train_eager_consistent.sh

# set -aux

DEVICE_NUM_PER_NODE=4
MASTER_ADDR=127.0.0.1
NUM_NODES=1
NODE_RANK=0

export PYTHONUNBUFFERED=1
echo PYTHONUNBUFFERED=$PYTHONUNBUFFERED
export NCCL_LAUNCH_MODE=PARALLEL
echo NCCL_LAUNCH_MODE=$NCCL_LAUNCH_MODE
# export NCCL_DEBUG=INFO

CHECKPOINT_SAVE_PATH=ddp_checkpoints
if [ ! -d "$CHECKPOINT_SAVE_PATH" ]; then
    mkdir $CHECKPOINT_SAVE_PATH
fi

CHECKPOINT_LOAD_PATH="./init_ckpt_by_lazy"

OFRECORD_PATH=/dataset/ImageNet/ofrecord/
OFRECORD_PART_NUM=256
LEARNING_RATE=0.256
MOM=0.875
EPOCH=90
TRAIN_BATCH_SIZE=64
VAL_BATCH_SIZE=50

# SRC_DIR=/path/to/models/resnet50
SRC_DIR=$(realpath $(dirname $0)/..)

python3 -m oneflow.distributed.launch \
    --nproc_per_node $DEVICE_NUM_PER_NODE \
    --nnodes $NUM_NODES \
    --node_rank $NODE_RANK \
    --master_addr $MASTER_ADDR \
    $SRC_DIR/train.py \
        --save $CHECKPOINT_SAVE_PATH \
        --load $CHECKPOINT_LOAD_PATH \
        --ofrecord-path $OFRECORD_PATH \
        --ofrecord-part-num $OFRECORD_PART_NUM \
        --num-devices-per-node $DEVICE_NUM_PER_NODE \
        --lr $LEARNING_RATE \
        --momentum $MOM \
        --num-epochs $EPOCH \
        --train-batch-size $TRAIN_BATCH_SIZE \
        --val-batch-size $VAL_BATCH_SIZE \