Had this happen on P14s on Ubuntu 23.10 while causally using Chromium. The screen went blank for a few seconds, but it apparently managed to reboot itself, and things started working again, except that and most windows were killed:
[drm:gfx_v11_0_priv_reg_irq [amdgpu]] *ERROR* Illegal register access in command stream
[drm:amdgpu_job_timedout [amdgpu]] *ERROR* ring gfx_0.0.0 timeout, signaled seq=5774109, emitted seq=5774111
[drm:amdgpu_job_timedout [amdgpu]] *ERROR* Process information: process chrome pid 14023 thread chrome:cs0 pid 14087
amdgpu 0000:64:00.0: amdgpu: GPU reset begin!
[drm:mes_v11_0_submit_pkt_and_poll_completion.constprop.0 [amdgpu]] *ERROR* MES failed to response msg=3
[drm:amdgpu_mes_unmap_legacy_queue [amdgpu]] *ERROR* failed to unmap legacy queue
[drm:mes_v11_0_submit_pkt_and_poll_completion.constprop.0 [amdgpu]] *ERROR* MES failed to response msg=3
[drm:amdgpu_mes_unmap_legacy_queue [amdgpu]] *ERROR* failed to unmap legacy queue
[drm:mes_v11_0_submit_pkt_and_poll_completion.constprop.0 [amdgpu]] *ERROR* MES failed to response msg=3
[drm:amdgpu_mes_unmap_legacy_queue [amdgpu]] *ERROR* failed to unmap legacy queue
[drm:mes_v11_0_submit_pkt_and_poll_completion.constprop.0 [amdgpu]] *ERROR* MES failed to response msg=3
[drm:amdgpu_mes_unmap_legacy_queue [amdgpu]] *ERROR* failed to unmap legacy queue
[drm:mes_v11_0_submit_pkt_and_poll_completion.constprop.0 [amdgpu]] *ERROR* MES failed to response msg=3
[drm:amdgpu_mes_unmap_legacy_queue [amdgpu]] *ERROR* failed to unmap legacy queue
[drm:mes_v11_0_submit_pkt_and_poll_completion.constprop.0 [amdgpu]] *ERROR* MES failed to response msg=3
[drm:amdgpu_mes_unmap_legacy_queue [amdgpu]] *ERROR* failed to unmap legacy queue
[drm:mes_v11_0_submit_pkt_and_poll_completion.constprop.0 [amdgpu]] *ERROR* MES failed to response msg=3
[drm:amdgpu_mes_unmap_legacy_queue [amdgpu]] *ERROR* failed to unmap legacy queue
[drm:mes_v11_0_submit_pkt_and_poll_completion.constprop.0 [amdgpu]] *ERROR* MES failed to response msg=3
[drm:amdgpu_mes_unmap_legacy_queue [amdgpu]] *ERROR* failed to unmap legacy queue
[drm:mes_v11_0_submit_pkt_and_poll_completion.constprop.0 [amdgpu]] *ERROR* MES failed to response msg=3
[drm:amdgpu_mes_unmap_legacy_queue [amdgpu]] *ERROR* failed to unmap legacy queue
[drm:gfx_v11_0_cp_gfx_enable.isra.0 [amdgpu]] *ERROR* failed to halt cp gfx
Dec 27 15:03:38 ciro-p14s kernel: amdgpu 0000:64:00.0: amdgpu: MODE2 reset
Dec 27 15:03:38 ciro-p14s kernel: amdgpu 0000:64:00.0: amdgpu: GPU reset succeeded, trying to resume
Dec 27 15:03:38 ciro-p14s kernel: [drm] PCIE GART of 512M enabled (table at 0x0000008000900
It appears to be a bug in the AMDGPU open source driver.
I think this was on Wayland. Possibly relatd but on X Window System, crashed the UI, showed message "oh no! Something has gone wrong."
2024-01-13_21-55-07@ciro@ciro-p14s$ cat /var/log/apport.log
ERROR: apport (pid 975172) 2024-01-13 21:41:02,087: host pid 3528 crashed in a separate mount namespace, ignoring
INFO: apport (pid 975227) 2024-01-13 21:41:02,398: called for pid 2728, signal 5, core limit 0, dump mode 1
INFO: apport (pid 975227) 2024-01-13 21:41:02,401: executable: /usr/bin/gnome-shell (command line "/usr/bin/gnome-shell")
INFO: apport (pid 975227) 2024-01-13 21:41:12,667: wrote report /var/crash/_usr_bin_gnome-shell.1000.crash
Ollama by Ciro Santilli 40 Updated 2025-07-16
Ollama is a highly automated open source wrapper that makes it very easy to run multiple Open weight LLM models either on CPU or GPU.
Its README alone is of great value, serving as a fantastic list of the most popular Open weight LLM models in existence.
Install with:
curl https://ollama.ai/install.sh | sh
The below was tested on Ollama 0.1.14 from December 2013.
Download llama2 7B and open a prompt:
ollama run llama2
On P14s it runs on CPU and generates a few tokens per second, which is quite usable for a quick interactive play.
As mentioned at github.com/jmorganca/ollama/blob/0174665d0e7dcdd8c60390ab2dd07155ef84eb3f/docs/faq.md the downloads to under /usr/share/ollama/.ollama/models/ and ncdu tells me:
--- /usr/share/ollama ----------------------------------
    3.6 GiB [###########################] /.ollama
    4.0 KiB [                           ]  .bashrc
    4.0 KiB [                           ]  .profile
    4.0 KiB [                           ]  .bash_logout
The file:
/usr/share/ollama/.ollama/models/manifests/hf.co/mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated-GGUF/Q2_K
gives a the exact model name and parameters.
We can also do it non-interactively with:
/bin/time ollama run llama2 'What is quantum field theory?'
which gave me:
0.13user 0.17system 2:06.32elapsed 0%CPU (0avgtext+0avgdata 17280maxresident)k
0inputs+0outputs (0major+2203minor)pagefaults 0swaps
but note that there is a random seed that affects each run by default. ollama-expect is an attempt to make the output deterministic.
Some other quick benchmarks from Amazon EC2 GPU on a g4nd.xlarge instance which had an Nvidia Tesla T4:
0.07user 0.05system 0:16.91elapsed 0%CPU (0avgtext+0avgdata 16896maxresident)k
0inputs+0outputs (0major+1960minor)pagefaults 0swaps
and on Nvidia A10G in an g5.xlarge instance:
0.03user 0.05system 0:09.59elapsed 0%CPU (0avgtext+0avgdata 17312maxresident)k
8inputs+0outputs (1major+1934minor)pagefaults 0swaps
So it's not too bad, a small article in 10s.
It tends to babble quite a lot by default, but eventually decides to stop.
GitHub book repo by Ciro Santilli 40 Updated 2025-07-16
Some amazing people have put book source codes on GitHub. This is a list of such repos.
g4nd.xlarge by Ciro Santilli 40 Updated 2025-07-16
TODO meaning of "nd"? "n" presumably means Nvidia, but what is the "d"? Compare it g4ad.xlarge which has AMD GPUs. aws.amazon.com/ec2/instance-types/g4/ mentions:
G4 instances are available with a choice of NVIDIA GPUs (G4dn) or AMD GPUs (G4ad).
Price:
Once you've done the Apache Jena CLI tools setup we can query all users with Full Name (FN) "John Smith" directly fom the rdf/vcard.ttl Turtle RDF file with the rdf/vcard.rq SPARQL query:
sparql --data=rdf/vcard.ttl --query=rdf/vcard.rq
and that outputs:
---------------------------------
| x                             |
=================================
| <http://somewhere/JohnSmith/> |
---------------------------------
The CLI tools don't appear to be packaged for Ubuntu 23.10? Annoying... There is a package libapache-jena-java but it doesn't contain any binaries, only Java library files.
To run the CLI tools easily we can download the prebuilt:
sudo apt install openjdk-22-jre
wget https://dlcdn.apache.org/jena/binaries/apache-jena-4.10.0.zip
unzip apache-jena-4.10.0.zip
cd apache-jena-4.10.0
export JENA_HOME="$(pwd)"
export PATH="$PATH:$(pwd)/bin"
and we can confirm it works with:
sparql -version
which outputs:
Apache Jena version 4.10.0
If your Java is too old then then running sparql with the prebuilts fails with:
Error: A JNI error has occurred, please check your installation and try again
Exception in thread "main" java.lang.UnsupportedClassVersionError: arq/sparql has been compiled by a more recent version of the Java Runtime (class file version 55.0), this version of the Java Runtime only recognizes class file versions up to 52.0
        at java.lang.ClassLoader.defineClass1(Native Method)
        at java.lang.ClassLoader.defineClass(ClassLoader.java:756)
        at java.security.SecureClassLoader.defineClass(SecureClassLoader.java:142)
        at java.net.URLClassLoader.defineClass(URLClassLoader.java:473)
        at java.net.URLClassLoader.access$100(URLClassLoader.java:74)
        at java.net.URLClassLoader$1.run(URLClassLoader.java:369)
        at java.net.URLClassLoader$1.run(URLClassLoader.java:363)
        at java.security.AccessController.doPrivileged(Native Method)
        at java.net.URLClassLoader.findClass(URLClassLoader.java:362)
        at java.lang.ClassLoader.loadClass(ClassLoader.java:418)
        at sun.misc.Launcher$AppClassLoader.loadClass(Launcher.java:352)
        at java.lang.ClassLoader.loadClass(ClassLoader.java:351)
        at sun.launcher.LauncherHelper.checkAndLoadMain(LauncherHelper.java:621)
Build from source is likely something like:
sudo apt install maven openjdk-22-jdk
git clone https://github.com/apache/jena --branch jena-4.10.0 --depth 1
cd jena
mvn clean install
TODO test it.
If you make the mistake of trying to run the source tree without build:
git clone https://github.com/apache/jena --branch jena-4.10.0 --depth 1
cd jena
export JENA_HOME="$(pwd)"
export PATH="$PATH:$(pwd)/apache-jena/bin"
it fails with:
Error: Could not find or load main class arq.sparql
as per: users.jena.apache.narkive.com/T5TaEszT/sparql-tutorial-querying-datasets-error-unrecognized-option-graph
Amazon EC2 GPU by Ciro Santilli 40 Updated 2025-07-16
As of December 2023, the cheapest instance with an Nvidia GPU is g4nd.xlarge, so let's try that out. In that instance, lspci contains:
00:1e.0 3D controller: NVIDIA Corporation TU104GL [Tesla T4] (rev a1)
so we see that it runs a Nvidia T4 GPU.
Be careful not to confuse it with g4ad.xlarge, which has an AMD GPU instead. TODO meaning of "ad"? "a" presumably means AMD, but what is the "d"?
Some documentation on which GPU is in each instance can seen at: docs.aws.amazon.com/dlami/latest/devguide/gpu.html (archive) with a list of which GPUs they have at that random point in time. Can the GPU ever change for a given instance name? Likely not. Also as of December 2023 the list is already outdated, e.g. P5 is now shown, though it is mentioned at: aws.amazon.com/ec2/instance-types/p5/
When selecting the instance to launch, the GPU does not show anywhere apparently on the instance information page, it is so bad!
Also note that this instance has 4 vCPUs, so on a new account you must first make a customer support request to Amazon to increase your limit from the default of 0 to 4, see also: stackoverflow.com/questions/68347900/you-have-requested-more-vcpu-capacity-than-your-current-vcpu-limit-of-0, otherwise instance launch will fail with:
You have requested more vCPU capacity than your current vCPU limit of 0 allows for the instance bucket that the specified instance type belongs to. Please visit aws.amazon.com/contact-us/ec2-request to request an adjustment to this limit.
When starting up the instance, also select:
Once you finally managed to SSH into the instance, first we have to install drivers and reboot:
sudo apt update
sudo apt install nvidia-driver-510 nvidia-utils-510 nvidia-cuda-toolkit
sudo reboot
and now running:
nvidia-smi
shows something like:
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 525.147.05   Driver Version: 525.147.05   CUDA Version: 12.0     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  Tesla T4            Off  | 00000000:00:1E.0 Off |                    0 |
| N/A   25C    P8    12W /  70W |      2MiB / 15360MiB |      0%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+
From there basically everything should just work as normal. E.g. we were able to run a CUDA hello world just fine along:
nvcc inc.cu
./a.out
One issue with this setup, besides the time it takes to setup, is that you might also have to pay some network charges as it downloads a bunch of stuff into the instance. We should try out some of the pre-built images. But it is also good to know this pristine setup just in case.
We then managed to run Ollama just fine with:
curl https://ollama.ai/install.sh | sh
/bin/time ollama run llama2 'What is quantum field theory?'
which gave:
0.07user 0.05system 0:16.91elapsed 0%CPU (0avgtext+0avgdata 16896maxresident)k
0inputs+0outputs (0major+1960minor)pagefaults 0swaps
so way faster than on my local desktop CPU, hurray.
After setup from: askubuntu.com/a/1309774/52975 we were able to run:
head -n1000 pap.txt | ARGOS_DEVICE_TYPE=cuda time argos-translate --from-lang en --to-lang fr > pap-fr.txt
which gave:
77.95user 2.87system 0:39.93elapsed 202%CPU (0avgtext+0avgdata 4345988maxresident)k
0inputs+88outputs (0major+910748minor)pagefaults 0swaps
so only marginally better than on P14s. It would be fun to see how much faster we could make things on a more powerful GPU.
Let's get SSH access, instal a package, and run a server.
As of December 2023 on a t2.micro instance, the only one part of free tier at the time with advertised 1 vCPU, 1 GiB RAM, 8 GiB disk for the first 12 months, on Ubuntu 22.04:
$ free -h
               total        used        free      shared  buff/cache   available
Mem:           949Mi       149Mi       210Mi       0.0Ki       590Mi       641Mi
Swap:             0B          0B          0B
$ nproc
1
$ df -h /
Filesystem      Size  Used Avail Use% Mounted on
/dev/root       7.6G  1.8G  5.8G  24% /
To install software:
sudo apt update
sudo apt install cowsay
cowsay asdf
Once HTTP inbound traffic is enabled on security rules for port 80, you can:
while true; do printf "HTTP/1.1 200 OK\r\n\r\n`date`: hello from AWS" | sudo nc -Nl 80; done
and then you are able to curl from your local computer and get the response.

Pinned article: Introduction to the OurBigBook Project

Welcome to the OurBigBook Project! Our goal is to create the perfect publishing platform for STEM subjects, and get university-level students to write the best free STEM tutorials ever.
Everyone is welcome to create an account and play with the site: ourbigbook.com/go/register. We belive that students themselves can write amazing tutorials, but teachers are welcome too. You can write about anything you want, it doesn't have to be STEM or even educational. Silly test content is very welcome and you won't be penalized in any way. Just keep it legal!
We have two killer features:
  1. topics: topics group articles by different users with the same title, e.g. here is the topic for the "Fundamental Theorem of Calculus" ourbigbook.com/go/topic/fundamental-theorem-of-calculus
    Articles of different users are sorted by upvote within each article page. This feature is a bit like:
    • a Wikipedia where each user can have their own version of each article
    • a Q&A website like Stack Overflow, where multiple people can give their views on a given topic, and the best ones are sorted by upvote. Except you don't need to wait for someone to ask first, and any topic goes, no matter how narrow or broad
    This feature makes it possible for readers to find better explanations of any topic created by other writers. And it allows writers to create an explanation in a place that readers might actually find it.
    Figure 1.
    Screenshot of the "Derivative" topic page
    . View it live at: ourbigbook.com/go/topic/derivative
  2. local editing: you can store all your personal knowledge base content locally in a plaintext markup format that can be edited locally and published either:
    This way you can be sure that even if OurBigBook.com were to go down one day (which we have no plans to do as it is quite cheap to host!), your content will still be perfectly readable as a static site.
    Figure 5. . You can also edit articles on the Web editor without installing anything locally.
    Video 3.
    Edit locally and publish demo
    . Source. This shows editing OurBigBook Markup and publishing it using the Visual Studio Code extension.
  3. https://raw.githubusercontent.com/ourbigbook/ourbigbook-media/master/feature/x/hilbert-space-arrow.png
  4. Infinitely deep tables of contents:
    Figure 6.
    Dynamic article tree with infinitely deep table of contents
    .
    Descendant pages can also show up as toplevel e.g.: ourbigbook.com/cirosantilli/chordate-subclade
All our software is open source and hosted at: github.com/ourbigbook/ourbigbook
Further documentation can be found at: docs.ourbigbook.com
Feel free to reach our to us for any help or suggestions: docs.ourbigbook.com/#contact