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⚙️ Installation

  • User Installation (for Non-Developers)
  • Developer Installation
  • Tutorial for Using the uv Package Manager

💡 Getting started

  • Install itwinai
  • Submitting jobs to SLURM on HPC
  • Using itwinai Container Images
  • Writing plugins for itwinai
  • Current List of itwinai Plugins

🪄 How it works

  • Training a neural network
  • Tracking ML workflows
  • Defining machine learning workflows
  • Hyperparameter Optimization

🚀 Tutorials

  • Distributed machine learning training
    • Distributed ML with PyTorch
      • 1. Introduction to distributed ML with PyTorch
      • 2. Distributed training on MNIST dataset
      • 3. Using the itwinai TorchTrainer Class
      • 4. GAN tutorial with PyTorch
      • 5. PyTorch scaling test
      • 6. itwinai and containers (Docker and Singularity)
      • 7. Tutorial on Kubeflow and TorchTrainer class
      • 8. Distributed Machine Learning on HPC from k8s using KubeRay operator and interLink
    • Distributed ML with TensorFlow
      • 1. Introduction on distributed training with TensorFlow
      • 2. Tensorflow ImageNet example
      • 3. Tensorflow scaling test
  • Machine learning workflows
    • Simple Workflow and Pipeline Components
    • Using Configuration Files in itwinai
    • DAG workflows
    • Integrating configuration with command line arguments
  • Hyperparameter Optimization Workflows
    • Hyperparameter Optimization with TorchTrainer on MNIST

📚 Scientific Use Cases

  • Introduction
  • Drought Early Warning in the Alps (EURAC)
  • Noise Simulation for Gravitational Waves Detector (Virgo, INFN)
  • Fast particle detector simulation (CERN)
  • Tropical Cyclones Detection (CMCC)
  • MNIST dataset
  • ML-based extreme events detection and characterization (xtclim, CERFACS)
  • Pulsar Segmentation and Analysis for Radio-Astronomy (HTW Berlin)
  • Normalizing flow for generating lattice field configurations (Lattice QCD, ETHZ/CSIC)

⚡ API reference

  • CLI
  • Python SDK

🎯 Github repository

  • itwinai
itwinai
  • Distributed machine learning training
  • View page source

Distributed machine learning training

Here you can find a collection of tutorials for distributing PyTorch and Tensorflow based workflows.

Distributed ML with PyTorch

  • 1. Introduction to distributed ML with PyTorch
  • 2. Distributed training on MNIST dataset
  • 3. Using the itwinai TorchTrainer Class
  • 4. GAN tutorial with PyTorch
  • 5. PyTorch scaling test
  • 6. itwinai and containers (Docker and Singularity)
  • 7. Tutorial on Kubeflow and TorchTrainer class
  • 8. Distributed Machine Learning on HPC from k8s using KubeRay operator and interLink

Distributed ML with TensorFlow

  • 1. Introduction on distributed training with TensorFlow
  • 2. Tensorflow ImageNet example
  • 3. Tensorflow scaling test

Machine learning workflows

Here you can find a collection of tutorials for various complexity ML workflows.

  • Simple Workflow and Pipeline Components
  • Using Configuration Files in itwinai
  • DAG workflows
  • Integrating configuration with command line arguments

Hyperparameter Optimization Workflows

This tutorial provides an overview of Hyperparameter Optimization (HPO) workflows.

  • Hyperparameter Optimization with TorchTrainer on MNIST
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