After forking this repository, replace orion_username in the config files with your actual orion username.

Setup in Orion cluster

The $HOME directory of your login machine ([username@login ~]) should have the following structure

$HOME
   ├── cnn_template (the forked repository)
   │   ├── config
   │   |   ├── orion_vnet_aug_malignant_c50perc_w200_2_chan_val_f0_test_f1.json.json
   │   |   └── (other configurations)
   |   ├── outputs
   │   |   └── README.md
   │   ├── customize_obj.py
   │   ├── experiment.py
   │   ├── slurm.sh
   │   ├── setup.sh
   │   └── (other files)
   ├── datasets (Put your datasets in here)
   │   └── headneck
   │       ├── canine_ds_3d_malignant_4fold.h5
   │       └── (other datasets)
   ├── hnperf (log files will be saved in here)
   │

Start by running setup.sh to download the singularity container

cd cnn-template
./setup.sh

Alternative you can directly download the image file

cd cnn-template
singularity pull --name deoxys.sif shub://huynhngoc/head-neck-analysis

Run experiments on Orion

Submit jobs

Submit slurm jobs like this:

sbatch slurm.sh config/2d_unet.json 2d_unet 200

Which will load the setup from the config/2d_unet.json file, train for 200 epochs and store the results in the folder $HOME/hnperf/2d_unet/.

To customize model and prediction checkpoints, add the model_checkpoint_period and prediction_checkpoint_period as arguments

sbatch slurm.sh config/2d_unet_CT_W_PET.json 2d_unet_CT_W_PET 100 --model_checkpoint_period 5 --prediction_checkpoint_period 5

Which will save the trained model every 5 epochs and predict the validation set every 5 epoch

Continue experiments and run test

To continue an experiment

sbatch slurm_cont.sh ../hnperf/2d_unet_CT_W_PET/model/model.030.h5 2d_unet_CT_W_PET 100 --model_checkpoint_period 5 --prediction_checkpoint_period 5

Which will load the saved model and continue training 100 more epochs

In the case the job ended unexpectedly before plotting the performance:

sbatch slurm_vis.sh 2d_unet_CT_W_PET

To run test

sbatch slurm_test.sh ../hnperf/2d_unet_CT_W_PET/model/model.030.h5 2d_unet_CT_W_PET

To run external validation

sbatch slurm_external.sh maastro.json 2d_unet_CT_W_PET --monitor val_dice

Misc

Manually build the singularity image file

singularity build --fakeroot deoxys.sif Singularity

Login to a gpu session to use the gpu

qlogin --partition=gpu --gres=gpu:1
singularity exec --nv deoxys.sif ipython

Results replication

We used deoxys version 0.0.11 in this project. Other configurations can be found in the associated paper: A doi will be provided.