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#!/bin/bash
#SBATCH --job-name=slurm-test # create a short name for your job
#SBATCH --nodes=1 # node count
#SBATCH --ntasks=1 # total number of tasks across all nodes
#SBATCH --cpus-per-task=2 # cpu-cores per task (>1 if multi-threaded tasks)
#SBATCH --mem-per-cpu=16G # memory per cpu-core (4G is default)
#SBATCH --gres=gpu:1 # number of gpus per node
#SBATCH --mail-type=ALL # send email when job begins, ends or failed etc.
MODEL_TYPE=fengshen-roformer
PRETRAINED_MODEL_PATH=IDEA-CCNL/Zhouwenwang-Unified-110M
ROOT_PATH=cognitive_comp
TASK=tnews
DATA_DIR=/$ROOT_PATH/yangping/data/ChineseCLUE_DATA/${TASK}_public/
CHECKPOINT_PATH=/$ROOT_PATH/yangping/checkpoints/modelevaluation/tnews/
OUTPUT_PATH=/$ROOT_PATH/yangping/nlp/modelevaluation/output/predict.json
DATA_ARGS="\
--data_dir $DATA_DIR \
--train_data train.json \
--valid_data dev.json \
--test_data test1.1.json \
--train_batchsize 32 \
--valid_batchsize 128 \
--max_length 128 \
--texta_name sentence \
--label_name label \
--id_name id \
"
MODEL_ARGS="\
--learning_rate 0.00002 \
--weight_decay 0.1 \
--num_labels 15 \
"
MODEL_CHECKPOINT_ARGS="\
--monitor val_acc \
--save_top_k 3 \
--mode max \
--every_n_train_steps 100 \
--save_weights_only True \
--dirpath $CHECKPOINT_PATH \
--filename model-{epoch:02d}-{val_acc:.4f} \
"
TRAINER_ARGS="\
--max_epochs 7 \
--gpus 1 \
--check_val_every_n_epoch 1 \
--val_check_interval 100 \
--default_root_dir ./log/ \
"
options=" \
--pretrained_model_path $PRETRAINED_MODEL_PATH \
--output_save_path $OUTPUT_PATH \
--model_type $MODEL_TYPE \
$DATA_ARGS \
$MODEL_ARGS \
$MODEL_CHECKPOINT_ARGS \
$TRAINER_ARGS \
"
DOCKER_PATH=/$ROOT_PATH/yangping/containers/pytorch21_06_py3_docker_image.sif
SCRIPT_PATH=/$ROOT_PATH/yangping/nlp/Fengshenbang-LM/fengshen/examples/classification/finetune_classification.py
python3 $SCRIPT_PATH $options
# singularity exec --nv -B /cognitive_comp/:/cognitive_comp/ $DOCKER_PATH python3 $SCRIPT_PATH $options
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