Docker on Snappy Ubuntu Core on a Raspberry Pi 2

In a previous post I gave a quick introduction to Snappy Ubuntu Core on the Raspberry Pi 2. This was based on the very early version of Ubuntu Snappy Core that was released around the time of the Rasberry Pi 2. There were various problems with that early release that have since been fixed, so in this post I want to give a quick update on the status of Snappy on the Pi, especially in relation to Docker.

If you have an early version of Snappy for the Pi 2, you should manually download a new image and re-flash your SD card. I did not have any luck in using the Snappy upgrade mechanism (which is somewhat ironic). You can download a new image here. I used ubuntu-15.04-snappy-armhf-rpi2.img for this post.

You can check if you have an old or new version of Snappy by running snappy list. That gave an error with the old version, but should work fine with the new version. The new version also sets the system clock using the internet, so date should return the correct time for any system that is connected to the internet. As before, Snappy runs an SSH server from first boot (ubuntu/ubuntu), so can be happily run completely headless.

Once logged in the snappy guides should more-or-less work. Commonly used Snappy commands are indicated below.

snappy
snappy --help
snappy list --help
snappy info
snappy list
snappy list -a
snappy list -u
snappy search docker
sudo snappy update ubuntu-core
sudo reboot
sudo snappy install docker

There is now a Snappy framework for Docker which can be easily installed, as indicated above. If you are a Docker person, this is essentially the only Snappy framework/app you need, as you can do everything else with Docker images.

The main issue with Docker on the Pi 2 (and on ARM more generally), is that the vast majority of images on DockerHub are for x86, and therefore will not work on ARM. For the Pi, the best bet is to search for images containing the text rpi or armhf. Again, some indicative Docker commands are given below.

docker search rpi
docker info
docker images
docker pull hypriot/rpi-java
docker pull resin/rpi-raspbian
docker images
docker run -i -t hypriot/rpi-java /bin/bash
docker ps
docker ps -a

Some useful base images include resin/rpi-raspbian which is a minimal Raspbian base, and hypriot/rpi-java, which includes OpenJDK7. With these (or other) base images, it is easy to layer on top with your own Dockerfile to create your own custom images. As discussed previously on this blog, OpenJDK is very slow relative to Oracle’s JVM on ARM. I’ve not yet found a public image containing Oracle’s JVM (perhaps due to licensing issues), but it’s easy enough to roll-your-own from a minimal base image. You can follow the standard Docker User Guides for information on how to build your own images.

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Getting started with Snappy Ubuntu Core on the Raspberry Pi 2

[UPDATE: Note that the version of Snappy described in this post is now obsolete – I have a new post describing a newer version which works better, and slightly differently.]

This post consists of a few notes which may be helpful for people trying to get started with Snappy Ubuntu Core on the new Raspberry Pi 2. First of all note that Ubuntu requires ARM7 so it won’t run on any model of Raspberry Pi prior to the Raspberry Pi 2 (model B), released February 2015.

The Ubuntu Core image can be downloaded from the Raspberry Pi downloads page, and written to uSD card in the usual way, just as you would a Raspbian image. From my Ubuntu laptop, I use a command like:

% sudo dd bs=1M if=pi-snappy.img of=/dev/mmcblk0

Be very careful to get the device name correct for of, as this command will completely trash the output device.

Once you have an image on a uSD card, you can insert it into your Pi 2 and boot it up as usual. If you have a keyboard and display hooked up you can log in on the console, but note that Ubuntu Core can be used headless from first boot via ssh. The default username and password are both ubuntu. I ssh in to my device with a command like ssh ubuntu@raspi08.home. Ubuntu Core uses the new containerised “snappy” system for managing packages, so “apt” doesn’t work. To get an idea of how snappy works, you can read through the snappy tour from Canonical, but note that much of this tour won’t actually work on the Pi 2 right now. Here’s a console session:

ubuntu@localhost:~$ snappy info
release: ubuntu-core/devel
frameworks: 
apps: 
ubuntu@localhost:~$ snappy versions
Part         Tag   Installed  Available  Fingerprint     Active  
ubuntu-core  edge  2          -          f442b1d8d6db3f  *              
This command needs root, please run with sudo
ubuntu@localhost:~$ snappy search docker
No matching packages found: docker
ubuntu@localhost:~$ 

You will note that Docker isn’t currently available for Ubuntu Core on the Pi 2, though hopefully that will change soon. However, at the time of writing you may also find that the final command bombs with a certificate error. This turns out to be due to the fact that the system time is incorrect. You can verify this by running date. You can manually set it with a command like

sudo date -s "Sat Feb  7 09:57:32 GMT 2015"

where you paste in the output from running date on a system which does have the correct time. You can also automate this by instead using a command like

sudo date -s "`ssh username@linuxserver date`"

where username and linuxserver are replaced appropriately. At the time of writing there are very few Snappy packages available, but one useful package is webdm. You can search for it with snappy search webdm and install it with

sudo snappy install webdm

Running snappy info will confirm that it has installed correctly. This runs a web based package manager on port 4200, so, for example, I can connect to this from a web browser on the local network using the URL http://raspi08.home:4200/. This allows the browsing of available frameworks and apps.

There currently isn’t any app or framework for Java, but manually downloading and installing Oracle’s JDK8 for ARM works fine, and runs code at the same speed as using the JVM which ships with Raspbian. It would be very easy to package up the JDK8 as a Snappy app or framework, but I guess that there are licensing issues, so I’ll leave that to others to sort out! You can find out more about how Snappy works by reading Canonical’s snappy guides.

I quite like the Snappy system, and running Ubuntu Core on a Pi 2 is potentially a great way to learn about Cloud computing in a very cheap, simple and safe way. However, we need a few key apps and frameworks before it will become genuinely useful. Ubuntu Core certainly isn’t about to replace Raspbian as the main OS for the Raspberry Pi 2 any time soon.

Benchmarking MCMC codes on the Raspberry Pi 2

Introduction

In the previous post I looked at running some MCMC codes in C and Scala on the Parallella. In that post I explained how the Parallella was significantly faster than the Raspberry Pi, and how it represented better “bang for buck” than the Raspberry Pi for computationally intensive MCMC codes. However, since that post was written, the Raspberry Pi 2 has been released. This board has a much better processor than the old Pi, and double the RAM, for the same price. This changes things, considerably. The processor is an ARM7 quad core. Each core is around twice as fast as the single core on the original Pi, and there are 4 of them. In this post I will re-run the codes from the previous post and compare against the Parallella.

Gibbs sampler in C

I’m using the new Raspbian image for the Pi 2. This includes gcc by default, but not the GSL library. This can be installed with sudo apt-get install libgsl0-dev. Then the file gibbs.c from the previous post will compile and run. On the Pi 2 this runs in around 75 seconds – very similar to the time on the Parallella, and around twice as fast as all of the previous Raspberry Pis.

Gibbs sampler in Scala

The Raspbian image ships with Oracle’s fast and efficient ARM-optimised JVM by default, so there’s no issue with installing Java at all. As usual, installing “sbt” is a simple matter of copying the launcher script (and jar) into your ~/bin directory. Then the Scala version of the Gibbs sampler can be run with a command like time sbt run > /dev/null. Again, it runs in around 4 minutes 40 seconds, just like on the Parallella. So, the ARM cores on the Parallella and the Pi 2 have very similar performance. However, the Parallella ARM chip has just two cores, whereas the Pi 2 is quad core.

Parallel Monte Carlo in Scala

Again, as for the previous post, I next ran the Monte Carlo example from this github repo. This gives output like:

$ sbt run
[info] Set current project to monte-carlo (in build file:/home/pi/src/git/statslang-scala/monte-carlo/)
[info] Running MonteCarlo 
Running with 1000000 iterations
Idiomatic vectorised solution
1.00136
time: 6768.504487ms
Fast efficient (serial) tail call
1.000705
time: 2473.331672ms
Parallelised version
1.0007
time: 1391.2828ms
Done
[success] 

Here again we see that the single threaded versions run in a similar time to the Parallella (and around twice as fast as the old Pis), but that the parallelised version runs significantly faster on the Pi 2 than on the Parallella (due to having 4 ARM cores rather than 2).

Summary

For my test MCMC codes, the cores on the Pi 2 are around twice as fast as the single core on the old Raspberry Pis, and a similar speed to the cores on the Parallella. However, multi-threaded codes run faster still, due to there being 4 cores on the Pi 2 (versus 2 on the Parallella and one on the old Pis). Furthermore, the Pi 2 is the same price as the old Pis (which are still being sold), and around a quarter of the price of the cheapest Parallella. So for standard single and multi-threaded codes running on the ARM cores, the Pi 2 wins hands down in terms of “bang for buck”, and is sufficiently quick and cheap that it starts looking like a credible platform for building cheap clusters for compute-intensive jobs like MCMC. Now to be fair to the Parallella, really the whole point of it is that it has a multi-core Epiphany co-processor that I’ve not been using or factoring in to the discussion at all so far. That said, the Pi 2 is so much cheaper than the Parallella (not to mention, less “fragile”), that I suspect that even for codes which effectively exploit the Epiphany chip it is unlikely that the Parallella will outperform the Pi 2 in terms of “bang for buck”. Now “bang per watt” is another matter entirely, and the Parallella may well outperform the Pi 2 in that regard if efficient use can be made of the Epiphany chip. But development time costs money too, and it’s really not clear that it’s going to be easy for me to run my multi-threaded Scala codes effectively on the Epiphany chip any time soon. So the Pi 2 currently looks like a real winner from my personal perspective.