[Issue]: ffmpeg logs

[hnotcold]

Please describe your bug

https://www.aliyundrive.com/s/QacEv33isD2，日志在这里，大佬辛苦看下

Jellyfin Version

Other

if other:

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Environment

- OS:
- Virtualization:
- Clients:
- Browser:
- FFmpeg Version:
- Playback Method:
- Hardware Acceleration:
- Plugins:
- Reverse Proxy:
- Base URL:
- Networking:
- Storage:

Jellyfin logs

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FFmpeg logs

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Please attach any browser or client logs here

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Code of Conduct

• I agree to follow this project's Code of Conduct

[hnotcold]

Cannot load libcuda.so.1
[AVHWDeviceContext @ 0x562894e1d340] Could not dynamically load CUDA
Device creation failed: -1313558101.
Failed to set value 'cuda=cu:0' for option 'init_hw_device': Unknown error occurred
Error parsing global options: Unknown error occurred

[nyanmisaka]

你是否已经按照官方教程添加了nvidia相关的启动参数？
https://jellyfin.org/docs/general/administration/hardware-acceleration.html#nvidia-nvenc

Start your container adding these parameters:

-e NVIDIA_DRIVER_CAPABILITIES=all \
-e NVIDIA_VISIBLE_DEVICES=all \
--gpus all \
A complete run command would look like this:

docker run -d \
 --name=jellyfin \
 -e NVIDIA_DRIVER_CAPABILITIES=all \
 -e NVIDIA_VISIBLE_DEVICES=all \
 --gpus all \
 -p 8096:8096 \
 -p 8920:8920 \
 -v /config:/config \
 -v /media:/media \
 -v /cache:/cache \
 --restart unless-stopped \
 jellyfin/jellyfin
Or with docker-compose >1.28, add the deploy and environment parts to your Jellyfin service:

services:
  jellyfin:
    image: jellyfin/jellyfin
  # ... your Jellyfin config
  environment:
    NVIDIA_DRIVER_CAPABILITIES: all
    NVIDIA_VISIBLE_DEVICES: all
  deploy:
    resources:
      reservations:
        devices:
        - capabilities: [gpu]
There are some special steps when running with the following option:

--user 1000:1000
You may need to add this user to the video group on your host machine:

usermod -aG video user
Once the container is started you can again validate access to the host resources:

docker exec -it jellyfin nvidia-smi
If you get driver information, everything is fine but if you get an error like couldn't find libnvidia-ml.so library in your system you need to run the following command:

docker exec -it jellyfin ldconfig
After that, you should ensure the NVIDIA driver loads correctly.

[hnotcold]

你是否已经按照官方教程添加了nvidia相关的启动参数？ https://jellyfin.org/docs/general/administration/hardware-acceleration.html#nvidia-nvenc

Start your container adding these parameters:

-e NVIDIA_DRIVER_CAPABILITIES=all \
-e NVIDIA_VISIBLE_DEVICES=all \
--gpus all \
A complete run command would look like this:

docker run -d \
 --name=jellyfin \
 -e NVIDIA_DRIVER_CAPABILITIES=all \
 -e NVIDIA_VISIBLE_DEVICES=all \
 --gpus all \
 -p 8096:8096 \
 -p 8920:8920 \
 -v /config:/config \
 -v /media:/media \
 -v /cache:/cache \
 --restart unless-stopped \
 jellyfin/jellyfin
Or with docker-compose >1.28, add the deploy and environment parts to your Jellyfin service:

services:
  jellyfin:
    image: jellyfin/jellyfin
  # ... your Jellyfin config
  environment:
    NVIDIA_DRIVER_CAPABILITIES: all
    NVIDIA_VISIBLE_DEVICES: all
  deploy:
    resources:
      reservations:
        devices:
        - capabilities: [gpu]
There are some special steps when running with the following option:

--user 1000:1000
You may need to add this user to the video group on your host machine:

usermod -aG video user
Once the container is started you can again validate access to the host resources:

docker exec -it jellyfin nvidia-smi
If you get driver information, everything is fine but if you get an error like couldn't find libnvidia-ml.so library in your system you need to run the following command:

docker exec -it jellyfin ldconfig
After that, you should ensure the NVIDIA driver loads correctly.

运行的时候提示以下提示：docker: Error response from daemon: could not select device driver "" with capabilities: [[gpu]].

[nyanmisaka]

运行的时候提示以下提示：docker: Error response from daemon: could not select device driver "" with capabilities: [[gpu]].

似乎你没有安装nvidia为docker提供的运行库 NVIDIA Container Toolkit
请查看以下链接来解决：

1. https://blog.csdn.net/dou3516/article/details/108314908
2. https://www.cnblogs.com/GengMingYan/p/14962878.html
3. https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#getting-started

[hnotcold]

运行的时候提示以下提示：docker: Error response from daemon: could not select device driver "" with capabilities: [[gpu]].

似乎你没有安装nvidia为docker提供的运行库 NVIDIA Container Toolkit 请查看以下链接来解决：

1. https://blog.csdn.net/dou3516/article/details/108314908
2. https://www.cnblogs.com/GengMingYan/p/14962878.html
3. https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#getting-started

老大你的这三个教程我看了，qts不支持自己安装这些，估计还是要寻找官方的人来支持一下了，qts不支持源

[jellyfin-bot]

This issue has gone 120 days without comment. To avoid abandoned issues, it will be closed in 21 days if there are no new comments.

If you're the original submitter of this issue, please comment confirming if this issue still affects you in the latest release or master branch, or close the issue if it has been fixed. If you're another user also affected by this bug, please comment confirming so. Either action will remove the stale label.

This bot exists to prevent issues from becoming stale and forgotten. Jellyfin is always moving forward, and bugs are often fixed as side effects of other changes. We therefore ask that bug report authors remain vigilant about their issues to ensure they are closed if fixed, or re-confirmed - perhaps with fresh logs or reproduction examples - regularly. If you have any questions you can reach us on Matrix or Social Media.

[athdesk]

this is still happening, it shouldn't be closed

[4n4n4s]

I could solve the issue by adding

  environment:
      - NVIDIA_DRIVER_CAPABILITIES=all
      - NVIDIA_VISIBLE_DEVICES=all

to the environment config as I am using the image: ghcr.io/linuxserver/jellyfin

[jellyfin-bot]

This issue has gone 120 days without comment. To avoid abandoned issues, it will be closed in 21 days if there are no new comments.

If you're the original submitter of this issue, please comment confirming if this issue still affects you in the latest release or master branch, or close the issue if it has been fixed. If you're another user also affected by this bug, please comment confirming so. Either action will remove the stale label.

This bot exists to prevent issues from becoming stale and forgotten. Jellyfin is always moving forward, and bugs are often fixed as side effects of other changes. We therefore ask that bug report authors remain vigilant about their issues to ensure they are closed if fixed, or re-confirmed - perhaps with fresh logs or reproduction examples - regularly. If you have any questions you can reach us on Matrix or Social Media.