Run a hardware accelerated KDE desktop in a container. This image is heavily influenced by Selkies Project to provide an accelerated desktop environment for NVIDIA, AMD and Intel machines.
Please see this important notice from the Selkies development team.
All AI-Dock containers share a common base which is designed to make running on cloud services such as vast.ai as straightforward and user friendly as possible.
Common features and options are documented in the base wiki but any additional features unique to this image will be detailed below.
The :latest tag points to :latest-cuda
Tags follow these patterns:
-
:cuda-[x.x.x]{-cudnn[x]}-[base|runtime|devel]-[ubuntu-version] -
:latest-cuda→:cuda-12.1.1-cudnn8-runtime-22.04
-
: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-[ubuntu-version] -
:latest-cpu→:cpu-22.04
Browse here for an image suitable for your target environment.
Supported Desktop Environments: KDE Plasma
Supported Platforms: NVIDIA CUDA, AMD ROCm, CPU/iGPU
Vast.ai
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.
The author (@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