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:
# cmd
# Install
pip install coralnet-toolbox
If you have CUDA, you should 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 11.8:
# cmd
# Example for CUDA 11.8
conda install nvidia/label/cuda-11.8.0::cuda-nvcc -y
conda install nvidia/label/cuda-11.8.0::cuda-toolkit -y
# Example for torch w/ CUDA 11.8
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu118 --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
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
# 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-11.8.0::cuda-nvcc -y
conda install nvidia/label/cuda-11.8.0::cuda-toolkit -y
# Example for torch w/ CUDA 11.8
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu118 --upgrade
# Run
coralnet-toolbox