Software¶

Language environments, machine learning frameworks, containers, and applications on CARC systems.

Python & Jupyter¶

Conda and Anaconda: introduction - What conda is, how environments work, and how to use Anaconda/Miniconda on CARC systems.
Managing conda environments - Create, activate, export, and remove conda environments on CARC clusters.
Conda channels and pip - Use conda channels (conda-forge, bioconda) and mix pip installs safely inside environments.
Conda environments in JupyterHub - Make your conda environments available as kernels in CARC JupyterHub.
Installing deep learning packages - Install GPU-enabled deep learning frameworks (PyTorch, TensorFlow) into conda environments.
Parallel Python with Dask and scikit-learn - Scale scikit-learn workloads across cluster nodes from JupyterHub using Dask.
MPI parallelization from JupyterHub - Run MPI-parallel Python (mpi4py/ipyparallel) from CARC JupyterHub sessions.
Julia in JupyterHub - Register a Julia kernel and use Julia notebooks in CARC JupyterHub.

R on CARC systems - Load R, run scripts in batch jobs, and use R interactively on CARC clusters.
Getting R software - Available R versions and how to load them with environment modules.
Installing R packages - Install R packages into your user library on CARC systems.
Parallel R with the future package - Parallelize R code across cores and nodes using the future framework.
Gurobi optimizer with R - Use the Gurobi optimization solver from R on CARC clusters.
R batch jobs with PBS (retired) - Historical instructions for submitting R jobs with PBS/Torque, which CARC has replaced with Slurm. (legacy)

Running MATLAB jobs - Run MATLAB non-interactively in Slurm batch jobs on CARC clusters.
Parallel MATLAB: profile setup and batch submission - Configure a cluster profile and submit parallel MATLAB jobs.
MATLAB Parallel Server - Use MATLAB Parallel Server to scale parpool jobs across multiple nodes.
MATLAB on GPUs - Accelerate MATLAB computations with GPUs on CARC clusters.
MATLAB deep learning - Train deep learning models in MATLAB using CARC GPU nodes.

PyTorch on CARC GPUs - Install and run GPU-enabled PyTorch on CARC clusters.
PyTorch image classifier walkthrough - End-to-end example: train an image classifier with PyTorch on a CARC GPU node.
TensorFlow on CARC GPUs - Install and run GPU-enabled TensorFlow on CARC clusters.
Multi-GPU TensorFlow - Distribute TensorFlow training across multiple GPUs on a CARC node.
AlphaFold - Run AlphaFold protein structure prediction on CARC systems.

Singularity / Apptainer containers - Build, pull, and run software containers on CARC clusters.
Apache Spark - Launch Apache Spark clusters inside Slurm allocations for large-scale data analysis.
ParaView remote visualization - Run the ParaView server on CARC compute nodes and connect from your desktop client.
CUDA-aware MPI - Pass GPU device pointers directly to MPI calls with the CUDA-aware OpenMPI/UCX stack, and fix the mixed-environment segfault.
Installing Perl libraries - Install Perl modules into your own home directory with cpan.
Haskell at CARC - Install GHC with ghcup and build a Stack project on CARC clusters.