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      • Using Python or Conda environments in the Jupyter App
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      • Desktop App (VNC)
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      • From Non-compliant Networks (2-FA)
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  • 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
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    • Best Practices for I/O
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  • Submitting jobs
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  • GPU Computing
    • GPUs on Oscar
      • Grace Hopper GH200 GPUs
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    • 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
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  • Where Can You Run
  • How to Load Singularity

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  1. Singularity Containers

Intro to Apptainer

This is designed to be an early users reference guide for how to build and run apptainer images on OSCAR at CCV for Brown University‌

Where Can You Run

Currently, apptainer 1.2.2-1.el9 has been installed and is operational on all compute nodes attached to the batch, gpu, and VNC partitions. If you experience any issues using Singularity, please contact support through the Support Ticket System.

How to Load Singularity

‌The current implementation of apptainer on OSCAR is not through the traditional module toolkit. Instead, it is natively installed on each node and automatically added to your $PATH such that you immediately have access to the apptainer command.

There is no need to load any modules specific for apptainer.

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Last updated 1 year ago

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