Making large AI models cheaper, faster and more accessible
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#!/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=""
PARENT_SAVE_DIR=""
PARENT_TENSORBOARD_DIR=""
PARENT_CONFIG_FILE=""
PRETRAINED_MODEL_PATH=""
declare -a dataset=(
"PATH TO THE DATASET"
)
TIMESTAMP=$(date +%Y-%m-%d-%H-%M-%S)
FULL_PROJECT_NAME="${PROJECT_NAME}-${TIMESTAMP}"
SAVE_DIR="${PARENT_SAVE_DIR}${FULL_PROJECT_NAME}"
TENSORBOARD_DIR="${PARENT_TENSORBOARD_DIR}${FULL_PROJECT_NAME}"
CONFIG_FILE="${PARENT_CONFIG_FILE}${FULL_PROJECT_NAME}.json"
colossalai run --nproc_per_node 8 --hostfile hostfile --master_port 30013 train.py \
--pretrained $PRETRAINED_MODEL_PATH \
--dataset ${dataset[@]} \
--plugin "zero2" \
--save_interval 400 \
--save_dir $SAVE_DIR \
--tensorboard_dir $TENSORBOARD_DIR \
--config_file $CONFIG_FILE \
--num_epochs 1 \
--micro_batch_size 8 \
--lr 1e-4 \
--mixed_precision "bf16" \
--grad_clip 1.0 \
--weight_decay 0.01 \
--warmup_steps 100 \
--use_grad_checkpoint \
--use_flash_attn \
--pad_token "unk"