I am looking to run the latest Cinelerra on Ubuntu 24.04. Nvidea RTX 2070, AMD Ryzen 7 3800X ( 8 core), 32 Gigs RAM, M2 NVME, Motu M2 audio card.
I have tried Cinelerra in the past and it was not usable. Am checking in to see if that has changed. However, there seems to be no way to install Cinelerra GG on my current OS. If anyone knows a quick and easy way to test, do let me know!
The easiest way to test is simply to download the file: CinGG-20251231-x86_64.AppImage at:
https://download.cinelerra-gg.org/?type=appimage
Be sure to make the downloaded file executable via:
chmod +x CinGG-20251231-x86_64.AppImage
If you have issues, be sure to let us know as it should work fine on Ubuntu 24 as far as I know.
I have indeed tried this and there are a lot of dependency issues that cannot be resolved. I could post the list of dependencies mentioned. It shows two that are missing and then the program gives up, as it were. Libfuse2, libflac8, I think were some of things the program clamored for...I tried to install those, but no candidate available...
I don't suppose you want to take the time to compile CinGG from source.
Initial problem appears to be that running appimage requires fuse3 instead of fuse2.
Here is a potential workaround that is easy to try.
1. /{path to appimage}/CinGG-20251231-x86_64.AppImage --appimage-extract
2. /{path to appimage}/squashfs-root/usr/bin/./cin
Per the web, below is what could fix the problem. Please let us know if this works for you. Thanks.
You can install FUSE 2 alongside FUSE 3 without issues. The older version doesn’t replace the newer package; they coexist ...
To install FUSE 2 on Ubuntu 24.04 LTS or newer, open a new Terminal window and type (or paste) the following command:
sudo apt install libfuse2t64
If you’re on Ubuntu 23.04 or 23.10 (both are EOL, FYI) then the package name is just libfuse2 as Ubuntu 24.04 LTS was when all of the 64-bit t_time bug problems were tackled.
Press the enter/return key, enter your password to authenticate, and blink while the necessary library is downloaded, unpacked, and installed.
That’s it; now if you double-click on an AppImage to open it (assuming it has permission to run first, of course) it will open and run as intended.
I did as you said and I was able to launch the appimage, thank you. I would like to integrate the program to my menu, but I guess that is down the road. I looked at the settings and it seems maybe Cinelerra supports CUDA? That would be great if this is the case.
The next step is to configure the system for optimal editing. I notice things like the aspect ratio is off. Hence, some documentation is needed to ensure Cinelerra is a viable pro option for editing.
Thanks again.
When you say "you did as I said" do you mean installing libfuset64 or squashfs?
You can add CinGG to your desktop by copying 3 files as shown below (it will not let me attach the 3 files here so I will have to put them on the website later). This worked for me on Fedora as root but Ubuntu may be slightly different or you may have to put into a local directory. Also, you may have to execute "chmod +x" on the first 2 files.
cp -a cin.svg /usr/share/pixmaps/
cp -a cin.xpm /usr/share/pixmaps/
cp -a cin.desktop /usr/share/applications/cin.desktop
Then edit /usr/share/applications/cin.desktop to change the line Exec=cin to Exec=location where you put your AppImage.
Finally you probably have to login again to see the application icon and move it where you want.
The files needed to create an Application Icon are now available along with a README at;
https://download.cinelerra-gg.org/?path=Desktop_icon&sort=name_asc
About Cuda -- yes CinGG can take advantage of Cuda as well as vaapi/vdpau/nvenc graphics board speedups BUT unfortunately due to the differences in all of the many graphics boards to include all of the variations of Nvidia boards, unless you build from source it does not matter. The computer on which the AppImage is created has a completely different graphics board than your Nvidea RTX 2070. However, you have a good computer setup seeing as how it is a Ryzen, so hopefully you will not miss using Cuda/vaapi/vdpau/nvenc. And if you start to get really serious about using CinGG for video editing, it really is not that hard to build CinGG following the instructions in the manual with help from us as needed.
Thank you for this. Kindly point me to documentation required to build from source so I can fully leverage my GPU. I am a professional videographer for 10+ years and have used most NLE's on the market. However, my main workstation is Linux where I run DaVinci Resolve Studio and Lightworks. Working with Cinelerra professionally would be ideal. But of course, some assembly is required.
To answer your earlier question, I installed "sudo apt install libfuse2t64" and thereafter, the appimage launched fine.
vThe specific instructions in the manual need to be updated, but the basic build is described at:
https://download.cinelerra-gg.org/files/CinelerraGG_Manual/single_user_build.html
for example the <os> would be ubuntu24 instead of just ubuntu.
And in your case the "configure" line would have added to the end: --with-vaapi --with-vdpau --with-nv --with-cuda
If you are not familiar with building packages from scratch, it would probably be better to try that first without the addition configure line "withs".Installing Cuda sdk ahead of time is also pretty difficult but must be done so I would suggest just trying to build without adding --with-cuda once you get past the first build. There has been some discussion that CinGG with cuda does not provide a boost but it has been proven that vaapi/vdpau/nv in most cases gives a nice boost.
More detailed information and requirements are described in the manual at:
https://download.cinelerra-gg.org/files/CinelerraGG_Manual/GPU_hardware_decoding.html#23967
@phylsmith2004 Thanks. I will invite some technical people over to my home studio to install and try to optimize. Every little ounce of performance I can get will be leveraged.
I don't know if Cinelerra can install on Intel OSX via 'homebrew'. Am not a Mac person, but someone loaned use of one until I can replace my old laptop. My preferred OS is Linux, always, but try to use what I got or can borrow.
Just an update. I have Cinelerra installed as you outlined above with the help of a dev. The dev encountered a lot of errors en route to installation and wishes to share with you some of the issues. The dev feels your team can learn from some of these issues. It is too long to post here. So the transcripts would need to be sent somewhere. The dev said that AI helped isolate a lot of the issues rapidly and showed where the code needed to be modified. More than one attempt was needed to compile the code to get the build 'just right'. CUDA and bluebanana were some of the sticking points. Of course, I will know issues a bit better once I actually start using the program over the next while.
That is great! You can send any information that you have that will help us improve things to [email protected] -- for large files, gmail will put on a shared google drive automatically. If this is not acceptable, we can find something else that works. I would really like this help.
