Installation

Requirements

D²MTOLab requires:

  • Python 3.10+

  • PyTorch 2.5+ with CUDA 12.1 support (for GPU acceleration)

  • BoTorch 0.16+

  • GPyTorch 1.14+

  • NumPy 2.0+

  • SciPy 1.15+

  • scikit-learn 1.7+

  • Pandas 2.3+

  • Matplotlib 3.10+

  • Seaborn 0.13+

  • tqdm 4.67+

  • OpenPyXL 3.1+

  • PyYAML 6.0+

  • Bottleneck 1.4+

  • Pyro-PPL 1.9+

Optional (for documentation):

  • Sphinx 7.4+

  • sphinx-rtd-theme 3.0+

  • myst-parser 3.0+

Installation Methods

Method 2: Using Conda

Step 1: Create Conda Environment

# Create a new conda environment
conda create -n ddmtolab python=3.10

# Activate the environment
conda activate ddmtolab

Step 2: Install PyTorch with CUDA Support

# For CUDA 12.1 (GPU acceleration)
conda install pytorch pytorch-cuda=12.1 -c pytorch -c nvidia

# For CPU only
conda install pytorch cpuonly -c pytorch

Step 3: Install D²MTOLab

pip install ddmtolab

Alternative: Install All Dependencies via Conda

# Install PyTorch
conda install pytorch pytorch-cuda=12.1 -c pytorch -c nvidia

# Install BoTorch and GPyTorch
conda install botorch -c conda-forge
conda install gpytorch -c gpytorch

# Install other dependencies
conda install numpy scipy scikit-learn pandas matplotlib seaborn tqdm openpyxl pyyaml bottleneck -c conda-forge
conda install pyro-ppl -c conda-forge

# Install D²MTOLab
pip install ddmtolab

Method 3: Install from Source

For the latest development version or if you want to contribute:

Step 1: Clone the Repository

git clone https://github.com/JiangtaoShen/DDMTOLab.git
cd DDMTOLab

Step 2: Create Virtual Environment

# Create virtual environment
python -m venv ddmtolab_env

# Activate on Windows
ddmtolab_env\Scripts\activate

# Activate on Linux/Mac
source ddmtolab_env/bin/activate

Step 3: Install PyTorch

# For CUDA 12.1
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121

# For CPU only
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu

Step 4: Install D²MTOLab and Dependencies

pip install -r requirements.txt
pip install -e .

Development Installation

For development work, install D²MTOLab in editable mode. This allows you to modify the code and see changes immediately without reinstalling.

Step 1: Clone the Repository

git clone https://github.com/JiangtaoShen/DDMTOLab.git
cd DDMTOLab

Step 2: Create Virtual Environment

# Create virtual environment
python -m venv ddmtolab_dev

# Activate on Windows
ddmtolab_dev\Scripts\activate

# Activate on Linux/Mac
source ddmtolab_dev/bin/activate

Step 3: Install PyTorch

# For CUDA 12.1
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121

# For CPU only
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu

Step 4: Install Dependencies

pip install -r requirements.txt

Step 5: Install in Editable Mode

pip install -e .

This installs D²MTOLab in development mode, allowing you to:

  • Modify source code and see changes immediately

  • Run tests and contribute to development

  • Use the package from any directory

Step 6: Install Development Tools (Optional)

# Install testing tools
pip install pytest pytest-cov

# Install documentation tools
pip install sphinx sphinx-rtd-theme myst-parser

# Install code quality tools
pip install black flake8 mypy

Verify Installation

To verify that D²MTOLab and all dependencies are correctly installed:

# Test basic imports
import numpy as np
import pandas as pd
import torch
import botorch
import gpytorch
import ddmtolab

# Check versions
print(f"DDMTOLab version: {ddmtolab.__version__}")
print(f"PyTorch version: {torch.__version__}")
print(f"BoTorch version: {botorch.__version__}")
print(f"GPyTorch version: {gpytorch.__version__}")

# Check CUDA availability
print(f"CUDA available: {torch.cuda.is_available()}")
if torch.cuda.is_available():
    print(f"CUDA version: {torch.version.cuda}")
    print(f"Number of GPUs: {torch.cuda.device_count()}")
    print(f"GPU name: {torch.cuda.get_device_name(0)}")

Quick Test

Run a quick test to ensure everything works:

from ddmtolab.Algorithms.STSO.DE import DE
from ddmtolab.Methods.mtop import MTOP
import numpy as np

# Define a simple optimization problem
def sphere(x):
    return np.sum(x**2, axis=1)

problem = MTOP()
problem.add_task(sphere, dim=10)

# Run optimization
optimizer = DE(problem, n=50, max_nfes=1000, save_data=False)
result = optimizer.optimize()

print(f"Best objective: {result.best_objs[0][0]:.6f}")
print("DDMTOLab installation successful!")

Upgrading D²MTOLab

To upgrade to the latest version:

# Upgrade from PyPI
pip install --upgrade ddmtolab

# Upgrade from source
cd DDMTOLab
git pull
pip install --upgrade -e .

Uninstalling D²MTOLab

To uninstall D²MTOLab:

pip uninstall ddmtolab

Troubleshooting

Common Issues:

  1. PyTorch not found:

    Install PyTorch first before installing D²MTOLab:

    pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
pip install ddmtolab

  2. CUDA compatibility issues:

    Ensure your CUDA version matches PyTorch requirements. Check compatibility at: https://pytorch.org/get-started/locally/

  3. Import errors:

    If you installed from source, make sure you ran pip install -e . in the project directory.

  4. Dependency conflicts:

    Use a fresh virtual environment or conda environment to avoid conflicts:

    python -m venv fresh_env
source fresh_env/bin/activate  # On Windows: fresh_env\Scripts\activate
pip install torch --index-url https://download.pytorch.org/whl/cu121
pip install ddmtolab

  5. BoTorch/GPyTorch installation fails:

    Install PyTorch first, then install D²MTOLab which will handle BoTorch and GPyTorch dependencies.

  6. Version conflicts:

    Check installed versions:

    pip list | grep -E "torch|botorch|gpytorch|ddmtolab"

    Reinstall with specific versions if needed:

    pip install torch==2.5.1 botorch==0.16.0 gpytorch==1.14.2
pip install ddmtolab

Getting Help

If you encounter issues:

  1. Check the GitHub Issues

  2. Review the documentation at https://jiangtaoshen.github.io/DDMTOLab/

  3. Submit a bug report on GitHub with:

    • Your operating system and Python version

    • Complete error message

    • Installation method used

    • Output of pip list

  4. Contact: j.shen5@exeter.ac.uk