https://github.com/ai-dock/linux-desktop
KDE Plasma desktop docker image for use in GPU cloud and local environments. Includes AI-Dock base for authentication and improved user experience.
https://github.com/ai-dock/linux-desktop
cloud desktop docker gpu kde kde-plasma runpod vast
Last synced: 6 months ago
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KDE Plasma desktop docker image for use in GPU cloud and local environments. Includes AI-Dock base for authentication and improved user experience.
- Host: GitHub
- URL: https://github.com/ai-dock/linux-desktop
- Owner: ai-dock
- License: other
- Created: 2024-01-05T18:26:58.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-08-08T17:31:45.000Z (9 months ago)
- Last Synced: 2024-08-09T18:28:08.900Z (9 months ago)
- Topics: cloud, desktop, docker, gpu, kde, kde-plasma, runpod, vast
- Language: Shell
- Homepage:
- Size: 140 KB
- Stars: 27
- Watchers: 2
- Forks: 3
- Open Issues: 2
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Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE.md
Awesome Lists containing this project
README
[](https://github.com/ai-dock/linux-desktop/actions/workflows/docker-build.yml)
# Linux Desktop
Run a hardware accelerated KDE desktop in a container. This image is heavily influenced by [Selkies Project](https://github.com/selkies-project) to provide an accelerated desktop environment for NVIDIA, AMD and Intel machines.
Please see this [important notice](#selkies-notice) from the Selkies development team.
## Documentation
All AI-Dock containers share a common base which is designed to make running on cloud services such as [vast.ai](https://link.ai-dock.org/vast.ai) as straightforward and user friendly as possible.
Common features and options are documented in the [base wiki](https://github.com/ai-dock/base-image/wiki) but any additional features unique to this image will be detailed below.
#### Version Tags
The `:latest` tag points to `:latest-cuda`
Tags follow these patterns:
##### _CUDA_
- `:cuda-[x.x.x]{-cudnn[x]}-[base|runtime|devel]-[ubuntu-version]`
- `:latest-cuda` → `:cuda-12.1.1-cudnn8-runtime-22.04`
##### _ROCm_
- `:rocm-[x.x.x]-[core|runtime|devel]-[ubuntu-version]`
- `:latest-rocm` → `:rocm-6.0-runtime-22.04`
ROCm builds are experimental. Please give feedback.
##### _CPU (iGPU)_
- `:cpu-[ubuntu-version]`
- `:latest-cpu` → `:cpu-22.04`
Browse [here](https://github.com/ai-dock/linux-desktop/pkgs/container/linux-desktop) for an image suitable for your target environment.
Supported Desktop Environments: `KDE Plasma`
Supported Platforms: `NVIDIA CUDA`, `AMD ROCm`, `CPU/iGPU`
## Pre-Configured Templates
**Vast.ai**
[linux-desktop:latest](https://link.ai-dock.org/template-vast-linux-desktop)
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## Selkies Notice
This project has been developed and is supported in part by the National Research Platform (NRP) and the Cognitive Hardware and Software Ecosystem Community Infrastructure (CHASE-CI) at the University of California, San Diego, by funding from the National Science Foundation (NSF), with awards #1730158, #1540112, #1541349, #1826967, #2138811, #2112167, #2100237, and #2120019, as well as additional funding from community partners, infrastructure utilization from the Open Science Grid Consortium, supported by the National Science Foundation (NSF) awards #1836650 and #2030508, and infrastructure utilization from the Chameleon testbed, supported by the National Science Foundation (NSF) awards #1419152, #1743354, and #2027170. This project has also been funded by the Seok-San Yonsei Medical Scientist Training Program (MSTP) Song Yong-Sang Scholarship, College of Medicine, Yonsei University, the MD-PhD/Medical Scientist Training Program (MSTP) through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea, and the Student Research Bursary of Song-dang Institute for Cancer Research, College of Medicine, Yonsei University.
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_The author ([@robballantyne](https://github.com/robballantyne)) may be compensated if you sign up to services linked in this document. Testing multiple variants of GPU images in many different environments is both costly and time-consuming; This helps to offset costs_