echo "PYTHONPATH: ${PYTHONPATH}" which_python=$(which python) echo "which python: ${which_python}" export PYTHONPATH=${PYTHONPATH}:${which_python} export PYTHONPATH=${PYTHONPATH}:. echo "PYTHONPATH: ${PYTHONPATH}" OUTPUT_DIR=./pllava_video_outputs/test_train_7b_reconstruct pooling_shape=(16,12,12) num_save_samples=80000 num_gpus=8 full_batch_size=128 batch_size=8 save_steps=$[$num_save_samples/($batch_size*$num_gpus)] ckpt_steps=$[$save_steps/10] gradient_accumulation_steps=$[$full_batch_size/($batch_size*$num_gpus)] echo $batch_size echo $gradient_accumulation_steps repo_id=llava-hf/llava-v1.6-vicuna-7b-hf accelerate launch --main_process_port 6876 --config_file scripts/accel_config_multigpu.yaml tasks/train/train_pllava_nframe_accel.py \ tasks/train/config_pllava_nframe.py \ output_dir ${OUTPUT_DIR} \ train_corpus videochat2_instruction_debug \ save_steps $save_steps \ ckpt_steps $ckpt_steps \ num_workers 8 \ num_frames 16 \ gradient_accumulation_steps $gradient_accumulation_steps \ batch_size $batch_size \ model.pooling_method avg \ model.use_lora True \ model.use_pooling True \ model.repo_id $repo_id \ gradient_checkpointing True \ preprocess.center_pad False \ preprocess.clip_transform False \ optimizer.lr 2e-5 \ scheduler.epochs 3 \ scheduler.warmup_ratio 0.2 \ scheduler.min_lr_multi 0.25 \ model.pooling_shape $pooling_shape \ scheduler.is_videochat2_custom True \ preprocess.mm_alone False \ preprocess.random_shuffle False \ preprocess.add_second_msg False