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RVC-WebUI-Docker

Newbie-Tutorials

For newbies there is a tutorial available for this repo! Check it out!
And now for the technical bits:

Purpose

This repo serves the purpose to make the build-process public, that is beeing used to build my RVC-WebUI-Containers on runpod. (See DockerHub)
I would kindly ask you to use the exported versions on runpod.io (just use their Explore-Feature and search for RVC. The ones with cherrymint in them are mine). With this you support me. :)
I do not add a licence to this project, since I am only taking the repos, pull them, download the models and then upload them to Dockerhub.
So what you do with them, is up to you, but I advise on checking if your local laws on copy- and personality-rights.

Main-Repos:

Credits and Cudos go to those repos and their respective contributers.

External Repos:

Credits and Cudos go to those repos and their respective contributers.

How to use

General

The build-process is mainly meant to be working with runpod.io (<--- I've put my referal-code in there, just so you know. Would be nice if you use it. :) ), but works locally aswell.
I can test this locally only on my system, which is Ubuntu 22.04 with a GTX 1070, Driver 535.171.04 and CUDA 12.2. Not top notch-tho, so your milage may vary!

Runpod-Config

You should be able to use the images build by this repo without any other configurations. If you don't want to build them yourself, just use mine: DockerHub
Just make sure you have your public-key set in your runpod-profile.
For training, I would recommend using 3x RTX A5000 or 3x RTX A4000 with 24 CPU-Processes and a Batchsize of 12. That gives you 1 epoch at about every 7 seconds, so 1000 epochs take about 2 hours, which will cost you 2-3$. Using newer or more cards did not do anything beneficial in my tests. (Written at 2024-06-10)

What does it do?

The created docker-images setup everything you need and even add Tensorboard. How to use Tensorboard can be read here:

Local usage

If you wanna use them locally, just open 0.0.0.0:7875 for the WebUI, 0.0.0.0:7865 for tensorboard and 0.0.0.0:8080 for filebrowser. Build the images by using this: docker build -t mydockerregistry/myusername/mycontainername:mytagname --build-arg REPO="<MYREPO>" .
MYREPO here supports this:

To run the container use: docker run -d -p 7875:7875 -p 7865:7865 -p 7895:7895 mydockerregistry/myusername/mycontainername:mytagname

If you wanna use ssh instead of the Filebrowser

  • add -p 22:22 to your docker run-command
  • add -e PUBLIC_KEY="<MY PUBLIC KEY>" to your docker run-command and connect through it.

Possible Questions

Why no volume?

Since I use those containers for training or one-time-interference, I don't need a volume most of the time. If you want to persist your data, just add some bind-mounts to the /app-subfolders you need. Infos on bind-mounts

Why no docker-compose.yml?

Again - I don't need it. You can do this yourself - I believe in you. :)

Why is there no info on the mangio-tag in your DockerHub-Repo?

The mangio-tag was an old version I had locally lying around on my homelab. I do not update that tag. But funny enough - the base-dockerfile is the one I used for that tag. :)

Changelog:

2024-06-13

2024-06-12

  • Eased up build-process (You can now build via a tag-name)
  • Added image-tag for Applio

2024-06-10:

  • Removed the pip-installation for tensorboard, since all Repos bring them themselves
  • Removed the need for an ssh-key to make this more "dummy"-approachable
  • Added a filebrowser
  • Added a Tutorial for dummies
  • Made Templates on runpod.io