task.sh

#!/bin/bash

R_DIR=`dirname $0`; MYDIR=`cd $R_DIR;pwd`

export FLAGS_eager_delete_tensor_gb=0.0
export FLAGS_sync_nccl_allreduce=1

if [[ -f ./model_conf ]];then
    source ./model_conf
else
    export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
fi


mkdir -p log/

lr=2e-5
batch_size=32
epoch=4

for i in {1..5};do

    timestamp=`date "+%Y-%m-%d-%H-%M-%S"`

    python -u run_classifier.py                                                \
           --use_cuda true                                                     \
           --for_cn False                                                      \
           --use_fast_executor ${e_executor:-"true"}                           \
           --tokenizer ${TOKENIZER:-"FullTokenizer"}                           \
           --use_fp16 ${USE_FP16:-"false"}                                     \
           --do_train true                                                     \
           --do_val true                                                       \
           --do_test true                                                      \
           --batch_size $batch_size                                            \
           --init_pretraining_params ${MODEL_PATH}/params                      \
           --verbose true                                                      \
           --train_set ${TASK_DATA_PATH}/QNLI/train.tsv                        \
           --dev_set   ${TASK_DATA_PATH}/QNLI/dev.tsv                          \
           --test_set  ${TASK_DATA_PATH}/QNLI/test.tsv                         \
           --vocab_path script/en_glue/ernie_base/vocab.txt                    \
           --checkpoints ./checkpoints                                         \
           --save_steps 30000                                                  \
           --weight_decay  0.0                                                 \
           --warmup_proportion 0.1                                             \
           --validation_steps 1000000000                                       \
           --epoch $epoch                                                      \
           --max_seq_len 128                                                   \
           --ernie_config_path script/en_glue/ernie_base/ernie_config.json     \
           --learning_rate $lr                                                 \
           --skip_steps 10                                                     \
           --num_iteration_per_drop_scope 1                                    \
           --num_labels 2                                                      \
           --test_save output/test_out.$i.$lr.$batch_size.$epoch.tsv           \
           --random_seed  1 2>&1 | tee log/job.$i.$lr.$batch_size.$epoch.log   \

done