Oscar
HomeServicesDocumentation
  • Overview
  • Quickstart
  • Getting Started
  • System Hardware
  • Account Information
  • Short "How to" Videos
  • Quick Reference
    • Common Acronyms and Terms
    • Managing Modules
    • Common Linux Commands
  • Getting Help
    • ❓FAQ
  • Citing CCV
  • CCV Account Information
  • Student Accounts
  • Offboarding
  • Connecting to Oscar
    • SSH (Terminal)
      • SSH Key Login (Passwordless SSH)
        • Mac/Linux/Windows(PowerShell)
        • Windows(PuTTY)
      • SSH Configuration File
      • X-Forwarding
      • SSH Agent Forwarding
        • Mac/Linux
        • Windows (PuTTY)
      • Arbiter2
    • Open OnDemand
      • Using File Explorer on OOD
      • Web-based Terminal App
      • Interactive Apps on OOD
      • Using Python or Conda environments in the Jupyter App
      • Using RStudio
      • Desktop App (VNC)
    • SMB (Local Mount)
    • Remote IDE (VS Code)
      • From Non-compliant Networks (2-FA)
      • Setup virtual environment and debugger
  • Managing files
    • Oscar's Filesystem
    • Transferring Files to and from Oscar
    • Transferring Files between Oscar and Campus File Storage (Replicated and Non-Replicated)
    • Resolving quota issues
      • Understanding Disk Quotas
    • Inspecting Disk Usage (Ncdu)
    • Restoring Deleted Files
    • Best Practices for I/O
    • Version Control
  • Submitting jobs
    • Running Jobs
    • Slurm Partitions
    • Interactive Jobs
    • Batch Jobs
    • Managing Jobs
    • Job Arrays
    • MPI Jobs
    • Condo/Priority Jobs
    • Dependent Jobs
    • Associations & Quality of Service (QOS)
  • GPU Computing
    • GPUs on Oscar
      • Grace Hopper GH200 GPUs
      • H100 NVL Tensor Core GPUs
      • Ampere Architecture GPUs
    • Submitting GPU Jobs
    • Intro to CUDA
    • Compiling CUDA
    • Installing Frameworks (PyTorch, TensorFlow, Jax)
      • Installing JAX
      • Installing TensorFlow
    • Mixing MPI and CUDA
  • Large Memory Computing
    • Large Memory Nodes on Oscar
  • Software
    • Software on Oscar
    • Using Modules
    • Migration of MPI Apps to Slurm 22.05.7
    • Python on Oscar
    • Python in batch jobs
    • Installing Python Packages
    • Installing R Packages
    • Using CCMake
    • Intro to Parallel Programming
    • Anaconda
    • Conda and Mamba
    • DMTCP
    • Screen
    • VASP
    • Gaussian
    • IDL
    • MPI4PY
  • Jupyter Notebooks/Labs
    • Jupyter Notebooks on Oscar
    • Jupyter Labs on Oscar
    • Tunneling into Jupyter with Windows
  • Debugging
    • Arm Forge
      • Configuring Remote Launch
      • Setting Job Submission Settings
  • MATLAB
    • Matlab GUI
    • Matlab Batch Jobs
    • Improving Performance and Memory Management
    • Parallel Matlab
  • Visualization 🕶
    • ParaView Remote Rendering
  • Singularity Containers
    • Intro to Apptainer
    • Building Images
    • Running Images
    • Accessing Oscar Filesystem
      • Example Container (TensorFlow)
    • Singularity Tips and Tricks
  • Installing Software Packages Locally
    • Installing your own version of Quantum Espresso
    • Installing your own version of Qmcpack
  • dbGaP
    • dbGaP Architecture
    • dbGaP Data Transfers
    • dbGaP Job Submission
  • RHEL9 Migration
    • RHEL-9 Migration
    • LMOD - New Module System
    • Module Changes
    • Testing Jupyter Notebooks on RHEL9 mini-cluster
  • Large Language Models
    • Ollama
Powered by GitBook
On this page

Was this helpful?

Export as PDF
  1. Submitting jobs

Interactive Jobs

To start an interactive session for running serial or threaded programs on an Oscar compute node, simply run the command interact from the login node:

interact

By default, this will create an interactive session that reserves 1 core and 4GB of memory for a period of 30 minutes. You can change the resources reserved for the session from these default limits by modifying the interact command:

usage: interact [-n cores] [-t walltime] [-m memory] [-q queue]
                [-o outfile] [-X] [-f featurelist] [-h hostname] [-g ngpus]

Starts an interactive job by wrapping the SLURM 'salloc' and 'srun' commands.

options:
  -n cores        (default: 1)
  -t walltime     as hh:mm:ss (default: 30:00)
  -m memory       as #[k|m|g] (default: 4g)
  -q queue        (default: 'batch')
  -o outfile      save a copy of the sessions output to outfile (default: off)
  -X              enable X forwarding (default: no)
  -f featurelist  CCV-defined node features (e.g., 'e5-2600'),
                  combined with '&' and '|' (default: none)
  -h hostname     only run on the specific node 'hostname'
                  (default: none, use any available node)
  -a account      user SLURM accounting account name
  -g ngpus        number of GPUs   

For example, the command

$ interact -n 20 -t 01:00:00 -m 10g

requests an interactive session with 20 cores and 10 GB of memory (per node) for a period of 1 hour.

Keeping Interactive Jobs Alive:

PreviousSlurm PartitionsNextBatch Jobs

Last updated 4 years ago

Was this helpful?

If you lose connectivity to your login node, you lose access to your interactive job. To mitigate this issue you can use screen to keep your connection alive. For more information on using screen on the login nodes, see the

software section