| #!/bin/bash | |
| #SBATCH --job-name=ses-01 | |
| #SBATCH --ntasks-per-node=1 | |
| #SBATCH --nodes=1 | |
| #SBATCH --gres=gpu:1 | |
| #SBATCH --constraint=gpu80 | |
| #SBATCH --gpus-per-task=1 # Set to equal gres=gpu:#! | |
| #SBATCH --cpus-per-task=40 # 40 / 80 / 176 distributed across node | |
| #SBATCH --time=01:20:00 # total run time limit (HH:MM:SS) | |
| #SBATCH -e slurms/%j.err # first create a "slurms" folder in current directory to store logs | |
| #SBATCH -o slurms/%j.out | |
| #SBATCH --no-requeue | |
| #SBATCH --array=0-19 | |
| #SBATCH --mail-type=END | |
| #SBATCH [email protected] | |
| module purge | |
| module load anaconda3/2023.3 | |
| # conda activate rt_mindEye2 | |
| source /scratch/gpfs/ri4541/MindEyeV2/src/fmri/bin/activate | |
| # The following line converts your jupyter notebook into a python script runnable with Slurm | |
| jupyter nbconvert main-multisession.ipynb --to python | |
| export NUM_GPUS=1 # Set to equal gres=gpu:#! | |
| export BATCH_SIZE=24 | |
| export GLOBAL_BATCH_SIZE=$((BATCH_SIZE * NUM_GPUS)) | |
| # Make sure another job doesnt use same port, here using random number | |
| export MASTER_PORT=$((RANDOM % (19000 - 11000 + 1) + 11000)) | |
| export HOSTNAMES=$(scontrol show hostnames "$SLURM_JOB_NODELIST") | |
| export MASTER_ADDR=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1) | |
| export COUNT_NODE=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | wc -l) | |
| echo MASTER_ADDR=${MASTER_ADDR} | |
| echo MASTER_PORT=${MASTER_PORT} | |
| echo WORLD_SIZE=${COUNT_NODE} | |
| # singlesubject finetuning | |
| model_name="sub-001_ses-01_bs24_MST_rishab_MSTsplit_orig" | |
| echo model_name=${model_name} | |
| accelerate launch --num_processes=$(($NUM_GPUS * $COUNT_NODE)) --num_machines=$COUNT_NODE --main_process_ip=$MASTER_ADDR --main_process_port=$MASTER_PORT orig_main.py --data_path=/scratch/gpfs/ri4541/MindEyeV2/src/mindeyev2 --model_name=${model_name} --no-multi_subject --subj=1 --batch_size=${BATCH_SIZE} --max_lr=3e-5 --mixup_pct=.33 --num_epochs=150 --use_prior --prior_scale=30 --clip_scale=1 --no-blurry_recon --blur_scale=.5 --no-use_image_aug --n_blocks=4 --hidden_dim=1024 --num_sessions=40 --ckpt_interval=999 --ckpt_saving --multisubject_ckpt=/scratch/gpfs/ri4541/MindEyeV2/src/mindeyev2/train_logs/multisubject_subj01_1024hid_nolow_300ep | |