Cardinal
HOWTO: Use RStudio on OnDemand
This page outlines how to use the RStudio Server interactive app on OnDemand.
Launching RStudio Server App
Log on to https://ondemand.osc.edu/ with your OSC credentials. Choose RStudio Server under the InteractiveApps option.
Ollama 0.32.0 available on Ascend and Cardinal
Ollama 0.32.0 is now available on Ascend and Cardinal clusters. To use, type: module load ollama/0.32.0 Refer to the Ollama software page for more usage details.
HOWTO: Use uv for Python at OSC
uv is a fast Python package and virtual environment manager developed by Astral. It provides functionality provided by both venv and pip, but is significantly faster at dependency resolution. Some users who do not need Conda packages or cross-language support in their project may prefer to use uv for its speed. Because uv is still under active development and its behavior may change between frequent releases, OSC does not currently provide it as a centrally managed software package.
HEASoft 6.36 now available on Cardinal
module load heasoft/6.36HEASoft
A Unified Release of the FTOOLS and XANADU Software Packages. XANADU: High-level, multi-mission tasks for X-ray astronomical spectral, timing, and imaging data analysis. FTOOLS: General and mission-specific tools to manipulate FITS files. FITSIO: Core library responsible for reading and writing FITS files. fv: General FITS file browser/editor/plotter with a graphical user interface. XSTAR: Tool for calculating the physical conditions and emission spectra of photoionized gases
Hosted Services
OSC provides hosted instances of specialized applications that require dedicated server infrastructure and authentication methods. These services run continuously on OSC systems, allowing research groups to access applications through a web browser.
vLLM 0.23.0 now available on Cardinal and Ascend
vLLM 0.23.0 is now available on Cardinal and Ascend. To use, run:
module load vllm/0.23.0
For detailed usage at OSC, see: vLLM software page
Topaz
Topaz is a software package for particle detection and image denoising in cryo-electron microscopy (cryo-EM) that uses convolutional neural networks.