GPU Docker Jellyfin not working

[ginjaturtles]

Here is my docker compose:

Code:

jellyfin:
    image: lscr.io/linuxserver/${MEDIA_SERVICE}
    container_name: ${MEDIA_SERVICE}
    environment:
      - PUID=${PUID}
      - PGID=${PGID}
      - VERSION=docker
      - NVIDIA_VISIBLE_DEVICES=all
      - NVIDIA_DRIVER_CAPABILITIES=all
    volumes:
      - ${MEDIA_DIRECTORY}/movies:/data/movies
      - ${MEDIA_DIRECTORY}/tvshows:/data/tvshows
      - ${INSTALL_DIRECTORY}/config/${MEDIA_SERVICE}:/config
    runtime: nvidia
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: ["gpu", "utility"]
    ports:
      - 8096:8096
    restart: unless-stopped

I installed nvidia toolkit container using this https://docs.nvidia.com/datacenter/cloud...guide.html and I'm able to run nvidia-smi with no issues. After running the compose I am alsi able to run docker exec jellyfin nvidia-smi and get back a response with no issues inside of the container:

Code:

Thu Sep 19 03:30:06 2024     
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 550.107.02            Driver Version: 550.107.02    CUDA Version: N/A      |
|-----------------------------------------+------------------------+----------------------+
| 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  NVIDIA GeForce GTX 980 Ti      Off |  00000000:01:00.0 Off |                  N/A |
|  0%  56C    P8            35W /  275W |      2MiB /  6144MiB |      0%      Default |
|                                        |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+
                                                                                       
+-----------------------------------------------------------------------------------------+
| Processes:                                                                              |
|  GPU  GI  CI        PID  Type  Process name                              GPU Memory |
|        ID  ID                                                              Usage      |
|=========================================================================================|
|  No running processes found                                                            |
+-----------------------------------------------------------------------------------------+

When trying to play a episode from a show I keep getting a transcoding error with the message:

Code:

/usr/lib/jellyfin-ffmpeg/ffmpeg -analyzeduration 200M -probesize 1G -init_hw_device cuda=cu:0 -filter_hw_device cu -noautorotate -i file:"/data/tvshows/Breaking Bad/Season 3/Breaking Bad - S03E01 - No Más Bluray-1080p.mkv" -map_metadata -1 -map_chapters -1 -threads 0 -map 0:0 -map 0:1 -map -0:0 -codec:v:0 h264_nvenc -preset p1 -b:v 19051125 -maxrate 19051125 -bufsize 38102250 -profile:v:0 high -g:v:0 72 -keyint_min:v:0 72 -filter_complex "[0:3]scale=-1:1080:fast_bilinear,scale,crop,pad=max(1920\,iw):max(1080\,ih):(ow-iw)/2:(oh-ih)/2:black@0,crop=1920:1080[sub];[0:0]setparams=color_primaries=bt709:color_trc=bt709:colorspace=bt709,scale=trunc(min(max(iw\,ih*a)\,min(1920\,1080*a))/2)*2:trunc(min(max(iw/a\,ih)\,min(1920/a\,1080))/2)*2,format=yuv420p[main];[main][sub]overlay=eof_action=pass:repeatlast=0" -start_at_zero -codec:a:0 libfdk_aac -ac 2 -ab 256000 -af "volume=2" -copyts -avoid_negative_ts disabled -max_muxing_queue_size 2048 -f hls -max_delay 5000000 -hls_time 3 -hls_segment_type fmp4 -hls_fmp4_init_filename "e26a6306ba622f193ae59116a0688317-1.mp4" -start_number 0 -hls_segment_filename "/config/cache/transcodes/e26a6306ba622f193ae59116a0688317%d.mp4" -hls_playlist_type vod -hls_list_size 0 -y "/config/cache/transcodes/e26a6306ba622f193ae59116a0688317.m3u8"


ffmpeg version 6.0.1-Jellyfin Copyright (c) 2000-2023 the FFmpeg developers
  built with gcc 13 (Ubuntu 13.2.0-23ubuntu4)
  configuration: --prefix=/usr/lib/jellyfin-ffmpeg --target-os=linux --extra-version=Jellyfin --disable-doc --disable-ffplay --disable-ptx-compression --disable-static --disable-libxcb --disable-sdl2 --disable-xlib --enable-lto --enable-gpl --enable-version3 --enable-shared --enable-gmp --enable-gnutls --enable-chromaprint --enable-opencl --enable-libdrm --enable-libxml2 --enable-libass --enable-libfreetype --enable-libfribidi --enable-libfontconfig --enable-libbluray --enable-libmp3lame --enable-libopus --enable-libtheora --enable-libvorbis --enable-libopenmpt --enable-libdav1d --enable-libsvtav1 --enable-libwebp --enable-libvpx --enable-libx264 --enable-libx265 --enable-libzvbi --enable-libzimg --enable-libfdk-aac --arch=amd64 --enable-libshaderc --enable-libplacebo --enable-vulkan --enable-vaapi --enable-amf --enable-libvpl --enable-ffnvcodec --enable-cuda --enable-cuda-llvm --enable-cuvid --enable-nvdec --enable-nvenc
  libavutil      58.  2.100 / 58.  2.100
  libavcodec    60.  3.100 / 60.  3.100
  libavformat    60.  3.100 / 60.  3.100
  libavdevice    60.  1.100 / 60.  1.100
  libavfilter    9.  3.100 /  9.  3.100
  libswscale      7.  1.100 /  7.  1.100
  libswresample  4. 10.100 /  4. 10.100
  libpostproc    57.  1.100 / 57.  1.100
[AVHWDeviceContext @ 0x5c7eb42e0300] Cannot load libcuda.so.1
[AVHWDeviceContext @ 0x5c7eb42e0300] Could not dynamically load CUDA
Device creation failed: -1.
Failed to set value 'cuda=cu:0' for option 'init_hw_device': Operation not permitted
Error parsing global options: Operation not permitted

I'm using Ubuntu Server Ubuntu 24.04.1 LTS with Docker. I have an old GTX 980TI in my home server. Any ideas? Can't figure out what I am missing

---

I also tried using intel quick sync since I have an i7-7700k in this home server but I had no luck there either. The whole reason I went down this rabbit hole is when using jellyfin from firefox and watching certain shows, my CPU usage would spike to 100% from ffmpeg in jellyfin doing the transcoding