podman-compose definitely got better over the past year…

But you can also use docker-compose itself with podman instead!

https://www.redhat.com/en/blog/podman-docker-compose

Basically, for system level containers, you can do:

sudo systemctl start podman.socket

(or enable --now instead of start if you want it to stick around after rebooting)

Then use docker-compose and it’ll communicate with podman instead of docker.

For user session “rootless” containers, it’s mainly the same thing, except you’ll need to remove sudo and then add --user after start or enable in that systemctl command. And you’d need to set an environment variable (either prefixing it on the command or using export to set it in your session), like this:

DOCKER_HOST=unix:///run/user/$UID/podman/podman

(Put that in front of the docker-compose command and it’ll connect to podman as your user instead, provided the service is available. Or toss it into your .bashrc with "export " before it and new bash sessions would have it.)

https://brandonrozek.com/blog/rootless-docker-compose-podman/

The one big gotcha I’ve hit is that if you have SELinux on your system, you’ll want to add :z to your volume(s) mount to have it automatically deal with SELinux stuff. (Lowercase z for volumes that can be mounted for multiplayer containers and uppercase Z for volumes that are tied to a specific container.)

But, I’ve found that using “quadlet” service files is much, much better than using podman-compose or docker-compose. There’s a program called “podlet” that can even convert compose files to service files (quadlet)… It can convert command line flags and kubes and other formats too.

Quadlets are basically systems service files that integrate with podman, letting you easily set up a container as a system (or even user level) service, making managing a container just like managing any other service.

Here’s the podlet command that’ll convert things to quadlets: https://github.com/containers/podlet

Peppers are the fruit of the plant. They’re what’s made after the flowers were pollinated and have seeds. They’re also sometimes sweet and not always so spicy.

Of course, there’s the botanical definition and culinary definition and there’s some overlap. The most famous would be a tomato, which is also a fruit and a vegetable from different points of view.

What’s mind-blowing to think about is that a pepper is not just a fruit but also technically a berry.

In cooking, peppers are used as a fruit, a vegetable, and even a spice. (Depending on the pepper variety.) So, anyone classifying it as any of those things is right. 👍

(Wikipedia mentions all this too.) https://en.m.wikipedia.org/wiki/Chili_pepper

Technically, it can be, depending on the type of pepperoni.

In parts of Europe, such as Germany, a pepperoni is a pickled pepper, not the salami named after it.

(And peppers are fruits of a capsicum plant.)

Yep, ArcMenu (@ https://gitlab.com/arcmenu/ArcMenu which is the maintained one, last updated days ago instead of years ago) has a ton of different layouts which can mimic any version of Windows, and so much more.

When using GNOME, use the “Extensions Manager” app (from Flathub) to search for “ArcMenu” and install it, then you can configure it there in the Extensions Manager app as well. In the ArcMenu configuration, go to layouts and select the modern group to see something like the screenshot above. (The previews are generic wireframe sketches; the result will look much more high fidelity.)

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Penpot works perfectly on Linux, and you can even host it yourself in your own computer if you want. It’s web-based and works in both Firefox and Chromium browsers. (I think WebKit ones too, but it’s been a little while since I’ve tried it with Epiphany.)

I use Penpot myself all the time on Linux, but I’m usually using the hosted version so I can collaborate with others without having to maintain a server. I have also run locally in a container using Podman, even with Podman’s rootless support.

But to start using it, all anyone needs to do is point their browser of choice to https://design.penpot.app/ and sign in. There is no setup process or installation needed; self-hosting is completely optional.

Just pointing this out, as there are non-free services that the apps use:

Frog is awesome, but note that while Frog works offline for OCR, it has TTS (text to speech) which uses an online service. As long as you avoid having it read to you, it’s all done locally.

And Dialect always uses an online service. Some of the servers are FOSS, but some aren’t. But everything you type or paste into it is sent somewhere else. (This is the case with using translation websites too, of course.) I’m not saying you shouldn’t use it; I’m just saying that you should be aware.

Hopefully Dialect will add Bergamot (what both Firefox by default & the “translate locally” extension use for translation) at some point. Dialect has a longstanding issue about it, but no forward motion yet. https://github.com/dialect-app/dialect/issues/183

For something open source that runs completely on your computer for translations, you’d want Speech Note. https://flathub.org/apps/net.mkiol.SpeechNote It’s Qt based, but works well. In addition to translation, it can do text to speech and speech to text too. You do have to download models first (easily available as a click in the app), but everything, including the text you’re working with, is all done locally.

I use both Frog and Speech Note all the time on my computer (GNOME on Fedora Linux). They’re excellent.

GNOME 46 (currently in release candidate mode and fully releasing later this month on March 20) is adding support for remote graphical logins via rdp:

https://9to5linux.com/gnome-46-to-introduce-headless-remote-logins-via-gnome-display-manager

So you’ll be able to do this pretty soon, after upgrading.

It’ll be in Fedora 40, scheduled for release around April 16.

https://fedorapeople.org/groups/schedule/f-40/f-40-all-tasks.html

Yeah, that’s a big, weird if though. Most modern apps can rely on the runtimes for their dependencies and not have to ship their own custom dependencies.

It’s different from something like AppImage, where everything is bundled (or Snap, where a lot more needs to be bundled than a typical Flatpak, but not as much as with an AppImage).

Additionally, there’s always some level of sandboxing in Flatpaks (and Snap packages) and none at all for RPMs, Debs, or AppImages.

Also, Flatpak dedupicates common files shared across flatpak apps and runtimes, so there isn’t “bloat” like what you’re talking about.

https://blogs.gnome.org/wjjt/2021/11/24/on-flatpak-disk-usage-and-deduplication/

That’s not how Flatpak works.

Flatpak has runtimes, which is where most shared libraries are. There’s a common base one called Freedesktop, a GNOME runtime, a KDE runtime , an Elementary runtime, and more. (The GNOME and KDE ones are built on top and inherit from the Freedesktop base runtime.)

https://docs.flatpak.org/en/latest/available-runtimes.html

Additionally, at least for Flathub, they have shared modules for commonly used libraries that aren’t in runtimes. (Many are related to games or legacy support like GTK2.)

https://github.com/flathub/shared-modules

Lastly, some distributions are building their own runtimes and apps on top, so the packages they build are available as flatpaks as well. This is the case for Fedora, Elementary, Endless, and others.

https://fedoraproject.org/wiki/Flatpak

Yeah, some of the smaller models are even reasonable on my old laptop in CPU mode.

General rule of thumb: The larger the model, the better it is. But not necessarily. 😉 I’ve found zephyr and mistral are both quite good for a tradeoff and work on CPU. Of the ones that really need more RAM and/or a GPU with a lot of vRAM, mixtral seems like the best.

Additional fun is to use a Modalfile (which is like a Containerfile, but is a recipe for models instead of containers) to customize a local model on top of one of the existing ones.

For a simple one to demonstrate, I have a system instruction to output everything in the form of the poem “This Is Just To Say”, but customized per topic.

It really works best with mixtral (I’ve tried other ones, especially smaller ones):

FROM mixtral
PARAMETER temperature 1
SYSTEM """
You will respond to everything in a modified poem in the form of "This Is Just To Say" by William Carlos Williams, except change all the specifics to be what the subject is. Do not say any other text. Try to make the syllables the same as the original and use the same formatting.

You can expand in length in responses when there is too much to talk about, but keep the format and style of the poem.

Do not respond in any other way.

For reference, the full poem is:

I have eaten
the plums
that were in
the icebox

and which
you were probably
saving
for breakfast

Forgive me
they were delicious
so sweet
and so cold
"""

Yes, you just instruct the system with natural text like that and it (usually) abides. I tried it without the poem being referenced inline, and it mostly worked fine… but it works even better being mentioned in the file.

I have that saved in ~/Projects/ollama/ as Modelfile.fun-plums

I run the server almost as above, but now also pass in my ollama project directory as a mounted volume with z (for SELinux mapping)… don’t forget to have run sudo setsebool container_use_devices=true first, else it won’t work:

podman run --detach --replace --device /dev/kfd --device /dev/dri --group-add video -v ollama:/root/.ollama -p 11434:11434 -v ~/Projects/ollama:/models:z --name ollama ollama/ollama:0.1.24-rocm

(You can run this command if you already have the server running. It will replace it with the new one. This is for AMD. You’d want to use the NVidia or CPU container if you don’t have an AMD card. The CPU container is the fasted to download. The version here is newer than the one for AMD that I listed above, so it might be a multi-gigabyte download if you don’t have this new one yet. The important and new part is ~/Projects/ollama:/models:z)

Then, create the model. This will be almost instant if you already have the base model downloaded (in this case, mixtral), otherwise it will auto-download the base model:

podman exec -it ollama ollama create fun-plums -f /models/Modelfile.fun-plums

(The path to the model in this command is the internal path from the point of view within the container.)

Then, you run it like any other model.

Here’s me running it, and bringing up the topic of leftover pizza.

$ podman exec -it ollama ollama run fun-plums
>>> pizza
 I have consumed
the pizza
that was on
the counter

and which
you were likely
saving
for lunch

Forgive me
it was satisfying
so tasty
and so warm

You can also paste the text from the reader mode of an article and it’ll summarize it with a poem based on that one. 🤣

For example, copying and pasting the text from https://www.theverge.com/2024/2/10/24068931/star-wars-phantom-menace-theater-showings-25th-anniversary-may resulted in:

 I have watched
the Phantom Menace
that was on
the silver screen

and which
you may have
missed or
disliked once

Forgive me
it has charm
a new sheen
and Darth Maul

FOSS apps (all on Flathub)

Some of the AI related apps I’ve been using that are both Free Software and offline (where it runs on your computer without using network services in the cloud) are:

• OCR: “Frog” can take screenshots, select images, accept drag and drop, and you can paste an image from the clipboard. It’ll read the text on the images and immediately have a text area with the result. https://flathub.org/apps/com.github.tenderowl.frog — it’s powered by Tesseract. Note: The completely optional text-to-speech that Frog has does use an online service. But the rest is offline.

• Speech to text: “Speech Note” does text to speech, speech to text, and translations… all locally on your computer, and it supports GPU acceleration (which isn’t needed, but it makes it a little faster). https://flathub.org/apps/net.mkiol.SpeechNote — This is basically the all-in-one “Swiss army knife” of ML text processing. Thanks to being a Flatpak, you don’t have to do anything special for the dependencies. It’s all taken care of for you. It also has tons of different models (for different voices, different backends) all available from within the UI, which just needs a click for downloading.

• Upscaling images: There are two that do something similar, using some of the same backends. A nice and simple one is “Upscaler”. https://flathub.org/apps/io.gitlab.theevilskeleton.Upscaler Another one that’s cross platform is “Upscayl” https://flathub.org/apps/org.upscayl.Upscayl — these both use ESRGAN and Waifu2x in the background.

• Closed captioning: “Live Captions” uses an ML model to transcribe text realtime. It’s wonderful for when a video doesn’t have subtitles, or when you’re participating in a video call (which might also not have CC). There’s also a toggle mode that will transcribe based on microphone input. The default is to use system audio. https://flathub.org/apps/net.sapples.LiveCaptions

• Web page translations: Firefox, for the past few releases, has the ability to translate web pages completely local in-browser. It does need to download a small model file (a quantized one around 20 megabytes per language pair), but this happens automatically on first use. All you need to do is click the translate icon (when it’s auto-detected) or go to the menu and select “Translate page…”. Firefox is located in your distribution already (and is usually installed by default in most Linux distributions) and is available as an official package from Mozilla on Flathub as well. Newer versions keep improving on this, improving speed (it’s pretty quick already), improving accuracy, improving reliability (sometimes you have to try to translate a couple of times on some pages), and adding languages. But what’s there in the release of Firefox is already great.

Chat and image generation (more advanced)

While all the above are graphical apps and on Flathub (some may have distro packages too), there are some additional AI/ML things you can run on Linux as well:

• Chat ML: “Ollama” (https://ollama.ai/) is a friendlier wrapper around llama.cpp and lets you run a variety of models (some FOSS, some just source-available-and-gratis, some not at all).

You can run Ollama in a container to make it even easier. Even a Podman container on your user account works. (You don’t need to set it up as a system container.) The instructions for Docker work on Podman (just swap the docker command for podman instead).

While the official instructions only list CPU (which is fine for some of the smaller models) and NVidia, it’s also possible to use an AMD GPU too:

# Enable device as user (run once per boot)
sudo setsebool container_use_devices=true

# Set up the ollama server for AMD acceleration (run once per session)
podman run --pull=always --replace --detach --device /dev/kfd --device /dev/dri --group-add video -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama:0.1.22-rocm

# Command-line interaction (run any time you want to use it — the last part is which model you want to use)
podman exec -it ollama ollama run llama2

llama2 is the default ML; there are so many others available. Mixtral is a good one if you have enough vram on your GPU. Whatever you specify, it will auto-download and set it up for you. You only need to wait the first time. (The ROCm version of takes a while to download. Each model varies. The good thing is, it’s all cached for subsequent uses.)

If you want a web UI like ChatGPT, then you could also run this instead of the command line interaction command:

podman run -d --replace -p 3000:8080 --add-host=host.docker.internal:host-gateway -v ollama-webui:/app/backend/data --name ollama-webui ghcr.io/ollama-webui/ollama-webui:main

…and visit http://localhost:3000/

When done, run podman stop ollama and podman stop ollama-webui to free up resources from your GPU.

There are also integrations for text editors and IDEs, similar to GitHub’s CoPilot. Neovim has a few already. VS Code (or VS Codium) has some too (like twinny and privy).

• Image generation: “Stable Diffusion” is the go-to here. There are a bunch of forks. Some of the better ones are:
  • “Fooocus” https://github.com/lllyasviel/Fooocus/ — an easy to use streamlined app (with some advanced settings) where you don’t need to prompt as much or tweak settings to get a good result by default.
  • “SD.next” https://github.com/vladmandic/automatic — A fork of the Automatic111 fork of the default Stable Diffusion web app that uses the Gradio UI.
  • “ComfyUI” https://github.com/comfyanonymous/ComfyUI — A node-based UI for Stable Diffusion. …and there are a ton others. Forks of forks of forks.

Krita, GIMP, and Blender all have plugins that can interface with some of these too (usually using a SD Automatic111 API).

For Stable Diffusion on AMD, you need to have ROCm installed and might need to set or use an environment variable to make it work with your card. Something like: HSA_OVERRIDE_GFX_VERSION=11.0.0 or HSA_OVERRIDE_GFX_VERSION=10.3.0 (depending on your GPU). Prefixing means just putting that at the beginning of the the command with a space and then the rest of the command. Setting it as a variable depends on your shell. You might need to export it for some (like for bash). Prefixing it is fine though, especially when you use ctrl+r to do a substrang search in your shell history (so you don’t need to retype it or remember silly-long commands).

As using these image generating apps pulls down a lot of Python libraries, I’d suggest considering setting up a separate user account instead of using your own, so the app doesn’t have access to your local files (like stuff in ~/.ssh/, ~/.local/, your documents, etc.). Setting up containers for these is not so easy (yet), sadly. Some people have done it. And they do run in a toolbox or distrobox podman container… but toolbox and distrobox containers don’t really contain so much, so you’re better off using podman (with a “docker” container) directly or running it as a separate account for some type of isolation from your user account files.

Everything else above is at least contained (via containers or Flatpak) to some degree… but stuff locally via pip installs can do anything. And it’s not just hypothetical either, for example: PyTorch nightly was compromised for a few days on Christmas of 2022.

There are some graphical apps on Flathub for connecting to Stable Diffusion and a ChatGPT AI (which ollama now has)… but in the course of setting them up, you basically have a web and/or text-based UI to interact with.

You can set up mount points on Linux, at least in GNOME, very easily. (It’s even fully automatic for external disks.) I’d be surprised if it isn’t as easy in KDE and other desktops too.

The problem here (at least from what it sounds like) isn’t setting up mount points. The problem is fixing an incorrect fstab on the disk that’s causing the system to hang on boot.

(This isn’t a typical situation, which is why I also asked about how the partition was added to the system.)

Good point! GNOME Disks can do this, actually. I didn’t think about that.

(Edit: However, I think it’ll just edit the /etc/fstab of the running system. In other words, the one of the live session, not the one on the installation.)

My first attempt to try to fix something like this would be to:

1. Download Fedora Workstation live media. (Within Windows or some other computer that boots.)
2. Flash it to a USB stick.
3. Reboot to the live desktop from the USB stick. (It might require pressing F12 or some other key combo during boot.)
4. “Try out” Fedora. (That is: do not install.)
5. Open GNOME Disks. (I think it’s included. Otherwise, you can sudo dnf install gnome-disks to install it temporarily on the live session.)
6. Try to mount the main filesystem that contains /etc/fstab (it should ask you for the LUKS password.
7. Comment out the Windows mount point. Or if you want to keep it (if the partition still exists and is just “dirty” and still needs a check from Windows) add “,nofail” after “auto” to the options in the line for the mount, so your system should still boot without that mount point.
8. Save the /etc/fstab file.
9. Shut down the computer.
10. Unplug USB stick.
11. Boot computer. Linux should successfully boot… hopefully. 😉

I’m also wondering: How did you add the Windows partition to Fedora? Was it from within Fedora’s installer (aka: “Anaconda”)? Or did you add it in a different way?

(BTW: I use Silverblue and have a long history with Fedora. 😁)