Search Results: "ag"

24 January 2024

Dirk Eddelbuettel: RApiDatetime 0.0.9 on CRAN: Maintenance

A new maintenance release of our RApiDatetime package is now on CRAN RApiDatetime provides a number of entry points for C-level functions of the R API for Date and Datetime calculations. The functions asPOSIXlt and asPOSIXct convert between long and compact datetime representation, formatPOSIXlt and Rstrptime convert to and from character strings, and POSIXlt2D and D2POSIXlt convert between Date and POSIXlt datetime. Lastly, asDatePOSIXct converts to a date type. All these functions are rather useful, but were not previously exported by R for C-level use by other packages. Which this package aims to change. This release responds to a CRAN request to clean up empty macros and sections in Rd files. Moreover, because the windows portion of the corresponding R-internal code underwent some changes, our (#ifdef conditional) coverage here is a little behind and created a warning under the newer UCRT setup. So starting with this release we are back to OS_type: unix meaning there will not be any Windows builds at CRAN. If you would like that to change, and ideally can work in the Windows portion, do not hesitate to get in touch. Details of the release follow based on the NEWS file.

Changes in RApiDatetime version 0.0.9 (2024-01-23)
  • Replace auto-generated stale RApitDatetime-package.Rd with macro-filled stanza to satisfy CRAN request.

Courtesy of my CRANberries, there is also a diffstat report for this release. If you like this or other open-source work I do, you can sponsor me at GitHub.

This post by Dirk Eddelbuettel originated on his Thinking inside the box blog. Please report excessive re-aggregation in third-party for-profit settings.

Thomas Lange: FAI 6.2 released

After more than one a year, a new minor FAI version is available, but it includes some interesting new features. Here a the items from the NEWS file: fai (6.2) unstable; urgency=low In the past the command fai-cd was only used for creating installation ISOs, that could be used from CD or USB stick. Now it possible to create a live ISO. Therefore you create your live chroot environment using 'fai dirinstall' and then convert it to a bootable live ISO using fai-cd. See man fai-cd(8) for an example. Years ago I had the idea to use the remaining disk space on an USB stick after copying an ISO onto it. I've blogged about this recently: https://blog.fai-project.org/posts/extending-iso-images/ The new FAI version includes the tool mk-data-partition for adding a data partition to the ISO itself or to an USB stick. FAI detects this data partition, mounts it to /media/data and can then use various configurations from it. You may want to copy your own set of .deb packages or your whole FAI config space to this partition. FAI now automatically searches this partition for usable FAI configuration data and packages. FAI will install all packages from pkgs/<CLASSNAME> if the equivalent class is defined. Setting FAI_CONFIG_SRC=detect:// now looks into the data partition for the subdirectory 'config' and uses this as the config space. So it's now possible to modify an existing ISO (that is read-only) and make changes to the config space. If there's no config directory in the data partition FAI uses the default location on the ISO. The tool fai-kvm, which starts virtual machines can now boot an ISO not only as CD but also as USB stick. Sometimes users want to adjust the list of disks before the partitioning is startet. Therefore FAI provides several new functions including You can select individual disks by their model name or even the serial number. Two new FAI flags were added (tmux and screen) that make it easy to run FAI inside a tmux or screen session. And finally FAI uses systemd. Yeah! This technical change was waiting since 2015 in a merge request from Moritz 'Morty' Str be, that would enable using systemd during the installation. Before FAI still was using old-style SYSV init scripts and did not started systemd. I didn't tried to apply the patch, because I was afraid that it would need much time to make it work. But then in may 2023 Juri Grabowski just gave it a try at MiniDebConf Hamburg, and voil it just works! Many, many thanks to Moritz and Juri for their bravery. The whole changelog can be found at https://tracker.debian.org/media/packages/f/fai/changelog-6.2 New ISOs for FAI are also available including an example of a Xfce desktop live ISO: https://fai-project.org/fai-cd/ The FAIme service for creating customized installation ISOs will get its update later. The new packages are available for bookworm by adding this line to your sources.list: deb https://fai-project.org/download bookworm koeln

Louis-Philippe V ronneau: Montreal Subway Foot Traffic Data, 2023 edition

For the fifth year in a row, I've asked Soci t de Transport de Montr al, Montreal's transit agency, for the foot traffic data of Montreal's subway. By clicking on a subway station, you'll be redirected to a graph of the station's foot traffic. Licences

Dirk Eddelbuettel: RcppAnnoy 0.0.22 on CRAN: Maintenance

annoy image A very minor maintenance release, now at version 0.0.22, of RcppAnnoy has arrived on CRAN. RcppAnnoy is the Rcpp-based R integration of the nifty Annoy library by Erik Bernhardsson. Annoy is a small and lightweight C++ template header library for very fast approximate nearest neighbours originally developed to drive the Spotify music discovery algorithm. It had all the buzzwords already a decade ago: it is one of the algorithms behind (drum roll ) vector search as it finds approximate matches very quickly and also allows to persist the data. This release responds to a CRAN request to clean up empty macros and sections in Rd files. Details of the release follow based on the NEWS file.

Changes in version 0.0.22 (2024-01-23)
  • Replace empty examples macro to satisfy CRAN request.

Courtesy of my CRANberries, there is also a diffstat report for this release. If you like this or other open-source work I do, you can sponsor me at GitHub.

This post by Dirk Eddelbuettel originated on his Thinking inside the box blog. Please report excessive re-aggregation in third-party for-profit settings.

22 January 2024

Dirk Eddelbuettel: x13binary 1.1.60 on CRAN: Upstream Update, Updated Build

The x13binary team is thrilled to share the availability of Release 1.1.60-1 of the x13binary package providing the X-13ARIMA-SEATS program by the US Census Bureau which arrived on CRAN earlier today. This release brings the package up to speed with the most current release by the Census Bureau. More importantly, we finally made good on an old promise to ourselves and now install the binary by compiling from its Fortran sources! No more pre-made binaries. This required some work by Kirill, Michael, and Jeroen to finalize matter because, as we all know, the CRAN build processes and tool chains can be a little byzantine in their details. Use on platforms not covered by binaries from CRAN (or r-universe) should just work too as the demands on the (Fortran) compiler are fairly standard. All in all the build is fairly lightweight and quick even when rebuilding from source. Courtesy of my CRANberries, there is also a diffstat report for this release showing changes to the previous release. If you like this or other open-source work I do, you can sponsor me at GitHub.

This post by Dirk Eddelbuettel originated on his Thinking inside the box blog. Please report excessive re-aggregation in third-party for-profit settings.

Chris Lamb: Increasing the Integrity of Software Supply Chains awarded IEEE Best Paper award

IEEE Software recently announced that a paper that I co-authored with Dr. Stefano Zacchiroli has recently been awarded their Best Paper award:
Titled Reproducible Builds: Increasing the Integrity of Software Supply Chains, the abstract reads as follows:
Although it is possible to increase confidence in Free and Open Source Software (FOSS) by reviewing its source code, trusting code is not the same as trusting its executable counterparts. These are typically built and distributed by third-party vendors with severe security consequences if their supply chains are compromised. In this paper, we present reproducible builds, an approach that can determine whether generated binaries correspond with their original source code. We first define the problem and then provide insight into the challenges of making real-world software build in a "reproducible" manner that is, when every build generates bit-for-bit identical results. Through the experience of the Reproducible Builds project making the Debian Linux distribution reproducible, we also describe the affinity between reproducibility and quality assurance (QA).
According to Google Scholar, the paper has accumulated almost 40 citations since publication. The full text of the paper can be found in PDF format.

Paul Tagliamonte: Writing a simulator to check phased array beamforming

Interested in future updates? Follow me on mastodon at @paul@soylent.green. Posts about hz.tools will be tagged #hztools.

If you're on the Fediverse, I'd very much appreciate boosts on my toot!
While working on hz.tools, I started to move my beamforming code from 2-D (meaning, beamforming to some specific angle on the X-Y plane for waves on the X-Y plane) to 3-D. I ll have more to say about that once I get around to publishing the code as soon as I m sure it s not completely wrong, but in the meantime I decided to write a simple simulator to visually check the beamformer against the textbooks. The results were pretty rad, so I figured I d throw together a post since it s interesting all on its own outside of beamforming as a general topic. I figured I d write this in Rust, since I ve been using Rust as my primary language over at zoo, and it s a good chance to learn the language better.
This post has some large GIFs

It make take a little bit to load depending on your internet connection. Sorry about that, I'm not clever enough to do better without doing tons of complex engineering work. They may be choppy while they load or something. I tried to compress an ensmall them, so if they're loaded but fuzzy, click on them to load a slightly larger version.
This post won t cover the basics of how phased arrays work or the specifics of calculating the phase offsets for each antenna, but I ll dig into how I wrote a simple simulator and how I wound up checking my phase offsets to generate the renders below.

Assumptions I didn t want to build a general purpose RF simulator, anything particularly generic, or something that would solve for any more than the things right in front of me. To do this as simply (and quickly all this code took about a day to write, including the beamforming math) I had to reduce the amount of work in front of me. Given that I was concerend with visualizing what the antenna pattern would look like in 3-D given some antenna geometry, operating frequency and configured beam, I made the following assumptions: All anetnnas are perfectly isotropic they receive a signal that is exactly the same strength no matter what direction the signal originates from. There s a single point-source isotropic emitter in the far-field (I modeled this as being 1 million meters away 1000 kilometers) of the antenna system. There is no noise, multipath, loss or distortion in the signal as it travels through space. Antennas will never interfere with each other.

2-D Polar Plots The last time I wrote something like this, I generated 2-D GIFs which show a radiation pattern, not unlike the polar plots you d see on a microphone. These are handy because it lets you visualize what the directionality of the antenna looks like, as well as in what direction emissions are captured, and in what directions emissions are nulled out. You can see these plots on spec sheets for antennas in both 2-D and 3-D form. Now, let s port the 2-D approach to 3-D and see how well it works out.

Writing the 3-D simulator As an EM wave travels through free space, the place at which you sample the wave controls that phase you observe at each time-step. This means, assuming perfectly synchronized clocks, a transmitter and receiver exactly one RF wavelength apart will observe a signal in-phase, but a transmitter and receiver a half wavelength apart will observe a signal 180 degrees out of phase. This means that if we take the distance between our point-source and antenna element, divide it by the wavelength, we can use the fractional part of the resulting number to determine the phase observed. If we multiply that number (in the range of 0 to just under 1) by tau, we can generate a complex number by taking the cos and sin of the multiplied phase (in the range of 0 to tau), assuming the transmitter is emitting a carrier wave at a static amplitude and all clocks are in perfect sync.
 let observed_phases: Vec<Complex> = antennas
.iter()
.map( antenna   
let distance = (antenna - tx).magnitude();
let distance = distance - (distance as i64 as f64);
((distance / wavelength) * TAU)
 )
.map( phase  Complex(phase.cos(), phase.sin()))
.collect();
At this point, given some synthetic transmission point and each antenna, we know what the expected complex sample would be at each antenna. At this point, we can adjust the phase of each antenna according to the beamforming phase offset configuration, and add up every sample in order to determine what the entire system would collectively produce a sample as.
 let beamformed_phases: Vec<Complex> = ...;
let magnitude = beamformed_phases
.iter()
.zip(observed_phases.iter())
.map( (beamformed, observed)  observed * beamformed)
.reduce( acc, el  acc + el)
.unwrap()
.abs();
Armed with this information, it s straight forward to generate some number of (Azimuth, Elevation) points to sample, generate a transmission point far away in that direction, resolve what the resulting Complex sample would be, take its magnitude, and use that to create an (x, y, z) point at (azimuth, elevation, magnitude). The color attached two that point is based on its distance from (0, 0, 0). I opted to use the Life Aquatic table for this one. After this process is complete, I have a point cloud of ((x, y, z), (r, g, b)) points. I wrote a small program using kiss3d to render point cloud using tons of small spheres, and write out the frames to a set of PNGs, which get compiled into a GIF. Now for the fun part, let s take a look at some radiation patterns!

1x4 Phased Array The first configuration is a phased array where all the elements are in perfect alignment on the y and z axis, and separated by some offset in the x axis. This configuration can sweep 180 degrees (not the full 360), but can t be steared in elevation at all. Let s take a look at what this looks like for a well constructed 1x4 phased array: And now let s take a look at the renders as we play with the configuration of this array and make sure things look right. Our initial quarter-wavelength spacing is very effective and has some outstanding performance characteristics. Let s check to see that everything looks right as a first test. Nice. Looks perfect. When pointing forward at (0, 0), we d expect to see a torus, which we do. As we sweep between 0 and 360, astute observers will notice the pattern is mirrored along the axis of the antennas, when the beam is facing forward to 0 degrees, it ll also receive at 180 degrees just as strong. There s a small sidelobe that forms when it s configured along the array, but it also becomes the most directional, and the sidelobes remain fairly small.

Long compared to the wavelength (1 ) Let s try again, but rather than spacing each antenna of a wavelength apart, let s see about spacing each antenna 1 of a wavelength apart instead. The main lobe is a lot more narrow (not a bad thing!), but some significant sidelobes have formed (not ideal). This can cause a lot of confusion when doing things that require a lot of directional resolution unless they re compensated for.

Going from ( to 5 ) The last model begs the question - what do things look like when you separate the antennas from each other but without moving the beam? Let s simulate moving our antennas but not adjusting the configured beam or operating frequency. Very cool. As the spacing becomes longer in relation to the operating frequency, we can see the sidelobes start to form out of the end of the antenna system.

2x2 Phased Array The second configuration I want to try is a phased array where the elements are in perfect alignment on the z axis, and separated by a fixed offset in either the x or y axis by their neighbor, forming a square when viewed along the x/y axis. Let s take a look at what this looks like for a well constructed 2x2 phased array: Let s do the same as above and take a look at the renders as we play with the configuration of this array and see what things look like. This configuration should suppress the sidelobes and give us good performance, and even give us some amount of control in elevation while we re at it. Sweet. Heck yeah. The array is quite directional in the configured direction, and can even sweep a little bit in elevation, a definite improvement from the 1x4 above.

Long compared to the wavelength (1 ) Let s do the same thing as the 1x4 and take a look at what happens when the distance between elements is long compared to the frequency of operation say, 1 of a wavelength apart? What happens to the sidelobes given this spacing when the frequency of operation is much different than the physical geometry? Mesmerising. This is my favorate render. The sidelobes are very fun to watch come in and out of existence. It looks absolutely other-worldly.

Going from ( to 5 ) Finally, for completeness' sake, what do things look like when you separate the antennas from each other just as we did with the 1x4? Let s simulate moving our antennas but not adjusting the configured beam or operating frequency. Very very cool. The sidelobes wind up turning the very blobby cardioid into an electromagnetic dog toy. I think we ve proven to ourselves that using a phased array much outside its designed frequency of operation seems like a real bad idea.

Future Work Now that I have a system to test things out, I m a bit more confident that my beamforming code is close to right! I d love to push that code over the line and blog about it, since it s a really interesting topic on its own. Once I m sure the code involved isn t full of lies, I ll put it up on the hztools org, and post about it here and on mastodon.

Russell Coker: Storage Trends 2024

It has been less than a year since my last post about storage trends [1] and enough has changed to make it worth writing again. My previous analysis was that for <2TB only SSD made sense, for 4TB SSD made sense for business use while hard drives were still a good option for home use, and for 8TB+ hard drives were clearly the best choice for most uses. I will start by looking at MSY prices, they aren't the cheapest (you can get cheaper online) but they are competitive and they make it easy to compare the different options. I'll also compare the cheapest options in each size, there are more expensive options but usually if you want to pay more then the performance benefits of SSD (both SATA and NVMe) are even more appealing. All prices are in Australian dollars and of parts that are readily available in Australia, but the relative prices of the parts are probably similar in most countries. The main issue here is when to use SSD and when to use hard disks, and then if SSD is chosen which variety to use. Small Storage For my last post the cheapest storage devices from MSY were $19 for a 128G SSD, now it s $24 for a 128G SSD or NVMe device. I don t think the Australian dollar has dropped much against foreign currencies, so I guess this is partly companies wanting more profits and partly due to the demand for more storage. Items that can t sell in quantity need higher profit margins if they are to have them in stock. 500G SSDs are around $33 and 500G NVMe devices for $36 so for most use cases it wouldn t make sense to buy anything smaller than 500G. The cheapest hard drive is $45 for a 1TB disk. A 1TB SATA SSD costs $61 and a 1TB NVMe costs $79. So 1TB disks aren t a good option for any use case. A 2TB hard drive is $89. A 2TB SATA SSD is $118 and a 2TB NVMe is $145. I don t think the small savings you can get from using hard drives makes them worth using for 2TB. For most people if you have a system that s important to you then $145 on storage isn t a lot to spend. It seems hardly worth buying less than 2TB of storage, even for a laptop. Even if you don t use all the space larger storage devices tend to support more writes before wearing out so you still gain from it. A 2TB NVMe device you buy for a laptop now could be used in every replacement laptop for the next 10 years. I only have 512G of storage in my laptop because I have a collection of SSD/NVMe devices that have been replaced in larger systems, so the 512G is essentially free for my laptop as I bought a larger device for a server. For small business use it doesn t make sense to buy anything smaller than 2TB for any system other than a router. If you buy smaller devices then you will sometimes have to pay people to install bigger ones and when the price is $145 it s best to just pay that up front and be done with it. Medium Storage A 4TB hard drive is $135. A 4TB SATA SSD is $319 and a 4TB NVMe is $299. The prices haven t changed a lot since last year, but a small increase in hard drive prices and a small decrease in SSD prices makes SSD more appealing for this market segment. A common size range for home servers and small business servers is 4TB or 8TB of storage. To do that on SSD means about $600 for 4TB of RAID-1 or $900 for 8TB of RAID-5/RAID-Z. That s quite affordable for that use. For 8TB of less important storage a 8TB hard drive costs $239 and a 8TB SATA SSD costs $899 so a hard drive clearly wins for the specific case of non-RAID single device storage. Note that the U.2 devices are more competitive for 8TB than SATA but I included them in the next section because they are more difficult to install. Serious Storage With 8TB being an uncommon and expensive option for consumer SSDs the cheapest price is for multiple 4TB devices. To have multiple NVMe devices in one PCIe slot you need PCIe bifurcation (treating the PCIe slot as multiple slots). Most of the machines I use don t support bifurcation and most affordable systems with ECC RAM don t have it. For cheap NVMe type storage there are U.2 devices (the enterprise form of NVMe). Until recently they were too expensive to use for desktop systems but now there are PCIe cards for internal U.2 devices, $14 for a card that takes a single U.2 is a common price on AliExpress and prices below $600 for a 7.68TB U.2 device are common that s cheaper on a per-TB basis than SATA SSD and NVMe! There are PCIe cards that take up to 4*U.2 devices (which probably require bifurcation) which means you could have 8+ U.2 devices in one not particularly high end PC for 56TB of RAID-Z NVMe storage. Admittedly $4200 for 56TB is moderately expensive, but it s in the price range for a small business server or a high end home server. A more common configuration might be 2*7.68TB U.2 on a single PCIe card (or 2 cards if you don t have bifurcation) for 7.68TB of RAID-1 storage. For SATA SSD AliExpress has a 6*2.5 hot-swap device that fits in a 5.25 bay for $63, so if you have 2*5.25 bays you could have 12*4TB SSDs for 44TB of RAID-Z storage. That wouldn t be much cheaper than 8*7.68TB U.2 devices and would be slower and have less space. But it would be a good option if PCIe bifurcation isn t possible. 16TB SATA hard drives cost $559 which is almost exactly half the price per TB of U.2 storage. That doesn t seem like a good deal. If you want 16TB of RAID storage then 3*7.68TB U.2 devices only costs about 50% more than 2*16TB SATA disks. In most cases paying 50% more to get NVMe instead of hard disks is a good option. As sizes go above 16TB prices go up in a more than linear manner, I guess they don t sell much volume of larger drives. 15.36TB U.2 devices are on sale for about $1300, slightly more than twice the price of a 16TB disk. It s within the price range of small businesses and serious home users. Also it should be noted that the U.2 devices are designed for enterprise levels of reliability and the hard disk prices I m comparing to are the cheapest available. If NAS hard disks were compared then the price benefit of hard disks would be smaller. Probably the biggest problem with U.2 for most people is that it s an uncommon technology that few people have much experience with or spare parts for testing. Also you can t buy U.2 gear at your local computer store which might mean that you want to have spare parts on hand which is an extra expense. For enterprise use I ve recently been involved in discussions with a vendor that sells multiple petabyte arrays of NVMe. Apparently NVMe is cheap enough that there s no need to use anything else if you want a well performing file server. Do Hard Disks Make Sense? There are specific cases like comparing a 8TB hard disk to a 8TB SATA SSD or a 16TB hard disk to a 15.36TB U.2 device where hard disks have an apparent advantage. But when comparing RAID storage and counting the performance benefits of SSD the savings of using hard disks don t seem to be that great. Is now the time that hard disks are going to die in the market? If they can t get volume sales then prices will go up due to lack of economy of scale in manufacture and increased stock time for retailers. 8TB hard drives are now more expensive than they were 9 months ago when I wrote my previous post, has a hard drive price death spiral already started? SSDs are cheaper than hard disks at the smallest sizes, faster (apart from some corner cases with contiguous IO), take less space in a computer, and make less noise. At worst they are a bit over twice the cost per TB. But the most common requirements for storage are small enough and cheap enough that being twice as expensive as hard drives isn t a problem for most people. I predict that hard disks will become less popular in future and offer less of a price advantage. The vendors are talking about 50TB hard disks being available in future but right now you can fit more than 50TB of NVMe or U.2 devices in a volume less than that of a 3.5 hard disk so for storage density SSD can clearly win. Maybe in future hard disks will be used in arrays of 100TB devices for large scale enterprise storage. But for home users and small businesses the current sizes of SSD cover most uses. At the moment it seems that the one case where hard disks can really compare well is for backup devices. For backups you want large storage, good contiguous write speeds, and low prices so you can buy plenty of them. Further Issues The prices I ve compared for SATA SSD and NVMe devices are all based on the cheapest devices available. I think it s a bit of a market for lemons [2] as devices often don t perform as well as expected and the incidence of fake products purporting to be from reputable companies is high on the cheaper sites. So you might as well buy the cheaper devices. An advantage of the U.2 devices is that you know that they will be reliable and perform well. One thing that concerns me about SSDs is the lack of knowledge of their failure cases. Filesystems like ZFS were specifically designed to cope with common failure cases of hard disks and I don t think we have that much knowledge about how SSDs fail. But with 3 copies of metadata BTFS or ZFS should survive unexpected SSD failure modes. I still have some hard drives in my home server, they keep working well enough and the prices on SSDs keep dropping. But if I was buying new storage for such a server now I d get U.2. I wonder if tape will make a comeback for backup. Does anyone know of other good storage options that I missed?

Dirk Eddelbuettel: RProtoBuf 0.4.22 on CRAN: Updated Windows Support!

A new maintenance release 0.4.22 of RProtoBuf arrived on CRAN earlier today. RProtoBuf provides R with bindings for the Google Protocol Buffers ( ProtoBuf ) data encoding and serialization library used and released by Google, and deployed very widely in numerous projects as a language and operating-system agnostic protocol. This release matches the recent 0.4.21 release which enabled use of the package with newer ProtoBuf releases. Tomas has been updating the Windows / rtools side of things, and supplied us with simple PR that will enable building with those updated versions once finalised. The following section from the NEWS.Rd file has full details.

Changes in RProtoBuf version 0.4.22 (2022-12-13)
  • Apply patch by Tomas Kalibera to support updated rtools to build with newer ProtoBuf releases on windows

Thanks to my CRANberries, there is a diff to the previous release. The RProtoBuf page has copies of the (older) package vignette, the quick overview vignette, and the pre-print of our JSS paper. Questions, comments etc should go to the GitHub issue tracker off the GitHub repo. If you like this or other open-source work I do, you can sponsor me at GitHub.

This post by Dirk Eddelbuettel originated on his Thinking inside the box blog. Please report excessive re-aggregation in third-party for-profit settings.

21 January 2024

Debian Brasil: MiniDebConf BH 2024 - patroc nio e financiamento coletivo

MiniDebConf BH 2024 J est rolando a inscri o de participante e a chamada de atividades para a MiniDebConf Belo Horizonte 2024, que acontecer de 27 a 30 de abril no Campus Pampulha da UFMG. Este ano estamos ofertando bolsas de alimenta o, hospedagem e passagens para contribuidores(as) ativos(as) do Projeto Debian. Patroc nio: Para a realiza o da MiniDebConf, estamos buscando patroc nio financeiro de empresas e entidades. Ent o se voc trabalha em uma empresa/entidade (ou conhece algu m que trabalha em uma) indique o nosso plano de patroc nio para ela. L voc ver os valores de cada cota e os seus benef cios. Financiamento coletivo: Mas voc tamb m pode ajudar a realiza o da MiniDebConf por meio do nosso financiamento coletivo! Fa a uma doa o de qualquer valor e tenha o seu nome publicado no site do evento como apoiador(a) da MiniDebConf Belo Horizonte 2024. Mesmo que voc n o pretenda vir a Belo Horizonte para participar do evento, voc pode doar e assim contribuir para o mais importante evento do Projeto Debian no Brasil. Contato Qualquer d vida, mande um email para contato@debianbrasil.org.br Organiza o Debian Brasil Debian Debian MG DCC

20 January 2024

Gunnar Wolf: Ruffle helps bring back my family history

Probably a trait of my family s origins as migrants from East Europe, probably part of the collective trauma of jews throughout the world or probably because that s just who I turned out to be, I hold in high regard the preservation of memory of my family s photos, movies and such items. And it s a trait shared by many people in my familiar group. Shortly after my grandmother died 24 years ago, my mother did a large, loving work of digitalization and restoration of my grandparent s photos. Sadly, the higher resolution copies of said photos is lost but she took the work of not just scanning the photos, but assembling them in presentations, telling a story, introducing my older relatives, many of them missing 40 or more years before my birth. But said presentations were built using Flash. Right, not my choice of tool, and I told her back in the day but given I wasn t around to do the work in what I d chosen (a standards-abiding format, naturally), and given my graphic design skills are nonexistant Several years ago, when Adobe pulled the plug on the Flash format, we realized they would no longer be accessible. I managed to get the photos out of the preentations, but lost the narration, that is a great part of the work. Three days ago, however, I read a post on https://www.osnews.com that made me jump to action: https://www.osnews.com/story/138350/ruffle-an-open-source-flash-player-emulator/. Ruffle is an open source Flash Player emulator, written in Rust and compiled to WASM. Even though several OSnews readers report it to be buggy to play some Flash games they long for, it worked just fine for a simple slideshow presentator. So I managed to bring it back to life! Yes, I d like to make a better index page, but that will come later I am now happy and proud to share with you:

Acariciando la ausencia: Familia Iszaevich Fajerstein, 1900 2000 (which would be roughly translated as Caressing the absence: Iszaevich Fajerstein family, 1900-2000).

Gunnar Wolf: A deep learning technique for intrusion detection system using a recurrent neural networks based framework

This post is a review for Computing Reviews for A deep learning technique for intrusion detection system using a recurrent neural networks based framework , a article published in Computer Communications
So let s assume you already know and understand that artificial intelligence s main building blocks are perceptrons, that is, mathematical models of neurons. And you know that, while a single perceptron is too limited to get interesting information from, very interesting structures neural networks can be built with them. You also understand that neural networks can be trained with large datasets, and you can get them to become quite efficient and accurate classifiers for data comparable to your dataset. Finally, you are interested in applying this knowledge to defensive network security, particularly in choosing the right recurrent neural network (RNN) framework to create an intrusion detection system (IDS). Are you still with me? Good! This paper might be right for you! The paper builds on a robust and well-written introduction and related work sections to arrive at explaining in detail what characterizes a RNN, the focus of this work, among other configurations also known as neural networks, and why they are particularly suited for machine learning (ML) tasks. RNNs must be trained for each problem domain, and publicly available datasets are commonly used for such tasks. The authors present two labeled datasets representing normal and hostile network data, identified according to different criteria: NSL-KDD and UNSW-NB15. They proceed to show a framework to analyze and compare different RNNs and run them against said datasets, segmented for separate training and validation phases, compare results, and finally select the best available model for the task measuring both training speed as well as classification accuracy. The paper is quite heavy due to both its domain-specific terminology many acronyms are used throughout the text and its use of mathematical notation, both to explain specific properties of each of the RNN types and for explaining the preprocessing carried out for feature normalization and selection. This is partly what led me to start the first paragraph by assuming that we, as readers, already understand a large body of material if we are to fully follow the text. The paper does begin by explaining its core technologies, but quickly ramps up and might get too technical for nonexpert readers. It is undeniably an interesting and valuable read, showing the state of the art in IDS and ML-assisted technologies. It does not detail any specific technology applying its findings, but we will probably find the information conveyed here soon enough in industry publications.

Niels Thykier: Making debputy: Writing declarative parsing logic

In this blog post, I will cover how debputy parses its manifest and the conceptual improvements I did to make parsing of the manifest easier. All instructions to debputy are provided via the debian/debputy.manifest file and said manifest is written in the YAML format. After the YAML parser has read the basic file structure, debputy does another pass over the data to extract the information from the basic structure. As an example, the following YAML file:
manifest-version: "0.1"
installations:
  - install:
      source: foo
      dest-dir: usr/bin
would be transformed by the YAML parser into a structure resembling:
 
  "manifest-version": "0.1",
  "installations": [
      
       "install":  
         "source": "foo",
         "dest-dir": "usr/bin",
        
      
  ]
 
This structure is then what debputy does a pass on to translate this into an even higher level format where the "install" part is translated into an InstallRule. In the original prototype of debputy, I would hand-write functions to extract the data that should be transformed into the internal in-memory high level format. However, it was quite tedious. Especially because I wanted to catch every possible error condition and report "You are missing the required field X at Y" rather than the opaque KeyError: X message that would have been the default. Beyond being tedious, it was also quite error prone. As an example, in debputy/0.1.4 I added support for the install rule and you should allegedly have been able to add a dest-dir: or an as: inside it. Except I crewed up the code and debputy was attempting to look up these keywords from a dict that could never have them. Hand-writing these parsers were so annoying that it demotivated me from making manifest related changes to debputy simply because I did not want to code the parsing logic. When I got this realization, I figured I had to solve this problem better. While reflecting on this, I also considered that I eventually wanted plugins to be able to add vocabulary to the manifest. If the API was "provide a callback to extract the details of whatever the user provided here", then the result would be bad.
  1. Most plugins would probably throw KeyError: X or ValueError style errors for quite a while. Worst case, they would end on my table because the user would have a hard time telling where debputy ends and where the plugins starts. "Best" case, I would teach debputy to say "This poor error message was brought to you by plugin foo. Go complain to them". Either way, it would be a bad user experience.
  2. This even assumes plugin providers would actually bother writing manifest parsing code. If it is that difficult, then just providing a custom file in debian might tempt plugin providers and that would undermine the idea of having the manifest be the sole input for debputy.
So beyond me being unsatisfied with the current situation, it was also clear to me that I needed to come up with a better solution if I wanted externally provided plugins for debputy. To put a bit more perspective on what I expected from the end result:
  1. It had to cover as many parsing errors as possible. An error case this code would handle for you, would be an error where I could ensure it sufficient degree of detail and context for the user.
  2. It should be type-safe / provide typing support such that IDEs/mypy could help you when you work on the parsed result.
  3. It had to support "normalization" of the input, such as
           # User provides
           - install: "foo"
           # Which is normalized into:
           - install:
               source: "foo"
4) It must be simple to tell  debputy  what input you expected.
At this point, I remembered that I had seen a Python (PYPI) package where you could give it a TypedDict and an arbitrary input (Sadly, I do not remember the name). The package would then validate the said input against the TypedDict. If the match was successful, you would get the result back casted as the TypedDict. If the match was unsuccessful, the code would raise an error for you. Conceptually, this seemed to be a good starting point for where I wanted to be. Then I looked a bit on the normalization requirement (point 3). What is really going on here is that you have two "schemas" for the input. One is what the programmer will see (the normalized form) and the other is what the user can input (the manifest form). The problem is providing an automatic normalization from the user input to the simplified programmer structure. To expand a bit on the following example:
# User provides
- install: "foo"
# Which is normalized into:
- install:
    source: "foo"
Given that install has the attributes source, sources, dest-dir, as, into, and when, how exactly would you automatically normalize "foo" (str) into source: "foo"? Even if the code filtered by "type" for these attributes, you would end up with at least source, dest-dir, and as as candidates. Turns out that TypedDict actually got this covered. But the Python package was not going in this direction, so I parked it here and started looking into doing my own. At this point, I had a general idea of what I wanted. When defining an extension to the manifest, the plugin would provide debputy with one or two definitions of TypedDict. The first one would be the "parsed" or "target" format, which would be the normalized form that plugin provider wanted to work on. For this example, lets look at an earlier version of the install-examples rule:
# Example input matching this typed dict.
#    
#       "source": ["foo"]
#       "into": ["pkg"]
#    
class InstallExamplesTargetFormat(TypedDict):
    # Which source files to install (dest-dir is fixed)
    sources: List[str]
    # Which package(s) that should have these files installed.
    into: NotRequired[List[str]]
In this form, the install-examples has two attributes - both are list of strings. On the flip side, what the user can input would look something like this:
# Example input matching this typed dict.
#    
#       "source": "foo"
#       "into": "pkg"
#    
#
class InstallExamplesManifestFormat(TypedDict):
    # Note that sources here is split into source (str) vs. sources (List[str])
    sources: NotRequired[List[str]]
    source: NotRequired[str]
    # We allow the user to write  into: foo  in addition to  into: [foo] 
    into: Union[str, List[str]]
FullInstallExamplesManifestFormat = Union[
    InstallExamplesManifestFormat,
    List[str],
    str,
]
The idea was that the plugin provider would use these two definitions to tell debputy how to parse install-examples. Pseudo-registration code could look something like:
def _handler(
    normalized_form: InstallExamplesTargetFormat,
) -> InstallRule:
    ...  # Do something with the normalized form and return an InstallRule.
concept_debputy_api.add_install_rule(
  keyword="install-examples",
  target_form=InstallExamplesTargetFormat,
  manifest_form=FullInstallExamplesManifestFormat,
  handler=_handler,
)
This was my conceptual target and while the current actual API ended up being slightly different, the core concept remains the same.
From concept to basic implementation Building this code is kind like swallowing an elephant. There was no way I would just sit down and write it from one end to the other. So the first prototype of this did not have all the features it has now. Spoiler warning, these next couple of sections will contain some Python typing details. When reading this, it might be helpful to know things such as Union[str, List[str]] being the Python type for either a str (string) or a List[str] (list of strings). If typing makes your head spin, these sections might less interesting for you. To build this required a lot of playing around with Python's introspection and typing APIs. My very first draft only had one "schema" (the normalized form) and had the following features:
  • Read TypedDict.__required_attributes__ and TypedDict.__optional_attributes__ to determine which attributes where present and which were required. This was used for reporting errors when the input did not match.
  • Read the types of the provided TypedDict, strip the Required / NotRequired markers and use basic isinstance checks based on the resulting type for str and List[str]. Again, used for reporting errors when the input did not match.
This prototype did not take a long (I remember it being within a day) and worked surprisingly well though with some poor error messages here and there. Now came the first challenge, adding the manifest format schema plus relevant normalization rules. The very first normalization I did was transforming into: Union[str, List[str]] into into: List[str]. At that time, source was not a separate attribute. Instead, sources was a Union[str, List[str]], so it was the only normalization I needed for all my use-cases at the time. There are two problems when writing a normalization. First is determining what the "source" type is, what the target type is and how they relate. The second is providing a runtime rule for normalizing from the manifest format into the target format. Keeping it simple, the runtime normalizer for Union[str, List[str]] -> List[str] was written as:
def normalize_into_list(x: Union[str, List[str]]) -> List[str]:
    return x if isinstance(x, list) else [x]
This basic form basically works for all types (assuming none of the types will have List[List[...]]). The logic for determining when this rule is applicable is slightly more involved. My current code is about 100 lines of Python code that would probably lose most of the casual readers. For the interested, you are looking for _union_narrowing in declarative_parser.py With this, when the manifest format had Union[str, List[str]] and the target format had List[str] the generated parser would silently map a string into a list of strings for the plugin provider. But with that in place, I had covered the basics of what I needed to get started. I was quite excited about this milestone of having my first keyword parsed without handwriting the parser logic (at the expense of writing a more generic parse-generator framework).
Adding the first parse hint With the basic implementation done, I looked at what to do next. As mentioned, at the time sources in the manifest format was Union[str, List[str]] and I considered to split into a source: str and a sources: List[str] on the manifest side while keeping the normalized form as sources: List[str]. I ended up committing to this change and that meant I had to solve the problem getting my parser generator to understand the situation:
# Map from
class InstallExamplesManifestFormat(TypedDict):
    # Note that sources here is split into source (str) vs. sources (List[str])
    sources: NotRequired[List[str]]
    source: NotRequired[str]
    # We allow the user to write  into: foo  in addition to  into: [foo] 
    into: Union[str, List[str]]
# ... into
class InstallExamplesTargetFormat(TypedDict):
    # Which source files to install (dest-dir is fixed)
    sources: List[str]
    # Which package(s) that should have these files installed.
    into: NotRequired[List[str]]
There are two related problems to solve here:
  1. How will the parser generator understand that source should be normalized and then mapped into sources?
  2. Once that is solved, the parser generator has to understand that while source and sources are declared as NotRequired, they are part of a exactly one of rule (since sources in the target form is Required). This mainly came down to extra book keeping and an extra layer of validation once the previous step is solved.
While working on all of this type introspection for Python, I had noted the Annotated[X, ...] type. It is basically a fake type that enables you to attach metadata into the type system. A very random example:
# For all intents and purposes,  foo  is a string despite all the  Annotated  stuff.
foo: Annotated[str, "hello world"] = "my string here"
The exciting thing is that you can put arbitrary details into the type field and read it out again in your introspection code. Which meant, I could add "parse hints" into the type. Some "quick" prototyping later (a day or so), I got the following to work:
# Map from
#      
#        "source": "foo"  # (or "sources": ["foo"])
#        "into": "pkg"
#      
class InstallExamplesManifestFormat(TypedDict):
    # Note that sources here is split into source (str) vs. sources (List[str])
    sources: NotRequired[List[str]]
    source: NotRequired[
        Annotated[
            str,
            DebputyParseHint.target_attribute("sources")
        ]
    ]
    # We allow the user to write  into: foo  in addition to  into: [foo] 
    into: Union[str, List[str]]
# ... into
#      
#        "source": ["foo"]
#        "into": ["pkg"]
#      
class InstallExamplesTargetFormat(TypedDict):
    # Which source files to install (dest-dir is fixed)
    sources: List[str]
    # Which package(s) that should have these files installed.
    into: NotRequired[List[str]]
Without me (as a plugin provider) writing a line of code, I can have debputy rename or "merge" attributes from the manifest form into the normalized form. Obviously, this required me (as the debputy maintainer) to write a lot code so other me and future plugin providers did not have to write it.
High level typing At this point, basic normalization between one mapping to another mapping form worked. But one thing irked me with these install rules. The into was a list of strings when the parser handed them over to me. However, I needed to map them to the actual BinaryPackage (for technical reasons). While I felt I was careful with my manual mapping, I knew this was exactly the kind of case where a busy programmer would skip the "is this a known package name" check and some user would typo their package resulting in an opaque KeyError: foo. Side note: "Some user" was me today and I was super glad to see debputy tell me that I had typoed a package name (I would have been more happy if I had remembered to use debputy check-manifest, so I did not have to wait through the upstream part of the build that happened before debhelper passed control to debputy...) I thought adding this feature would be simple enough. It basically needs two things:
  1. Conversion table where the parser generator can tell that BinaryPackage requires an input of str and a callback to map from str to BinaryPackage. (That is probably lie. I think the conversion table came later, but honestly I do remember and I am not digging into the git history for this one)
  2. At runtime, said callback needed access to the list of known packages, so it could resolve the provided string.
It was not super difficult given the existing infrastructure, but it did take some hours of coding and debugging. Additionally, I added a parse hint to support making the into conditional based on whether it was a single binary package. With this done, you could now write something like:
# Map from
class InstallExamplesManifestFormat(TypedDict):
    # Note that sources here is split into source (str) vs. sources (List[str])
    sources: NotRequired[List[str]]
    source: NotRequired[
        Annotated[
            str,
            DebputyParseHint.target_attribute("sources")
        ]
    ]
    # We allow the user to write  into: foo  in addition to  into: [foo] 
    into: Union[BinaryPackage, List[BinaryPackage]]
# ... into
class InstallExamplesTargetFormat(TypedDict):
    # Which source files to install (dest-dir is fixed)
    sources: List[str]
    # Which package(s) that should have these files installed.
    into: NotRequired[
        Annotated[
            List[BinaryPackage],
            DebputyParseHint.required_when_multi_binary()
        ]
    ]
Code-wise, I still had to check for into being absent and providing a default for that case (that is still true in the current codebase - I will hopefully fix that eventually). But I now had less room for mistakes and a standardized error message when you misspell the package name, which was a plus.
The added side-effect - Introspection A lovely side-effect of all the parsing logic being provided to debputy in a declarative form was that the generated parser snippets had fields containing all expected attributes with their types, which attributes were required, etc. This meant that adding an introspection feature where you can ask debputy "What does an install rule look like?" was quite easy. The code base already knew all of this, so the "hard" part was resolving the input the to concrete rule and then rendering it to the user. I added this feature recently along with the ability to provide online documentation for parser rules. I covered that in more details in my blog post Providing online reference documentation for debputy in case you are interested. :)
Wrapping it up This was a short insight into how debputy parses your input. With this declarative technique:
  • The parser engine handles most of the error reporting meaning users get most of the errors in a standard format without the plugin provider having to spend any effort on it. There will be some effort in more complex cases. But the common cases are done for you.
  • It is easy to provide flexibility to users while avoiding having to write code to normalize the user input into a simplified programmer oriented format.
  • The parser handles mapping from basic types into higher forms for you. These days, we have high level types like FileSystemMode (either an octal or a symbolic mode), different kind of file system matches depending on whether globs should be performed, etc. These types includes their own validation and parsing rules that debputy handles for you.
  • Introspection and support for providing online reference documentation. Also, debputy checks that the provided attribute documentation covers all the attributes in the manifest form. If you add a new attribute, debputy will remind you if you forget to document it as well. :)
In this way everybody wins. Yes, writing this parser generator code was more enjoyable than writing the ad-hoc manual parsers it replaced. :)

Thomas Koch: Know your tools - simple backup with rsync

Posted on June 9, 2022
I ve been using rsync for years and still did not know its full powers. I just wanted a quick and dirty simple backup but realised that rsnapshot is not in Debian anymore. However you can do much of rsnapshot with rsync alone nowadays. The --link-dest option (manpage) solves the part of creating hardlinks to a previous backup (found here). So my backup program becomes this shell script in ~/backups/backup.sh:
#!/bin/sh
SERVER="$ 1 "
BACKUP="$ HOME /backups/$ SERVER "
SNAPSHOTS="$ BACKUP /snapshots"
FOLDER=$(date --utc +%F_%H-%M-%S)
DEST="$ SNAPSHOTS /$ FOLDER "
LAST=$(ls -d1 $ SNAPSHOTS /????-??-??_??-??-?? tail -n 1)
rsync \
  --rsh="ssh -i $ BACKUP /sshkey -o ControlPath=none -o ForwardAgent=no" \
  -rlpt \
  --delete --link-dest="$ LAST " \
  $ SERVER ::backup "$ DEST "
The script connects to rsync in daemon mode as outlined in section USING RSYNC-DAEMON FEATURES VIA A REMOTE-SHELL CONNECTION in the rsync manpage. This allows to reference a module as the source that is defined on the server side as follows:
[backup]
path = /
read only = true
exclude from = /srv/rsyncbackup/excludelist
uid = root
gid = root
The important bit is the read only setting that protects the server against somebody with access to the ssh key to overwrit files on the server via rsync and thus gaining full root access. Finally the command prefix in ~/.ssh/authorized_keys runs rsync as daemon with sudo and the specified config file:
command="sudo rsync --config=/srv/rsyncbackup/config --server --daemon ."
The sudo setup is left as an exercise for the reader as mine is rather opinionated. Unfortunately I have not managed to configure systemd timers in the way I wanted and therefor opened an issue: Allow retry of timer triggered oneshot services with failed conditions or asserts . Any help there is welcome!

Fran ois Marier: Proper Multicast DNS Handling with NetworkManager and systemd-resolved

Using NetworkManager and systemd-resolved together in Debian bookworm does not work out of the box. The first sign of trouble was these constant messages in my logs:
avahi-daemon[pid]: Host name conflict, retrying with hostname-2
Then I realized that CUPS printer discovery didn't work: my network printer could not be found. Since this discovery now relies on Multicast DNS, it would make sense that both problems are related to an incompatibility between NetworkManager and Avahi.

What didn't work The first attempt I made at fixing this was to look for known bugs in Avahi. Neither of the work-arounds I found worked:

What worked The real problem turned out to be the fact that NetworkManager turns on full mDNS support in systemd-resolved which conflicts with the mDNS support in avahi-daemon. You can see this in the output of resolvectl status:
Global
       Protocols: -LLMNR +mDNS -DNSOverTLS DNSSEC=no/unsupported
resolv.conf mode: stub
Link 2 (enp6s0)
    Current Scopes: DNS mDNS/IPv4 mDNS/IPv6
         Protocols: +DefaultRoute -LLMNR +mDNS -DNSOverTLS
                    DNSSEC=no/unsupported
Current DNS Server: 192.168.1.1
       DNS Servers: 192.168.1.1
        DNS Domain: lan
which includes +mDNS for the main network adapter. I initially thought that I could just uninstall avahi-daemon and rely on the systemd-resolved mDNS stack, but it's not actually compatible with CUPS. The solution was to tell NetworkManager to set mDNS to resolve-only mode in systemd-resolved by adding the following to /etc/NetworkManager/conf.d/mdns.conf:
[connection]
connection.mdns=1
leaving /etc/avahi/avahi-daemon.conf to the default Debian configuration.

Verifying the configuration After rebooting, resolvectl status now shows the following:
Global
       Protocols: -LLMNR +mDNS -DNSOverTLS DNSSEC=no/unsupported
resolv.conf mode: stub
Link 2 (enp6s0)
    Current Scopes: DNS mDNS/IPv4 mDNS/IPv6
         Protocols: +DefaultRoute -LLMNR mDNS=resolve -DNSOverTLS
                    DNSSEC=no/unsupported
Current DNS Server: 192.168.1.1
       DNS Servers: 192.168.1.1
        DNS Domain: lan
Avahi finally sees my printer (called hp in the output below):
$ avahi-browse -at   grep Printer
+ enp6s0 IPv6 hp @ myprintserver   Secure Internet Printer local
+ enp6s0 IPv4 hp @ myprintserver   Secure Internet Printer local
+ enp6s0 IPv6 hp @ myprintserver   Internet Printer        local
+ enp6s0 IPv4 hp @ myprintserver   Internet Printer        local
+ enp6s0 IPv6 hp @ myprintserver   UNIX Printer            local
+ enp6s0 IPv4 hp @ myprintserver   UNIX Printer            local
and so does CUPS:
$ sudo lpinfo --include-schemes dnssd -v
network dnssd://myprintserver%20%40%20hp._ipp._tcp.local/cups?uuid=d46942a2-b730-11ee-b05c-a75251a34287

Firewall rules Since printer discovery in CUPS relies on mDNS, another thing to double-check is that the correct ports are open on the firewall. This is what I have in /etc/network/iptables.up.rules:
# Allow mDNS for local service discovery
-A INPUT -d 100.64.0.0/10 -p udp --dport 5353 -j ACCEPT
-A INPUT -d 192.168.1.0/24 -p udp --dport 5353 -j ACCEPT
and in etc/network/ip6tables.up.rules:
# Allow mDNS for local service discovery
-A INPUT -d ff02::/16 -p udp --dport 5353 -j ACCEPT

19 January 2024

Russell Coker: 2.5Gbit Ethernet

I just decided to upgrade the core of my home network from 1Gbit to 2.5Gbit. I didn t really need to do this, it was only about 5 years ago that I upgrade from 100Mbit to 1Gbit. but it s cheap and seemed interesting. I decided to do it because a 2.5Gbit switch was listed as cheap on Ozbargain Computing [1], that was $40.94 delivered. If you are in Australia and like computers then Ozbargain is a site worth polling, every day there s interesting things at low prices. The seller of the switch is KeeplinkStore [2] who distinguished themselves by phoning me from China to inform me that I had ordered a switch with a UK plug for delivery to Australia and suggesting that I cancel the order and make a new order with an Australian plug. It wouldn t have been a big deal if I had received a UK plug as I ve got a collection of adaptors but it was still nice of them to make it convenient for me. The switch basically does what it s expected to do and has no fan so it s quiet. I got a single port 2.5Gbit PCIe card for $18.77 and a dual port card for $34.07. Those cards are a little expensive when compared to 1Gbit cards but very cheap when compared to the computers they are installed in. These cards use the Realtek RTL8125 chipset and work well. I got a USB-3 2.5Gbit device for $17.43. I deliberately didn t get USB-C because I still use laptops without USB-C and most of the laptops with USB-C only have a single USB-C port which is used for power. I don t plan to stop using my 100Mbit USB ethernet device because most of the time I don t need a lot of speed. But sometimes I do things like testing auto-install on laptops and then having something faster than Gigabit is good. This card worked at 1Gbit speed on a 1Gbit network when used with a system running Debian/Bookworm with kernel 6.1 and worked at 2.5Gbit speed when connected to my LicheePi RISC-V system running Linux 5.10, but it would only do 100Mbit on my laptop running Debian/Unstable with kernel 6.6 (Debian Bug #1061095) [3]. It s a little disappointing but not many people have such hardware so it probably doesn t get a lot of testing. For the moment I plan to just use a 1Gbit USB Ethernet device most of the time and if I really need the speed I ll just use an older kernel. I did some tests with wget and curl to see if I could get decent speeds. When using wget 1.21.3 on Debian/Bookworm I got transfer speeds of 103MB/s and 18.8s of system CPU time out of 23.6s of elapsed time. Curl on Debian/Bookworm did 203MB/s and took 10.7s of system CPU time out of 11.8s elapsed time. The difference is that curl was using 100KB read buffers and a mix of 12K and 4K write buffers while wget was using 8KB read buffers and 4KB write buffers. On Debian/Unstable wget 1.21.4 uses 64K read buffers and a mix of 4K and 60K write buffers and gets a speed of 208MB/s. As an experiment I changed the read buffer size for wget to 256K and that got the speed up to around 220MB/s but it was difficult to measure as the occasional packet loss slowed things down. The pattern of writing 4K and then writing the rest continued, it seemed related to fwrite() buffering. For anyone else who wants to experiment with the code, the wget code is simpler (due to less features) and the package builds a lot faster (due to fewer tests) so that s the one to work on. The client machine for these tests has a E5-2696 v3 CPU, this doesn t compare well to some of the recent AMD CPUs on single-core performance but is still a decently powerful system. Getting good performance at Gigabit speeds on an ARM or RISC-V system is probably going to be a lot harder than getting good performance at 2.5Gbit speeds on this system. In conclusion 2.5Gbit basically works apart from a problem with new kernels and a problem with the old version of wget. I expect that when Debian/Trixie is released (probably mid 2025) things will work well. For good transfer rates use wget version 1.21.4 or newer or use curl. As an aside I use a 1500byte MTU because I have some 100baseT systems on my LAN and the settings regarding TCP acceleration etc are all the defaults.

Reproducible Builds (diffoscope): diffoscope 254 released

The diffoscope maintainers are pleased to announce the release of diffoscope version 254. This version includes the following changes:
[ Chris Lamb ]
* Reflow some code according to black.
[ Seth Michael Larson ]
* Add support for comparing the 'eXtensible ARchive' (.XAR/.PKG) file format.
[ Vagrant Cascadian ]
* Add external tool on GNU Guix for 7z.
You find out more by visiting the project homepage.

18 January 2024

Russell Coker: LicheePi 4A (RISC-V) First Look

I Just bought a LicheePi 4A RISC-V embedded computer (like a RaspberryPi but with a RISC-V CPU) for $322.68 from Aliexpress (the official site for buying LicheePi devices). Here is the Sipheed web page about it and their other recent offerings [1]. I got the version with 16G of RAM and 128G of storage, I probably don t need that much storage (I can use NFS or USB) but 16G of RAM is good for VMs. Here is the Wiki about this board [2]. Configuration When you get one of these devices you should make setting up ssh server your first priority. I found the HDMI output to be very unreliable. The first monitor I tried was a Samsung 4K monitor dating from when 4K was a new thing, the LicheePi initially refused to operate at a resolution higher than 1024*768 but later on switched to 4K resolution when resuming from screen-blank for no apparent reason (and the window manager didn t support this properly). On the Dell 4K monitor I use on my main workstation it sometimes refused to talk to it and occasionally worked. I got it running at 1920*1080 without problems and then switched it to 4K and it lost video sync and never talked to that monitor again. On my Desklab portabable 4K monitor I got it to display in 4K resolution but only the top left 1/4 of the screen displayed. The issues with HDMI monitor support greatly limit the immediate potential for using this as a workstation. It doesn t make it impossible but would be fiddly at best. It s quite likely that a future OS update will fix this. But at the moment it s best used as a server. The LicheePi has a custom Linux distribution based on Ubuntu so you want too put something like the following in /etc/network/interfaces to make it automatically connect to the ethernet when plugged in:
auto end0
iface end0 inet dhcp
Then to get sshd to start you have to run the following commands to generate ssh host keys that aren t zero bytes long:
rm /etc/ssh/ssh_host_*
systemctl restart ssh.service
It appears to have wifi hardware but the OS doesn t recognise it. This isn t a priority for me as I mostly want to use it as a server. Performance For the first test of performance I created a 100MB file from /dev/urandom and then tried compressing it on various systems. With zstd -9 it took 16.893 user seconds on the LicheePi4A, 0.428s on my Thinkpad X1 Carbon Gen5 with a i5-6300U CPU (Debian/Unstable), 1.288s on my E5-2696 v3 workstation (Debian/Bookworm), 0.467s on the E5-2696 v3 running Debian/Unstable, 2.067s on a E3-1271 v3 server, and 7.179s on the E3-1271 v3 system emulating a RISC-V system via QEMU running Debian/Unstable. It s very impressive that the QEMU emulation is fast enough that emulating a different CPU architecture is only 3.5* slower for this test (or maybe 10* slower if it was running Debian/Unstable on the AMD64 code)! The emulated RISC-V is also more than twice as fast as real RISC-V hardware and probably of comparable speed to real RISC-V hardware when running the same versions (and might be slightly slower if running the same version of zstd) which is a tribute to the quality of emulation. One performance issue that most people don t notice is the time taken to negotiate ssh sessions. It s usually not noticed because the common CPUs have got faster at about the same rate as the algorithms for encryption and authentication have become more complex. On my i5-6300U laptop it takes 0m0.384s to run ssh -i ~/.ssh/id_ed25519 localhost id with the below server settings (taken from advice on ssh-audit.com [3] for a secure ssh configuration). On the E3-1271 v3 server it is 0.336s, on the QMU system it is 28.022s, and on the LicheePi it is 0.592s. By this metric the LicheePi is about 80% slower than decent x86 systems and the QEMU emulation of RISC-V is 73* slower than the x86 system it runs on. Does crypto depend on instructions that are difficult to emulate?
HostKey /etc/ssh/ssh_host_ed25519_key
KexAlgorithms -ecdh-sha2-nistp256,ecdh-sha2-nistp384,ecdh-sha2-nistp521,diffie-hellman-group14-sha256
MACs -umac-64-etm@openssh.com,hmac-sha1-etm@openssh.com,umac-64@openssh.com,umac-128@openssh.com,hmac-sha2-256,hmac-sha2-512,hmac-sha1
I haven t yet tested the performance of Ethernet (what routing speed can you get through the 2 gigabit ports?), emmc storage, and USB. At the moment I ve been focused on using RISC-V as a test and development platform. My conclusion is that I m glad I don t plan to compile many kernels or anything large like LibreOffice. But that for typical development that I do it will be quite adequate. The speed of Chromium seems adequate in basic tests, but the video output hasn t worked reliably enough to do advanced tests. Hardware Features Having two Gigabit Ethernet ports, 4 USB-3 ports, and Wifi on board gives some great options for using this as a router. It s disappointing that they didn t go with 2.5Gbit as everyone seems to be doing that nowadays but Gigabit is enough for most things. Having only a single HDMI port and not supporting USB-C docks (the USB-C port appears to be power only) limits what can be done for workstation use and for controlling displays. I know of people using small ARM computers attached to the back of large TVs for advertising purposes and that isn t going to be a great option for this. The CPU and RAM apparently uses a lot of power (which is relative the entire system draws up to 2A at 5V so the CPU would be something below 5W). To get this working a cooling fan has to be stuck to the CPU and RAM chips via a layer of thermal stuff that resembles a fine sheet of blu-tack in both color and stickyness. I am disappointed that there isn t any more solid form of construction, to mount this on a wall or ceiling some extra hardware would be needed to secure this. Also if they just had a really big copper heatsink I think that would be better. 80386 CPUs with similar TDP were able to run without a fan. I wonder how things would work with all USB ports in use. It s expected that a USB port can supply a minimum of 2.5W which means that all the ports could require 10W if they were active. Presumably something significantly less than 5W is available for the USB ports. Other Devices Sipheed has a range of other devices in the works. They currently sell the LicheeCluster4A which support 7 compute modules for a cluster in a box. This has some interesting potential for testing and demonstrating cluster software but you could probably buy an AMD64 system with more compute power for less money. The Lichee Console 4A is a tiny laptop which could be useful for people who like the 7 laptop form factor, unfortunately it only has a 1280*800 display if it had the same resolution display as a typical 7 phone I would have bought one. The next device that appeals to me is the soon to be released Lichee Pad 4A which is a 10.1 tablet with 1920*1200 display, Wifi6, Bluetooth 5.4, and 16G of RAM. It also has 1 USB-C connection, 2*USB-3 sockets, and support for an external card with 2*Gigabit ethernet. It s a tablet as a laptop without keyboard instead of the more common larger phone design model. They are also about to release the LicheePadMax4A which is similar to the other tablet but with a 14 2240*1400 display and which ships with a keyboard to make it essentially a laptop with detachable keyboard. Conclusion At this time I wouldn t recommend that this device be used as a workstation or laptop, although the people who want to do such things will probably do it anyway regardless of my recommendations. I think it will be very useful as a test system for RISC-V development. I have some friends who are interested in this sort of thing and I can give them VMs. It is a bit expensive. The Sipheed web site boasts about the LicheePi4 being faster than the RaspberryPi4, but it s not a lot faster and the RaspberryPi4 is much cheaper ($127 or $129 for one with 8G of RAM). The RaspberryPi4 has two HDMI ports but a limit of 8G of RAM while the LicheePi has up to 16G of RAM and two Gigabit Ethernet ports but only a single HDMI port. It seems that the RaspberryPi4 might win if you want a cheap low power desktop system. At this time I think the reason for this device is testing out RISC-V as an alternative to the AMD64 and ARM64 architectures. An open CPU architecture goes well with free software, but it isn t just people who are into FOSS who are testing such things. I know some corporations are trying out RISC-V as a way of getting other options for embedded systems that don t involve paying monopolists. The Lichee Console 4A is probably a usable tiny laptop if the resolution is sufficient for your needs. As an aside I predict that the tiny laptop or pocket computer segment will take off in the near future. There are some AMD64 systems the size of a phone but thicker that run Windows and go for reasonable prices on AliExpress. Hopefully in the near future this device will have better video drivers and be usable as a small and quiet workstation. I won t rule out the possibility of making this my main workstation in the not too distant future, all it needs is reliable 4K display and the ability to decode 4K video. It s performance for web browsing and as an ssh client seems adequate, and that s what matters for my workstation use. But for the moment it s just for server use.

17 January 2024

Colin Watson: Task management

Now that I m freelancing, I need to actually track my time, which is something I ve had the luxury of not having to do before. That meant something of a rethink of the way I ve been keeping track of my to-do list. Up to now that was a combination of things like the bug lists for the projects I m working on at the moment, whatever task tracking system Canonical was using at the moment (Jira when I left), and a giant flat text file in which I recorded logbook-style notes of what I d done each day plus a few extra notes at the bottom to remind myself of particularly urgent tasks. I could have started manually adding times to each logbook entry, but ugh, let s not. In general, I had the following goals (which were a bit reminiscent of my address book): I didn t do an elaborate evaluation of multiple options, because I m not trying to come up with the best possible solution for a client here. Also, there are a bazillion to-do list trackers out there and if I tried to evaluate them all I d never do anything else. I just wanted something that works well enough for me. Since it came up on Mastodon: a bunch of people swear by Org mode, which I know can do at least some of this sort of thing. However, I don t use Emacs and don t plan to use Emacs. nvim-orgmode does have some support for time tracking, but when I ve tried vim-based versions of Org mode in the past I ve found they haven t really fitted my brain very well. Taskwarrior and Timewarrior One of the other Freexian collaborators mentioned Taskwarrior and Timewarrior, so I had a look at those. The basic idea of Taskwarrior is that you have a task command that tracks each task as a blob of JSON and provides subcommands to let you add, modify, and remove tasks with a minimum of friction. task add adds a task, and you can add metadata like project:Personal (I always make sure every task has a project, for ease of filtering). Just running task shows you a task list sorted by Taskwarrior s idea of urgency, with an ID for each task, and there are various other reports with different filtering and verbosity. task <id> annotate lets you attach more information to a task. task <id> done marks it as done. So far so good, so a redacted version of my to-do list looks like this:
$ task ls
ID A Project     Tags                 Description
17   Freexian                         Add Incus support to autopkgtest [2]
 7   Columbiform                      Figure out Lloyds online banking [1]
 2   Debian                           Fix troffcvt for groff 1.23.0 [1]
11   Personal                         Replace living room curtain rail
Once I got comfortable with it, this was already a big improvement. I haven t bothered to learn all the filtering gadgets yet, but it was easy enough to see that I could do something like task all project:Personal and it d show me both pending and completed tasks in that project, and that all the data was stored in ~/.task - though I have to say that there are enough reporting bells and whistles that I haven t needed to poke around manually. In combination with the regular backups that I do anyway (you do too, right?), this gave me enough confidence to abandon my previous text-file logbook approach. Next was time tracking. Timewarrior integrates with Taskwarrior, albeit in an only semi-packaged way, and it was easy enough to set that up. Now I can do:
$ task 25 start
Starting task 00a9516f 'Write blog post about task tracking'.
Started 1 task.
Note: '"Write blog post about task tracking"' is a new tag.
Tracking Columbiform "Write blog post about task tracking"
  Started 2024-01-10T11:28:38
  Current                  38
  Total               0:00:00
You have more urgent tasks.
Project 'Columbiform' is 25% complete (3 of 4 tasks remaining).
When I stop work on something, I do task active to find the ID, then task <id> stop. Timewarrior does the tedious stopwatch business for me, and I can manually enter times if I forget to start/stop a task. Then the really useful bit: I can do something like timew summary :month <name-of-client> and it tells me how much to bill that client for this month. Perfect. I also started using VIT to simplify the day-to-day flow a little, which means I m normally just using one or two keystrokes rather than typing longer commands. That isn t really necessary from my point of view, but it does save some time. Android integration I left Android integration for a bit later since it wasn t essential. When I got round to it, I have to say that it felt a bit clumsy, but it did eventually work. The first step was to set up a taskserver. Most of the setup procedure was OK, but I wanted to use Let s Encrypt to minimize the amount of messing around with CAs I had to do. Getting this to work involved hitting things with sticks a bit, and there s still a local CA involved for client certificates. What I ended up with was a certbot setup with the webroot authenticator and a custom deploy hook as follows (with cert_name replaced by a DNS name in my house domain):
#! /bin/sh
set -eu
cert_name=taskd.example.org
found=false
for domain in $RENEWED_DOMAINS; do
    case "$domain" in
        $cert_name)
            found=:
            ;;
    esac
done
$found   exit 0
install -m 644 "/etc/letsencrypt/live/$cert_name/fullchain.pem" \
    /var/lib/taskd/pki/fullchain.pem
install -m 640 -g Debian-taskd "/etc/letsencrypt/live/$cert_name/privkey.pem" \
    /var/lib/taskd/pki/privkey.pem
systemctl restart taskd.service
I could then set this in /etc/taskd/config (server.crl.pem and ca.cert.pem were generated using the documented taskserver setup procedure):
server.key=/var/lib/taskd/pki/privkey.pem
server.cert=/var/lib/taskd/pki/fullchain.pem
server.crl=/var/lib/taskd/pki/server.crl.pem
ca.cert=/var/lib/taskd/pki/ca.cert.pem
Then I could set taskd.ca on my laptop to /usr/share/ca-certificates/mozilla/ISRG_Root_X1.crt and otherwise follow the client setup instructions, run task sync init to get things started, and then task sync every so often to sync changes between my laptop and the taskserver. I used TaskWarrior Mobile as the client. I have to say I wouldn t want to use that client as my primary task tracking interface: the setup procedure is clunky even beyond the necessity of copying a client certificate around, it expects you to give it a .taskrc rather than having a proper settings interface for that, and it only seems to let you add a task if you specify a due date for it. It also lacks Timewarrior integration, so I can only really use it when I don t care about time tracking, e.g. personal tasks. But that s really all I need, so it meets my minimum requirements. Next? Considering this is literally the first thing I tried, I have to say I m pretty happy with it. There are a bunch of optional extras I haven t tried yet, but in general it kind of has the vim nature for me: if I need something it s very likely to exist or easy enough to build, but the features I don t use don t get in my way. I wouldn t recommend any of this to somebody who didn t already spend most of their time in a terminal - but I do. I m glad people have gone to all the effort to build this so I didn t have to.

16 January 2024

Jonathan Dowland: Reading hack

My to-read shelf My to-read shelf
This year, with respect to my ever-growing reading backlog, I'm going to try something new: when I acquire a new book, I'm going to try to read at least a few pages of it immediately. My theory is this will help me to have a better idea of what to expect when I come to pick the next book to start, later on. A few pages may not be very representative of a full book (In "How to read a Novel: A User's Guide John Sullivan suggests reading 69 pages before giving up on a book), but it may be better than nothing. I'll report back if it seems to work.

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