System Description

Resource Limits per User

Each user has 100GB of persistent file storage. The following table outlines the resource allocations of each server option.

Server Option Resource Descriptions

CPU or GPU

Resource Option

Description

CPU

(default)

In each CPU image option you have access to the following:

  • Up to 4 CPUs when system demand allows (1 CPU guaranteed).

  • Up to 16GB of RAM when system demand allows (2GB guaranteed).

GPU

A100 GPU, 2CPU/8GB

You have access to:

  • Part of one A100 GPU; GPUs are shared with other users.

  • Up to 2 CPUs when system demand allows (1 CPU guaranteed).

  • Up to 8GB of RAM when system demand allows (2GB guaranteed).

GPU

V100 GPU, 3CPU/7GB

You have access to:

  • Part of one V100 GPU; GPUs are shared with other users.

  • Up to 3 CPUs when system demand allows (1 CPU guaranteed).

  • Up to 7GB of RAM when system demand allows (2GB guaranteed).

Notebook Duration Limits

Notebooks are temporary and intended to last a maximum of 24 hours. Notebooks older than 24 hours may be ended without warning.

Notebooks run live in a web browser, if you close your web browser or lose your internet connection for more than 1 hour, your notebook and running processes may end. It is expected that running processes will continue if your connection is interrupted for less than 1 hour.

User Directory

Unlike notebooks, files in your user directory are persistent.

ICRN does not backup data, you are responsible for backing up your files.

Server Options

Python Server

CPU option. The Python server option has Python and Conda installed. Python is a general-purpose programming language. Conda is an open source package management system.

R Server

CPU option. The R server option has popular R packages installed. R is a programming language and software environment for statistical computing and graphics.

PyTorch Server

GPU option. The PyTorch server option has PyTorch installed. NVIDIA provides a brief overview of GPU computing at the beginning of the Using GPUs with Python webinar hosted by NCSA. You can view past recordings of this webinar, and others, in the HPC Moodle Past NCSA Training Events Repository.