LLaMA-Factory-310P3/run_once.sh

#!/bin/bash

run_type="$1"
model="$2"
gpu_cnt="$3"
max_steps="$4"

current_datetime=$(date +%Y%m%d%H%M%S)

if [ "${run_type}"="lora_sft" ]; then
    run_name="${run_type}_${model}_${gpu_cnt}_gpu_${max_steps}_step_${current_datetime}"
    
else
    run_name="${run_type}_${model}_${gpu_cnt}_gpu_${current_datetime}"
fi

output_dir="./results/${run_name}"

if [ ! -d "$output_dir" ]; then
    mkdir -p "$output_dir"
    echo "路径不存在，已创建: $output_dir"
else
    echo "路径已存在: $output_dir"
fi

echo "${run_type} ${model} ${gpu_cnt} ${max_steps} ${run_name} ${output_dir}"
python prepare_yaml_file.py ${run_type} ${model} ${max_steps} ${run_name} ${output_dir}



# export USE_MODELSCOPE_HUB=1

# # 0 means not printing gpu status
# python gpu_status.py ${output_dir} 0 &
# gpu_status_pid=$!
# echo "Start recording gpu status "


# if [ "${gpu_cnt}"="1" ]; then
#    ASCEND_RT_VISIBLE_DEVICES=0 llamafactory-cli train ${output_dir}/${run_name}.yml \
#    | tee ${output_dir}/log.txt" &
#    train_pid=$!
#    echo "Start train"
# else
#     FORCE_TORCHRUN=1 llamafactory-cli train ${output_dir}/${run_name}.yml \
#    | tee ${output_dir}/log.txt" &
#    train_pid=$!
#    echo "Start train"
# fi

# wait $train_pid
# echo "Train ended"
# sleep 90
# kill $gpu_status_pid
# echo "Gpu status ended"