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3cd1076 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 | #!/bin/bash
#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
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