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Installation

Requirements​

  • Python 3.7+
  • PyTorch 1.9+
  • CUDA 10.2+ (for GPU acceleration)

Quick Install​

pip install pytorch_segmentation_models_trainer
GPU Acceleration

If you want GPU acceleration and are not using Docker, install pytorch_scatter first:

pip install torch-scatter -f https://data.pyg.org/whl/torch-1.12.0+cu113.html

Replace cu113 with your CUDA version (cu102, cu113, cu116, etc.)

Option 2: From Source​

git clone https://github.com/phborba/pytorch_segmentation_models_trainer.git
cd pytorch_segmentation_models_trainer
pip install -e .

Option 3: Docker (Easiest for GPU)​

We provide pre-built Docker images with all dependencies:

# Pull the latest image
docker pull phborba/pytorch_segmentation_models_trainer:latest

# Run interactively
docker run -it --gpus all \
    -v /path/to/your/data:/data \
    -v /path/to/your/configs:/configs \
    phborba/pytorch_segmentation_models_trainer:latest bash

# Run training directly
docker run --gpus all \
    -v /path/to/your/data:/data \
    -v /path/to/your/configs:/configs \
    phborba/pytorch_segmentation_models_trainer:latest \
    pytorch-smt --config-dir /configs --config-name my_config +mode=train

Verify Installation​

Test your installation:

import pytorch_segmentation_models_trainer
print("Installation successful!")

# Check available modes
from pytorch_segmentation_models_trainer.main import main

Or use the CLI:

pytorch-smt --help

Docker Compose Setup​

For development with database support:

version: '3'
services:
  db:
    image: postgis/postgis
    environment:
      POSTGRES_DB: test_db
      POSTGRES_USER: postgres
      POSTGRES_PASSWORD: postgres
    ports:
      - "5432:5432"

  app:
    image: phborba/pytorch_segmentation_models_trainer:latest
    volumes:
      - .:/workspace
    depends_on:
      - db

Run with:

docker-compose up -d

Optional Dependencies​

For Advanced Features​

# For visualization and plotting
pip install matplotlib seaborn

# For additional image processing
pip install opencv-python-headless

# For COCO dataset support
pip install pycocotools

# For PostGIS database integration
pip install psycopg2-binary geopandas

# For advanced metrics
pip install scikit-learn

Development Dependencies​

pip install pytest black flake8 pre-commit

Common Issues​

CUDA/GPU Issues​

Problem: CUDA out of memory

RuntimeError: CUDA out of memory

Solution: Reduce batch size in your config:

hyperparameters:
  batch_size: 1  # Reduce from higher value

Problem: No CUDA devices available

AssertionError: Torch not compiled with CUDA support

Solution: Install PyTorch with CUDA:

pip install torch torchvision --extra-index-url https://download.pytorch.org/whl/cu113

Import Errors​

Problem: ModuleNotFoundError: No module named 'pytorch_scatter'

Solution: Install pytorch-scatter for your CUDA version:

pip install torch-scatter -f https://data.pyg.org/whl/torch-1.12.0+cu113.html

Problem: ImportError: cannot import name 'instantiate'

Solution: Update Hydra:

pip install --upgrade hydra-core

Tips​

  1. Use virtual environments:

    python -m venv venv
    source venv/bin/activate  # On Windows: venv\Scripts\activate
    pip install pytorch_segmentation_models_trainer

  2. Check your CUDA version:

    nvidia-smi

  3. For M1/M2 Macs: Use the Docker option or install with MPS support:

    pip install torch torchvision --extra-index-url https://download.pytorch.org/whl/cpu

Next Steps​