Installation

Requirements

• Python ≥ 3.10

• PyTorch ≥ 1.9 (installed separately for CUDA compatibility)

Note

PyTorch is installed separately to allow users to choose the appropriate CUDA version for their system. See [pytorch.org](https://pytorch.org) for installation options.

Quick install (recommended)

# Create environment and install PyTorch first
conda create -n velot python=3.10 -y
conda activate velot

# Install PyTorch (adjust for your CUDA version)
# GPU:
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
# CPU only:
# pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu

# Install VelOT
pip install velot

Developer installation

For contributing, modifying the code, or reproducing the paper:

# Create environment and install PyTorch first
conda create -n velot python=3.10 -y
conda activate velot

# Install PyTorch (adjust for your CUDA version — see https://pytorch.org)
# GPU:
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
# CPU only:
# pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu

# Install VelOT
git clone https://github.com/lucas-rdlr/velot.git
cd velot
pip install -e .

Verify Installation

import velot
print(velot.__version__)
# 1.0.0

Dependencies

VelOT automatically installs the following packages:

• numpy, scipy, pandas

• anndata, scanpy

• scikit-learn

• matplotlib

• POT (Python Optimal Transport)

• scvelo

• tqdm