Transcoding Issues on Windows Docker

[mistamoronic]

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 interaction

However, when I try to do the "docker exec -it jellyfin nvidia-smi" command from this page https://jellyfin.org/docs/general/admini...ualization
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: unknown

Not sure why that is happening, because when I do the nvidia-smi test from this page: https://docs.nvidia.com/datacenter/cloud...kload.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      |
+-----------------------------------------------------------------------------------------+