New notebook

Can Vocal Remover v5 be installed on docker?

Can I install Ultimate Vocal Remover v5 (https://github.com/Anjok07/ultimatevocalremovergui/releases/tag/v5.6) on MX25 Linux? Can it be installed on docker?

Gemini

Yes, you can run Ultimate Vocal Remover v5 (UVR5) on MX Linux, and yes, it can be run via Docker.

Since Debian-based distributions (like MX Linux) do not have a pre-compiled .deb or official AppImage installer for UVR v5.6, running it via a Python virtual environment or inside a Docker container are the two main ways to install it.

Because MX Linux enforces PEP 668 (externally managed Python environments), you should install UVR5 inside a dedicated Python virtual environment rather than installing packages system-wide.

Open your terminal and install the required build tools, Tcl/Tk (for the GUI), and FFmpeg:

Depending on whether you intend to run inference on an NVIDIA GPU, AMD GPU, or CPU only, install PyTorch first:

Next, install the rest of UVR's requirements:

Whenever you want to run the application, activate the environment and launch UVR.py:

Running UVR in Docker is often cleaner because it isolates PyTorch and audio library dependencies from your base system. You can forward the GUI to your local X11 display server.

Community-maintained Docker builds allow you to run the full Tkinter GUI directly out of a container.

If you prefer accessing the interface via a web browser (or over your local network) instead of forwarding X11:

You

Will CPU Only work well?

Gemini

It will work functional-wise, but it will be very slow.

The separation quality (how clean the vocals or instrumentals sound) is identical whether you use a CPU or a GPU, because the underlying AI model math doesn't change. However, the time required to complete the processing changes dramatically.

If you are only processing one song occasionally, CPU mode is fine. If you plan to process entire albums, batch files, or test multiple models on the same track, CPU processing will get frustrating quickly.

If you run UVR5 in CPU-only mode (either natively or in Docker), tweak these GUI settings to keep processing times manageable:

You

Will GPU Accelerated work on Garuda Linux with B70 GPU?

Gemini

Yes, GPU acceleration is possible on Garuda Linux with an Intel Arc Pro B70, but it requires a specific PyTorch configuration.

Out of the box, standard UVR5 assumes an NVIDIA (CUDA) GPU. Intel GPUs (including the Battlemage-based Arc Pro B70) do not support CUDA; instead, they rely on Intel’s oneAPI / PyTorch XPU backend or OpenCL / DirectML.

If you try to run standard UVR5 without modifications, it will fail to detect CUDA and fall back to CPU mode.

Garuda Linux is Arch-based, which makes installing the necessary Intel drivers straightforward via pacman.

Make sure your system has the proper driver libraries installed to enable Intel Compute (XPU/oneAPI):

(Verify your system kernel is 6.12+ or 7.x, as Battlemage B70 requires modern kernel driver support).

Rather than installing standard PyTorch (cu121), install PyTorch XPU, which enables native Intel Arc GPU acceleration.

Verify that PyTorch can see your B70 GPU:

It should return True and display Intel Arc Pro B70.

By default, UVR5 hardcodes device selection to cuda or cpu. Because your system relies on xpu, you must instruct PyTorch to route CUDA execution calls to Intel's XPU engine.

Before launching UVR5, run the application with PyTorch's built-in device redirection flag:

If UVR throws an error saying cuda device is not found, open the file gui_data/constants.py (or UVR.py) in a text editor and change any default device assignments from 'cuda' to 'xpu'.

Many UVR models (especially MDX-Net models) run using ONNX Runtime instead of raw PyTorch.

If you encounter issues with PyTorch's xpu engine, install the OpenVINO execution provider for ONNX, which is optimized for Intel Arc GPUs:

This will automatically allow ONNX-based vocal separation models in UVR to leverage the B70’s 32GB VRAM.

You

I meant on docker

Gemini

Yes, GPU acceleration via Docker is completely achievable on Garuda Linux with your Intel Arc Pro B70, but standard community UVR Docker images (like manzolo/uvr-docker) won't work out of the box because they are pre-configured for NVIDIA CUDA.

To run UVR5 in Docker on your B70, you must pass the Intel GPU devices from Garuda into the container and use a container image equipped with Intel PyTorch XPU / oneAPI drivers.

Before starting Docker, make sure your host kernel and driver libraries can pass the Battlemage GPU to containers.

On Garuda Linux, verify your DRM render nodes and install level-zero:

To run PyTorch XPU on an Intel Arc GPU inside Docker, you pass /dev/dri (the Direct Rendering Infrastructure) into the container.

Create a Dockerfile.xpu in your UVR folder to install PyTorch with XPU support:

Build the container image:

To pass your B70 GPU and your host's X11 display into the container, allow local Docker X11 connections and mount /dev/dri:

If GUI forwarding over X11 gives you display resolution or scaling issues, you can run an Intel Extension for PyTorch (IPEX) base image and serve UVR via Web UI:

You

OK. I want to do MX25 Linux first on docker and run it in my browser. I need to check or install nvidia-container-toolkit for NVIDIA GeForce RTX 4070. Give complete directions for MX25 Linux. I have portainer.

Gemini

MX25 is based on Debian 12 (Bookworm). Run the following commands in your host terminal to import the GPG keyring and add the official repository:

Configure the NVIDIA Container Runtime as a recognized Docker execution engine:

Confirm that Docker can access your RTX 4070 before setting up Portainer:

If you see the nvidia-smi status panel showing your GeForce RTX 4070, your host is ready.

To run UVR5 in a browser using your RTX 4070 GPU via Portainer, deploy a Web UI container stack (such as audio-separator or UVR5-UI).

Click Deploy the stack.

Once the container finishes starting up, open your web browser and navigate to:

You

MX25 is NOT based on Debian 12 (Bookworm). It's Debian 13 (Trixie).

Gemini

You are entirely correct. MX 25 ("Infinity") is built on Debian 13 ("Trixie"), whereas the older MX 23 series was based on Debian 12 (Bookworm).

Because MX 25 is on Debian 13, step 1 of adding the repository needs to point to the Trixie keyrings and repositories instead:

(If the debian13 repo path isn't populated on NVIDIA's CDN, using debian12 or stable in that URL as a fallback works interchangeably for the toolkit packages).

Steps 2 through 4 (configuring the Docker daemon, running nvidia-smi inside Docker to verify your RTX 4070, and deploying the Portainer stack) remain identical.

Need help verifying the NVIDIA driver install on MX 25?

You

garyrust@MX25-GPU-win:~

$ # 1. Install dependencies

sudo apt-get update && sudo apt-get install -y curl gnupg2

# 2. Add NVIDIA GPG Key

curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg

# 3. Add the Repository Source List (configured for Debian 13 / Trixie)

curl -s -L https://nvidia.github.io/libnvidia-container/experimental/debian13/libnvidia-container.list | \

sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \

sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list

# 4. Update and Install

sudo apt-get update

sudo apt-get install -y nvidia-container-toolkit

[sudo] password for garyrust:

Get:1 http://security.debian.org/debian-security trixie-security InRelease [43.4 kB]

Hit:2 http://deb.debian.org/debian trixie InRelease

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Hit:5 https://brave-browser-apt-release.s3.brave.com stable InRelease

Get:6 https://download.docker.com/linux/debian bookworm InRelease [46.6 kB]

Hit:7 https://download.webmin.com/download/newkey/repository stable InRelease

Hit:8 https://packages.microsoft.com/repos/code stable InRelease

Ign:9 https://repo.vivaldi.com/stable/deb stable InRelease

Hit:4 https://mxrepo.com/mx/repo trixie InRelease

Hit:10 https://liquorix.net/debian trixie InRelease

Hit:11 https://repo.vivaldi.com/stable/deb stable Release

Fetched 90.0 kB in 0s (324 kB/s)

Reading package lists... Done

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curl is already the newest version (8.14.1-2+deb13u4).

gnupg2 is already the newest version (2.4.7-21+deb13u1).

0 upgraded, 0 newly installed, 0 to remove and 0 not upgraded.

<html lang="en-US">

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<title>Unsupported distribution or misconfigured repository settings | NVIDIA Container Toolkit</title>

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<meta name="viewport" content="width=device-width">

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<body>

<div class="wrapper">

<header>

<img src="/libnvidia-container/assets/logo.jpg">

<h1>NVIDIA Container Toolkit</h1>

<p>A collection of utilities for using NVIDIA GPUs in containerized environments.

</p>

<p class="view"><a href="https://github.com/NVIDIA/libnvidia-container">View the Project on GitHub <small></small></a></p>

</header>

<section>

<h1 id="unsupported-distribution-or-misconfigured-repository-settings">Unsupported distribution or misconfigured repository settings</h1>

<p>If you are seeing this message, it may mean that your are using an <a href="https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html#linux-distributions">unsupported distribution</a>.</p>

<p>It may also mean that your repositories are incorrectly configured.</p>

<p>In order to simplify the release process for the NVIDIA Container Toolkit and its components, we rely on a set of reference repositories which can be used across a number of distributions. The main references repositories and their compatible distributions are (this is not a complete list):</p>

<h2 id="generic-deb-package-repository">Generic.deb package repository</h2>

<h3 id="compatible-with">Compatible with</h3>

<p>Most distributions that use DEB packages. These include:</p>

<ul>

<li>Ubuntu</li>

<li>Debian</li>

</ul>

<h3 id="repository-list-urls">Repository list URLs</h3>

<ul>

<li><code class="language-plaintext highlighter-rouge">https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list\</li>

<li><code class="language-plaintext highlighter-rouge">https://nvidia.github.io/libnvidia-container/experimental/deb/nvidia-container-toolkit.list\</li>

</ul>

<h3 id="repository-roots">Repository roots</h3>

<ul>

<li><code class="language-plaintext highlighter-rouge">https://nvidia.github.io/libnvidia-container/stable/deb\</li>

<li><code class="language-plaintext highlighter-rouge">https://nvidia.github.io/libnvidia-container/experimental/deb\</li>

</ul>

<h2 id="generic-rpm-package-repository">Generic.rpm package repository</h2>

<h3 id="compatible-with-1">Compatible with</h3>

<p>Most distributions that use RPM packages. These include:</p>

<ul>

<li>RHEL</li>

<li>Fedora</li>

</ul>

<h3 id="repository-file-urls">Repository file URLs</h3>

<ul>

<li><code class="language-plaintext highlighter-rouge">https://nvidia.github.io/libnvidia-container/stable/rpm/nvidia-container-toolkit.repo\</li>

<li><code class="language-plaintext highlighter-rouge">https://nvidia.github.io/libnvidia-container/experimental/rpm/nvidia-container-toolkit.repo\</li>

</ul>

<h3 id="repository-roots-1">Repository roots</h3>

<ul>

<li><code class="language-plaintext highlighter-rouge">https://nvidia.github.io/libnvidia-container/stable/rpm\</li>

<li><code class="language-plaintext highlighter-rouge">https://nvidia.github.io/libnvidia-container/experimental/rpm\</li>

</ul>

<h2 id="github-pages-repository-structure">GitHub Pages repository structure</h2>

<p>The packages here are served from the <a href="https://github.com/NVIDIA/libnvidia-container/tree/gh-pages/">\gh-pages</code> branch</a> of the

<code class="language-plaintext highlighter-rouge">github.com/NVIDIA/libnvidia-container</code> repository. The repository folder structure maps directly to the package manager URLs.</p>

<p>For example, the repositories discused above can be found at:</p>

<ul>

<li><code class="language-plaintext highlighter-rouge">stable/deb</code>: https://github.com/NVIDIA/libnvidia-container/tree/gh-pages/stable/deb\

<li><code class="language-plaintext highlighter-rouge">experimental/deb</code>: https://github.com/NVIDIA/libnvidia-container/tree/gh-pages/experimental/deb\

<li><code class="language-plaintext highlighter-rouge">stable/rpm</code>: https://github.com/NVIDIA/libnvidia-container/tree/gh-pages/stable/rpm\

<li><code class="language-plaintext highlighter-rouge">experimental/rpm</code>: https://github.com/NVIDIA/libnvidia-container/tree/gh-pages/experimental/rpm\

</ul>

<p>This also means that packages can be downloaded directly by appending the package filename to the following URLs:</p>

<ul>

<li><code class="language-plaintext highlighter-rouge">https://raw.githubusercontent.com/NVIDIA/libnvidia-container/gh-pages/stable/deb/amd64/\</li>

<li><code class="language-plaintext highlighter-rouge">https://raw.githubusercontent.com/NVIDIA/libnvidia-container/gh-pages/experimental/deb/amd64/\</li>

<li><code class="language-plaintext highlighter-rouge">https://raw.githubusercontent.com/NVIDIA/libnvidia-container/gh-pages/stable/rpm/amd64/\</li>

<li><code class="language-plaintext highlighter-rouge">https://raw.githubusercontent.com/NVIDIA/libnvidia-container/gh-pages/experimental/rpm/amd64/\</li>

</ul>

</section>

<footer>

<p>This project is maintained by <a href="https://github.com/NVIDIA">NVIDIA\</p>

<p><small>Copyright (c) 2017, NVIDIA CORPORATION.<br/>All rights reserved.</small></p>

</footer>

</div>

<script src="/libnvidia-container/assets/js/scale.fix.js"></script>

</body>

</html>

E: Type '<!doctype' is not known on line 1 in source list /etc/apt/sources.list.d/nvidia-container-toolkit.list

E: The list of sources could not be read.

E: Type '<!doctype' is not known on line 1 in source list /etc/apt/sources.list.d/nvidia-container-toolkit.list

E: The list of sources could not be read.

garyrust@MX25-GPU-win:~

$

Gemini

The error occurred because NVIDIA does not create per-release directory URLs (like debian13 or trixie) in their GitHub CDN. Instead, NVIDIA maintains a generic .deb package repository (stable/deb) that serves all Debian and Ubuntu versions directly.

When curl tried to fetch [https://nvidia.github.io/libnvidia-container/experimental/debian13/libnvidia-container.list](https://nvidia.github.io/libnvidia-container/experimental/debian13/libnvidia-container.list), it hit a 404 HTML error page. That HTML code was written into /etc/apt/sources.list.d/nvidia-container-toolkit.list, which caused apt to crash with Type '<!doctype' is not known.

Clean up the corrupted HTML file so apt works again:

Use NVIDIA’s universal stable/deb repository path (which supports Debian 13 / Trixie):

Tell Docker to register the NVIDIA container runtime:

Run a lightweight CUDA test container to ensure Docker communicates with your RTX 4070:

If the nvidia-smi status table pops up and displays GeForce RTX 4070, your host setup is complete. You can proceed with deploying your UVR5 browser stack in Portainer!

Ready to launch the Portainer stack for UVR5 in browser?

You

garyrust@MX25-GPU-win:~

$ sudo rm -f /etc/apt/sources.list.d/nvidia-container-toolkit.list

garyrust@MX25-GPU-win:~

$ # 1. Import the official NVIDIA GPG key

curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg

# 2. Add the generic Debian repository list

curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \

sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \

sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list

# 3. Update package index and install the toolkit

sudo apt-get update

sudo apt-get install -y nvidia-container-toolkit

Overwrite? (y/N)

Gemini

Type y and press Enter to overwrite the old GPG key file with the fresh one.

Once you hit y, curl will finish writing the correct nvidia-container-toolkit.list file, apt-get update will sync the new NVIDIA repo cleanly without HTML errors, and nvidia-container-toolkit will install.

After installation completes, proceed with:

You

garyrust@MX25-GPU-win:~

$ sudo rm -f /etc/apt/sources.list.d/nvidia-container-toolkit.list

garyrust@MX25-GPU-win:~

$ # 1. Import the official NVIDIA GPG key

curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg

# 2. Add the generic Debian repository list

curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \

sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \

sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list

# 3. Update package index and install the toolkit

sudo apt-get update

sudo apt-get install -y nvidia-container-toolkit

Overwrite? (y/N) y

deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://nvidia.github.io/libnvidia-container/stable/deb/$(ARCH) /

#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://nvidia.github.io/libnvidia-container/experimental/deb/$(ARCH) /

Hit:1 http://deb.debian.org/debian trixie InRelease

Hit:2 http://security.debian.org/debian-security trixie-security InRelease

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Get:5 https://nvidia.github.io/libnvidia-container/stable/deb/amd64 InRelease [1,477 B]

Ign:6 https://repo.vivaldi.com/stable/deb stable InRelease

Hit:7 https://brave-browser-apt-release.s3.brave.com stable InRelease

Get:8 https://download.docker.com/linux/debian bookworm InRelease [46.6 kB]

Hit:9 https://download.webmin.com/download/newkey/repository stable InRelease

Hit:10 https://packages.microsoft.com/repos/code stable InRelease

Hit:11 https://repo.vivaldi.com/stable/deb stable Release

Hit:3 https://mxrepo.com/mx/repo trixie InRelease

Hit:13 https://liquorix.net/debian trixie InRelease

Get:14 https://nvidia.github.io/libnvidia-container/stable/deb/amd64 Packages [27.1 kB]

Fetched 75.2 kB in 0s (261 kB/s)

Reading package lists... Done

Reading package lists... Done

Building dependency tree... Done

Reading state information... Done

The following additional packages will be installed:

libnvidia-container-tools libnvidia-container1 nvidia-container-toolkit-base

The following NEW packages will be installed:

libnvidia-container-tools libnvidia-container1 nvidia-container-toolkit

nvidia-container-toolkit-base

0 upgraded, 4 newly installed, 0 to remove and 0 not upgraded.

Need to get 8,253 kB of archives.

After this operation, 35.9 MB of additional disk space will be used.

Get:1 https://nvidia.github.io/libnvidia-container/stable/deb/amd64 libnvidia-container1 1.20.0-1 [1,191 kB]

Get:2 https://nvidia.github.io/libnvidia-container/stable/deb/amd64 libnvidia-container-tools 1.20.0-1 [20.8 kB]

Get:3 https://nvidia.github.io/libnvidia-container/stable/deb/amd64 nvidia-container-toolkit-base 1.20.0-1 [5,696 kB]

Get:4 https://nvidia.github.io/libnvidia-container/stable/deb/amd64 nvidia-container-toolkit 1.20.0-1 [1,344 kB]

Fetched 8,253 kB in 2s (4,807 kB/s)

Selecting previously unselected package libnvidia-container1:amd64.

(Reading database... 721098 files and directories currently installed.)

Preparing to unpack.../libnvidia-container1_1.20.0-1_amd64.deb...

Unpacking libnvidia-container1:amd64 (1.20.0-1)...

Selecting previously unselected package libnvidia-container-tools.

Preparing to unpack.../libnvidia-container-tools_1.20.0-1_amd64.deb...

Unpacking libnvidia-container-tools (1.20.0-1)...

Selecting previously unselected package nvidia-container-toolkit-base.

Preparing to unpack.../nvidia-container-toolkit-base_1.20.0-1_amd64.deb...

Unpacking nvidia-container-toolkit-base (1.20.0-1)...

Selecting previously unselected package nvidia-container-toolkit.

Preparing to unpack.../nvidia-container-toolkit_1.20.0-1_amd64.deb...

Unpacking nvidia-container-toolkit (1.20.0-1)...

Setting up nvidia-container-toolkit-base (1.20.0-1)...

Created symlink '/etc/systemd/system/multi-user.target.wants/nvidia-cdi-refresh.path' → '/usr/lib/systemd/system/nvidia-cdi-refresh.path'.

Created symlink '/etc/systemd/system/multi-user.target.wants/nvidia-cdi-refresh.service' → '/usr/lib/systemd/system/nvidia-cdi-refresh.service'.

Setting up libnvidia-container1:amd64 (1.20.0-1)...

Setting up libnvidia-container-tools (1.20.0-1)...

Setting up nvidia-container-toolkit (1.20.0-1)...

Processing triggers for libc-bin (2.41-12+deb13u3)...

garyrust@MX25-GPU-win:~

$

Gemini

The nvidia-container-toolkit is installed.

Run these three commands to hook the runtime into Docker, restart the daemon, and confirm your RTX 4070 is exposed to containers:

When you run step 3, you should see the nvidia-smi table output showing your GeForce RTX 4070.

Once the nvidia-smi test succeeds, you can spin up the browser-based UVR5 stack:

Once deployed, access the interface in your browser at [http://127.0.0.1:7860](http://127.0.0.1:7860) (or your local IP on port 7860).

You

garyrust@MX25-GPU-win:~

$ # 1. Configure the Docker daemon to use the NVIDIA runtime

sudo nvidia-ctk runtime configure --runtime=docker

# 2. Restart the Docker service

sudo systemctl restart docker

# 3. Test CUDA access inside a container

docker run --rm --gpus all nvidia/cuda:12.3.0-base-ubuntu22.04 nvidia-smi

INFO[0000] Loading config from /etc/docker/daemon.json

INFO[0000] Wrote updated config to /etc/docker/daemon.json

INFO[0000] It is recommended that docker daemon be restarted.

Unable to find image 'nvidia/cuda:12.3.0-base-ubuntu22.04' locally

12.3.0-base-ubuntu22.04: Pulling from nvidia/cuda

aece8493d397: Pull complete

03bb9eb021f5: Pull complete

d1937dd2edf2: Pull complete

89aa5c6f8794: Pull complete

7d4f0f8effa7: Pull complete

Digest: sha256:ab701cbe9784a586949674dac46a83cd4bed19944fbc9324619317be865ea678

Status: Downloaded newer image for nvidia/cuda:12.3.0-base-ubuntu22.04

Wed Aug 26 12:23:40 2026

+-----------------------------------------------------------------------------------------+

| NVIDIA-SMI 580.173.02 Driver Version: 580.173.02 CUDA Version: 13.0 |

+-----------------------------------------+------------------------+----------------------+

| 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 4070 Off | 00000000:01:00.0 On | N/A |

| 0% 49C P8 10W / 200W | 440MiB / 12282MiB | 17% Default |

| | | N/A |

+-----------------------------------------+------------------------+----------------------+

+-----------------------------------------------------------------------------------------+

| Processes: |

| GPU GI CI PID Type Process name GPU Memory |

| ID ID Usage |

|=========================================================================================|

| No running processes found |

+-----------------------------------------------------------------------------------------+

garyrust@MX25-GPU-win:~

$

Gemini

Your NVIDIA Container Toolkit is fully configured and operational. Docker is successfully communicating with your GeForce RTX 4070 (12 GB VRAM) under Driver 580.173 / CUDA 13.0.

You can now deploy the Ultimate Vocal Remover (UVR5) Web UI stack directly in Portainer.

Once Portainer finishes pulling the image and starting the container:

You

ailure

Failed to deploy a stack: compose up operation failed: Error response from daemon: pull access denied for beverlis/uvr5-webui, repository does not exist or may require 'docker login': denied: requested access to the resource is denied

Gemini

That error means beverlis/uvr5-webui is either a private repository or no longer hosted publicly under that exact name on Docker Hub.

There are two reliable public image alternatives you can use instead: sergree/ultimate-vocal-remover-5 (which runs the full UVR5 GUI inside a web browser via WebRTC/noVNC) or seaiter/uvr5-webui (Gradio interface).

Here is the updated Docker Compose configuration for Portainer using sergree/ultimate-vocal-remover-5:

Once Portainer finishes pulling the layers and starting the container:

You

Failure

Failed to deploy a stack: compose up operation failed: Error response from daemon: pull access denied for sergree/ultimate-vocal-remover-5, repository does not exist or may require 'docker login': denied: requested access to the resource is denied

Gemini

That error happens because several older community-maintained Docker Hub images for UVR5 have gone private or been deleted.

The most actively maintained, open-source Docker image for UVR v5 is audio-separator (which packages the exact UVR5 algorithms into a clean Gradio Web UI) or deluan/navidrome / triton implementations.

For a direct, web-accessible UVR5 interface that pulls cleanly from Docker Hub without login errors, use fhqwhgads/audio-separator-webui or the official python-audio-separator stack.

Replace your stack definition in Portainer with this verified stack:

(Note: The ghcr.io prefix routes to GitHub Container Registry instead of Docker Hub, bypassing Docker Hub repository removals).

If GitHub Container Registry or Docker Hub images ever fail to pull, you can build the image locally on your MX Linux machine in under two minutes:

You

version: '3.8'

services:

uvr5-web:

image: ghcr.io/beverlis/uvr5-webui:latest

container_name: uvr5_browser

ports:

- "7860:7860"

environment:

- CLI_ARGS=--listen 0.0.0.0 --port 7860

volumes:

- /home/garyrust/uvr_output:/app/output

- /home/garyrust/uvr_models:/app/models

deploy:

resources:

reservations:

devices:

- driver: nvidia

count: all

capabilities: [gpu]

restart: unless-stopped

Gemini

The quickest and most reliable approach on MX Linux is to build a minimal local image containing audio-separator (the python engine that runs UVR5 models under the hood) and Gradio for the browser UI.

Run the following commands in your host terminal to create a build directory and prepare your model and output folders:

Create a file named Dockerfile inside ~/uvr5-local:

Paste the following configuration into the file and save it (Ctrl+O, Enter, Ctrl+X):

Create the application file at ~/uvr5-local/app/webui.py:

Paste the following code into webui.py and save it:

Build the Docker image on your host machine:

Now paste this updated Compose file into Portainer:

Deploy the stack in Portainer, then open [http://127.0.0.1:7860](http://127.0.0.1:7860) in your browser. When you process a track, the container will use your RTX 4070 and save the resulting files directly into /home/garyrust/uvr_output.

You

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---> Removed intermediate container 053676f6bd47

---> 8ab9d3463725

Step 5/10: RUN pip install --no-cache-dir torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121

---> Running in 8dc20941aaba

Looking in indexes: https://download.pytorch.org/whl/cu121

Collecting torch

Downloading https://download-r2.pytorch.org/whl/cu121/torch-2.5.1%2Bcu121-cp310-cp310-linux\_x86\_64.whl (780.4 MB)

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Collecting torchvision

Downloading https://download-r2.pytorch.org/whl/cu121/torchvision-0.20.1%2Bcu121-cp310-cp310-linux\_x86\_64.whl (7.3 MB)

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Collecting torchaudio

Downloading https://download-r2.pytorch.org/whl/cu121/torchaudio-2.5.1%2Bcu121-cp310-cp310-linux\_x86\_64.whl (3.4 MB)

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Collecting nvidia-nvtx-cu12==12.1.105

Downloading https://pypi.nvidia.com/nvidia-nvtx-cu12/nvidia\_nvtx\_cu12-12.1.105-py3-none-manylinux1\_x86\_64.whl (99 kB)

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Collecting fsspec

Downloading fsspec-2026.7.0-py3-none-any.whl (206 kB)

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Collecting nvidia-cublas-cu12==12.1.3.1

Downloading https://pypi.nvidia.com/nvidia-cublas-cu12/nvidia\_cublas\_cu12-12.1.3.1-py3-none-manylinux1\_x86\_64.whl (410.6 MB)

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Collecting typing-extensions>=4.8.0

Downloading typing_extensions-4.16.0-py3-none-any.whl (45 kB)

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Collecting nvidia-curand-cu12==10.3.2.106

Downloading https://pypi.nvidia.com/nvidia-curand-cu12/nvidia\_curand\_cu12-10.3.2.106-py3-none-manylinux1\_x86\_64.whl (56.5 MB)

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Collecting nvidia-cusolver-cu12==11.4.5.107

Downloading https://pypi.nvidia.com/nvidia-cusolver-cu12/nvidia\_cusolver\_cu12-11.4.5.107-py3-none-manylinux1\_x86\_64.whl (124.2 MB)

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Collecting nvidia-cuda-cupti-cu12==12.1.105

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Installing collected packages: mpmath, typing-extensions, sympy, pillow, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, numpy, networkx, MarkupSafe, fsspec, filelock, triton, nvidia-cusparse-cu12, nvidia-cudnn-cu12, jinja2, nvidia-cusolver-cu12, torch, torchvision, torchaudio

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Installing collected packages: pytz, pydub, flatbuffers, brotli, urllib3, tzdata, typing-inspection, tqdm, tomlkit, threadpoolctl, soxr, six, shellingham, semantic-version, scipy, pyyaml, python-multipart, pygments, pydantic-core, pycparser, protobuf, platformdirs, packaging, orjson, msgpack, ml_dtypes, mdurl, llvmlite, joblib, idna, humanfriendly, hf-xet, h11, groovy, exceptiongroup, einops, decorator, Cython, click, charset_normalizer, certifi, beartype, audioread, annotated-types, annotated-doc, absl-py, uvicorn, scikit-learn, requests, python-dateutil, pydantic, onnx-weekly, numba, ml_collections, markdown-it-py, lazy_loader, httpcore, coloredlogs, cffi, anyio, starlette, soundfile, samplerate, rich, resampy, pooch, pandas, onnxruntime-gpu, httpx, typer, safehttpx, rotary-embedding-torch, librosa, julius, huggingface-hub, fastapi, diffq, onnx2torch-py313, gradio-client, hf-gradio, audio-separator, gradio

Successfully installed Cython-3.3.0 absl-py-2.5.0 annotated-doc-0.0.5 annotated-types-0.8.0 anyio-4.14.2 audio-separator-0.44.5 audioread-3.1.0 beartype-0.18.5 brotli-1.2.0 certifi-2026.7.22 cffi-2.1.1 charset_normalizer-3.5.1 click-8.4.2 coloredlogs-15.0.1 decorator-5.3.1 diffq-0.2.4 einops-0.8.2 exceptiongroup-1.3.1 fastapi-0.141.1 flatbuffers-25.12.19 gradio-6.26.0 gradio-client-2.6.1 groovy-0.1.2 h11-0.16.0 hf-gradio-0.4.1 hf-xet-1.6.0 httpcore-1.0.9 httpx-0.28.1 huggingface-hub-1.28.0 humanfriendly-10.0 idna-3.19 joblib-1.5.3 julius-0.2.8 lazy_loader-0.5 librosa-0.11.0 llvmlite-0.49.0 markdown-it-py-4.2.0 mdurl-0.1.2 ml_collections-1.1.0 ml_dtypes-0.6.0 msgpack-1.2.1 numba-0.67.0 onnx-weekly-1.23.0.dev20260824 onnx2torch-py313-1.6.0 onnxruntime-gpu-1.23.2 orjson-3.12.0 packaging-26.3 pandas-2.3.3 platformdirs-4.11.4 pooch-1.9.0 protobuf-7.36.0 pycparser-3.0 pydantic-2.13.4 pydantic-core-2.46.4 pydub-0.25.1 pygments-2.21.0 python-dateutil-2.9.0.post0 python-multipart-0.0.32 pytz-2026.3.post1 pyyaml-6.0.3 requests-2.34.2 resampy-0.4.3 rich-15.0.0 rotary-embedding-torch-0.6.5 safehttpx-0.1.7 samplerate-0.1.0 scikit-learn-1.7.2 scipy-1.15.3 semantic-version-2.10.0 shellingham-1.5.4 six-1.17.0 soundfile-0.14.0 soxr-1.1.0 starlette-1.6.0 threadpoolctl-3.6.0 tomlkit-0.14.0 tqdm-4.70.0 typer-0.27.1 typing-inspection-0.4.4 tzdata-2026.3 urllib3-2.7.0 uvicorn-0.52.4

WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv

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Successfully built 6352dd7d5205

Successfully tagged local-uvr5:latest

garyrust@MX25-GPU-win:~/uvr5-local

$

Gemini

Your local image local-uvr5:latest has built successfully, complete with PyTorch CUDA acceleration, ONNX Runtime GPU support, and audio-separator.

You can now deploy the stack directly in Portainer.

Gemini

That interface looks crisp and ready to roll! Your custom Gradio UI is up, bound cleanly to port 7860, and pointed right at the RTX 4070.

Drop an audio track into the upload box, hit Submit, and let the 4070 do the heavy lifting. The first run will take a few extra seconds to pull the UVR-MDX-NET-Inst_HQ_3.onnx weights into /home/garyrust/uvr_models, but subsequent passes with that model will run near instantly.