| #SBATCH -J owt_duo_anneal # Job name | |
| #SBATCH -o watch_folder/%x_%j.out # log file (out & err) | |
| #SBATCH -N 1 # Total number of nodes requested | |
| #SBATCH --get-user-env # retrieve the users login environment | |
| #SBATCH --mem=100000 # server memory requested (per node) | |
| #SBATCH -t 960:00:00 # Time limit (hh:mm:ss) | |
| #SBATCH --partition=anonymous # Request partition | |
| #SBATCH --constraint="[a5000|a6000|a100|3090]" | |
| #SBATCH --ntasks-per-node=1 | |
| #SBATCH --gres=gpu:1 # Type/number of GPUs needed | |
| #SBATCH --open-mode=append # Do not overwrite logs | |
| #SBATCH --requeue # Requeue upon preemption | |
| checkpoint_path="${1:-CKPT_PATH}" | |
| sampler="${2:-meanflow}" | |
| num_steps="${3:-10}" | |
| seed="${4}" | |
| if [ -z "$checkpoint_path" ] || [ -z "$sampler" ] || [ -z "$num_steps" ] || [ -z "$seed" ]; then | |
| echo "Usage: $0 <checkpoint_path> <sampler> <num_steps> <seed>" | |
| exit 1 | |
| fi | |
| export HYDRA_FULL_ERROR=1 | |
| python -u -m main \ | |
| mode=sample_eval \ | |
| seed=$seed \ | |
| model=small \ | |
| algo=duo_finetune \ | |
| algo.use_curriculum=True \ | |
| eval.checkpoint_path=$checkpoint_path \ | |
| loader.batch_size=2 \ | |
| loader.eval_batch_size=8 \ | |
| sampling.num_sample_batches=16 \ | |
| sampling.noise_removal=$sampler \ | |
| training.pred_type=x0 \ | |
| sampling.steps=$num_steps \ | |
| training.loss_type=$sampler \ | |
| +wandb.offline=true | |