File size: 2,147 Bytes
c4b1fea | 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 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 | #!/bin/bash
#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=64000 # server memory requested (per node)
#SBATCH -t 960:00:00 # Time limit (hh:mm:ss)
#SBATCH --constraint="[a100|a6000|a5000|3090]"
#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 preemption
<<comment
# Usage:
cd scripts/
PROP=<qed|ring_count>
sbatch \
--export=ALL,PROP=${PROP} \
--job-name=train_qm9_fudge_classifier_${PROP} \
train_qm9_fudge_classifier.sh
comment
# Setup environment
cd ../ || exit # Go to the root directory of the repo
source setup_env.sh
export HYDRA_FULL_ERROR=1
export NCCL_P2P_LEVEL=NVL
# Expecting:
# - PROP (qed or ring_count)
if [ -z "${PROP}" ]; then
echo "PROP is not set"
exit 1
fi
LABEL_SMOOTHING=FALSE
RUN_NAME="${PROP}"
# To enable preemption re-loading, set `hydra.run.dir` or
srun python -u -m main \
mode=train_classifier \
+is_fudge_classifier=True \
+use_label_smoothing=${LABEL_SMOOTHING} \
parameterization=ar \
data=qm9 \
data.label_col="${PROP}" \
data.label_col_pctile=90 \
data.num_classes=2 \
loader.global_batch_size=2048 \
loader.eval_global_batch_size=4096 \
classifier_model=tiny-classifier \
classifier_backbone=dit \
classifier_model.pooling=no_pooling \
model.length=32 \
optim.lr=3e-4 \
lr_scheduler=cosine_decay_warmup \
lr_scheduler.warmup_t=1000 \
lr_scheduler.lr_min=3e-6 \
training.guidance=null \
+training.use_label_smoothing=${LABEL_SMOOTHING} \
callbacks.checkpoint_every_n_steps.every_n_train_steps=5_000 \
callbacks.checkpoint_monitor.monitor=val/cross_entropy \
trainer.val_check_interval=1.0 \
trainer.max_steps=25_000 \
wandb.group=train_classifier \
wandb.name="qm9-fudge_classifier_${RUN_NAME}" \
hydra.run.dir="./outputs/qm9/fudge_classifier/${RUN_NAME}"
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