The Rosetta@home project is a not-for-profit distributed computing project created by the Baker laboratory at the University of Washington. The project uses idle compute capacity from volunteer computers to study protein structure, which is used in research into diseases such as HIV, Malaria, Cancer, and Alzheimer’s.
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In common with many other scientific organizations, Rosetta@home is currently expending significant resources on the search for vaccines and treatments for COVID-19.
- Rosetta Stone English Torrent – Double click all language iso files (to open with DiskImageMounter), then go open Rosetta Stone, and at the top-right, one of the drop-down menus will give you the option to “Add or Remove Languages”. Click that and proceed through the steps.
- This transfer was tested on WED 2016-08-17 from Windows XP running Rosetta Stone V4 to Windows 10 x64 running the same Rosetta Stone V4. File tracking.db3 contains progress for all users, defined on this computer. It means, you need to preserve the existing tracking.db3 file and rename it back, when you are done.
Mkdir Chain Next, run the ont-rosetta image using the command specified below. Note that we mount three volumes into the container. The three volumes store log data, block data, and allow access to the host configuration file.
Rosetta@home uses the open source BOINC platform to manage donated compute resources. BOINC was originally developed to support the SETI@home project searching for Extraterrestrial Intelligence. These days, it is used by a number of projects in many different scientific fields. A single BOINC client can contribute compute resources to many such projects, though not all projects support all architectures.
For the example shown in this article a Raspberry Pi 3 Model B was used, which is one of the tested reference devices for Fedora IoT. This device, with only 1GB of RAM, is only just powerful enough to be able to make a meaningful contribution to Rosetta@home, and there’s certainly no way the Raspberry Pi can be used for anything else – such as running a desktop environment – at the same time.
It’s also worth mentioning at this point that the first rule of Raspberry Pi computing is to get the recommended power supply. It is important to get as close to the specified 2.5A as you can, and use a good quality micro-usb cable.
Getting Fedora IoT
To install Fedora IoT on a Raspberry Pi, the first step is to download the aarch64 Raw Image from the iot.fedoraproject.org download page.
Then use the arm-image-installer utility (sudo dnf install fedora-arm-installer) to write the image to the SD card. As always, be very sure which device name corresponds to your SD Card before continuing. Check the device with the lsblk command like this:
If you’re still not sure, try running lsblk with the SD card removed, then again with the SD card inserted and comparing the outputs. In this case it lists the SD card as /dev/sdb. If you’re really unsure, there are some more tips described in the Getting Started guide.
We need to tell arm-image-installer which image file to use, what type of device we’re going to be using, and the device name – determined above – to use for writing the image. The arm-image-installer utility is also able to expand the filesystem to use the entire SD card at the point of writing the image.
Since we’re not going to use the zezere provisioning server to deploy SSH keys to the Raspberry Pi, we need to specify the option to remove the root password so that we can log in and set it at first boot.
In my case, the full command was:
After a final confirmation prompt:
the image is written to the SD Card.
Booting the Raspberry Pi
For the initial setup, you’ll need to attach a keyboard and mouse to the Raspberry Pi. Alternatively, you can follow the instructions for connecting with a USB-to-Serial cable.
When the Raspberry Pi boots up, just type root at the login prompt and press enter.
The first task is to set a password for the root user.
Verifying Network Connectivity
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To verify the network connectivity, the checklist in the Fedora IoT Getting Started guide was followed. This system is using a wired ethernet connection, which shows as eth0. If you need to set up a wireless connection this can be done with nmcli.
ip addr will allow you to check that you have a valid IP address.
ip route will check that the network has a default gateway configured.
To verify internet access and name resolution, use ping
Optional: Configuring sshd so we can disconnect the keyboard and monitor
Before disconnecting the keyboard and monitor, we need to ensure that we can connect to the Raspberry Pi over the network.
First we verify that sshd is running
and that there is a firewall rule present to allow ssh.
In the file /etc/ssh/sshd_config, find the section named
and add the line
There will already be a line
which you can edit by removing the # comment character and changing the value to yes.
Restart the sshd service to pick up the change
If all this is in place, we should be able to ssh to the Raspberry Pi.
It’s now safe to log out from the console (exit) and disconnect the keyboard and monitor.
Disabling unneeded services
Since we’re right on the lower limit of viable hardware for Rosetta@home, it’s worth disabling any unneeded services. Fedora IoT is much more lightweight than desktop distributions, but there are still a few optimizations we can do.
Like disabling bluetooth, Modem Manager (used for cellular data connections), WPA supplicant (used for Wi-Fi) and the zezere services, which are used to centrally manage a fleet of Fedora IoT devices.
Getting the BOINC client
Instead of installing the BOINC client directly onto the operating system with rpm-ostree, we’re going to use podman to run the containerized version of the client.
This image uses a volume mount to store its data, so we create the directories it needs in advance.
We also need to add a firewall rule to allow the container to resolve external DNS names.
Finally we are ready to pull and run the BOINC client container.
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We can inspect the container logs to make sure everything is working as expected:
Configuring the BOINC container to run at startup
We can automatically generate a systemd unit file for the container with podman generate systemd.
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This creates a systemd unit file in root’s home directory.
We install the file by moving it to the appropriate directory.
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Connecting to the Rosetta Stone project
You need to create an account at the Rosetta@home signup page, and retrieve your account key from your account home page. The key to copy is the “Weak Account Key”.
Finally, we execute the boinccmd configuration utility inside the container using podman exec, passing the Rosetta@home url and our account key.
Running podman logs –follow boinc will allow us to see the container connecting to the project. You will probably see errors of the form
This is because most, but not all, of the work units in Rosetta@Home require more memory than we have to offer. However, if you leave the device running for a while, it should eventually get some jobs to process. The polling interval seems to be approximately 10 minutes. We can also tweak the memory settings using BOINC manager to allow BOINC to use slightly more memory. This will increase the probability that Rosetta@home will be able to find tasks for us.
Installing BOINC Manager for remote access
You can use dnf to install the BOINC manager component to remotely manage the BOINC client on the Raspberry Pi.
If you switch to “Advanced View” , you will be able to select “File -> Select Computer” and connect to your Raspberry Pi, using the IP address of the Pi and the value supplied for BOINC_GUI_RPC_PASSWORD in the podman run command, in my case “blah“.
Under “Options -> Computing Preferences”, increase the value for “When Computer is not in use, use at most _ %”. I’ve been using 93%; this seems to allow Rosetta@home to schedule work on the pi, whilst still leaving it just about usable. It is possible that further fine tuning of the operating system might allow this percentage to be increased.
These settings can also be changed through the Rosetta@home website settings page, but bear in mind that changes made through the BOINC Manager client override preferences set in the web interface.
Wait
It may take a while, possibly several hours, for Rosetta@home to send work to our newly installed client, particularly as most work units are too big to run on a Raspberry Pi. COVID-19 has resulted in a large number of new computers being joined to the Rosetta@home project, which means that there are times when there isn’t enough work to do.
When we are assigned some work units, BOINC will download several hundred megabytes of data. This will be stored on the SD Card and can be viewed using BOINC manager.
We can also see the tasks running in the Tasks pane:
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The client has downloaded four tasks, but only one of them is currently running due to memory constraints. At times, two tasks can run simultaneously, but I haven’t seen more than that. This is OK as long as the tasks are completed by the deadline shown on the right. I’m fairly confident these will be completed as long as the Raspberry Pi is left running. I have found that the additional memory overhead created by the BOINC Manager connection and sshd services can reduce parallelism, so I try to disconnect these when I’m not using them.
Conclusion
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Rosetta@home, in common with many other distributed computing projects, is currently experiencing a large spike in participation due to COVID-19. That aside, the project has been doing valuable work for many years to combat a number of other diseases.
Whilst a Raspberry Pi is never going to appear at the top of the contribution chart, I think this is a worthwhile project to undertake with a spare Raspberry Pi. The existence of work units aimed at low-spec ARM devices indicates that the project organizers agree with this sentiment. I’ll certainly be leaving mine running for the foreseeable future.