Welcome to itwinai

Accelerate AI & ML workflows for Scientific Digital Twins.

itwinai streamlines distributed training, hyperparameter optimization, logging, and modular workflows, so you can focus on science, not plumbing.

Features

🚀 Seamless Scaling: Run training and inference on HPC clusters or cloud with a single CLI command.
🔍 Effortless Logging: Built-in support for MLflow, Weights & Biases, TensorBoard, and more.
🧩 Modular Workflows: Define reusable pipelines for end-to-end experiment management.
🤖 HPO Made Easy: Native hyperparameter optimization with minimal configuration.
🔌 Extensible Plugins: Add custom integrations or contribute new features.

Quick Start

# Install via pip
pip install itwinai

# Launch a complete workflow with SLURM integration using the MNIST example
itwinai run -c https://raw.githubusercontent.com/interTwin-eu/itwinai/refs/heads/main/use-cases/mnist/torch/run-example.yaml

# View logs in MLflow
itwinai mlflow-ui --path mllogs/mlflow

🚀 Begin Here

🛠️ Core Guides

🎓 Tutorials

📚 Use Cases & 🧩 Plugins

For the full list of scientific use cases refer to the navigation side bar.

⚡ API Reference

Integration with EuroHPC centers

Our code has been run and tested on the following EuroHPC systems:

LUMI
JSC
Vega
Deucalion (coming soon)

Community & Support

itwinai is an open-source Python library primarily developed by CERN, in collaboration with Forschungszentrum Jülich (FZJ). As the primary contributor, CERN will retain administrative rights to the repository during and after the interTwin project, except in cases where CERN is unable to maintain it.

How to contribute

Want to help improve itwinai? Here are a few good ways to get involved:

Report a bug / request a feature: open a GitHub issue.
Contribute code or docs: fork the repository and submit a pull request.
Ask questions or float ideas: start a GitHub discussion or join us on Discord.