Containers on HPC

Tip

Apptainer is installed on the operating systems of all HPC compute nodes, so can be easily accessed either from an interactive session or batch script without worrying about software modules.

Available Containers

We support the use of HPC and ML/DL containers available on NVIDIA GPU Cloud (NGC). Many of the popular HPC applications including NAMD, LAMMPS and GROMACS containers are optimized for performance and available to run in Apptainer on Ocelote or Puma. The containers and respective README files can be found in /contrib/singularity/nvidia. They are only available from compute nodes, so start an interactive session if you want to view them.

Container	Description
nvidia-caffe.20.01-py3.simg	Caffe is a deep learning framework made with expression, speed, and modularity in mind. It was originally developed by the Berkeley Vision and Learning Center (BVLC).
nvidia-gromacs.2018.2.simg	GROMACS is designed to simulate biochemical molecules like proteins, lipids, and nucleic acids
nvidia-julia.1.2.0.simg	The Julia programming language is a flexible dynamic language, appropriate for scientific and numerical computing, with performance comparable to traditional statically-typed languages.
nvidia-lammps.24Oct2018.sif	The main use case of the Large-scale Atomic / Molecular Massively Parallel Simulator is atom scale particle modeling or, more generically, as a parallel particle simulator at the atomic, meson, or continuum scale
nvidia-namd_2.13-multinode.sif	NAMD is a parallel molecular dynamics code designed for high-performance simulation of large biomolecular systems. NAMD uses the popular molecular graphics program VMD for simulation setup and trajectory analysis, but is also file-compatible with AMBER, CHARMM, and X-PLOR.
nvidia-pytorch.20.01-py3.simg	PyTorch is a Python package that provides two high-level features: - Tensor computation (like numpy) with strong GPU acceleration - Deep Neural Networks built on a tape-based autograd system
nvidia-rapidsai.sif	RAPIDS provides unmatched speed with familiar APIs that match the most popular PyData libraries. Built on state-of-the-art foundations like NVIDIA CUDA and Apache Arrow.
nvidia-relion_2.1.b1.simg	RELION (REgularized LIkelihood OptimizatioN) implements an empirical Bayesian approach for analysis of electron cryo-microscopy (Cryo-EM). Specifically, RELION provides refinement methods of singular or multiple 3D reconstructions as well as 2D class averages.
nvidia-tensorflow_2.0.0-py3.sif	TensorFlow is an open source software library for numerical computation using data flow graphs. TensorFlow was originally developed by researchers and engineers working on the Google Brain team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research.
nvidia-theano.18.08.simg	Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently.

Sharing Your Containers

If you have containers that you would like to share with your research group or broader HPC community, you may do so by creating a directory in the space /contrib/singularity/shared.

Cache Directory

To speed up image downloads for faster, less redundant builds and pulls, Apptainer sets a cache directory in your home under ~/.apptainer. This directory stores images, metadata, and docker layers that can wind up being reasonably large. If you're struggling with space usage and your home's 50 GB quota, one option is to set a new Apptainer cache directory. You can do this by setting the environment variable APPTAINER_CACHEDIR to a new location. From Apptainer's documentation:

If you change the value of APPTAINER_CACHEDIR be sure to choose a location that is:

1. Unique to you. Permissions are set on the cache so that private images cached for one user are not exposed to another. This means that APPTAINER_CACHEDIR cannot be shared.
2. Located on a filesystem with sufficient space for the number and size of container images anticipated.
3. Located on a filesystem that supports atomic rename, if possible.

For example, if you wanted to set your cache directory to your PI's /groups directory under a directory you own, you could use:

export APPTAINER_CACHEDIR=/groups/pi/your_netid/.apptainer

To make the change permanent, add this line to the hidden file in your home directory ~/.bashrc. If you are unfamiliar with this file and would like more information, see our Linux cheat sheet guide.