#!/bin/bash set_n_least_used_CUDA_VISIBLE_DEVICES() { local n=${1:-"9999"} echo "GPU Memory Usage:" local FIRST_N_GPU_IDS=$(nvidia-smi --query-gpu=memory.used --format=csv | tail -n +2 | nl -v 0 | tee /dev/tty | sort -g -k 2 | awk '{print $1}' | head -n $n) export CUDA_VISIBLE_DEVICES=$(echo $FIRST_N_GPU_IDS | sed 's/ /,/g') echo "Now CUDA_VISIBLE_DEVICES is set to:" echo "CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES" } set_n_least_used_CUDA_VISIBLE_DEVICES 8 PROJECT_NAME="rm" PARENT_SAVE_DIR="" # Path to a folder to save checkpoints PARENT_TENSORBOARD_DIR="" # Path to a folder to save logs PARENT_CONFIG_FILE="" # Path to a folder to save training config logs PRETRAINED_MODEL_PATH="" # huggingface or local model path PRETRAINED_TOKENIZER_PATH="" # huggingface or local tokenizer path declare -a dataset=( YOUR/PREFERENCE/DATA/DIR/arrow/part-00000 YOUR/PREFERENCE/DATA/DIR/arrow/part-00001 YOUR/PREFERENCE/DATA/DIR/arrow/part-00002 YOUR/PREFERENCE/DATA/DIR/arrow/part-00003 YOUR/PREFERENCE/DATA/DIR/arrow/part-00004 YOUR/PREFERENCE/DATA/DIR/arrow/part-00005 YOUR/PREFERENCE/DATA/DIR/arrow/part-00006 YOUR/PREFERENCE/DATA/DIR/arrow/part-00007 YOUR/PREFERENCE/DATA/DIR/arrow/part-00008 YOUR/PREFERENCE/DATA/DIR/arrow/part-00009 ) TIMESTAMP=$(date +%Y-%m-%d-%H-%M-%S) FULL_PROJECT_NAME="${PROJECT_NAME}-${TIMESTAMP}" SAVE_DIR="${PARENT_SAVE_DIR}${FULL_PROJECT_NAME}" CONFIG_FILE="${PARENT_CONFIG_FILE}-${FULL_PROJECT_NAME}.json" colossalai run --nproc_per_node 8 --hostfile hostfile --master_port 31312 train_rm.py \ --pretrain $PRETRAINED_MODEL_PATH \ --tokenizer_dir $PRETRAINED_TOKENIZER_PATH \ --dataset ${dataset[@]} \ --plugin "zero2" \ --save_interval 1000 \ --save_dir $SAVE_DIR \ --config_file $CONFIG_FILE \ --max_epochs 3 \ --accumulation_steps 1 \ --batch_size 8 \ --lr 5e-6 \ --mixed_precision "bf16" \ --grad_clip 1.0 \ --weight_decay 0.01 \ --warmup_steps 40 \ --grad_checkpoint \ --use_wandb