Installation

Choose your preferred installation method below.

Via PyPI (Recommended)

The easiest way to get started is using pip:

pip install GaugePredict

This installs the latest stable release with all core dependencies.

For development or to use the latest features:

git clone https://github.com/caitlinturner/GaugePredict.git
cd GaugePredict
pip install -e ".[dev]"

The -e flag installs in editable mode, allowing you to modify the source code and see changes immediately.

For a conda-based environment:

conda env create -f environment.yml
conda activate gaugepredict-dev

This creates an isolated environment with all required dependencies.

GaugePredict supports GPU acceleration via PyTorch and CUDA. Install the CUDA version of PyTorch for your system:

For CUDA 12.6:

pip3 install torch torchvision --index-url https://download.pytorch.org/whl/cu126

For other CUDA versions:

Visit PyTorch’s installation guide to find the correct command for your system.

GPU acceleration is optional—the package works fine with CPU-only PyTorch.

Core Requirements:

See requirements.txt in the repository for the complete list of dependencies and their versions.

Verify the installation was successful:

import GaugePredict
print(GaugePredict.__version__)

This should print the version number (e.g., 1.0.1) without errors.

Import Error: “No module named ‘GaugePredict’”: Ensure the package is installed in your active Python environment. Run pip list | grep GaugePredict to verify.
PyTorch Installation Issues: Refer to the official PyTorch installation guide for your specific operating system and hardware.
GPU Not Detected: Run torch.cuda.is_available() to check if CUDA is properly installed. For support, see the PyTorch CUDA compatibility guide.

If you encounter other issues, please report them on the GitHub Issues page.