| #SBATCH -J train_sedd # Job name | |
| #SBATCH -o watch_folder/%x_%j.out # output file (%j expands to jobID) | |
| #SBATCH -N 1 # Total number of nodes requested | |
| #SBATCH --get-user-env # retrieve the users login environment | |
| #SBATCH --mem=32000 # 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 --constraint="gpu-mid|gpu-high" | |
| #SBATCH --ntasks-per-node=4 | |
| #SBATCH --gres=gpu:4 # Type/number of GPUs needed | |
| #SBATCH --open-mode=append # Do not overwrite logs | |
| #SBATCH --requeue # Requeue upon pre-emption | |
| # To enable preemption re-loading, set `hydra.run.dir` or | |
| # `checkpointing.save_dir` explicitly. | |
| srun python -u -m main \ | |
| loader.batch_size=16 \ | |
| loader.eval_batch_size=16 \ | |
| model=small \ | |
| data=openwebtext-split \ | |
| wandb.name=sedd-owt \ | |
| algo=sedd \ | |
| model.length=1024 \ | |
| eval.compute_generative_perplexity=True \ | |
| sampling.predictor=analytic |