0. Prepare for batch jobs (**Only needed first time**)
1. open ThinLinc - red.uits.iu.edu
2. log in using your IU username and passphrase
3. once you’re logged in, you’ll see a screen that looks like an old computer – open “terminal”
4. navigate to the subdirectory with the batch job files
cd slurm_DLC
5. start the job you want:
to train a network:
sbatch slurm_trainNetwork.sh
to label videos:
sbatch slurm_analyzeVideosBulk.sh
6. monitor how your job is doing
1. open ThinLinc
2. log in using your IU username and passphrase
3. once you’re logged in, you’ll see a screen that looks like an old computer – open “terminal”
a. terminal will look somewhat similar to Anaconda Powershell Prompt
4. Start an interactive job on a GPU compute node with 20GB of RAM, and 20 hours of allowed compute time
srun -A c00735 -p gpu --time=48:00:00 --gpus-per-node=1 --mem=32G --x11 --pty bash
5. Pull python module into corrent workspace
module load python/gpu/3.10.10
6. Activate the virtual environment
source ~/pvenv/bin/activate
7. Start python environment
ipython
8. import deeplabcut tools into workspace
import deeplabcut
9. run your desired command
deeplabcut.train_network(<config_path>,shuffle=1,saveiters=2000)
or
deeplabcut.analyze_videos(<config_path>,<video_path>)