| #SBATCH -J sample_ar # 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=16000 # server memory requested (per node) | |
| #SBATCH -t 24:00:00 # Time limit (hh:mm:ss) | |
| #SBATCH --partition=anonymous,gpu # 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="YOUR_CHECKPOINT_PATH" | |
| seed=1 | |
| export HYDRA_FULL_ERROR=1 | |
| srun python -u -m main \ | |
| mode=sample_eval \ | |
| seed=$seed \ | |
| loader.batch_size=2 \ | |
| loader.eval_batch_size=8 \ | |
| data=openwebtext-split \ | |
| algo=ar \ | |
| model=small \ | |
| eval.checkpoint_path=$checkpoint_path/last.ckpt \ | |
| sampling.num_sample_batches=100 \ | |
| +wandb.offline=true \ | |
| eval.generated_samples_path=$checkpoint_path/$seed-ckpt-last.json |