Lumi-supercomputer / LUMI-AI-Guide
The LUMI AI Guide is designed to assist users in migrating their machine learning applications from smaller-scale computing environments to the LUMI supercomputer.
☆38Updated last month
Alternatives and similar repositories for LUMI-AI-Guide
Users that are interested in LUMI-AI-Guide are comparing it to the libraries listed below
Sorting:
- Jupyter notebooks, jobscripts and other files for the "Getting started with AI on LUMI" workshop☆30Updated this week
- Transformer eXplainability and eXploration☆19Updated 6 months ago
- A simple Python wrapper for Slurm with flexibility in mind.☆140Updated 3 months ago
- SciML Benchmarking Suite for AI for Science☆40Updated 9 months ago
- Tool to wrap installations into a container designed for use on HPC systems☆33Updated 2 months ago
- PyTorch training at CSCS☆15Updated last year
- Tutorial for using Singularity containers☆115Updated 4 years ago
- AI Training Series Material☆35Updated 7 months ago
- upload big files to Zenodo using cURL, jq and bash☆269Updated 3 months ago
- SC23 Deep Learning at Scale Tutorial Material☆44Updated 8 months ago
- ☆117Updated 5 months ago
- The JUBE benchmarking environment provides a script based framework to easily create benchmark sets, run those sets on different computer…☆38Updated 11 months ago
- Distance-based Analysis of DAta-manifolds in python☆127Updated last month
- ☆55Updated last year
- ☆36Updated 3 weeks ago
- Library for steering campaigns of simulations on supercomputers☆53Updated 2 weeks ago
- This is a repository with examples to run inference endpoints on various ALCF clusters☆19Updated last week
- Guidelines on using Weights and Biases logging for deep learning applications on NERSC machines☆12Updated last year
- ☆33Updated last year
- Train across all your devices, ezpz 🍋☆20Updated last week
- Running Jax in PyTorch Lightning☆100Updated 5 months ago
- [ICML 2024] SIRFShampoo: Structured inverse- and root-free Shampoo in PyTorch (https://arxiv.org/abs/2402.03496)☆14Updated 6 months ago
- Linux productivity tools and practices for researchers☆82Updated last week
- Compatibility layer for common array libraries to support the Array API☆96Updated this week
- Provides differentiable versions of common HEP operations and objectives.☆24Updated last year
- Material for the SC22 Deep Learning at Scale Tutorial☆41Updated last year
- Aalto scientific computing guide: former Triton user guide + more info☆32Updated this week
- python library for atomistic machine learning☆77Updated this week
- ☆17Updated this week
- Stencil computations in JAX☆71Updated last year