#!/bin/bash
#SBATCH -J train_owt_sedd                # Job name
#SBATCH -o watch_folder/%x_%j.out     # log file (out & err)
#SBATCH -e watch_folder/%x_%j.err     # 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=gpu          # Request partition
#SBATCH --constraint="[a5000|a6000|3090|a100]"
#SBATCH --ntasks-per-node=8
#SBATCH --gres=gpu:8                  # Type/number of GPUs needed
#SBATCH --open-mode=append            # Do not overwrite logs
#SBATCH --requeue                     # Requeue upon preemption

python -u main.py \
    loader.global_batch_size=512 \
    loader.eval_global_batch_size=512 \
    loader.batch_size=16 \
    loader.eval_batch_size=16 \
    model=small \
    algo=sedd \
    data=openwebtext-split \
    data.insert_train_special=False \
    data.insert_valid_special=False \
    data.insert_valid_eos=False \
    model.length=1024 \
    wandb.name=sedd-owt