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+--- Thread: Transcoding Issues on Windows Docker (/t-transcoding-issues-on-windows-docker)
Transcoding Issues on Windows Docker - mistamoronic - 2025-01-12
Hello, I am using docker 10.10.3 on Docker for Windows and I am having trouble getting transcoding to work. I tried following the steps listed in https://jellyfin.org/docs/general/administration/hardware-acceleration/nvidia#configure-with-linux-virtualization
But I am not sure how to complete step 4 that says to add my user to the video group with the
Code:
sudo usermod -aG video $USERThen step 5 says to run the "docker exec -it jellyfin ldconfig" command and when I try that I get
Code:
docker container exec -it jellyfin ldconfig
ldconfig: Can't create temporary cache file /etc/ld.so.cache~: Permission deniedAnyway I tried to just enable the transcoding without that step to see what would happen and I get "Playback failed due to a fatal player error" when I try to watch something that requires transcoding. Checking the logs, it says this at the end
Code:
ffmpeg version 7.0.2-Jellyfin Copyright (c) 2000-2024 the FFmpeg developers
built with gcc 12 (Debian 12.2.0-14)
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=auto --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-libharfbuzz --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 59. 8.100 / 59. 8.100
libavcodec 61. 3.100 / 61. 3.100
libavformat 61. 1.100 / 61. 1.100
libavdevice 61. 1.100 / 61. 1.100
libavfilter 10. 1.100 / 10. 1.100
libswscale 8. 1.100 / 8. 1.100
libswresample 5. 1.100 / 5. 1.100
libpostproc 58. 1.100 / 58. 1.100
[AVHWDeviceContext @ 0x564d10bf4d40] Cannot load libcuda.so.1
[AVHWDeviceContext @ 0x564d10bf4d40] 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 permittedSo I obviously doing something wrong lol, is it because my user is not added to the video group? Here is my docker-compose yaml file btw
Code:
services:
jellyfin:
image: jellyfin/jellyfin:latest
container_name: jellyfin
user: 1000:1000
network_mode: 'host'
ports:
- 8096:8096
volumes:
- C:\Users\User1\docker\jellyfin\config:/config
- C:\Users\User1\docker\jellyfin\cache:/cache
- type: bind
source: D:\Documents\Videos\Movies
target: /media
read_only: true
restart: 'unless-stopped'
runtime: nvidia
deploy:
resources:
reservations:
devices:
- driver: nvidia
count: all
capabilities: [all]RE: Transcoding Issues on Windows Docker - Efficient_Good_5784 - 2025-01-12
You're using WSL right? Make sure you're on WSL2 and have an Nvidia GPU (as it's the only brand of GPU that's supported on Docker with WSL2).
This might be helpful: https://docs.docker.com/desktop/features/gpu/
RE: Transcoding Issues on Windows Docker - mistamoronic - 2025-01-12
Yes I've done that part and it works when I use that benchmark command inside that page
RE: Transcoding Issues on Windows Docker - mistamoronic - 2025-01-13
(2025-01-12, 08:20 AM)Efficient_Good_5784 Wrote: You're using WSL right? Make sure you're on WSL2 and have an Nvidia GPU (as it's the only brand of GPU that's supported on Docker with WSL2).
This might be helpful: https://docs.docker.com/desktop/features/gpu/
Following up with this, here is the output when I run the "docker run --rm -it --gpus=all nvcr.io/nvidia/k8s/cuda-sample:nbody nbody -gpu -benchmark" command from that page.
Code:
docker run --rm -it --gpus=all nvcr.io/nvidia/k8s/cuda-sample:nbody nbody -gpu -benchmark
Run "nbody -benchmark [-numbodies=<numBodies>]" to measure performance.
-fullscreen (run n-body simulation in fullscreen mode)
-fp64 (use double precision floating point values for simulation)
-hostmem (stores simulation data in host memory)
-benchmark (run benchmark to measure performance)
-numbodies=<N> (number of bodies (>= 1) to run in simulation)
-device=<d> (where d=0,1,2.... for the CUDA device to use)
-numdevices=<i> (where i=(number of CUDA devices > 0) to use for simulation)
-compare (compares simulation results running once on the default GPU and once on the CPU)
-cpu (run n-body simulation on the CPU)
-tipsy=<file.bin> (load a tipsy model file for simulation)
NOTE: The CUDA Samples are not meant for performance measurements. Results may vary when GPU Boost is enabled.
> Windowed mode
> Simulation data stored in video memory
> Single precision floating point simulation
> 1 Devices used for simulation
GPU Device 0: "Ampere" with compute capability 8.6
> Compute 8.6 CUDA device: [NVIDIA GeForce RTX 3070]
47104 bodies, total time for 10 iterations: 40.967 ms
= 541.601 billion interactions per second
= 10832.025 single-precision GFLOP/s at 20 flops per interactionHowever, when I try to do the "docker exec -it jellyfin nvidia-smi" command from this page https://jellyfin.org/docs/general/administration/hardware-acceleration/nvidia/#configure-with-linux-virtualization
This is the result I get:
Code:
docker exec -it jellyfin nvidia-smi
OCI runtime exec failed: exec failed: unable to start container process: exec: "nvidia-smi": executable file not found in $PATH: unknownNot sure why that is happening, because when I do the nvidia-smi test from this page: https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/sample-workload.html just by itself, it works:
Code:
docker run --rm --runtime=nvidia --gpus all ubuntu nvidia-smi
Tue Jan 14 05:26:59 2025
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 565.77.01 Driver Version: 566.36 CUDA Version: 12.7 |
|-----------------------------------------+------------------------+----------------------+
| 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 RTX 3070 On | 00000000:09:00.0 On | N/A |
| 30% 32C P8 23W / 220W | 1626MiB / 8192MiB | 1% Default |
| | | N/A |
+-----------------------------------------+------------------------+----------------------+
+-----------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=========================================================================================|
| 0 N/A N/A 37 G /Xwayland N/A |
+-----------------------------------------------------------------------------------------+RE: Transcoding Issues on Windows Docker - mistamoronic - 2025-01-14
Just wanted to put my transcoding settings here for additional information. Looking at the encode/decode support codex listed here: https://developer.nvidia.com/video-encode-and-decode-gpu-support-matrix-new
I should be able to decode everything and encode everything except AV1
![[Image: ikanhBZ.png]](https://i.imgur.com/ikanhBZ.png)