iterations of each runTime for an average speed.

# setup output file
file = open("Time_FileCheck_Results.txt", "w")

file.write(str('eval ' + str(loopNum) + ' loops for ' + str(len(ListOptions)) + ' FileCheck Options' + "\n\\"))

def iterateWithArgs(loops, args, runTime):
    for l in range(loops):
        start = time.time()
        subprocess.run(args)
        end = time.time()
        runTime.append(end-start)

for option in ListOptions:
    runTime = []
    iterateWithArgs(loopNum, option, runTime)
    file.write("average runTime for FileCheck with "+ str(option) + "options is " + "\n\\")
    file.write(str(sum(runTime) / loopNum) +"\n\\")
    print("average runTime for FileCheck with " + str(option) + " options is " + "\n\\")
    print(str(sum(runTime) / loopNum) +"\n\\")

file.close()

Evaluating Ubuntu Pop OS: Dual Boot Setup

Dual OS on a 2015 MacBook pro

As the costs of Apple computers continue to skyrocket and the price of useable amounts of storage zoom past a neighboring galaxy (for a college student at least), I am always on on the hunt for cost effective solutions to house and process big projects and large data.

Pop OS (a neatly wrapped Ubuntu) is the in-house OS from System76.  After looking through their catalog of incredible computers and servers, I thought it would be a good time to see how far I can go with an Ubuntu daily driver.  Of course, there are many major and do-not-pass-go downsides- see the below list:

  • Logic Pro X → There is no replacement 🙁   A killer DAW with fantastic AU libraries. I am versed with Reaper and Bitwig, but neither is as complete as Logic Pro.  I will be evaluating POP with an installation of Reaper, but with so few plugins (I own very few third party sets) this is not a fair replacement.
  • Adobe PS and LR:  I do not like Adobe, but these programs are... ...kind of crucial for most project of mine that involve 2d, raster graphics.  I continue to use Inkscape for many tasks, but it is irrelevant when it comes to pixel-based work and photo management / bulk operations.
  • AutoCAD / Fusion 360 / Sketchup:  I like FreeCAD a lot, but it is not at all like the other programs.  Not worse or better, but these are all very different animals for different uses.
  • Apple notes and other apple-y things:  OSX is extremely refined. Inter-device solutions are superb.  I have gotten myself used to Google Keep, but it is not quite at the in-house Apple level.
  • XCode and IOS Simulator environments:  I do use Expo, but frankly to make products for Apple you need a Mac.

Dual Boot (OSX and Pop Ubuntu) Installation on a 2015 MBP:

This process is quite simple, and only calls for a small handful of post-installation tweaks.  My intent is to create a small sandbox with minimal use of “extras” (no extra boot managers or anything like that)

Steps:

Partition separate “boot”, “home”, and other drives

  • I am using a 256gb micro sd partitioned in half for OSX and Pop_OS (Sandisk extreme, “v3” speed rating version card via a BaseQi slot adapter)

Use the partition tool in Mac disk utility.  Be sure to set these new partitions as FAT 32- we will be using ext4 and other more linux-y filesystems upon installation, so these need to be as generic as possible.

Get a copy of Pop_OS from System76.

Use Etcher (recommended) or any other image burning tool to create a boot key for Pop.  

The USB key only has one small job, in which Pop_os will be burned into a better location in your boot partition made in the previous step.  If you are coming from a hackintosh experience, fear not: everything will stay in the Macbook Pro, not extra USB safety dongles or Kexts, or Plist mods…!

BOOT INTO POP_OS:

Restart your computer and hold down the alt-option Key.  THIS IS HOW TO SWITCH from Pop_os, OSX, Bootcamp, and anything else you have in there.  You should see an “efi” option next to the default OSX. (note- at least in my case, the built-in bootloader defaults to the last used OS at each restart.)

Once you are in the Pop_OS installer, click through and select the appropriate partitions when prompted.  After this installation, you may remove the USB key and continue to select
“efi” in the bootloader.


ASSUMING ALL GOES WELL:

You are now in Pop_OS!  Using the alt/option key will become second nature… but some Pop key mappings may not.  Continue for a list of Macbook Pro - specific tweaks and notes.

First moves:

Go to the Pop Shop and get the “Tweaks” tool.  I made one or two small keymap changes, but this is likely personal preference.  

Default, important Key Mappings:

Command will act as a “control center-ish” thing.  It will not copy or paste anything for you.

Control does what Command did on OSX.  

Terminal uses Control+Shift for copy and paste, but only in Terminal:  if you pull a Control+Shift+C in Chrome, you will get the Dev tool GUI...  The Shift key thing is needed unless you are inclined to root around and change it.

Custom Boot Scripts and Services:

In an effort to make things simple, I made a shell script to house the processes I want running when I turn on the computer- this is to streamline the “.service” making process.  While it may only take marginally more time to make a new service, this way I can keep track of what is doing what from a file in my documents folder.

In terminal, go to where your services live if you want to look:

cd /etc/systemd/system

Or, cut to the chase:

sudo nano /etc/systemd/system/startsh.sh.service

Paste the following into this new file:

_____________Begin _After_This_Line____________________

[Unit]

Description=Start at Open plz

[Service]

ExecStart=/Documents/startsh.sh

[Install]

WantedBy=multi-user.target

_____________End _Above_This_Line____________________

Exit nano (saving as you go) and cd back to “/”.

cd

sudo nano /Documents/startsh.sh

Paste the following (and any scripts you may want, see the one I have commented out for odrive CLI) into this new file:

_____________Begin _After_This_Line____________________

#!/bin/bash

# Uncomment the following if you want 24/7 odrive in your system

# otherwise do whatever you want

#nohup "$HOME/.odrive-agent/bin/odriveagent" > /dev/null 2>&1 &

# end

_____________End _Above_This_Line____________________

After exiting the shell script, start it all up with the following:

sudo systemctl start startsh.sh

sudo systemctl enable startsh.sh

Cloud file management with Odrive CLI and Odrive Utilities:

Visit one of the two Odrive CLI pages- this one has linux in it:

https://forum.odrive.com/t/odrive-sync-agent-a-cli-scriptable-interface-for-odrives-progressive-sync-engine-for-linux-os-x-and-windows/499#linuxinst

Please visit this repo to get going with --recursive and other odrive utilities

https://github.com/amagliul/odrive-utilities


These are the two commands I ended up putting in a markdown file on my desktop for easy access.  Nope, not nearly as cool as it is on OSX. But it works…

Odrive sync: [-h] for help

```

python "$HOME/.odrive-agent/bin/odrive.py" sync

```

Odrive utilities:

```

python "$HOME/odrive-utilities/odrivecli.py" sync --recursive

```

Next, Get Some Apps:

Download Chrome.  Sign into Chrome to get your chrome OS apps loaded into the launcher- in my case, I needed Chrome remote desktop.  DO NOT DOWNLOAD ADDITIONAL PACKAGES for Chrome Remote Desktop, if that is your thing. They will halt all system tools (disk utils, Gnome terminal, graphical file viewer…   !!See this thread, it happened to me!! )

Stock up!  

Get Atom editor:  https://atom.io/

...Or my favorites: https://www.jetbrains.com/toolbox/app/

Rstudio:  https://www.rstudio.com/products/rstudio/download/#download

Mysql:  https://dev.mysql.com/downloads/mysql/

MySQL Workbench:  https://dev.mysql.com/downloads/workbench/

If you get stuck:  make sure you have tried installing as root ($ sudo su -) and verified passwords with ($ sudo mysql_secure_installation)  

See here to start “rooting around” MySQL issues:  https://stackoverflow.com/questions/50132282/problems-installing-mysql-in-ubuntu-18-04/50746032#50746032

Get some GIS tools:

QGIS!

sudo apt-get install qgis python-qgis qgis-plugin-grass

uGet for bulk USGS data download!

sudo add-apt-repository ppa:plushuang-tw/uget-stable

sudo apt install uget

That's all for now- Cheers!

-Jess

Deploy Shiny R apps along Node.JS

Find the tools in action on Heroku as a node.js app!

https://kml-tools.herokuapp.com/

See the code on GitHub:

https://github.com/Jesssullivan/Shiny-Apps

After many iterations of ideas regarding deployment for a few research Shiny R apps, I am glad to say the current web-only setup is 100% free and simple to adapt.   I thought I'd go through some of the Node.JS bits I have been fussing with. 

The Current one:  

Heroku has a free tier for node.js apps.  See the pricing and limitations here: https://www.heroku.com/pricing as far as I can tell, there is little reason to read too far into a free plan; they don’t have my credit card, and thy seem to convert enough folks to paid customers to be nice enough to offer a free something to everyone.  

Shiny apps- https://www.shinyapps.io/- works straight from RStudio.  They have a free plan. Similar to Heroku, I can care too much about limitations as it is completely free.  

The reasons to use Node.JS (even if it just a jade/html wrapper) are numerous, though may not be completely obvious.  If nothing else, Heroku will serve it for free….

Using node is nice because you get all the web-layout-ux-ui stacks of stuff if you need them.  Clearly, I have not gone to many lengths to do that, but it is there.

Another big one is using node.js with Electron.  https://electronjs.org/ The idea is a desktop app framework serves up your node app to itself, via the chromium.  I had a bit of a foray with Electron- the node execa npm install execa package let me launch a shiny server from electron, wait a moment, then load a node/browser app that acts as a interface to the shiny process.  While this mostly worked, it is definitely overkill for my shiny stuff.  Good to have as a tool though.

-Jess

Recycled Personal “Cloud Computing” under NAT

As many may intuit, I like the AWS ecosystem; it is easy to navigate and usually just works.  

...However- more than 1000 dollars later, I no longer use AWS for most things....

🙁   

My goals: 

Selective sync:  I need a unsync function for projects and files due to the tiny 256 SSD on my laptop (odrive is great, just not perfect for cloud computing.

Shared file system:  access files from Windows and OSX, locally and remote

Server must be headless, rebootable, and work remotely from under a heavy enterprise NAT (College)

Needs more than 8gb ram

Runs windows desktop remotely for gis applications, (OSX on my laptop)

 

Have as much shared file space as possible: 12TB+

 

Server:  recycled, remote, works-under-enterprise-NAT:

Recycled Dell 3010 with i5: https://www.plymouth.edu/webapp/itsurplus/

- Cost: $75 (+ ~$200 in windows 10 pro, inevitable license expense) 

free spare 16gb ram laying around, local SSD and 2TB HDD upgrades

- Does Microsoft-specific GIS bidding, can leave running without hampering productivity

Resilio (bittorrent) Selective sync: https://www.resilio.com/individuals/

- Cost: $60

- p2p Data management for remote storage + desktop

- Manages school NAT and port restrictions well (remote access via relay server)

Drobo 5c:

Attached and syncs to 10TB additional drobo raid storage, repurposed for NTFS

  • Instead of EBS (or S3)

 

What I see:  front end-

Jump VNC Fluid service: https://jumpdesktop.com/

- Cost: ~$30

- Super efficient Fluid protocol, clients include chrome OS and IOS,  (with mouse support!)

- Manages heavy NAT and port restrictions well

- GUI for everything, no tunneling around a CLI

  • Instead of Workspaces, EC2

Jetbrains development suite:  https://www.jetbrains.com/ (OSX)

- Cost:  FREE as a verified GitHub student user.

- PyCharm IDE, Webstorm IDE

  • Instead of Cloud 9

 

Total (extra) spent: ~$165

(Example:  my AWS bill for only October was $262)

 

-Jess

Quick fix: 254 character limit in ESRI Story Map?

https://gis.stackexchange.com/questions/75092/maximum-length-of-text-fields-in-shapefile-and-geodatabase-formats

https://en.wikipedia.org/wiki/GeoJSON

https://gis.stackexchange.com/questions/92885/ogr2ogr-converting-kml-to-geojson

If you happened to be working with....  KML data (or any data with large description strings) and transitioning it into the ESRI Story Map toolset, there is a very good chance  you hit the the dBase 254 character length limit with the ESRI Shapefile upload.  Shapefiles are always a terrible idea.

 

the solution:  with GDAL or QGIS (alright, even in ArcMap), one can use GeoJSON as an output format AND import into the story map system- with complete long description strings!

 

QGIS:

Merge vector layers -> save to file -> GeoJSON

arcpy:
import arcpy

import os

arcpy.env.workspace = "/desktop/arcmapstuff"

arcpy.FeaturesToJSON_conversion(os.path.join("outgdb.gdb", "myfeatures"), "output.json")

GDAL:
<
ogr2ogr -f GeoJSON output.json input.kml

New App:  KML Search and Convert

Written in R; using GDAL/EXPAT libraries on Ubuntu and hosted with AWS EC2.

New App:  KML Search and Convert

Here is an simple (beta) app of mine that converts KML files into Excel-friendly CSV documents.  It also has a search function, so you can download a subset of data that contains keywords.   🙂

The files will soon be available in Github.

I'm still working on a progress indicator; it currently lets you download before it is done processing.   Know a completely processed file is titled with "kml2csv_<yourfile>.csv".

...YMMV.  xD

GDAL for R Server on Ubuntu – KML Spatial Libraries and More

GDAL for R Server on Red Hat Xenial Ubuntu - KML Spatial Libraries and More

If you made the (possible mistake) of running with a barebones Red Hat Linux instance, you will find it is missing many things you may want in R.   I rely on GDAL (the definitive Geospatial Data Abstraction Library) on my local OSX R setup, and want it on my server too.  GDAL contains many libraries you need to work with KML, RGDAL, and other spatial packages.  It is massive and usually take a long time to sort out on any machine.

These notes assume you are already involved with a R server (usually port 8787 in a browser).  I am running mine from an EC2 instance with AWS.

! Note this is a fresh server install, using Ubuntu; I messed up my original ones while trying to configure GDAL against conflicting packages. If you are creating a new one, opt for at least a T2 medium (or go bigger) and find the latest Ubuntu server AMI.  For these instructions, you want an OS that is as generic as possible.

On Github:

https://github.com/Jesssullivan/rhel-bits

From Bash:

# SSH into the EC2 instance: (here is the syntax just in case)

#ssh -i "/Users/YourSSHKey.pem" ec2-user@yourAWSinstance.amazonaws.com

sudo su -

apt-get update

apt-get upgrade

nano /etc/apt/sources.list

#enter as a new line at the bottom of the doc:

deb https://cloud.r-project.org/bin/linux/ubuntu xenial/

#exit nano

wget https://raw.githubusercontent.com/Jesssullivan/rhel-bits/master/xen-conf.sh

chmod 777 xen-conf.sh

./xen-conf.sh

Or...

From SSH:

# SSH into the EC2 instance: (here is the syntax just in case)

ssh -i "/Users/YourSSHKey.pem" ec2-user@yourAWSinstance.amazonaws.com

# if you can, become root and make some global users- these will be your access to

# RStudio Server and shiny too!

sudo su –

adduser <Jess>

# Follow the following prompts carefully to create the user

apt-get update

nano /etc/apt/sources.list

# enter as a new line at the bottom of the doc:

deb https://cloud.r-project.org/bin/linux/ubuntu xenial/

# exit nano

# Start, or try bash:

apt-get install r-base

apt-get install r-base-dev

apt-get update

apt-get upgrade

wget http://download.osgeo.org/gdal/2.3.1/gdal-2.3.1.tar.gz

tar xvf gdal-2.3.1.tar.gz

cd  gdal-2.3.1

# begin making GDAL: this all takes a while

./configure  [if your need proper kml support (like me), search on configuring with expat or libkml.   There are many more options for configuration based on other packages that can go here, and this is the step to get them in order...]

sudo make

sudo make install

cd # Try entering R now and check the version!

# Start installing RStudio server and Shiny

apt-get update

apt-get upgrade
sudo apt-get install gdebi-core
wget https://download2.rstudio.org/rstudio-server-1.1.456-amd64.deb
sudo gdebi rstudio-server-1.1.456-amd64.deb

# Enter R or go to the graphical R Studio installation in your browser

R

# Authenticate if using the graphical interface using the usr:pwd you defined earlier

# this will take a long time

install.packages(“rgdal”)

# Note any errors carefully!

Then:

install.packages(“dplyr”)

install.packages(c("data.table", "tidyverse”, “shiny”)  # etc

Well, there you have it!

-Jess

Extras:

##Later, ONLY IF you NEED Anaconda, FYI:

# Get Anaconda: this is a large package manager, and is could be used for patching up missing # dependencies:

#Use  "ls" followed by rm -r <anaconda> (fill in with ls results) to remove conflicting conda

# installers if you have any issue there, I am starting fresh:

mkdir binconda

# *making a weak attempt at sandboxing the massive new package manager installation*

cd binconda
wget http://repo.continuum.io/archive/Anaconda2-4.3.0-Linux-x86_64.sh
# install and follow the prompts
bash Anaconda2-5.2.0-Linux-x86_64.sh

# Close the terminal window completely and start a new one, and ssh back to where you left

# off.  Conda install requires this.

# open and SSH back into your instance.  You should now have either additional flexibility in

# either patching holes in dependencies, or created some large holes in your server.  YMMV.

### Done

Red Hat stuff:

Follow these AWS instructions if you are doing something else:

https://aws.amazon.com/blogs/big-data/running-r-on-aws/

See my notes on this here:

https://www.transscendsurvival.org/2018/03/08/how-to-make-a-aws-r-server/

and notes on Shiny server:

https://www.transscendsurvival.org/2018/07/16/deploy-a-shiny-web-app-in-r-using-aws-ec2-red-hat/

GDAL on Red Hat:- Existing threads on this:

https://gis.stackexchange.com/questions/120101/building-gdal-with-libkml-support/120103#120103

This is a nice short thread about building from source:

https://gis.stackexchange.com/questions/263495/how-to-install-gdal-on-centos-7-4

neat RPM package finding tool, just in case:

https://rpmfind.net/linux/rpm2html/

Info on the LIBKML driver if you end up with issues there:

http://www.gdal.org/drv_libkml.html

 

I hope this is useful- GDAL is important and best to set it up early.  It will be a pain, but so is losing work while trying to patch it in later.  xD

 

-Jess

 

INFO: Deploy a Shiny web app in R using AWS (EC2 Red Hat)

Info on deploying a Shiny web app in R using AWS (EC2 Redhat)

As a follow-up to my post on how to create an AWS RStudio server, the next logical step is to host some useful apps you created in R for people to use.  A common way to do this is the R-specific tool Shiny, which is built in to RStudio.  Learning the syntax to convert R code into a Shiny app is rather subtle, and can be hard.  I plan to do a more thorough demo on this- particularly the use of the $ symbol, as in “input$output”- later. 🙂

 

It turns out hosting a Shiny Web app provides a large number of opportunities for things to go wrong….  I will share what worked for me.  All of this info is accessed via SSH, to the server running Shiny and RStudio.

 

I am using the AWS “Linux 2” AMI, which is based on the Red Hat OS.  For reference, here is some extremely important Red Hat CLI language worth being familiar with and debugging:

 

sudo yum install” and “wget” are for fetching and installing things like shiny.  Don’t bother with instructions that include “apt-get install”, as they are for a different Linux OS!

 

sudo chmod -R 777” is how you change your directory permissions for read, write, and execute (all of those enabled).  This is handy if your server disconnecting when the app tries to run something- it is a simple fix to a problem not always evident in the logs.  The default root folder from which shiny apps are hosted and run is “/srv/shiny-server” (or just “/srv” to be safe).

 

nano /var/log/shiny-server.log” is the location of current shiny logs.

 

sudo stop shiny-server” followed by “sudo start shiny-server” is the best way to restart the server- “sudo restart shiny-server” is not a sure bet on any other process.  It is true, other tools like a node.js server or nginx could impact the success of Shiny- If you think nginx is a problem, “cd /ect/nginx” followed by “ls” will get you in the right direction.  Others have cited problems with Red Hat not including the directories and files at “/etc/nginx/sites-available”.  You do not need these directories.  (though they are probably important for other things).

 

sudo rm -r” is a good way to destroy things, like a mangled R studio installation.  Remember, it is easy enough to start again fresh!  🙂

 

sudo nano /etc/shiny-server/shiny-server.conf” is how to access the config file for Shiny.  The fresh install version I used did not work!  There will be lots of excess in that file, much of which can causes issues in a bare-bones setup like mine.  One important key is to ensure Shiny is using a root user- see my example file below.  I am the root user here (jess)- change that to mirror- at least for the beginning- the user defined as root in your AWS installation.  See my notes HERE on that- that is defined in the advanced settings of the EC2 instance.

 

BEGIN CONFIG FILE:   (or click to download) *Download is properly indented


# Define user: this should be the same user as the AWS root user!
#
run_as jess;
#
# Define port and where the home (/) directory is
# Define site_dir/log_dir - these are the defaults
#
server{
listen 3838;
location / {
site_dir /srv/shiny-server;
log_dir /var/log/shiny-server;
directory_index on;
}
}

END CONFIG FILE

Well, the proof is in the pudding.   At least for now, you can access a basic app I made that cleans csv field data files that where entered into excel by hand.  They start full of missing fields and have a weird two-column setup for distance- the app cleans all these issues and returns a 4 column (from 5 column) csv.

Download the test file here:   2012_dirt_PCD-git

And access the app here:  Basic Shiny app on AWS!

Below is an iFrame into the app, just to show how very basic it is.  Give it a go!

-Jess

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