Lightning Segmentation

This repo is for practicing using our workflow.

Branch-specific setup on HiPerGator (banks only)

For HiPerGator, create a conda environment from hpg-environment.yml using conda env create --file=hpg_environment.yml. This should create an environment called hpg. It may take a while (and it uses pip, sorry!) but it should eventually work. This is the environment that should be used in the training script (currently training.sh).

Setup:

Conda environment

1. Install Anaconda package manager with Python version 3.9 from Miniconda (recommended because of small size) or full Anaconda (includes graphical user interface for package management).
2. Verify that the pip3 (Python 3's official package manager) is installed by entering pip3 -v in the terminal. If it is not installed, install it, perhaps using this tutorial.
3. Create the conda environment jtml from the environment.yml using the command conda env create -f environment.yml.
4. Activate the conda env with conda activate jtml.
5. There may be other dependencies that you can install using conda or pip3.

WandB - our logging system.

1. Create an account from the website and send the email you used to Sasank (to get invited to the Wandb team).

CUDA (Optional)

If you have an NVIDIA graphics card, please install CUDA. This will allow you to use your GPU for training, which is useful when running a couple batches during development to ensure the code runs.

Data:

Large data is in the Files section of the Microsoft Teams team. Please copy these files/folders locally. This includes the image folder of X-ray images and segmentation masks (you need to unzip this folder) and the .ckpt model checkpoint file needed for loading a pretrained model for testing.

After you download these files/folders locally, remember to edit the config file (in config/config.py) to specify the location of your local image directory and checkpoint file.

Use:

1. Be in the LitJTML directory (use the cd command to change the directory to the blah/blah/LitJTML/ directory).
2. To fit (train) a model, call python scripts/fit.py my_config where my_config is the name of the config.py file in the config/ directory.
   • The config file should specify the model, data, and other parameters.