CGFormer/scripts/train_gref.sh

#!/bin/bash
#SBATCH --job-name=sanity_check
#SBATCH --partition=a4000
#SBATCH --gres=gpu:2
#SBATCH --time=13-11:30:00 # d-hh:mm:ss
#SBATCH --mem=52000 # CPU memory size
#SBATCH --cpus-per-task=8 # Number of CPU cores
#SBATCH --output=./filter_exp/sanity.log

ml purge
ml load cuda/11.8
eval "$(conda shell.bash hook)"
conda activate ris_all

cd /data2/projects/chaeyun/CGFormer/


export NVIDIA_TF32_OVERRIDE=1
export NCCL_DEBUG=INFO
export NCCL_IB_TIMEOUT=100
export NCCL_IB_RETRY_CNT=15

if [ "$#" -ne 2 ]; then
    echo "Usage: sbatch train.sh <OUTPUT_DIR> <EXP_NAME>"
    exit 1
fi

# to change
GPUS=2
MASTER_PORT=7028

# input
OUTPUT_DIR=$1
EXP_NAME=$2

MARGIN=12
TEMP=0.07
MODE=hardpos_only_sbertsim_refined
MLW=0.1
BATCH_SIZE=10
MIXUP_FQ=True

echo "Starting distributed training with ${GPUS} GPUs on port ${MASTER_PORT}..."
echo "Experiment Name: ${EXP_NAME}, Output Dir: ${OUTPUT_DIR}"


python -m torch.distributed.launch \
    --nproc_per_node=${GPUS} \
    --master_port=${MASTER_PORT} \
    train_gref.py \
    --config config/config_gref_ace.yaml \
    --opts TRAIN.batch_size ${BATCH_SIZE} \
           TRAIN.exp_name ${EXP_NAME} \
           TRAIN.output_folder ${OUTPUT_DIR} \
           TRAIN.metric_mode ${MODE} \
           TRAIN.metric_loss_weight ${MLW} \
           TRAIN.margin_value ${MARGIN} \
           TRAIN.temperature ${TEMP} \
           TRAIN.mixup_lasttwo ${MIXUP_FQ}