#!/bin/bash
#SBATCH --partition=orion --qos=normal
# #SBATCH --time=96:00:00  --> this is a comment, you can choose to not specify a nodelist, it will randomly assign to a GPU
#SBATCH --nodes=1
#SBATCH --cpus-per-task=8
#SBATCH --mem=12G
#SBATCH --account=orion

# only use the following on partition with GPUs

# only use the following if yo####SBATCH --mail-user=youremailaddress
####SBATCH --mail-type=ALLu want email notification


# list out some useful information (optional)
echo "SLURM_JOBID="$SLURM_JOBID
echo "SLURM_JOB_NODELIST"=$SLURM_JOB_NODELIST
echo "SLURM_NNODES"=$SLURM_NNODES
echo "SLURMTMPDIR="$SLURMTMPDIR
echo "working directory = "$SLURM_SUBMIT_DIR

# sample process (list hostnames of the nodes you've requested)
# NPROCS=`srun --nodes=${SLURM_NNODES} bash -c 'hostname' |wc -l`
# echo NPROCS=$NPROCS

# can try the following to list out which GPU you have access to
#srun /usr/local/cuda/samples/1_Utilities/deviceQuery/deviceQuery
source ~/.bashrc
export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/usr/local/cuda-$cuda_version/lib64
export PATH=${PATH}:/usr/local/cuda-$cuda_version/bin
export CUDA_HOME=/usr/local/cuda-$cuda_version
cd $path
conda activate $conda
accelerate launch textual_inversion.py \
  --pretrained_model_name_or_path=runwayml/stable-diffusion-v1-5 \
  --train_data_dir=scnnt_710  \
  --learnable_property="style" \
  --placeholder_token="<scnnt_710>" --initializer_token="room" \
  --resolution=512 \
  --train_batch_size=1 \
  --gradient_accumulation_steps=4 \
  --max_train_steps=3000 \
  --learning_rate=5.0e-04 --scale_lr \
  --lr_scheduler="constant" \
  --lr_warmup_steps=0 \
  --push_to_hub \
  --output_dir="textual_inversion_scnnt_710"