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export PYTHONPATH="${PYTHONPATH}:/workspace/code"
export GPU_ID=0,1
export TRAIN_FILE=../data/datasets/wikipedia_json_64/
# CUDA_VISIBLE_DEVICES=$GPU_ID python examples/big_ae/run_lm_vae_pretraining.py \
# --output_dir=../output/local_lm_vae_wikipedia_pretraining \
# --dataset wikipedia \
# --encoder_model_type=bert \
# --encoder_model_name_or_path=bert-base-cased \
# --decoder_model_type=gpt2 \
# --decoder_model_name_or_path=gpt2 \
# --beta 0.0 \
# --ratio_zero 1.0 \
# --ratio_increase 0.1 \
# --do_train \
# --fb_mode 1 \
# --train_data_file=$TRAIN_FILE \
# --num_train_epochs 1.0 \
# --save_steps 10000 \
# --logging_steps 1000 \
# --overwrite_output_dir \
# --per_gpu_train_batch_size=8 \
# --block_size 256
CUDA_VISIBLE_DEVICES=$GPU_ID python -m torch.distributed.launch --nproc_per_node 2 examples/big_ae/run_lm_vae_pretraining_distributed.py \
--output_dir=../output/local_lm_vae_wikipedia_pretraining \
--dataset wikipedia \
--encoder_model_type=bert \
--encoder_model_name_or_path=bert-base-cased \
--decoder_model_type=gpt2 \
--decoder_model_name_or_path=gpt2 \
--beta 0.0 \
--ratio_zero 1.0 \
--ratio_increase 0.1 \
--do_train \
--fb_mode 1 \
--train_data_file=$TRAIN_FILE \
--num_train_epochs 1.0 \
--save_steps 10000 \
--logging_steps 1000 \
--overwrite_output_dir \
--per_gpu_train_batch_size=8 \
--block_size 256
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