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Quick Start Guide

Get PyTorch Connectomics running in 5 minutes! 🚀

What You'll Do

  1. Install PyTorch Connectomics (2-3 minutes)
  2. Run a demo to verify installation (30 seconds)
  3. Try a tutorial with real data (optional)

Step 1: Install

curl -fsSL https://raw.githubusercontent.com/zudi-lin/pytorch_connectomics/master/quickstart.sh | bash
cd pytorch_connectomics
conda activate pytc

For CUDA-specific options, manual install, or installing via an AI coding agent (Claude Code / Codex), see INSTALLATION.md.

Step 2: Verify Installation

Quick Demo (30 seconds)

conda activate pytc
python scripts/main.py --demo

This creates synthetic data and trains a small model for 5 epochs. If this works, your installation is successful! ✅

Expected output:

🎯 PyTorch Connectomics Demo Mode
...
✅ DEMO COMPLETED SUCCESSFULLY!
Your installation is working correctly! 🎉

Step 3: Try a Real Tutorial (Optional)

Download Tutorial Data

The Lucchi++ dataset contains mitochondria segmentation data from EM images.

# Download from HuggingFace (recommended)
mkdir -p datasets/
wget https://huggingface.co/datasets/pytc/tutorial/resolve/main/lucchi%2B%2B.zip
unzip lucchi++.zip -d datasets/
rm lucchi++.zip

Size: ~100 MB

Run Training

# Quick test (1 batch, ~30 seconds)
python scripts/main.py --config tutorials/monai_lucchi++.yaml --fast-dev-run

# Full training (~2 hours on GPU)
python scripts/main.py --config tutorials/monai_lucchi++.yaml

Monitor Progress

# Launch TensorBoard (in a separate terminal)
tensorboard --logdir outputs/lucchi++_monai_unet

# Open browser to http://localhost:6006

Common Issues

For installation problems, see INSTALLATION.md.

Issue: "CUDA out of memory"

Solution: Reduce batch size in config:

python scripts/main.py --config tutorials/lucchi.yaml data.dataloader.batch_size=1

Next Steps

Learn More

Get Help

Customize Your Workflow

Train on your own data:

# Create a config file (e.g., my_config.yaml)
# See tutorials/*.yaml for examples

python scripts/main.py --config my_config.yaml

Use different models:

# In your config file:
model:
  architecture: mednext  # Try MedNeXt (state-of-the-art)
  mednext_size: S        # S, B, M, or L
  deep_supervision: true

Distributed training:

system:
  training:
    num_gpus: 4  # Automatically uses DDP

Tips for Success

  1. Start small: Use --fast-dev-run to test configs quickly
  2. Monitor training: Always use TensorBoard to watch loss curves
  3. GPU memory: Start with small batch sizes, increase gradually
  4. Ask questions: Join our Slack community - we're friendly! 😊

What's Next?

Now that you're set up, explore:

  1. Different architectures: MONAI models, MedNeXt
  2. Advanced features: Mixed precision, deep supervision
  3. Custom data: HDF5, TIFF, Zarr formats
  4. Deployment: Docker/Singularity containers

Happy segmenting! 🔬🧠