LLaMA-Factory-310P3/run_once.sh

57 lines
1.3 KiB
Bash

#!/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 "output_dir created: $output_dir"
else
echo "output_dir exists: $output_dir"
fi
# echo "${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
echo "Start recording gpu status "
# 0 means not printing gpu status
python gpu_status.py ${output_dir} 1 10 &
gpu_status_pid=$!
echo "${gpu_status_pid}"
sleep 60
# if [ "${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"