export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/lib/nvidia
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/data/user/jfeng644/.mujoco/mujoco210/bin  
export PYTHONLOGLEVEL=ERROR
export ACCELERATE_LOG_LEVEL=error
export TRANSFORMERS_VERBOSITY=error
export HF_DATASETS_VERBOSITY=error
export MUJOCO_GL=egl
export PYOPENGL_PLATFORM=egl


# export STAGE1_MODEL_PATH=/data/user/jfeng644/code/vpp/output/svd3/train_2025-10-21T03-58-20/checkpoint-64000
export STAGE1_MODEL_PATH=/data/user/jfeng644/code/vpp/output/svd3/livingellipse115move_studymove/singleview/train_2025-10-29T04-42-47/checkpoint-30000
# export STAGE1_MODEL_PATH=/data/user/jfeng644/code/vpp/output/svd/train_2025-10-03T03-11-11/checkpoint-80000
# export STAGE1_MODEL_PATH=/data/user/jfeng644/code/vpp/output/svd/vppmvlibero/checkpoint-80000
export GLOBAL_FRAME_NUM=13
# 映射 EGL 渲染到可见 GPU：基于 CUDA_VISIBLE_DEVICES 与 LOCAL_RANK 选择物理 GPU 索引
PREPARE_GPU=1,0
if [ -n "$PREPARE_GPU" ]; then
    IFS=',' read -r -a __CUDA_DEV_ARR <<< "$PREPARE_GPU"
    __LOCAL_IDX=${LOCAL_RANK:-0}
    export MUJOCO_EGL_DEVICE_ID=${__CUDA_DEV_ARR[$__LOCAL_IDX]}
fi
# python /hpc2hdd/home/jfeng644/anaconda3/envs/uva/lib/python3.9/site-packages/robosuite/scripts/setup_macros.py
    # model.policy.autoregressive_model_params.pretrained_model_path=checkpoints/libero10.ckpt \
# python train.py \
CUDA_VISIBLE_DEVICES=1,0 NCCL_ASYNC_ERROR_HANDLING=1 accelerate launch --num_processes=2 --main_process_port=29512  train.py \
    --config-dir=. \
    --config-name=vpp_xc.yaml \
    model.policy.action_model_params.predict_action=True \
    model.policy.optimizer.learning_rate=1e-4 \
    logging.project=vpp \
    hydra.run.dir="../xcres/siglip" \
    # model.policy.autoregressive_model_params.pretrained_model_path=checkpoints/libero10.ckpt \