pmohanj / selkies-gstreamer

Open-Source Low-Latency Linux WebRTC HTML5 Remote Desktop / GStreamer WebRTC Components of Selkies

Moonlight, Google Stadia, or GeForce NOW in noVNC form factor for Linux X11, in any HTML5 web interface you wish to embed inside, with at least 60 frames per second on Full HD resolution.

We are in need of maintainers and community contributors. Please consider stepping up, as we can never have too much help!

Selkies-GStreamer is an open-source low-latency high-performance Linux-native GPU/CPU-accelerated WebRTC HTML5 remote desktop streaming platform, for self-hosting, containers, Kubernetes, or Cloud/HPC platforms, started out first by Google engineers, then expanded by academic researchers.

Selkies-GStreamer is designed for researchers including people in the graphical AI/robotics/autonomous driving/drug discovery field, SLURM supercomputer/HPC system administrators, Jupyter/Kubernetes/Docker®/Coder infrastructure administrators, and Linux cloud gaming enthusiasts.

While designed for clustered or unprivileged containerized environments, Selkies-GStreamer can also be deployed in desktop computers, and any performance issue that would be problematic in cloud gaming platforms is also considered a bug.

Read the Documentation to get started.

Citations in Academic Publications

NOTE: This section is also applicable for developers applying, embedding, forking, deriving, or taking influence from this project.

Citations are the currency of scientific research. Citing in your publications is the main driver of keeping this project alive.

Remote desktop software projects have always been primarily proprietary. It is difficult to maintain such a project without consistent full-time compensation or financial upkeep.

Therefore, citations in academic publications are crucial for keeping this project under academic community governance, as well as to track and report the demographics of our users.

Therefore, we kindly, but strongly ask all software projects which are applying into, embedding in, forking from, deriving from, or taking influence from this project to retain the below sections in a clearly visible location of your project and/or documentation.

Users of this open-source software project should cite the following publications when publishing in academic form to keep this project and original upstream projects sustainable:

(Please note that this is currently a placeholder, an upcoming publication will be available after article review.)

Kim, S., Isla, D., Hejtmánek, L., et al., Selkies-GStreamer, (2024), GitHub repository, https://github.com/selkies-project/selkies-gstreamer

Maintainers of derivative open-source projects should also place this text in a clearly visible location of your project.

---

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.