It’s recommended to use Anaconda to create an environment for the toolbox:
# cmd
# Create and activate an environment
conda create --name coralnet10 python=3.10 -y
conda activate coralnet10
Once this has finished, install the toolbox using uv:
# cmd
# Install uv first
pip install uv
# Install with uv
uv pip install coralnet-toolbox
Although fast, uv is still relatively new; if this fails, simply fall back to using pip:
# cmd
# Install
pip install coralnet-toolbox
If you have CUDA, you can install the versions of cuda-nvcc and cudatoolkit that you
need, and then install the corresponding versions of torch and torchvision. Below is an example of how that can be
done using CUDA version 12.9:
# cmd
# Example for CUDA 12.9
conda install nvidia/label/cuda-12.9.0::cuda-nvcc -y
conda install nvidia/label/cuda-12.9.0::cuda-toolkit -y
# Example for torch w/ CUDA 12.9
uv pip install torch torchvision --index-url https://download.pytorch.org/whl/cu129 --upgrade
If CUDA is installed on your computer, and torch was built with it properly, you should see a 🐇 icon in the
toolbox instead of a 🐢; if you have multiple CUDA devices available, you should see a 🚀 icon,
and if you’re using a Mac with Metal, you should see an 🍎 icon (click on the icon to see the device information).
See here for more details on versions for the following:
Finally, you can run the toolbox from the command line:
# cmd
# Run
coralnet-toolbox
When opening the toolbox, you will be notified if there is an update available, and you have the option to do so,
if you so choose. To upgrade, run the following command from your terminal:
# cmd
uv pip install -U coralnet-toolbox==[enter_newest_version_here]
Again, fall back to using just pip and not uv if this fails.
If you prefer to clone the repository and run the toolbox from the source code, you can do so with the following:
# cmd
# Create and activate an environment
conda create --name coralnet10 python=3.10 -y
conda activate coralnet10
# Install git via conda, if not already installed
conda install git -y
# Change to the desired directory (e.g., Documents)
cd Documents
# Clone and enter the repository
git clone https://github.com/Jordan-Pierce/CoralNet-Toolbox.git
cd CoralNet-Toolbox
# Install the latest
pip install -e .
# Install CUDA requirements (if applicable)
conda install nvidia/label/cuda-12.9.0::cuda-nvcc -y
conda install nvidia/label/cuda-12.9.0::cuda-toolkit -y
# Example for torch w/ CUDA 12.9
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu118 --upgrade
# Run
coralnet-toolbox
To update your repository to match the current version on main, run fetch and pull commands:
# cmd
# Change to the proper directory
cd Coralnet-Toolbox
# Ask for the updates on main
git fetch
# Pull the updates from main
git pull
# Update your conda environment
pip install -e . -U
Or, if you want to simply install the toolbox from the GitHub repo directly you can also do the following:
# cmd
pip install git+https://github.com/Jordan-Pierce/CoralNet-Toolbox.git@main -U
# replace @main with a different branch if you want to test out experimental code