this post was submitted on 21 Sep 2024
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45
Jellyfin hardware acceleration docker issues. (lemmy.world)
submitted 1 month ago by hoxbug@lemmy.world to c/selfhosted@lemmy.world
17 comments fedilink hide all child comments
 

Having a bit of trouble getting hardware acceleration working on my home server. The cpu of the server is an i7-10700 and has a discrete GPU, RTX 2060. I was hoping to use intel quick sync for the hardware acceleration, but not having much luck.

From the guide on the jellyfin site https://jellyfin.org/docs/general/administration/hardware-acceleration/intel

I have gotten the render group ID using "getent group render | cut -d: -f3" though it mentions on some systems it might not be render, it may be video or input which i tried with those group ID's as well.

When I run "docker exec -it jellyfin /usr/lib/jellyfin-ffmpeg/vainfo" I get back

libva info: VA-API version 1.22.0
libva info: Trying to open /usr/lib/jellyfin-ffmpeg/lib/dri/nvidia_drv_video.so
libva info: Trying to open /usr/lib/x86_64-linux-gnu/dri/nvidia_drv_video.so
libva info: Trying to open /usr/lib/dri/nvidia_drv_video.so
libva info: Trying to open /usr/local/lib/dri/nvidia_drv_video.so
libva info: va_openDriver() returns -1
vaInitialize failed with error code -1 (unknown libva error),exit

I feel like I need to do something on the host system since its trying to use the discrete card? But I am unsure.

This is the compose file just in case I am missing something

version: "3.8"
services:
  jellyfin:
    image: jellyfin/jellyfin
    user: 1000:1000
    ports:
      - 8096:8096
    group_add:
      - "989" # Change this to match your "render" host group id and remove this comment
      - "985"
      - "994"
    # network_mode: 'host'
    volumes:
      - /home/hoxbug/Docker/jellyfin/config:/config
      - /home/hoxbug/Docker/jellyfin/cache:/cache
      - /mnt/External/Movies:/Movies
    devices:
      - /dev/dri/renderD128:/dev/dri/renderD128
networks:
  external:
    external: true

Thank you for the help.

top 17 comments
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[–] sk@hub.utsukta.org 14 points 1 month ago

On my system i was able to use my integrated iGPU wit the follwing:

    devices:     - /dev/dri:/dev/dri

The rest of your compose looks fine.

[–] sneezycat@sopuli.xyz 9 points 1 month ago (1 children)

Isn't your GPU an Nvidia RTX 2060? Why are you trying to use the Intel GPU acceleration method? I'm confused

[–] hoxbug@lemmy.world 2 points 1 month ago (1 children)

It just seemed the easiest route, but I may just give using the GPU a go.

[–] __ghost__@lemmy.ml 2 points 1 month ago (1 children)

From personal experience intel QSV wasn't worth the trouble to txshoot on my hardware. Mine is a lot older than yours though. Vaapi has worked well on my arc card

[–] entropicdrift@lemmy.sdf.org 8 points 1 month ago

QSV is the highest quality video transcoding hardware acceleration out there. It's worth using if you have a modern Intel CPU (8th gen or newer)

[–] bobslaede@feddit.dk 5 points 1 month ago (1 children)

This is how mine works, with a Nvidia GPU

services:
  jellyfin:
    volumes:
      - jellyfin_config:/config
      - jellyfin_cache:/cache
      - type: tmpfs
        target: /cache/transcodes
        tmpfs:
          size: 8G
      - media:/media
    image: jellyfin/jellyfin:latest
    restart: unless-stopped
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              device_ids:
                - "0"
              capabilities:
                - gpu
[–] notfromhere@lemmy.ml 3 points 1 month ago (1 children)

What is the tmpfs for?

[–] bobslaede@feddit.dk 8 points 1 month ago (1 children)

Temp files for transcoding. No need to hit the disk.

[–] __ghost__@lemmy.ml 4 points 1 month ago (3 children)

Is the tmpfs on RAM?

[–] baduhai@sopuli.xyz 3 points 1 month ago

Aye.

[–] 486@lemmy.world 2 points 1 month ago (1 children)

It is. It might end up on disk in swap, if you run low on memory (and have some sort of disk-based swap enabled), but usually it is located in RAM.

[–] __ghost__@lemmy.ml 1 points 1 month ago* (last edited 1 month ago) (1 children)

You can create a tmpfs on other storage devices as well, just curious what their setup looked like

[–] 486@lemmy.world 1 points 1 month ago* (last edited 1 month ago)

No, tmpfs is always located in virtual memory. Have a look at the kernel documentation for more information about tmpfs.

[–] bobslaede@feddit.dk 1 points 1 month ago

Hmm. I would think so. But I haven't actually checked. That was my thought.

[–] HumanPerson@sh.itjust.works 1 points 1 month ago* (last edited 1 month ago)

I have an arc for transcoding, and I had to set the device to /dev/dri without the renderD128 part. If I were you, I would just use the 2060. If it's there for llama or something I'd still try it and see how it does doing both at once, as it should be separate parts of the gpu handling that.

[–] entropicdrift@lemmy.sdf.org 1 points 1 month ago* (last edited 1 month ago)

If you switch the devices line to

- /dev/dri:/dev/dri

as other have suggested, that should expose the Intel iGPU to your Jellyfin docker container. Presently you're only exposing the Nvidia GPU.

[–] JustEnoughDucks@feddit.nl 1 points 1 month ago

Do you have the Intel drivers installed on your machine? Are GuC and HuC working?

sudo reboot
sudo dmesg | grep i915
sudo cat /sys/kernel/debug/dri/0/gt/uc/guc_info
sudo cat /sys/kernel/debug/dri/0/gt/uc/huc_info

On Debian I had to manually download the i915 full driver Zip, extract it, take out the Intel drivers, and put it in /usr/lib/firmware

Then hardware acceleration worked on my Arc380.

If you use QSV, your CPU iGPU will be the one that can use it, so make sure to set your render device in docker to the iGPU and not the RTX 2060