OCTOPI 🐙🐙🐙

Object deteCTion Of ProteIns. A deep learning framework for Cryo-ET 3D particle picking with autonomous model exploration capabilities.

🚀 Introduction

octopi addresses a critical bottleneck in cryo-electron tomography (cryo-ET) research: the efficient identification and extraction of proteins within complex cellular environments. As advances in cryo-ET enable the collection of thousands of tomograms, the need for automated, accurate particle picking has become increasingly urgent.

Our deep learning-based pipeline streamlines the training and execution of 3D autoencoder models specifically designed for cryo-ET particle picking. Built on copick, a storage-agnostic API, octopi seamlessly accesses tomograms and segmentations across local and remote environments.

🧩 Core Features

• 3D U-Net Training: Train and evaluate custom 3D U-Net models for particle segmentation
• Automatic Architecture Search: Explore optimal model configurations using Bayesian optimization via Optuna
• Flexible Data Access: Seamlessly work with tomograms from local storage or remote data portals
• HPC Ready: Built-in support for SLURM-based clusters
• Experiment Tracking: Integrated MLflow support for monitoring training and optimization
• Dual Interface: Use via command-line or Python API

🚀 Quick Start

Installation

Octopi is availableon PyPI and can be installed using pip:

pip install octopi

⚠️ Note: One of the current dependencies is currently not working with pip 25.1. We recommend using pip 25.2 or higher, or uv pip when installing octopi.

pip install --upgrade "pip>=25.2"

Basic Usage

octopi provides two main command-line interfaces:

# Main CLI for training, inference, and data processing
octopi

📚 Documentation

For detailed documentation, tutorials, CLI and API reference, visit our documentation.

🤝 Contributing

Code of Conduct

This project adheres to the Contributor Covenant code of conduct. By participating, you are expected to uphold this code. Please report unacceptable behavior to opensource@chanzuckerberg.com.

🔒 Security

If you believe you have found a security issue, please responsibly disclose by contacting us at security@chanzuckerberg.com.