Andrew B. Collier / @datawookie

I know that global variables are from the Devil, but sometimes you just can’t get around them.

I’m building a small package for a client that relies on a data file. For various reasons that file is not part of the package and can reside in different locations on users’ machines. Furthermore there are users on both Windows and Linux machines.

Although it’s very handy (and easy) to set up some cloud resources using the AWS Management Console, once you know what you need it makes a lot of sense to automate the process. Fortunately there’s a handy little command line tools, aws, which makes this eminently possible. The AWS CLI Command Reference is the definitive resource for this tool. There’s a mind boggling array of possibilities. We’ll take a look at a small selection of them.

I have been retrieving some route information using Rodrigo Azuero’s gmapsdistance package and noted that there was some asymmetry in the results: the time and distance for the trip from A to B was not necessarily always the same as the time and distance for the trip from B to A. Although in retrospect this seems self-evident, it merited further investigation.

We’ve been building some models for Kaggle competitions using an EC2 instance for compute. I initially downloaded the data locally and then pushed it onto EC2 using SCP. But there had to be a more efficient way to do this, especially given the blazing fast bandwidth available on AWS.

Enter kaggle-cli.

Update: Apparently that project has been deprecated in favour of kaggle-api. More information below.

Ensuring that your account is configured to run with appropriate time zone information can make your life a lot easier.

Of course, if you administer your own system then you can simply set your system time to local time. However, it’s generally a better idea to set system time to Universal Time (UTC) and then configure time zone information on a per-user basis.

Why does this make sense? Well, suppose that you have remote users logging onto your system. It’s very likely that a remote user will be operating in a different time zone and it’d be handy for them to have system time converted into their local time.

I’m in the process of setting up a Zinnia blog on one of my Django sites. After putting all of the necessary plumbing in place I got the following message on first visiting the blog URL:

Database returned an invalid value in QuerySet.datetimes(). Are time zone definitions for your database and pytz installed?

The solution to this is to copy your system’s time zone information across to the database.

Some quick notes on adding a storage volume to an EC2 instance.

A couple of options for remote access to desktop applications on a EC2 host.

A record of some more or less important events in the history of R.

This is a work in progress. The information is cobbled together from a range of sources. If you have pertinent items to add, please let me know via the comments.

By default an EC2 instance has only a single user other than root. For example, on a Ubuntu instance, that user is ubuntu. If there will be multiple people accessing the instance then it’s generally necessary for each of them to have their own account. Setting this up is pretty simple, it just requires sorting out some authentication details.

I’m busy putting together a Docker image for a multi-user Jupyter Notebook installation. I am to have an independent login for each of the users and each of them should also have their own storage space. That space should exist elsewhere from on the container though, so that even if the container stops, the data lives on. This should mitigate user rage.

Amazon’s EC2 Container Services (ECS) is an orchestrated system for deploying Docker containers on AWS. This post is about not using ECS.

I’ll be running a training course in a few weeks which will use RStudio as the main computational tool. Since it’s a short course I don’t want to spend a lot of time sorting out technical issues. And with multiple operating systems (and versions) these issues can be numerous and pervasive. Setting up a RStudio server which everyone can access (and that requires no individual configuration!) makes a lot of sense.

This is what I did to set up Hadoop on my Ubuntu machine.

I’m busy experimenting with Spark. This is what I did to set up a local cluster on my Ubuntu machine. Before you embark on this you should first set up Hadoop.

In the process of uploading a massive CSV file to my Django application my session data are getting pretty big. As a the result I’m getting these errors:

• (1153, "Got a packet bigger than 'max_allowed_packet' bytes") and
• (2006, 'MySQL server has gone away').

The second error is potentially unrelated.

After some research it became apparent that the source of the problem is my max_allowed_packet setting.

I’ve been meaning to set up a VPN and this morning seemed like a good time to tick it off the bucket list. This is a quick outline of my experience, which included one minor hiccup.

A short note on how to set up Jupyter Notebooks with Python 3 on Ubuntu. The instructions are specific to Xenial Xerus (16.04) but are likely to be helpful elsewhere too.